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dennisthiessenandClaude Opus 5 247a7889b9 fix(tickers): honor the effective date instead of retiring on the mark
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active_only tested delisted_on IS NULL, so a symbol dropped out of signals the
moment a Form 25 was detected — ten days before Rule 12d2-2 makes the removal
effective, while it was demonstrably still trading. A manual future-dated mark
behaved the same way. It now compares against the database's own date, so a
pending delisting stays live until the day it takes effect.

That exposes a second problem the fix would otherwise create. Trading typically
stops before the ten-day delay expires, so across that window the symbol is
correctly active yet produces no bars — and confirm_delisting returned None for
an already-marked row, which would have fired the staleness warning daily for
ten days, the exact noise this flow exists to remove. It now reports the known
effective date on every path where the delisting is established, so the caller
warns only about gaps that are still unexplained.

CURRENT_DATE renders identically on postgres and sqlite, and the OR is
parenthesized when callers chain further where clauses.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
 2026-08-11 16:58:51 +02:00
dennisthiessenandClaude Opus 5 1d4ed39fd2 fix(tickers): close the delisting review findings 
Detection could retire an actively traded symbol — silently, since it then
vanishes from every signal. Three causes:

- Form 25 is filed per security class. An issuer removing its notes, preferred
  or warrants files one while the common keeps trading. The filing's own
  descriptionClassSecurity distinguishes them, so the primary document is now
  fetched and read; anything not recognisably common equity is rejected, as is
  anything unreadable (pre-2009 filings have no primary_doc.xml). Fail closed.
- Form 15 ends a reporting obligation and is no evidence trading stopped. The
  whole family is dropped.
- A historical filing for a long-gone class could retire a symbol whose bars ran
  years later, stamping the old date. Filings before the last bar (less a 30-day
  lead for the exchange) are now ignored.

Rule 12d2-2 makes removal effective ten days after filing, so delisted_on is the
effective date rather than the filing date.

bootstrap_universe(prune_missing=True) still ran a cascading delete over
delisted rows, undoing the retention this branch exists for; it now skips them
and reports kept_delisted so the count is explicable.

clear_delisted had no route, which made "safe to automate because it is
reversible" false — reversal needed SQL. POST/DELETE /tickers/{symbol}/delisting
now mark and un-mark, giving an operator a non-destructive alternative to the
cascading DELETE that was the only option.

Shared-CIK siblings (GOOG/GOOGL) stay safe by construction: the probe is
per-symbol and gated on that symbol's own staleness, so a class that still
trades is never probed.

Not addressed: pruning a symbol merely dropped from the index still destroys its
history — the same survivorship problem in a different costume, needing a
tracked/membership state separate from delisting.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
 2026-08-11 16:58:51 +02:00
dennisthiessenandClaude Opus 5 d950fcf70e fix(tickers): let an SEC confirmation upgrade a manual delisting mark 
mark_delisted returned early on any already-delisted row, so the sequence an
operator actually hits — mark EA by hand today, Form 25-NSE surfaces three days
later dated 2026-08-04 — left the estimated date and "manual" reason in place
permanently. Form 25 carries the real effective date, so it now replaces an
operator's estimate; a confirmed row is never downgraded or re-probed.

Also cover _get_ohlcv_priority_tickers, the one place active_only wraps a
compound select rather than a bare one — the unit suite reached none of it.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
 2026-08-11 16:58:51 +02:00
dennisthiessenandClaude Opus 5 6501b7e9a0 feat(tickers): record delisting instead of deleting the symbol 
Retiring a symbol meant delete_ticker or bootstrap_universe(prune_missing),
both of which cascade through OHLCV, setups and scores. That destroys exactly
the history four research documents already apologise for: today's tracked
universe projected backward is survivorship-biased, and hard-deleting every
delisted name is what causes it. Keeping the rows preserves the option to fix
that — it does not fix it, which needs the replay to model a delisting as an
exit event.

tickers gains delisted_on / delisted_reason (migration 032). NULL means
actively traded.

The filter is opt-in via ticker_service.active_only rather than folded into a
shared getter: the registry and admin views deliberately keep delisted rows so
the delisting is visible, and a silent default would undo that. Applied to the
live path only — scanner, momentum ranking, scoring, breadth, fundamentals
candidates, SEC universe, earnings import, ingestion loops. run_backtest keeps
them on purpose.

Detection runs off OHLCV staleness, not off the SEC fundamentals import: that
importer stalls for days on unrelated Company-Facts gaps and would take
detection down with it. On a stale symbol the scheduler asks SEC for a Form
25/25-NSE/15 and retires it only on a hit, so a halt or a rename (SATS->ECHO)
keeps the existing warning. The probe waits 3 stale days so a market-data
outage cannot turn into one SEC request per symbol per run.

Safe to automate because it is reversible: clear_delisted un-retires a false
positive, where a delete had already taken the history.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
 2026-08-11 16:58:51 +02:00
dennisthiessenandClaude Opus 5 486fb500d1 test(sec): cover the ceiling's promote/queue/alert path end to end 
The ceiling tests asserted validate()'s verdict but nothing proved the claim
the design rests on: that a forced promotion actually queues the filings it
released and says that it did. Drive it through run_import with a filing that
stays inside the per-filing window, so only the aggregate ceiling can release
it, and assert the SecFilingGap row and the promotion_ceiling_forced event.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
 2026-08-11 16:58:46 +02:00
dennisthiessenandClaude Opus 5 77570557db feat(sec): cap how long the fundamentals import can stay deferred 
MISSING_XBRL_RETRY_DAYS bounds how long ONE filing blocks promotion. It does
not bound the import as a whole, and the two come apart because a blocking
filing is only queued by promote(), which a deferred run never reaches. During
a rolling supply of unresolvable filings — earnings season, when SEC's
Company-Facts aggregation lags furthest — each new arrival restarts the 3-day
clock before the previous one clears, and nothing is written at all: not the
good rows, not the gap rows that would stop those filings blocking again.

Add an aggregate ceiling. Once promotions have been stale for
PROMOTION_CEILING_DAYS (7), every unresolved filing is aged past the retry
window in place, so promote() queues them all through the path that already
exists, source_max_date advances, and _missing() keeps queued rows aged-out on
later runs. The import self-heals instead of compounding.

Deliberately not the alternative of queueing gap rows on a deferred run: that
would drop the grace period to a single run for every filing, including the
common case of a Company-Facts lag that resolves in a day, and it needs a write
on a run that failed validation.

The per-filing window is untouched, a never-promoted source never trips (that
is initial setup, not a wedge), and affected symbols stay barred from setups
either way since setup_blocked_ciks ignores the window. A forced promotion
raises promotion_ceiling_forced so the safety valve is never silent.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
 2026-08-11 16:58:46 +02:00
dennisthiessenandClaude Opus 5 fbca38e144 fix(backtest): roll back the portfolio-sim DB failures too 
The first pass guarded the replay loop but not the portfolio-simulation block,
which re-fetches price columns and loads the benchmark and the live exit policy
from the same session much later. A failure in any of those swallows the
exception without clearing the transaction — the identical failure mode, with
the identical symptom: the report write is the first unguarded statement and
takes the blame.

The outer handler is the backstop for the price_columns loop, which has no
handler of its own; rolling back a session an inner handler already cleared is
a no-op.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
 2026-08-11 16:10:57 +02:00
dennisthiessenandClaude Opus 5 6ca7f13779 fix(backtest): roll back the session after a swallowed DB failure 
Every DB call in run_backtest is best-effort so one unreadable ticker cannot
abort the whole replay, but the handlers swallowed the exception without
clearing the transaction. asyncpg then reports "current transaction is
aborted" for every later statement, and the first unguarded one — the report
write — surfaced it as the job error, long after the real cause.

Add _rollback_quietly at the three swallowing sites (benchmark load, parallel
fetch, sequential replay), matching the guard price_service already uses.

Load plain symbols instead of Ticker instances: a rollback expires ORM objects
held across it, and touching an expired attribute afterwards triggers sync
lazy-loading, which raises on an AsyncSession. rr_scanner_service hit this
same trap. Only .symbol was ever used.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
 2026-08-11 14:20:19 +02:00
dennisthiessenandClaude Opus 5 d02fd82ced docs(research): regenerate the artifact under the v2-mandatory harness
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Supersedes the previous artifact, which predates v2_reconstruction becoming a
required variant and the P1-cap conditional reporting. Generated from a clean
tree (git_dirty false at rev 43ee619), all hard gates passing, so the recorded
source hashes actually identify the code that produced it.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
 2026-08-08 23:16:52 +02:00
dennisthiessenandClaude Opus 5 43ee619412 fix(research): require the v2 reproduction, and correct the P1-cap denominator 
Two review findings, plus a lost-edit repair.

v2_reconstruction is now a required variant. It carries every published figure
the reproduction rests on (avg, p80, max, P3-pegged, W1-live), so a run without
it could emit a confident, non-provisional recommendation having checked nothing
against v2 at all -- while the methodology doc claims v2 and v3 are reproduced
first. The default invocation is now derived from REQUIRED_VARIANTS so the two
cannot drift, and a test asserts the default satisfies its own requirement.

The doc and the P1_TREND_BREAK_ANCHORS comment still justified skipping the
P1_SCORE_CAP with 17/408 = 4.2%, which is the all-session share and does not
evaluate the rule. The rule names sessions with State >= 40: 47 of them, P1 sole
argmax on 17 = 36.2%, against P2's 16 and P3's 14. Conclusion unchanged -- well
under the 80% trigger -- but the published rationale now states the metric that
actually decided it.

Root cause of that survival: the earlier correction WAS made, but in a script
that applied several substitutions and wrote the file once at the end. A later
substitution raised, so the successful edits were discarded with it. The
"Unlike P3 and V1 ... P3's do not" fix was lost the same way and is restored.

Also adds tests for the refusal paths themselves -- missing required variant,
unknown variant, custom window with no calendar anchor. They were verified by
hand last round but left unpinned, which is the same shape of problem as the
optional gates they exist to enforce. All return before any network call.

Deliberately not done, as not load-bearing: recording the oas400 variant's
missing-credit session count (the truncation conclusion rests on the
distribution mismatch, which is already recorded), and generalising
_pipeline_gates for arbitrary --end/--sessions windows.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
 2026-08-08 23:16:03 +02:00
dennisthiessenandClaude Opus 5 87224a1451 docs(research): regenerate the calibration artifact from a clean tree 
The previous artifact was produced from a dirty working tree while HEAD still
pointed at the harness commit, so its recorded revision could not reproduce it.
This one records git_dirty false alongside sha256 of the three source files it
depends on, so the claim "checking out this revision reproduces this artifact"
is now checkable rather than implied.

All hard gates pass, including the first-scored-date anchor and the row-wise
state_v4 <= state_v3 invariant, so it carries a non-null recommendation.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
 2026-08-08 22:46:41 +02:00
dennisthiessenandClaude Opus 5 ec1b0acfad fix(research): make the calibration artifact live up to its refusal guarantees 
Review of the v4 evidence path. The shipped sensors, bands, methodology bump and
categorical allowlist were found sound; these are gaps in the harness that
produced the evidence for them.

The recommendation gates were optional, so they were not gates. The calendar
anchor lived behind --expected-first-session, which defaulted to None -- so the
committed artifact had no first-date check at all, leaving only a session COUNT
that is tautological (the harness slices the tail of the price series to whatever
was asked for). And the state_v4 <= state_v3 invariant was appended only when
both variants were present, so `--methodology v3` alone could still emit a v4
recommendation having never evaluated v4. The anchor is now a published constant
asserted unconditionally, required explicitly whenever --end/--sessions are
overridden, and v3+v4 are mandatory. Both refusals exit 2.

The P1_SCORE_CAP decision was taken on the wrong population. The agreed rule was
"sole price argmax on >80% of sessions with State >= 40"; the harness reported
only all-session counts and the doc concluded from 17/408 = 4.2%. Measured on the
actual population: 47 qualifying sessions, P1 sole argmax on 17 = **36.2%** (P2
16, P3 14). Still well under 80, so the conclusion holds -- but it was reached
from a denominator that did not test the rule, and 36.2% is a materially
different number to have on the page.

Provenance did not identify the code that produced the artifact. It recorded
git_rev c3ae5ad while the live v4 variant depended on app changes that were still
uncommitted, so checking out that revision would not reproduce it. Now records
git_dirty plus sha256 of regime_monitor_service, breadth_service and the script
itself, and this artifact is regenerated from a clean tree.

The 400- vs 700-day OAS question was described as settled but was not
reproducible: the artifact carried only oas_fetch_days 4748, and
v2_reconstruction patches the per-session window to 3653 regardless, so
--oas-window-days 400 could not simulate it. Patching a window cannot stand in
for data that was simply absent, so v2_reconstruction_oas400 truncates the OAS
SOURCE series instead: avg 26.54, p80 42.52, max 100.00 against published
22.6 / 35.1 / 91.2. Full coverage reproduces all three, so the published figures
predate the truncation. Now recorded in the doc.

v4-vix-only and v4-p1-only had become no-ops: after the cutover the shipped
sensors ARE v4, so patching one candidate in left the other shipped and both
variants evaluated full v4. Each now restores the other sensor to its v3 formula,
and they separate properly (v3 18.13, v4-vix-only 16.64, v4-p1-only 16.28,
v4 14.78 -- each fix contributing about half the move).

Docs: the copy-paste invocation was mangled by a backslash-escaping bug and is
now a fenced, forward-slash command; "Unlike P3 and V1 ... P3's do not" corrected
to "Unlike P1 and V1"; the point-in-time section updated from 400 sessions to the
672-calendar-day / ~464-session window production actually replays; the exercised
52.33 VIX print recorded so the top anchors are not merely asserted.

Tests: band_for now pinned at 64.9/65 from both sides so a silent revert to 80
cannot pass, and the categorical carry-forward test stores locked=True and
asserts it survives -- losing it is half the failure mode, since
update_regime_monitor only auto-refreshes when locked is false.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
 2026-08-08 22:46:10 +02:00
dennisthiessenandClaude Opus 5 3143477a62 feat(regime): cut the risk monitor to v4 — desaturate VIX and the trend break 
Two sensors saturated in exactly the range where resolution matters, and the top
State band had no headroom. Calibrated with scripts/run_regime_monitor_calibration.py
over the 408 sessions ending 2026-07-24; the shipped code reproduces that run's
band shares exactly (78.9 / 13.0 / 4.7 / 3.4).

V1 read VIX 30, 50 and 82 as an identical 100 — the same defect v3 had just
removed from P3, left in place one sensor over. In the window it flattened five
distinct April-2025 prints (52.33, 46.98, 45.31, 40.72, 38.57) into one value.
Now an anchor table reaching full scale at 55, not at 2020's ~82: anchoring the
top at a once-in-a-generation print would make VIX 50 read only ~70. Pegged on
14 of 408 sessions before; none now.

_under_200 returned a bare 0/100, so P1 printed 100 the moment SMH and QQQ were
both under their average — and since the price pillar takes max(P1, P2, P3),
that pinned the pillar and stopped P3's ladder resolving for the whole of a
selloff. Now graded by depth below the 200-DMA, with a deliberate floor of 20 at
the crossing: the break is a genuine binary event, only its depth is graded.
Pegged on 46 of 408 sessions before; none now. A 2% break reads ~30, not 100.

max() was KEPT — the defect was the step function feeding it, not the vote, and
v3's "one capped vote for correlated reads" rationale still holds. P1 is the sole
price argmax on 17 of 408 sessions (4.2%), so the P1_SCORE_CAP fallback drafted
during design was measured as unnecessary and not shipped.

STATE_BANDS breaking 80 -> 65, and only that threshold. Credit returns 0.0 (not
None) when calm, so it holds its 20 points pinned at zero and price + breadth +
volatility at literal maximum summed to exactly 80.0 — v3's threshold to the
decimal, with nothing above it. The sensor is deliberately unchanged: a
calm-credit selloff genuinely is less stressed. What was stale is the band, fit
on v2 while credit's since-removed percentile leg still contributed. A
2022-style AI/tech drawdown with calm credit computes to 70.3 (no death cross) or
74.0 (with one); 70 would have left 0.33 points of headroom, reproducing the
defect. Chosen by scenario arithmetic, and the realized breaking share then lands
on 3.4% — the same as v3's, arrived at independently.

"v4" added to CATEGORICAL_FUNDAMENTAL_METHODOLOGIES in this same commit, which is
load-bearing: that set is checked against the STORED blob, so bumping without it
discards the collected observation on first write, leaving fetched_at null and
locked false — and update_regime_monitor then fires a paid LLM refresh on every
run, forever. Now guarded by a test parametrised over v2 and v3 stored blobs.

SENSOR_REVISION deliberately stays 2: a METHODOLOGY change already forces a full
reseed via _parse_snapshot, and bumping both would imply the reseed was
revision-driven.

QUADRANT_STATE_DIVIDER stays 50 because only breaking moved, so alert_service,
RegimeChart and the quadrant tests need no change. A new test enforces
divider == band boundary on both axes, which nothing did before.

Doc renamed to regime-monitor-v4.md with a tombstone at the old path (commit
messages cite it), the three open questions converted to resolved with the
reasoning that closed them, and indexed in docs/research/README.md for the first
time. The P2 limit is stated honestly: _death_cross pegs at a -5% MA gap, so a
deep selloff still reaches 100 via P2 — v4 repairs the shallow-to-moderate break,
not "the price pillar no longer pegs".

DEPLOY: the first run reseeds ~464 sessions. Expect one phantom quadrant alert
(the dedup key carries basket_hash, not methodology) and re-run the Event Study
manually — its cached report self-invalidates but does not self-regenerate.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
 2026-08-08 20:34:25 +02:00
dennisthiessenandClaude Opus 5 c3ae5ad949 feat(research): commit the regime-monitor replay harness, and reproduce v2/v3 
v3 was calibrated by replaying the series offline, but that harness was never
committed -- so its published numbers could not be re-derived, and a v4 cut would
have had to choose anchors by argument rather than measurement. This is that
harness, and it reproduces the published figures.

scripts/run_regime_monitor_calibration.py replays State/Warning session by
session from the same inputs the live job uses (Alpaca for all 33 symbols, FRED
for VIX and HY OAS), with no database: breadth and divergence come from
breadth_service's pure helpers. It never reimplements an unchanged live sensor --
_compute_index, _score_pillars, P2, P4 and the Warning sensors are imported and
called. Only candidate formulas (proposed v4) and retired ones (v2, gone from the
codebase) are defined here and patched onto the module for a variant's duration.

Reproduction of the 408 sessions ending 2026-07-24, against the figures in
docs/research/regime-monitor-v3.md:

  v2 State avg      22.6   ->  22.68
  v2 State p80      35.1   ->  35.1     exact
  v2 State max      91.2   ->  91.2     exact
  v2 P3 pegged        39   ->    39     exact
  v2 W1 live         108   ->   108     exact
  v3 State max      87.4   ->  87.4     exact
  v3 band shares  73.3/15.0/8.3/3.4 -> 73.0/15.4/8.1/3.4

Three things the harness had to get right to reach that, each of which was
initially wrong and caught by a gate rather than by inspection:

  - "W1 live 108" counts NONZERO sessions, not non-null ones. v2's divergence
    gate returned 0.0 during any decline (v3 tapers instead), so the retired
    divergence formula had to be reconstructed too.
  - v2 sliced HY_OAS_REFERENCE_YEARS = 10.0 per session, not v3's 700 days. The
    percentile leg ranks against that window, so replaying it short shifted the
    middle of the distribution while leaving the max exact.
  - The published v2 numbers correspond to FULL OAS coverage. Replaying v2 with
    the 400-calendar-day fetch it shipped with yields max 100.0 and 133
    credit-less sessions -- so that truncation was not in force when the figures
    were taken. Recorded rather than assumed.

The script refuses to emit a band recommendation unless every hard gate passes
(33 symbols fetched, per-symbol warm-up and final bar, full basket on every
session, calendar anchors, 100% coverage, and a row-wise state_v4 <= state_v3
invariant), and exits non-zero. It is meant to be structurally impossible to read
a calibration result out of a run whose pipeline did not validate. No v4 code
ships in this commit.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
 2026-08-08 20:05:12 +02:00
dennisthiessenandClaude Opus 5 f22313deaf chore: remove dead frontend code and one unused service helper
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Scan of every module and exported symbol, with each candidate verified by hand
rather than trusted from the scan.

Deleted outright:
  frontend/src/lib/fundamentals.ts  (112 lines, 12 exports) — imported by
    nothing, including FundamentalsPanel, which reads backend values. It mirrors
    scoring_service._compute_fundamental_score, so it is the same *kind* of
    thing as lib/qualification.ts — but nothing consumes it, so it mirrored
    nothing and could drift out of sync unnoticed.
  Skeleton.SkeletonLine, paperTrades.getEquityCurve, regime.regimeColor
  breadth_service.compute_breadth_today — self-described "thin wrapper, for
    future live use"; that future did not arrive.

Kept, but unexported — used inside their own module, so the dead part was the
public surface, not the code: Button.Spinner, exitPlan.SETUP_STOP_ATR_MULTIPLIER,
client.ApiError.

Three things the scan flagged that are NOT dead, recorded so the next sweep does
not re-raise them:
  RegimeChart.tsx — lazy(() => import(...)) in RegimePage, so it looks orphaned
    to any importer-graph scan. Deleting it would break the risk page.
  qualification.ts MIN_TARGET_PROBABILITY / liveRiskReward — that file is a live
    mirror of app/services/qualification.py used in five places, and the
    constant is exported to document the backend value it tracks.
  ssl_bootstrap.ssl_status — called from an inline python snippet inside
    scripts/run_tier1_macbook.sh, invisible to a .py-only search.

No orphaned backend modules across app/.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
 2026-08-08 17:24:04 +02:00
dennisthiessenandClaude Opus 5 d116fcc146 chore(frontend): drop the dead FundamentalsPanel dev harness
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Left over from the fundamentals work. Verified orphaned before removing: not in
vite.config.ts (default build input is index.html alone, and dist/ only ever
contained index.html, so it never shipped), not referenced by any script,
config or module, and imported by nothing. Its only mention anywhere was its own
header comment — the many other "harness" hits in the repo are the factor /
backtest IC harness, which is unrelated and stays.

Removes frontend/src/dev entirely.

The pattern it embodied — a fixture-seeded page for eyeballing one component
without a backend — is still the only way to see a component render, since the
frontend has no test runner. But that is worth recreating per component on the
spot, not preserving as a stale file pinned to one panel.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
 2026-08-08 17:13:11 +02:00
dennisthiessenandClaude Opus 5 94baa89423 fix(jobs): make last-run writes atomic and drain them properly on shutdown
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Two review findings on 22bee28, both reproduced before fixing.

[1] Concurrent writes could lose or rewind a row. record_finish did
select-then-insert-or-update, and pipelines are separate scheduler jobs that can
overlap while sharing step ids -- data_collector belongs to all four. Reproduced
both halves: two sessions that SELECT before either INSERTs make the second
commit raise IntegrityError, which _persist_job_run swallows, so the run
silently vanishes; and a later write carrying an OLDER finished_at rewound the
row from 12:00 back to 09:00, dragging the status with it, so the panel would
report a stale outcome as the latest.

Now a single atomic INSERT ... ON CONFLICT (job_name) DO UPDATE, guarded by
WHERE job_run_state.finished_at < excluded.finished_at so an older completion
can never overwrite a newer one. Dialect-specific because prod is Postgres and
tests are SQLite; both support it (SQLite >= 3.24, ours is 3.45). updated_at is
set explicitly, as the model's onupdate hook does not fire for a core upsert,
and get_map now uses populate_existing since core writes leave any
previously-loaded ORM instance stale in the identity map.

[2] Shutdown could dispose the engine underneath a pending write.
scheduler.shutdown(wait=False) returns before APScheduler dispatches its
completion events, and those events are what create persist tasks -- so a single
snapshot of the task set missed writes still to be queued. flush_job_run_persists
now settles briefly for pending callbacks, then drains in a loop until the set
stays empty, with the deadline still bounding total shutdown time. Left
shutdown(wait=False) alone deliberately: waiting would block a deploy restart
behind a long-running scan.

Six regression tests: interleaved first writes, older-never-rewinds,
newer-still-wins, a task queued mid-drain, prompt return when idle, and giving
up rather than hanging shutdown.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
 2026-08-08 14:15:05 +02:00
dennisthiessenandClaude Opus 5 4b4a1084cb refactor(jobs): group Admin -> Jobs into sections instead of one flat list 
Nineteen jobs rendered as one alphabetical list in which a pipeline, one of its
steps, a standalone cron job and a manual-only job were indistinguishable.

Four sections, ordered by the API (category rank, then trading-day order within
it) so the client does not re-derive ordering: Pipelines, Pipeline steps,
Standalone scheduled, Manual only. An unrecognised category still renders, under
"Other" -- a stray section beats a job silently vanishing from the admin page.

Sections rather than nesting steps under their parent, which is what the flat
"runs via pipeline" label invited. Membership is many-to-many -- data_collector
runs in all four pipelines, alerts and outcome_evaluator in two each -- so
nesting means duplicating those rows, and the duplicates would each carry a
Trigger button despite not being distinct actions: only plain collect_ohlcv is
registered, while the near-close and after-close variants are different
coroutines that are not individually triggerable. Instead each pipeline card
lists its step sequence and each step says which pipelines run it, which is the
same information without a button that lies.

Every job now answers "when does this next run" the same way: its own timer, its
soonest enabled parent's ("Next via Intraday Pipeline in 42m"), or "manual
only". Jobs with no recorded run say so explicitly rather than showing nothing.

The status chip and the rate-limit banner still read runtime_* only, so a
persisted failure cannot pin either to a stale state.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
 2026-08-08 12:25:05 +02:00
dennisthiessenandClaude Opus 5 22bee28ac7 feat(jobs): persist each job's last run so it survives a restart 
Job outcomes lived only in scheduler._job_runtime, an in-memory dict. Every
deploy wiped it, so Admin -> Jobs could report "Active" with no indication a job
had ever run or how it ended -- which is the main thing that page is for.

New job_run_state table (migration 031): one row per job, upserted on job_name.
Deliberately not history -- system_events already grows unbounded with no
retention job, and a second append-only operational table would repeat that
debt. Adding history later is purely additive.

Written from two hooks, NOT from _runtime_finish. That looked cheapest (one
function, ~40 call sites) but unit tests invoke job coroutines directly, so it
would fire detached DB writes at the real session factory throughout the suite,
and there is no testing flag to guard on.

  - An APScheduler EVENT_JOB_EXECUTED/ERROR listener covers everything the
    scheduler fires, including manual triggers. Its detached task is held in a
    module-level set (a bare create_task result can be collected mid-flight) and
    drained in the app lifespan before engine.dispose().
  - _run_pipeline persists directly, and must: pipeline steps are plain
    coroutine calls that emit no scheduler events, so the listener cannot see
    them. The step persist sits AFTER the except that swallows step errors --
    inside it, exactly the failed runs worth seeing would be skipped. The
    orchestrator persists in the finally, and the disabled early-return persists
    too, or "skipped" is silently dropped.

_persist_job_run never raises: a persistence failure must not break an otherwise
successful pipeline.

The API reports this as last_run_* and leaves runtime_* meaning strictly live
in-memory state. Reusing runtime_status would have been a regression, not a
no-op: JobControls drives the status chip from it (a job that errored eight days
ago would read "Last run error" forever instead of "Active") and picks the
rate-limit banner from it (a week-old rate limit would pin the banner
permanently). Tests pin the split.

The table starts empty; each job fills its row the next time it finishes. No
backfill from system_events, which records only warning/error outcomes under a
different status vocabulary and would invent successes that never happened.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
 2026-08-08 12:19:29 +02:00
dennisthiessenandClaude Opus 5 083c9dbf7c refactor(jobs): derive job topology from one catalog, make next-run coherent 
Groundwork for the Admin -> Jobs cleanup. Three sources of truth collapse into
app/job_catalog.py, which imports nothing from app so both the scheduler and
admin_service can import it at module level (admin_service otherwise has to
import the scheduler inside functions to dodge a cycle).

PIPELINE_MEMBERS is now DERIVED from the four pipeline step lists instead of
being a literal set in admin_service duplicating four lists in scheduler.py with
nothing asserting they agreed. A test pins that the derivation reproduces the
previous hand-maintained 9 names exactly, so this is behaviour-preserving.

Deletes the private _JOB_NAMES list, which held 16 of the 19 jobs:
benchmark_collector, outcome_evaluator and shadow_book had no runtime row, and
so no "last run" line in the panel, until their first run in a given process.
_job_runtime is now seeded from the catalog, and a test pins the invariant.

Next-run is decided by category rather than by reading a timestamp. A pipeline
step has no schedule of its own, so it reports its parent's ("next via Morning
Pipeline in 3h") instead of nothing; a manual job says manual_only rather than
rendering a date. This also fixes a real bug: triggering a paused job set
next_run_time=now, APScheduler re-armed the 520-week backstop behind it, and the
panel displayed "next run in ~87600h". Two independent guards -- the category
rule, plus _visible_next_run dropping anything past a year -- and an APScheduler
listener that re-pauses steps and manual jobs once their run finishes. The
listener is registered at module level because configure_scheduler is called
more than once and add_listener does not deduplicate.

Migrates backtest and ticker_universe_sync from interval to cron (Sun 03:00 ET
and 01:00 ET). configure_scheduler calls remove_all_jobs() on every startup, so
an interval countdown restarts each deploy -- a 168h backtest needed a week of
uninterrupted uptime to fire even once. The codebase already documented this
pitfall as the reason cron was adopted; these two were never migrated. Both are
now editable in Admin -> Schedule.

Also: list_jobs went from one settings query per job (19) to one for all of
them, and data_backfill is hidden from the listing while staying registered and
API-triggerable.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
 2026-08-08 12:08:01 +02:00
dennisthiessenandClaude Opus 5 7fdcac3b55 docs: carry the risk-monitor wording through docs, comments and logs
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Follows 5ea0785, which renamed the user-visible labels. This finishes the pass
so code, docs and operator output use one vocabulary: README (pipeline list,
route table, FRED row), the methodology doc title, .env.example and config
comments, the snapshot model / event-study / service / test docstrings, the
scheduler section headers and morning-pipeline docstring, the TopBar status
text ("bullish regime" -> "bullish trend"), and the four "Regime monitor:" log
prefixes.

Deliberately NOT changed, because "market regime" is also a standard finance
term and most occurrences are not this job: the backtest caveat "~6 months is
roughly one market regime" in backtest_service, README, BacktestPanel and every
generated reports/*.json; "a regime shift" in TrackRecordPanel; and the
capacity-bracket findings doc. Renaming those would have made the text wrong.

Also unchanged, being persisted or externally linked rather than wording: the
regime_monitor / market_regime job ids, the regime_quadrant_enabled setting key,
the /regime route, METHODOLOGY and the snapshot fields, the service/test module
filenames, and docs/research/regime-monitor-v3.md's path (referenced from commit
messages). The doc now carries a one-line note recording the old name and why
those identifiers still use it.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
 2026-08-07 22:51:49 +02:00
dennisthiessenandClaude Opus 5 5ea0785be6 refactor(ui): name the two regime jobs for what they actually do 
"Market Regime" and "Regime Monitor" sat next to each other in Admin -> Jobs
(pipeline steps 4 and 5) reading as the same job. They are unrelated, and the
names had it backwards: "Market Regime" is the SPY 50/200 guard that drives the
TopBar trend dot and the counter-trend warning on setups, so it changes what a
setup shows; "Regime Monitor" is the observational AI/Tech thermometer that
explicitly feeds no trades. The more consequential job had the vaguer name.

  market_regime   "Market Regime"   -> "Market Trend (SPY)"
  regime_monitor  "Regime Monitor"  -> "AI/Tech Risk Monitor"

Display strings only. The job *ids* are persisted -- they key the pipeline step
list, cron config, runtime tracking and run history -- so they are untouched,
as is the /regime route, which keeps existing links working.

The label the admin UI renders comes from JOB_LABELS in admin_service (via
routers/jobs.py), not from the scheduler's APScheduler `name=`. Both are updated;
only the former is user-visible.

Carries the vocabulary through the rest of the surface so it does not half-land:
page title, nav ("Regime" -> "Risk"), the empty-state instruction that names the
job to run, the quadrant alert toggle, the morning-pipeline hint, and the
Telegram alert headline ("Regime quadrant change" -> "AI/Tech risk quadrant
change"). No test asserts any of these strings.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
 2026-08-07 19:48:15 +02:00
dennisthiessenandClaude Opus 5 98b41629e7 ci: lint the whole repo, and pin the rule set so it stays deterministic
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Widens the lint step from `ruff check app/` to `ruff check .`, since tests/ and
scripts/ had drifted to 11 findings while unchecked (fixed in 1c6ccce).

Widening alone would have been unsafe, and the check turned up something worse
than the drift: CI installs ruff unpinned, the repo had no [tool.ruff] config,
and ruff's default rule set is not stable across releases. Local 0.15.4 reports
0 findings in app/; 0.16.2 -- what `pip install ruff` resolves to today --
reports 376. 168 of those are B008 flagging FastAPI's `Depends()` in a signature
default, which is the framework's documented idiom, not a defect. So the lint
job would have failed the deploy pipeline on untouched code at the next push,
independent of this change.

Pinning the ruff version would freeze the bug in place. Pinning the *rule set*
is the actual fix: select = ["E4", "E7", "E9", "F"] in pyproject.toml, the set
the tree was already clean under, now enforced repo-wide. The ruff version can
float freely without changing what CI enforces.

Verified both scopes against both versions with caches disabled: `ruff check .`
passes under 0.15.4 and 0.16.2. Confirmed the pin actually binds rather than
passing by luck -- a probe file using `Depends()` in a default is clean under
the committed config and reports B008 + I001 under `--isolated` 0.16.2 defaults.
Full suite still 852 passed, 1 skipped.

Adding rules is welcome; do it in pyproject.toml with the fixes in the same
commit.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
 2026-08-07 19:28:26 +02:00
dennisthiessenandClaude Opus 5 1c6ccceb12 chore: make the whole tree ruff-clean, not just app/ 
CI only lints app/, so 11 findings had accumulated in tests/ and scripts/.

Mechanical and behaviour-neutral, but two were not auto-fixable and needed a
judgement call rather than `ruff --fix`:

- E741 in run_fip_breadth_diagnostics: `l` is the OHLCV low and is genuinely
  used, so this was a naming fix (`l` -> `lo`), not a deletion.
- F841 in the same file: `vol_ix`/`momr_ix` are assigned from a pure local
  `_index()` and never read, so removing them cannot change any output. Their
  upstream `vol_weeks`/`momr_weeks` maps *are* used further down and stay; the
  comment above `_index` was corrected to say so.

The rest are unused imports and f-strings without placeholders (literal
markdown table headers, so identical output).

Verified beyond the linter, since py_compile does not catch a removed-but-used
import: every removed symbol has zero remaining references, all scripts compile,
and the full unit suite passes (852 passed, 1 skipped).

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
 2026-08-07 18:43:46 +02:00
dennisthiessenandClaude Opus 5 3483797e75 fix(regime): reseed stored history on a sensor change, and stop faking an observation 
Two review findings on 46ace50.

[P1] Raising HY_OAS_WINDOW_DAYS to 700 only reached newly computed rows. A
routine run recomputes the latest trading date alone, and `rebuilding` was keyed
on "no v3 snapshot exists at all", which is false once the cutover has run --
so every row already written kept the credit gap the wider window exists to
close, indefinitely.

Adds SENSOR_REVISION: stamped into each snapshot, absent on pre-marker rows
(read as 1), and a stored revision below the current one triggers exactly one
reseed. Deliberately not METHODOLOGY, which would partition the history API and
discard the cached event study -- neither warranted, since the study recomputes
its Warning series from source rather than reading snapshots and so cannot be
staled by a reseed.

The reseed is bounded by REBUILD_LOOKBACK_DAYS in calendar days rather than a
session count, because the binding constraint is the OAS fetch: each replayed
row needs W3's 20-business-day lookback inside HY_OAS_WINDOW_DAYS. Replaying by
session count would have left the oldest stored rows unrepaired -- the exact
rows the fix targets. At 672 days the replay covers ~464 sessions, W3's oldest
requirement lands on the first fetched OAS day, and the ~400-session series the
cutover wrote is fully covered. A test asserts that relationship so the two
constants cannot drift back into recreating the gap.

[P2] With nothing ever collected, current_observation returned available=true
and the default placeholders -- "unknown" for every hyperscaler, "mixed" for the
reaction -- so the card announced a reading that never happened. Those are the
absence of an observation, not an observation of absence. Gated on `observed`
(non-null fetched_at, the one field every path writing real content stamps),
which blanks the content and drives a proper empty state naming where an admin
collects one. This was a regression from 46ace50; fundamental_overlay never had
it, since no observation means no effective date means pending.

Also renames the leftover v2 identifiers in the touched paths
(rewrite_existing_v2, latest_v2).

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
 2026-08-07 18:22:25 +02:00
dennisthiessenandClaude Opus 5 7dc804be2b test(dolt): anchor the real-clone smoke test to the clone, not the wall clock 
The test built the importer with today=date.today() while running against a
fixed local clone with do_pull=False, so its forward horizon shrank by a day
per real day. It has now decayed past the initial-load gate -- 19d against the
21d MIN_FORWARD_HORIZON_DAYS floor -- and would have kept failing, worse each
day.

Anchors today to the clone's own calendar (max reporting date across the seeded
dot-free symbols, minus 35 days, mirroring the ~35d horizon the importer's own
comment cites) and uses that date in the forward-calendar assertion. Also
surfaces run.error_details on failure, which is how the cause was found.

Test-only. MIN_FORWARD_HORIZON_DAYS and the importer are untouched: production
pulls fresh data on every run and was never affected by this.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
 2026-08-07 18:22:11 +02:00
dennisthiessenandClaude Opus 5 46ace501a2 refactor(regime): collapse the monitor page, fix the OAS rebuild window 
The page had twelve stacked blocks, several of them different views of the
same numbers. The quadrant plot and the score-history chart drew the same two
series from the same query key, which read as two datasets; they are now one
card with a Time | Path toggle. The two pillar disclosures become one grouped
table, and three prose blocks (data quality, basket, coverage) become one
provenance chip strip. Page text is now limited to what changes how the reader
interprets today's number; the rest moved to the methodology doc.

Removes three stale-threshold bugs of one class. The quadrant fell back to v2's
60/60 dividers when quadrant_config was absent -- the real values are 50/40 and
they feed alert_service, so the chart could disagree with what actually fires.
The gauge fell back to v2's 30/60/80 band ticks, and drew a divider line that
always landed on its own "elevated" tick. The time series' reference lines were
at 30/60/80, which correspond to nothing in v3; they are now per-axis dashed
lines read from the same quadrant_config. Rendering also surfaced a live
clipping bug inherited from the old chart: margin.left -18 against YAxis
width 28 left ~10px for a 3-digit label, so every Y tick was cut off.

HY_OAS_WINDOW_DAYS was 400 *calendar* days while a rebuild replays
REBUILD_SESSIONS = 400 *trading* sessions (~579 calendar days), so the oldest
~180 days of any rebuild got no OAS at all and both credit sensors returned
None. State then lands at 80% coverage and Warning at exactly MIN_COVERAGE, so
both still publish bands -- a series that looks homogeneous while its oldest
rows were scored without credit. Widened to 700. This needs no methodology
bump: C1 reads [-1] and W3 reads [-21], both from the end, so widening only
prepends and every live score is bit-identical. Sequenced deliberately, since
acting on the open findings below bumps METHODOLOGY and fires the rebuild.

A just-collected fundamental observation was hidden until its effective date --
one day, three over a weekend -- because the live reading called the
point-in-time function, so refreshing appeared to do nothing. That was the
opposite of what the doc claimed. fundamental_overlay stays the gated record
(it runs for every replayed date during a rebuild); current_observation is the
live reading and reports the effective date instead of blanking the content.
Nothing in the overlay is scored, so showing it early cannot reach a published
number.

Documents four calculation findings. Three are not implemented, since each
changes a published score and so requires a v4 cut: State's top band is a
credit-event band (credit returns 0.0 rather than None below the 3.5 anchor, so
it is pinned at zero at weight 20 -- with everything else pegged State computes
to exactly 80.0, the breaking threshold); V1 saturates at VIX 30; and the
deliberate max(P1,P2,P3) defeats P3's anchoring because P1 is binary.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
 2026-08-07 17:53:45 +02:00
dennisthiessenandClaude Opus 5 23de8c9540 docs(dolt-plan): drop workstream B and record why
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B would have replaced Alpaca historical OHLCV with the DoltHub stocks repo. It
was scoped inside a plan whose goal was killing the quota-limited free-tier APIs
— which A6 achieved without it, since Alpaca was never one of them.

Its only concrete benefit was `corporate_actions` for the KLAC-class post-filing
split (TTM EPS pre-split against a post-split price). That needs split events,
not a 4.7 GB clone, and the Alpaca SDK already in the venv exposes them. Against
that: fundamentals are 20% of the composite and P/E one of three inputs, so the
wart is small and self-correcting at the next filing; and B would have made
symbol history mutable as a routine event, which the backtest/prod parity guard
exists to catch.

The design is kept as a record, struck through rather than deleted, with the
reasoning next to it so this isn't re-derived. Also corrects the doc's status
header, the never-written migration 027 (that number went to
weighted_avg_diluted_shares), and the note that the stocks repo's license was
never reviewed.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
 2026-08-07 13:03:32 +02:00
dennisthiessenandClaude Opus 5 c2a3b56aaa chore: drop the A6 rollback tombstones
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A6 deployed cleanly and the provider keys are gone from the production `.env`,
which makes the legacy collector inert regardless of any settings row. The two
tombstones migration 029 pinned have no remaining job, and nothing in the
codebase reads either key.

Migration 030 deletes them and drops the Admin filter that hid them. Unlike
029's, its downgrade is meaningful — it restores both rows at their safe values,
since going back past this revision means going back toward code that reads them.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
 2026-08-07 12:34:28 +02:00
dennisthiessenandClaude Opus 5 e1607ddbff fix: don't double-report a failed SEC run, and correct the rollback doc
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Three follow-ups from review of the A6 commits.

The cache-summary re-finalize raised a second durable system event on a failed
run: _runtime_finish emits for `error`/`rate_limited`, and the dedup key
includes the message, so "SEC unavailable" and "SEC unavailable · cache 511 · 2
score inputs changed" landed as two unacknowledged Admin events. Adds
`emit_event` so a re-finalize that only rewords an outcome stays silent, with a
regression test asserting exactly one event.

The rollback section still claimed disabling the SEC job freezes the cache — the
opposite of what the same page says two lines earlier, and of what the code now
does. Rewritten: there is no Admin cache-off switch, restoring `fundamental_data`
alone is temporary because the next run rebuilds it from the same snapshots and
code, and a real freeze means stopping the service.

Remaining "shadow" wording: the two import jobs have never been shadow since
activation, so `_run_shadow_import` -> `_run_source_import`, its section heading,
the deployment doc's job label, and the plan doc's "production switch remains"
handoff paragraph are all brought up to date.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
 2026-08-07 11:46:34 +02:00
dennisthiessenandClaude Opus 5 13f3636b6a fix: surface the fundamentals cache result on every SEC job outcome 
The runtime message only appended the cache summary when the import itself
completed. On a deferred, failed or source-locked run Admin → Jobs showed just
the import outcome, so an operator had no signal that `fundamental_data` had
advanced — contradicting the claim now made in the docstring, the schedule hint
and the deployment doc.

The import status still varies and stays the headline; the cache summary is
appended to all of them. Adds a source-locked regression test.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
 2026-08-07 11:22:28 +02:00
dennisthiessenandClaude Opus 5 3e83d63b05 chore: decommission FMP, Finnhub and Alpha Vantage (A6) 
The A5 cutover has been on and observed in production, so SEC Company Facts +
DoltHub earnings are already the live source for `fundamental_data`. This
removes everything the legacy path still occupied.

Gone: the three providers and their config/env keys; the weekly
`fundamental_collector` job; the cutover toggle (SEC + Dolt is now the
unconditional path, so `off` can no longer silently freeze scoring inputs); the
A5 parity report, whose deltas became structurally zero once the candidate
builder started writing the table it compared against; and the FMP tier of
universe bootstrap.

Two behavioral notes:

- Disabling **SEC Fundamentals Import** now stops the SEC network fetch only.
  The local cache refresh moved outside the job-enable check, because candidates
  also derive from daily closes and earnings events — freezing those on an
  ingestion pause would stale scoring with no fallback left to recover from.
- `/ingestion/fetch?sources=fundamentals` still accepts the key and reports
  `skipped`; there is no per-ticker fetch any more.

Migration 029 does not blanket-delete the leftover settings rows. Migrations run
before the service restart, and pre-A6 code reads an absent `job_*_enabled` row
as *enabled* — so the two behavior-bearing keys become tombstones pinned to safe
values (hidden in Admin) and only the inert three are deleted. Removing the
provider keys from the production `.env` is the matching rollout step.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
 2026-08-07 11:19:28 +02:00
dennisthiessenandClaude Opus 5 f5d4b516ab docs: land capacity-study evidence and share the rank-map helper
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Brings the durable artifacts of research/portfolio-capacity-rebalancing onto
main so the rationale for raising the count cap lives with the code that cites
it. The matrix runner, the research simulator hooks and the study's unit tests
are deliberately left behind; they remain at tag research/portfolio-capacity-final.

Corrects conclusions that were reached on EV per trade and are now superseded:
the findings doc's decisions 1 (keep cap 10) and 4 (run the risk-floor A/B) are
struck through and answered in a new correction section, and the research README
and phase-A matrix entries are updated to match. The frozen specification itself
is untouched -- its recorded SHA-256 f1e37783 still verifies.

effective-risk-floor-ab.md is retained but marked CLOSED/NEGATIVE: the study it
proposes is already answered by cap15 vs cash_unbounded (-0.753pp CAGR while
EV/trade rises), and its EV-based pass rule would have shipped it.

scripts/research_rankings.py replaces a fourth copy of the historical rank-map
helper; run_research_matrix, run_execution_recovery_matrix and
run_daily_reentry_matrix now share it. The shared version adds a duplicate
observation guard and a deterministic symbol tie-break the copies lacked.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
 2026-08-05 23:08:29 +02:00
dennisthiessenandClaude Opus 5 3ff0fd9f1c feat: stop the position count cap from binding (10 -> 15) 
The capacity bracket study (reports/portfolio-construction-prod505-capacity-
bracket-daily-v1) showed a book whose count cap never binds earns +1.1pp CAGR
over the old 10 -- 51 of 175 paired cohorts better, 2 worse -- at unchanged
drawdown (+0.007pp) and better Calmar in 51 of the 52 cohorts that moved.

The headline EV-per-trade delta is ~0 (+0.001), which is the trap: capacity
does not change trade quality, it changes trade COUNT. Flat EV/trade means the
blocked entries were just as good as the taken ones, so refusing them cost their
whole contribution to return. Judge capacity on CAGR, never on EV per trade.

15 is headroom, not a target. cap15 peaked at 12 positions with zero full-book
skips, so cash plus SIM_NOTIONAL_CAP is the real ceiling and 15/20/None are the
same experiment.

SIM_MAX_POSITIONS and the shadow book's DEFAULT_CAPACITY move together to keep
backtest and production in parity. Historical research arms pass max_positions
explicitly, so their labels and past results are unaffected.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
 2026-08-05 23:04:21 +02:00
124 changed files with 8261 additions and 8769 deletions
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.env.example
+4 -18
View File
@@ -18,16 +18,9 @@ OPENAI_API_KEY=
OPENAI_MODEL=gpt-4o-mini
OPENAI_SENTIMENT_BATCH_SIZE=5
# Fundamentals Provider — Financial Modeling Prep
FMP_API_KEY=
# Fundamentals Provider — Finnhub (optional fallback)
FINNHUB_API_KEY=
# Fundamentals Provider — Alpha Vantage (optional fallback)
ALPHA_VANTAGE_API_KEY=
# Dolt bulk data — local clone of post-no-preference/earnings (workstream A).
# Dolt bulk data — local clone of post-no-preference/earnings. Together with the
# SEC EDGAR block below this is the ONLY fundamentals source; there is no
# provider-API fallback.
# DOLT_BINARY: path to the dolt CLI (set the full path in dev if it's not on PATH,
# e.g. Windows: C:\Program Files\Dolt\bin\dolt.exe). DOLT_DATA_DIR holds the
# clones; in PRODUCTION it MUST be outside the deploy tree (deploy is
@@ -52,21 +45,14 @@ SEC_REQUEST_SPACING_SECONDS=0.2
SEC_MAX_RETRIES=4
SEC_REQUEST_TIMEOUT_SECONDS=30.0
# A5 read-only parity report archive. In production keep this outside the
# rsync deployment tree, e.g. /var/lib/signal-platform/reports/fundamentals-parity.
FUNDAMENTALS_PARITY_REPORT_DIR=reports/fundamentals-parity
# Regime Monitor — FRED (VIX + HY credit spreads). Free key: https://fred.stlouisfed.org/docs/api/api_key.html
# AI/Tech Risk Monitor — FRED (VIX + HY credit spreads). Free key: https://fred.stlouisfed.org/docs/api/api_key.html
# Optional: without it the volatility (V1) and credit (C1) pillars show as n/a.
FRED_API_KEY=
# Scheduled Jobs
DATA_COLLECTOR_FREQUENCY=daily
SENTIMENT_POLL_INTERVAL_MINUTES=30
FUNDAMENTAL_FETCH_FREQUENCY=daily
RR_SCAN_FREQUENCY=daily
FUNDAMENTAL_RATE_LIMIT_RETRIES=3
FUNDAMENTAL_RATE_LIMIT_BACKOFF_SECONDS=15
# Scoring Defaults
DEFAULT_WATCHLIST_AUTO_SIZE=10
.gitea/workflows/deploy.yml
+4 -1
View File
@@ -38,7 +38,10 @@ jobs:
python-version: "3.12"
cache: "pip"
- run: pip install ruff
- run: ruff check app/
# Whole repo, not just app/: tests/ and scripts/ drifted to 11 findings
# while unchecked. Rules are pinned in pyproject.toml, so the unpinned
# ruff above cannot change what this enforces.
- run: ruff check .
test:
needs: lint
.gitignore
+3
View File
@@ -54,3 +54,6 @@ reports/.cache/
# Runtime A5 parity bundles are generated on the production server. Research
# conclusions belong in docs/research, not as an ever-growing artifact archive.
reports/fundamentals-parity/
# Calibration harness raw-pull cache (Alpaca/FRED); regenerable, not a record.
.calib-cache/
README.md
+8 -21
View File
@@ -133,8 +133,8 @@ indicators.
1. **OHLCV** — latest daily bars (Alpaca); new tickers backfill ~5 years.
2. **Sentiment** — stale names that matter (top-pick feeders, watchlist, open paper, discovery net). Display context only; the activation gate is price-only.
3. **Market Regime** + **Regime Monitor** — breadth/trend and the v3 risk thermometer; feed no trades.
4. **Telegram alerts** — change-driven (regime-quadrant etc.); quiet days stay quiet. Setup alerts still fire on the near-close pipeline after the scan.
3. **Market Trend (SPY)** + **AI/Tech Risk Monitor** — the SPY trend guard and the v4 risk thermometer; feed no trades.
4. **Telegram alerts** — change-driven (risk-quadrant etc.); quiet days stay quiet. Setup alerts still fire on the near-close pipeline after the scan.
**Near-close** (~15:30 ET Mon–Fri) — the only full-universe qualifying observation:
@@ -155,7 +155,7 @@ Hourly mid-session (Mon–Fri ~10:00–15:00 ET): only **OHLCV → Outcome Eval*
### Other jobs
Fundamentals (weekly, early Monday ET) · Backtest (weekly) · Ticker-universe sync (daily). Alerts auto-fire only via the near-close pipeline (still manually triggerable). Deep history backfill and event study are manual-only (Admin → Jobs).
Dolt earnings import (daily 02:30 ET) · SEC fundamentals import (daily 04:00 ET, also refreshes the fundamentals cache scoring reads) · Backtest (weekly) · Ticker-universe sync (daily). Alerts auto-fire only via the near-close pipeline (still manually triggerable). Deep history backfill and event study are manual-only (Admin → Jobs).
### From score to "top pick"
@@ -255,18 +255,11 @@ A systematic single-variable sweep (offline prod snapshot, production gate/rank/
| ATR trail multiple {1.5–4.0} | **Keep 3.0** | Return+Sharpe peak; ≤2.0 whipsaws out the momentum right tail; ≥2.5 is a plateau |
| SPY 200d-MA regime overlay (block entries / go flat) | **Reject** | Halves return (315%→138%) with zero drawdown benefit — the ATR trail already manages downside, and the filter blocks the recovery-phase entries that make the money |
| Momentum lookback: 6-1, 3-1, 12-7 (Novy-Marx), composites | **Keep residual 12-1** | 6-1/3-1 rank-IC ≈ 0; 12-7 IC 0.045 / t 1.58 — weaker than residual 12-1 (0.055 / t 1.98) |
| Selection cutoff {70, 75, 85, 90} × book size {10, 15, 20} | **Keep cutoff 80; capacity reopened** | The older weekly replay favored 80 × 10, but its no-cap-pressure conclusion is superseded by 519 book-full rejections versus 472 trades under the current daily gate-reset control |
| Selection cutoff {70, 75, 85, 90} × book size {10, 15, 20} | **Keep 80 × 10** | Monotonically worse in both directions from 80; the 10-slot cap never binds (<10 concurrent) |
| Position sizing: equal-weight, inverse-vol, risk-% sweep | **Keep 1% fixed-fractional** | See the inverse-vol warning below |
| Post-stop re-entry: immediate, fixed 2–5 sessions, gate resets, confirmation filters | **Keep normal gate reset for the 10-position production book** | Sharpe 1.77 vs 1.67 immediate and 1.47 cooldown 5; rerun before changing portfolio capacity |
| FIP path-smoothness as an in-book tie-breaker/filter | **Reject** (but see the lead below) | Non-monotonic across FIP quintiles within the qualified set; either half of a median split underperforms the full book — thinning the entry stream costs more compounding than the tilt returns |
> **Capacity correction (2026-08-05):** the table's older weekly conclusion
> that the ten-slot cap never binds is superseded. Under the current daily
> gate-reset Phase A control, 472 trades were admitted and 519 qualified entries
> were rejected because the book was full (52.4% of admitted+blocked
> opportunities). Cutoff 80 remains the signal setting; portfolio capacity is
> reopened in the focused capacity-bracket study.
Two findings future sessions must not re-litigate:
- **The "inverse-vol sizing win" (July 2026) was mis-attributed — do not resurrect.** The diagnostic sized `notional = equity × 1% / vol_6m`, and the 20% notional cap bound on 95% of entries, so it actually measured "~5 positions × 20% notional each" — a concentration/risk-appetite bump economically equivalent to raising risk to 1.5%, not vol-managed sizing. Genuine inverse-vol sizing (risk budget × median-vol/vol) cuts max drawdown to −18.2% but costs ~58pp total return at flat Sharpe: a risk-preference trade, not edge.
@@ -308,13 +301,13 @@ Corollaries: never let an unvalidated score gate setups; the outcome evaluator m
| Charts | Canvas 2D candlestick chart with S/R overlays |
| Routing | React Router v6 (SPA) |
| HTTP | Axios with JWT interceptor |
| Data providers | Alpaca (OHLCV); OpenAI / Gemini / DeepSeek / xAI (sentiment, pluggable); Fundamentals chain: FMP → Finnhub → Alpha Vantage; FRED (regime); Telegram (alerts) |
| Data providers | Alpaca (OHLCV); OpenAI / Gemini / DeepSeek / xAI (sentiment, pluggable); SEC EDGAR Company Facts + DoltHub earnings (fundamentals, bulk import); FRED (regime); Telegram (alerts) |
## Features
### Backend
- Ticker registry with full cascade delete
- Universe bootstrap for `sp500`, `nasdaq100`, `nasdaq_all` via admin endpoint
- Universe bootstrap for `sp500`, `nasdaq100`, `nasdaq_all` via admin endpoint — free public sources (Wikipedia / NASDAQ Trader), then the cached snapshot, then a built-in seed list. The seeds are representative, not complete, so a *fresh* install bootstrapped while the public source is unreachable gets a partial universe; a warm instance falls through to its cache.
- OHLCV price storage with upsert and validation
- Technical indicators: ADX, EMA, RSI, ATR, Volume Profile, Pivot Points, EMA Cross
- Structural Support/Resistance detection with rejection/recency strength, ATR-adaptive merging and a hard cap; persisted for charts and alerts
@@ -358,7 +351,7 @@ Corollaries: never let an unvalidated score gate setups; the outcome evaluator m
| `/` | Dashboard — top setups, open trades, regime (default) | Authenticated |
| `/market` | Market — watchlist + rankings tabs | Authenticated |
| `/signals` | Signals — scanner + track record tabs | Authenticated |
| `/regime` | Market Regime | Authenticated |
| `/regime` | AI/Tech Risk Monitor | Authenticated |
| `/ticker/:symbol` | Ticker Detail | Authenticated |
| `/admin` | Admin Panel | Admin only |
@@ -590,18 +583,12 @@ Configure in `.env` (copy from `.env.example`):
| `OPENAI_API_KEY` | For sentiment (OpenAI path) | — | OpenAI API key |
| `OPENAI_MODEL` | No | `gpt-4o-mini` | OpenAI model name |
| `OPENAI_SENTIMENT_BATCH_SIZE` | No | `5` | Micro-batch size for sentiment collector |
| `FMP_API_KEY` | Optional (fundamentals) | — | Financial Modeling Prep API key (first provider in chain) |
| `FINNHUB_API_KEY` | Optional (fundamentals) | — | Finnhub API key (fallback provider) |
| `ALPHA_VANTAGE_API_KEY` | Optional (fundamentals) | — | Alpha Vantage API key (fallback provider) |
| `FRED_API_KEY` | Optional (regime) | — | FRED key for the regime monitor (VIX, credit spreads) |
| `FRED_API_KEY` | Optional (risk monitor) | — | FRED key for the AI/Tech risk monitor (VIX, credit spreads) |
| `TELEGRAM_BOT_TOKEN` | Optional (alerts) | — | Telegram bot token for alerts (can also be set in Admin) |
| `TELEGRAM_CHAT_ID` | Optional (alerts) | — | Telegram chat id for alerts |
| `DATA_COLLECTOR_FREQUENCY` | No | `daily` | OHLCV collection schedule (legacy — see note below) |
| `SENTIMENT_POLL_INTERVAL_MINUTES` | No | `30` | Sentiment polling interval |
| `FUNDAMENTAL_FETCH_FREQUENCY` | No | `weekly` | Fundamentals fetch cadence |
| `RR_SCAN_FREQUENCY` | No | `daily` | R:R scanner schedule |
| `FUNDAMENTAL_RATE_LIMIT_RETRIES` | No | `3` | Retries per ticker on fundamentals rate-limit |
| `FUNDAMENTAL_RATE_LIMIT_BACKOFF_SECONDS` | No | `15` | Base backoff seconds for fundamentals retry (exponential) |
| `DEFAULT_WATCHLIST_AUTO_SIZE` | No | `10` | Auto-watchlist size |
| `DEFAULT_RR_THRESHOLD` | No | `1.5` | Minimum R:R ratio for setups |
| `DB_POOL_SIZE` | No | `5` | Database connection pool size |
alembic/versions/029_retire_legacy_fundamentals_settings.py
+96
View File
@@ -0,0 +1,96 @@
"""Retire the legacy fundamentals settings (A6)
Revision ID: 029
Revises: 028
Create Date: 2026-08-07 00:00:00.000000
A6 removed the FMP/Finnhub/Alpha Vantage providers, the weekly
``fundamental_collector`` job and the A5 parity report. Five SystemSetting rows
are left over. They are NOT all deleted, because the deploy runs migrations
before restarting the service: for a short window — and for the whole of any
rollback — pre-A6 code is still live, and it reads absent rows permissively
(cutover absent -> disabled; ``job_<name>_enabled`` absent -> enabled). Deleting
both would hand a rolled-back process a re-armed legacy collector writing over
the SEC/Dolt cache.
So the two rows that carry behavior become tombstones pinned to the safe value,
and only the inert ones are deleted. The tombstones are dropped in a later
release once the rollback window has closed; ``SettingsForm`` hides them
meanwhile.
"""
from typing import Sequence, Union
from alembic import op
import sqlalchemy as sa
revision: str = "029"
down_revision: Union[str, None] = "028"
branch_labels: Union[str, Sequence[str], None] = None
depends_on: Union[str, Sequence[str], None] = None
# Behavior-bearing under pre-A6 code -> pin to the safe value, keep the row.
_TOMBSTONES: dict[str, str] = {
"fundamental_data_sec_dolt_cutover_enabled": "true",
"job_fundamental_collector_enabled": "false",
}
# Inert either way: an absent cron falls back to a default for a job that no
# longer registers, and the parity report never wrote anything.
_OBSOLETE: tuple[str, ...] = (
"schedule_fundamentals_cron",
"schedule_fundamentals_parity_cron",
"job_fundamentals_parity_report_enabled",
)
_settings = sa.table(
"system_settings",
sa.column("id", sa.Integer),
sa.column("key", sa.String),
sa.column("value", sa.Text),
sa.column("updated_at", sa.DateTime(timezone=True)),
)
def upgrade() -> None:
conn = op.get_bind()
now = sa.func.now()
for key, pinned in _TOMBSTONES.items():
row = conn.execute(
sa.select(_settings.c.value).where(_settings.c.key == key)
).fetchone()
old_value = row[0] if row is not None else None
print(f"a6_tombstone {key}: {old_value!r} -> {pinned!r}", flush=True)
if row is None:
conn.execute(
sa.insert(_settings).values(key=key, value=pinned, updated_at=now)
)
elif old_value != pinned:
conn.execute(
sa.update(_settings)
.where(_settings.c.key == key)
.values(value=pinned, updated_at=now)
)
# Print the value before deleting — a bare DELETE cannot be undone from the
# migration output.
for key in _OBSOLETE:
row = conn.execute(
sa.select(_settings.c.value).where(_settings.c.key == key)
).fetchone()
if row is None:
print(f"a6_delete {key}: absent", flush=True)
continue
print(f"a6_delete {key}: {row[0]!r}", flush=True)
conn.execute(sa.delete(_settings).where(_settings.c.key == key))
def downgrade() -> None:
"""No-op.
The deleted rows configured jobs this revision's code no longer registers,
and the tombstones already hold the values pre-A6 code needs. Recreating
them would restore nothing useful; the printed values above cover recovery.
"""
alembic/versions/030_drop_legacy_fundamentals_tombstones.py
+71
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@@ -0,0 +1,71 @@
"""Drop the A6 rollback tombstones
Revision ID: 030
Revises: 029
Create Date: 2026-08-07 00:00:00.000000
Migration ``029`` kept two SystemSetting rows alive as rollback tombstones,
pinned to the values a pre-A6 process needed to behave safely. A6 is deployed
and healthy, and the provider keys are gone from the production ``.env`` — which
makes the legacy collector inert regardless of any settings row — so the
tombstones have no remaining job.
Nothing in the current codebase reads either key.
"""
from typing import Sequence, Union
from alembic import op
import sqlalchemy as sa
revision: str = "030"
down_revision: Union[str, None] = "029"
branch_labels: Union[str, Sequence[str], None] = None
depends_on: Union[str, Sequence[str], None] = None
# The safe values 029 pinned. Kept here so downgrade restores real protection
# rather than leaving a rolled-back process reading absent rows permissively.
_TOMBSTONES: dict[str, str] = {
"fundamental_data_sec_dolt_cutover_enabled": "true",
"job_fundamental_collector_enabled": "false",
}
_settings = sa.table(
"system_settings",
sa.column("id", sa.Integer),
sa.column("key", sa.String),
sa.column("value", sa.Text),
sa.column("updated_at", sa.DateTime(timezone=True)),
)
def upgrade() -> None:
conn = op.get_bind()
for key in _TOMBSTONES:
row = conn.execute(
sa.select(_settings.c.value).where(_settings.c.key == key)
).fetchone()
if row is None:
print(f"a6_tombstone_drop {key}: absent", flush=True)
continue
print(f"a6_tombstone_drop {key}: {row[0]!r}", flush=True)
conn.execute(sa.delete(_settings).where(_settings.c.key == key))
def downgrade() -> None:
"""Restore the tombstones at their safe values.
Unlike 029's no-op downgrade, this one is meaningful: going back past this
revision implies going back toward code that still reads these keys.
"""
conn = op.get_bind()
now = sa.func.now()
for key, pinned in _TOMBSTONES.items():
exists = conn.execute(
sa.select(_settings.c.id).where(_settings.c.key == key)
).fetchone()
if exists is None:
conn.execute(
sa.insert(_settings).values(key=key, value=pinned, updated_at=now)
)
alembic/versions/031_job_run_state.py
+51
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@@ -0,0 +1,51 @@
"""Durable last-run state per scheduled job
Revision ID: 031
Revises: 030
Create Date: 2026-08-08 00:00:00.000000
Job run state lived only in an in-memory dict in ``app.scheduler``, so every
process restart wiped it. Admin → Jobs could then only report "Active" with no
indication of whether a job had ever run, or how it ended — which is exactly
the information an operator opens that page for.
One row per job, upserted on ``job_name``. Not history: ``system_events``
already grows unbounded with no retention job, and a second append-only
operational table would repeat that debt.
The table starts empty; each job populates its row the next time it finishes.
No backfill from ``system_events`` — that table only records warning/error
outcomes and uses a different status vocabulary, so seeding from it would
invent successful runs that never happened.
"""
from typing import Sequence, Union
from alembic import op
import sqlalchemy as sa
revision: str = "031"
down_revision: Union[str, None] = "030"
branch_labels: Union[str, Sequence[str], None] = None
depends_on: Union[str, Sequence[str], None] = None
def upgrade() -> None:
op.create_table(
"job_run_state",
sa.Column("id", sa.Integer(), nullable=False),
sa.Column("job_name", sa.String(length=64), nullable=False),
sa.Column("status", sa.String(length=32), nullable=False),
sa.Column("started_at", sa.DateTime(timezone=True), nullable=True),
sa.Column("finished_at", sa.DateTime(timezone=True), nullable=False),
sa.Column("processed", sa.Integer(), nullable=True),
sa.Column("total", sa.Integer(), nullable=True),
sa.Column("message", sa.Text(), nullable=True),
sa.Column("updated_at", sa.DateTime(timezone=True), nullable=False),
sa.PrimaryKeyConstraint("id"),
sa.UniqueConstraint("job_name", name="uq_job_run_state_job_name"),
)
def downgrade() -> None:
op.drop_table("job_run_state")
alembic/versions/032_ticker_delisting.py
+49
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@@ -0,0 +1,49 @@
"""Record delisting on tickers instead of deleting them
Revision ID: 032
Revises: 031
Create Date: 2026-08-11 00:00:00.000000
Until now the only way to retire a symbol was ``delete_ticker`` (or
``bootstrap_universe(prune_missing=True)``), both of which cascade through
OHLCV, setups and scores. That destroys exactly the history four research
documents already apologise for: today's tracked universe projected backward
is survivorship-biased, and hard-deleting every delisted name is what causes
it. Keeping the rows preserves the option to fix that later — it does not fix
it by itself, which needs the replay to model a delisting as an exit event.
``delisted_on`` is the effective date (from SEC Form 25/25-NSE/15 where we can
confirm it, else the day it was marked); ``delisted_reason`` is a short code
for how we learned. NULL in both means actively traded — the live signal path
filters on that, while list and admin views keep showing the row so the
delisting is visible rather than silently absent.
Nullable and reversible by design: clearing ``delisted_on`` un-retires a
symbol, which is what makes automatic marking safe where a delete would not be.
"""
from typing import Sequence, Union
from alembic import op
import sqlalchemy as sa
revision: str = "032"
down_revision: Union[str, None] = "031"
branch_labels: Union[str, Sequence[str], None] = None
depends_on: Union[str, Sequence[str], None] = None
def upgrade() -> None:
op.add_column("tickers", sa.Column("delisted_on", sa.Date(), nullable=True))
op.add_column(
"tickers", sa.Column("delisted_reason", sa.String(length=32), nullable=True)
)
# The live path filters "actively traded" on every universe scan; the index
# keeps that predicate cheap as delisted rows accumulate.
op.create_index("ix_tickers_delisted_on", "tickers", ["delisted_on"])
def downgrade() -> None:
op.drop_index("ix_tickers_delisted_on", table_name="tickers")
op.drop_column("tickers", "delisted_reason")
op.drop_column("tickers", "delisted_on")
app/config.py
+1 -21
View File
@@ -28,15 +28,6 @@ class Settings(BaseSettings):
deepseek_api_key: str = ""
xai_api_key: str = ""
# Fundamentals Provider — Financial Modeling Prep
fmp_api_key: str = ""
# Fundamentals Provider — Finnhub (optional fallback)
finnhub_api_key: str = ""
# Fundamentals Provider — Alpha Vantage (optional fallback)
alpha_vantage_api_key: str = ""
# Dolt bulk-data — local clone of post-no-preference/earnings (workstream A).
# dolt_binary: full path when not on PATH (dev/Windows install). dolt_data_dir
# holds the clones; in production it MUST be outside the deploy tree (deploy is
@@ -61,11 +52,7 @@ class Settings(BaseSettings):
sec_max_retries: int = 4
sec_request_timeout_seconds: float = 30.0
# A5 read-only comparison artifacts. Production must keep this outside the
# rsync deployment tree so the 5-7 day review window survives deploys.
fundamentals_parity_report_dir: str = "reports/fundamentals-parity"
# Regime Monitor — FRED (VIX level + HY credit spreads). Optional: without it
# AI/Tech Risk Monitor — FRED (VIX level + HY credit spreads). Optional: without it
# the volatility (P5) and credit-spread (F2) signals are reported as n/a.
fred_api_key: str = ""
@@ -86,15 +73,8 @@ class Settings(BaseSettings):
# the score window is 7 days).
sentiment_fresh_hours: int = 120
sentiment_top_composite: int = 30
fundamental_fetch_frequency: str = "weekly" # quarterly-ish data; weekly conserves API quota
rr_scan_frequency: str = "daily" # legacy label; qualifying scan is cron near-close
# alerts_frequency removed: alerts fire only via morning + near-close pipelines
fundamental_rate_limit_retries: int = 3
fundamental_rate_limit_backoff_seconds: int = 15
# Pause between tickers in the bulk fundamentals job. Free tiers throttle
# hard (Finnhub ~60 calls/min, ~3 calls/ticker → ~3s/ticker); without
# spacing the job bursts straight into 429s. 0 disables.
fundamental_request_spacing_seconds: float = 3.0
# Scoring Defaults
default_watchlist_auto_size: int = 10
app/job_catalog.py
+207
View File
@@ -0,0 +1,207 @@
"""Job topology: names, labels, pipeline membership, categories, ordering.
The single source of truth for *what the jobs are*, as opposed to how they run.
It deliberately imports nothing from ``app`` so both ``app.scheduler`` and
``app.services.admin_service`` can import it at module level -- admin_service
otherwise has to do ``from app.scheduler import ...`` inside functions to dodge a
cycle.
The pipeline step lists live here rather than in the scheduler because three
separate things need them and used to keep private copies: the runner, the
``PIPELINE_MEMBERS`` set the admin API reports, and the UI's grouping. Steps are
``(step_name, coroutine_name)``; ``_run_pipeline`` resolves the coroutine late
out of the scheduler's own globals, so nothing here depends on those functions
existing.
"""
from __future__ import annotations
# ---------------------------------------------------------------------------
# Pipelines
# ---------------------------------------------------------------------------
_DAILY_PIPELINE_STEPS = [
("data_collector", "collect_ohlcv"),
("benchmark_collector", "collect_benchmark"),
("sentiment_collector", "collect_sentiment"),
("market_regime", "compute_market_regime"),
# Observational only — display/alerts; not trade selection.
("regime_monitor", "compute_regime_monitor"),
# Alerts after regime so quadrant changes reach Telegram in the morning.
# Dispatcher is change-driven; quiet days stay quiet. Setup alerts still
# fire on the near-close pipeline after the qualifying scan.
("alerts", "dispatch_alerts_job"),
]
# Near-close (~15:30 ET Mon–Fri): refresh in-progress day-t bars (incremental
# ingestion overlaps the latest stored session), then the only daily
# qualifying R:R scan, then Telegram immediately so manual fills can still hit
# MOC cutoffs (~15:50/15:55). Under a 15-minute delayed SIP feed a 15:30 scan
# may see ~15:15 prices — immaterial for a 12-1 momentum signal.
#
# US early-close days (~3/year, 13:00 ET close): this job runs post-close and
# entries behave like stale_close (still acceptable per execution-recovery matrix).
# No exchange calendar dependency.
_NEAR_CLOSE_PIPELINE_STEPS = [
# Must land today's in-progress bar (~20 min behind live), or the scan falls
# back to the previous close and execution degrades to the stale_close floor.
("data_collector", "collect_ohlcv_for_scan"),
("rr_scanner", "scan_rr"),
# Straight after the scan so shadow entries mark at the same near-close
# prices the discretionary book is looking at.
("shadow_book", "run_shadow_book"),
("alerts", "dispatch_alerts_job"),
]
# After close (~16:45 ET Mon–Fri): fresh OHLCV fetch so outcomes resolve on the
# final bar, not the near-close partial bar, then outcome/paper close.
_AFTER_CLOSE_PIPELINE_STEPS = [
("data_collector", "collect_ohlcv_final"),
("outcome_evaluator", "evaluate_outcomes"),
]
# Intraday (light): keep prices current and resolve outcomes through the day,
# without the expensive scan/sentiment. The dashboard recomputes live R:R from
# the latest price, so refreshing OHLCV is enough to stop prices lagging; the
# outcome step also closes paper trades that hit their stop/target intraday.
_INTRADAY_PIPELINE_STEPS = [
("data_collector", "collect_ohlcv"),
("outcome_evaluator", "evaluate_outcomes"),
]
# Ordered by trading day, not alphabetically: this is the sequence an operator
# reads down the page, and it drives the UI's ordering too.
PIPELINE_STEPS: dict[str, list[tuple[str, str]]] = {
"daily_pipeline": _DAILY_PIPELINE_STEPS,
"intraday_pipeline": _INTRADAY_PIPELINE_STEPS,
"near_close_pipeline": _NEAR_CLOSE_PIPELINE_STEPS,
"after_close_pipeline": _AFTER_CLOSE_PIPELINE_STEPS,
}
# Derived, never hand-maintained: this used to be a literal set in admin_service
# duplicating the four lists above from another module, with nothing asserting
# the two agreed.
PIPELINE_MEMBERS: frozenset[str] = frozenset(
step for steps in PIPELINE_STEPS.values() for step, _ in steps
)
def _pipelines_by_member() -> dict[str, tuple[str, ...]]:
"""Member -> the orchestrators that run it, in trading-day order.
Membership is many-to-many: data_collector runs in all four pipelines (via
three different coroutines), alerts and outcome_evaluator in two each.
"""
out: dict[str, list[str]] = {}
for pipeline, steps in PIPELINE_STEPS.items():
for step, _ in steps:
bucket = out.setdefault(step, [])
if pipeline not in bucket:
bucket.append(pipeline)
return {member: tuple(pipelines) for member, pipelines in out.items()}
PIPELINES_BY_MEMBER: dict[str, tuple[str, ...]] = _pipelines_by_member()
# ---------------------------------------------------------------------------
# Job identity
# ---------------------------------------------------------------------------
# Orchestrators, in trading-day order.
PIPELINE_JOBS: tuple[str, ...] = tuple(PIPELINE_STEPS)
# Own timer, independent of any pipeline.
SCHEDULED_JOBS: tuple[str, ...] = (
"dolt_earnings_import",
"sec_fundamentals_import",
"ticker_universe_sync",
"backtest",
)
# Registered but never auto-fired; run only when a human asks.
MANUAL_JOBS: tuple[str, ...] = ("event_study", "data_backfill")
# Steps in the order an operator meets them across the trading day, so the UI
# reads as a sequence rather than an alphabetical jumble.
PIPELINE_STEP_JOBS: tuple[str, ...] = tuple(
dict.fromkeys(step for steps in PIPELINE_STEPS.values() for step, _ in steps)
)
VALID_JOB_NAMES: frozenset[str] = frozenset(
PIPELINE_JOBS + PIPELINE_STEP_JOBS + SCHEDULED_JOBS + MANUAL_JOBS
)
JOB_LABELS: dict[str, str] = {
"data_collector": "Data Collector (OHLCV)",
"data_backfill": "Data Backfill (deep history)",
"benchmark_collector": "Benchmark Collector",
"sentiment_collector": "Sentiment Collector",
"dolt_earnings_import": "Dolt Earnings Import",
"sec_fundamentals_import": "SEC Fundamentals Import",
"rr_scanner": "R:R Scanner",
"ticker_universe_sync": "Ticker Universe Sync",
"outcome_evaluator": "Outcome Evaluator",
"alerts": "Alerts Dispatcher",
# Keys are persisted job ids and must not change; these are display only.
"market_regime": "Market Trend (SPY)",
"regime_monitor": "AI/Tech Risk Monitor",
"event_study": "Event Study",
"backtest": "Backtest",
"daily_pipeline": "Morning Pipeline",
"near_close_pipeline": "Near-Close Pipeline (scan+alert)",
"after_close_pipeline": "After-Close Pipeline (outcome)",
"intraday_pipeline": "Intraday Pipeline",
"shadow_book": "Shadow Book (auto-traded strategy)",
}
CATEGORY_PIPELINE = "pipeline"
CATEGORY_STEP = "pipeline_step"
CATEGORY_SCHEDULED = "scheduled"
CATEGORY_MANUAL = "manual"
# Order the sections appear in.
CATEGORY_ORDER: tuple[str, ...] = (
CATEGORY_PIPELINE,
CATEGORY_STEP,
CATEGORY_SCHEDULED,
CATEGORY_MANUAL,
)
CATEGORY_LABELS: dict[str, str] = {
CATEGORY_PIPELINE: "Pipelines",
CATEGORY_STEP: "Pipeline steps",
CATEGORY_SCHEDULED: "Standalone scheduled",
CATEGORY_MANUAL: "Manual only",
}
_CATEGORY_MEMBERS: dict[str, tuple[str, ...]] = {
CATEGORY_PIPELINE: PIPELINE_JOBS,
CATEGORY_STEP: PIPELINE_STEP_JOBS,
CATEGORY_SCHEDULED: SCHEDULED_JOBS,
CATEGORY_MANUAL: MANUAL_JOBS,
}
JOB_CATEGORY: dict[str, str] = {
name: category
for category, names in _CATEGORY_MEMBERS.items()
for name in names
}
# Registered and triggerable through the API, but kept out of Admin → Jobs.
# data_backfill's only capability beyond collect_ohlcv (which already backfills
# full history for *new* tickers) is re-deepening *existing* ones after
# ohlcv_history_days is raised -- a rare one-off, not something to scan past
# every time you open the page.
HIDDEN_JOBS: frozenset[str] = frozenset({"data_backfill"})
_SORT_INDEX: dict[str, tuple[int, int]] = {
name: (CATEGORY_ORDER.index(category), position)
for category, names in _CATEGORY_MEMBERS.items()
for position, name in enumerate(names)
}
def sort_order(job_name: str) -> tuple[int, int]:
"""(category rank, position within category). Unknown jobs sort last."""
return _SORT_INDEX.get(job_name, (len(CATEGORY_ORDER), 0))
app/main.py
+9 -1
View File
@@ -21,7 +21,12 @@ from app.config import settings
from app.database import async_session_factory, engine
from app.middleware import register_exception_handlers
from app.models.user import User
from app.scheduler import configure_scheduler, load_schedule_config, scheduler
from app.scheduler import (
configure_scheduler,
flush_job_run_persists,
load_schedule_config,
scheduler,
)
from app.routers.admin import router as admin_router
from app.routers.auth import router as auth_router
from app.routers.health import router as health_router
@@ -91,6 +96,9 @@ async def lifespan(_app: FastAPI) -> AsyncGenerator[None, None]:
scheduler.shutdown(wait=False)
logger.info("Scheduler stopped")
# Drain detached last-run writes before the engine goes away, or a job that
# finished during shutdown loses the row it just wrote.
await flush_job_run_persists()
await engine.dispose()
logger.info("Shutting down")
app/models/__init__.py
+2
View File
@@ -18,6 +18,7 @@ from app.models.benchmark_price import BenchmarkPrice
from app.models.signal_context_snapshot import SignalContextSnapshot
from app.models.system_event import SystemEvent
from app.models.sec_filing_gap import SecFilingGap
from app.models.job_run_state import JobRunState
__all__ = [
"Ticker",
@@ -42,4 +43,5 @@ __all__ = [
"SignalContextSnapshot",
"SystemEvent",
"SecFilingGap",
"JobRunState",
]
app/models/job_run_state.py
+37
View File
@@ -0,0 +1,37 @@
from datetime import datetime
from sqlalchemy import DateTime, Integer, String, Text
from sqlalchemy.orm import Mapped, mapped_column
from app.database import Base
class JobRunState(Base):
"""How each scheduled job last finished. One row per job, overwritten.
The scheduler's ``_job_runtime`` dict is the live view and is deliberately
in-memory, but it is also wiped by every process restart -- so after a deploy
Admin → Jobs could only say "Active" with no indication of whether a job had
ever run. This is the durable half.
Deliberately not history: ``system_events`` already grows without a reaper,
and a second append-only operational table would repeat that. Rows are
upserted on ``job_name``; adding history later is purely additive.
"""
__tablename__ = "job_run_state"
id: Mapped[int] = mapped_column(primary_key=True)
job_name: Mapped[str] = mapped_column(String(64), unique=True, nullable=False)
# Scheduler vocabulary: completed | skipped | error | rate_limited | deferred.
# Distinct from data_import_runs' statuses, which is one reason this is its
# own table rather than a widened column there.
status: Mapped[str] = mapped_column(String(32), nullable=False)
started_at: Mapped[datetime | None] = mapped_column(DateTime(timezone=True), nullable=True)
finished_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), nullable=False)
processed: Mapped[int | None] = mapped_column(Integer, nullable=True)
total: Mapped[int | None] = mapped_column(Integer, nullable=True)
message: Mapped[str | None] = mapped_column(Text, nullable=True)
updated_at: Mapped[datetime] = mapped_column(
DateTime(timezone=True), default=datetime.utcnow, onupdate=datetime.utcnow, nullable=False
)
app/models/regime_snapshot.py
+1 -1
View File
@@ -8,7 +8,7 @@ from app.database import Base
class RegimeSnapshot(Base):
"""Daily point-in-time snapshot of the AI/Tech Regime Monitor.
"""Daily point-in-time snapshot of the AI/Tech Risk Monitor.
One row per calendar date (unique). ``breakdown_json`` holds the full
``breakdown_json`` is authoritative for v2 State, Warning, source dates,
app/models/ticker.py
+9 -2
View File
@@ -1,6 +1,6 @@
from datetime import datetime
from datetime import date, datetime
from sqlalchemy import String, DateTime
from sqlalchemy import Date, String, DateTime
from sqlalchemy.orm import Mapped, mapped_column, relationship
from app.database import Base
@@ -21,6 +21,13 @@ class Ticker(Base):
cik: Mapped[str | None] = mapped_column(String(10), nullable=True)
sic: Mapped[str | None] = mapped_column(String(4), nullable=True)
sic_description: Mapped[str | None] = mapped_column(String(160), nullable=True)
# Delisting is recorded, never deleted: the rows carry the price history that
# makes a backtest less survivorship-biased, and a delete cascades it away.
# NULL == actively traded. The live signal path filters on this (see
# ticker_service.active_only); list/admin views keep the row and show it.
delisted_on: Mapped[date | None] = mapped_column(Date, nullable=True, index=True)
# How we learned: "form_25" (SEC confirmed), "manual" (operator).
delisted_reason: Mapped[str | None] = mapped_column(String(32), nullable=True)
created_at: Mapped[datetime] = mapped_column(
DateTime(timezone=True), default=datetime.utcnow, nullable=False
)
app/providers/fmp.py
-174
View File
@@ -1,174 +0,0 @@
"""Financial Modeling Prep (FMP) fundamentals provider using httpx.
Uses the stable API endpoints (https://financialmodelingprep.com/stable/)
which replaced the legacy /api/v3/ endpoints deprecated in Aug 2025.
"""
from __future__ import annotations
import logging
import os
from datetime import datetime, timezone
from pathlib import Path
import httpx
from app.exceptions import ProviderError, RateLimitError
from app.providers.protocol import FundamentalData
logger = logging.getLogger(__name__)
_FMP_STABLE_URL = "https://financialmodelingprep.com/stable"
# Resolve CA bundle for explicit httpx verify
_CA_BUNDLE = os.environ.get("SSL_CERT_FILE", "")
if not _CA_BUNDLE or not Path(_CA_BUNDLE).exists():
_CA_BUNDLE_PATH: str | bool = True # use system default
else:
_CA_BUNDLE_PATH = _CA_BUNDLE
class FMPFundamentalProvider:
"""Fetches fundamental data from Financial Modeling Prep REST API."""
def __init__(self, api_key: str) -> None:
if not api_key:
raise ProviderError("FMP API key is required")
self._api_key = api_key
# Mapping from FMP endpoint name to the FundamentalData field it populates
_ENDPOINT_FIELD_MAP: dict[str, str] = {
"ratios-ttm": "pe_ratio",
"financial-growth": "revenue_growth",
"earnings": "earnings_surprise",
}
async def fetch_fundamentals(self, ticker: str) -> FundamentalData:
"""Fetch P/E, revenue growth, earnings surprise, and market cap.
Fetches from multiple stable endpoints. If a supplementary endpoint
(ratios, growth, earnings) returns 402 (paid tier), we gracefully
degrade and return partial data rather than failing entirely, and
record the affected field in ``unavailable_fields``.
"""
try:
endpoints_402: set[str] = set()
async with httpx.AsyncClient(timeout=30.0, verify=_CA_BUNDLE_PATH) as client:
params = {"symbol": ticker, "apikey": self._api_key}
# Profile is the primary source — must succeed
profile = await self._fetch_json(client, "profile", params, ticker)
# Supplementary sources — degrade gracefully on 402
ratios, was_402 = await self._fetch_json_optional(client, "ratios-ttm", params, ticker)
if was_402:
endpoints_402.add("ratios-ttm")
growth, was_402 = await self._fetch_json_optional(client, "financial-growth", params, ticker)
if was_402:
endpoints_402.add("financial-growth")
earnings, was_402 = await self._fetch_json_optional(client, "earnings", params, ticker)
if was_402:
endpoints_402.add("earnings")
pe_ratio = self._safe_float(ratios.get("priceToEarningsRatioTTM"))
revenue_growth = self._safe_float(growth.get("revenueGrowth"))
market_cap = self._safe_float(profile.get("marketCap"))
earnings_surprise = self._compute_earnings_surprise(earnings)
# Build unavailable_fields from 402 endpoints
unavailable_fields: dict[str, str] = {
self._ENDPOINT_FIELD_MAP[ep]: "requires paid plan"
for ep in endpoints_402
if ep in self._ENDPOINT_FIELD_MAP
}
return FundamentalData(
ticker=ticker,
pe_ratio=pe_ratio,
revenue_growth=revenue_growth,
earnings_surprise=earnings_surprise,
market_cap=market_cap,
fetched_at=datetime.now(timezone.utc),
unavailable_fields=unavailable_fields,
)
except (ProviderError, RateLimitError):
raise
except Exception as exc:
logger.error("FMP provider error for %s: %s", ticker, exc)
raise ProviderError(f"FMP provider error for {ticker}: {exc}") from exc
async def _fetch_json(
self,
client: httpx.AsyncClient,
endpoint: str,
params: dict,
ticker: str,
) -> dict:
"""Fetch a stable endpoint and return the first item (or empty dict)."""
url = f"{_FMP_STABLE_URL}/{endpoint}"
resp = await client.get(url, params=params)
self._check_response(resp, ticker, endpoint)
data = resp.json()
if isinstance(data, list):
return data[0] if data else {}
return data if isinstance(data, dict) else {}
async def _fetch_json_optional(
self,
client: httpx.AsyncClient,
endpoint: str,
params: dict,
ticker: str,
) -> tuple[dict, bool]:
"""Fetch a stable endpoint, returning ``({}, True)`` on 402 (paid tier).
Returns a tuple of (data_dict, was_402) so callers can track which
endpoints required a paid plan.
"""
url = f"{_FMP_STABLE_URL}/{endpoint}"
resp = await client.get(url, params=params)
if resp.status_code == 402:
logger.warning("FMP %s requires paid plan — skipping for %s", endpoint, ticker)
return {}, True
self._check_response(resp, ticker, endpoint)
data = resp.json()
if isinstance(data, list):
return (data[0] if data else {}, False)
return (data if isinstance(data, dict) else {}, False)
def _compute_earnings_surprise(self, earnings_data: dict) -> float | None:
"""Compute earnings surprise % from the most recent actual vs estimated EPS."""
actual = self._safe_float(earnings_data.get("epsActual"))
estimated = self._safe_float(earnings_data.get("epsEstimated"))
if actual is None or estimated is None or estimated == 0:
return None
return ((actual - estimated) / abs(estimated)) * 100
def _check_response(
self, resp: httpx.Response, ticker: str, endpoint: str
) -> None:
"""Raise appropriate errors for non-200 responses."""
if resp.status_code == 429:
raise RateLimitError(f"FMP rate limit hit for {ticker} ({endpoint})")
if resp.status_code == 403:
raise ProviderError(
f"FMP {endpoint} access denied for {ticker}: HTTP 403 — check API key validity and plan tier"
)
if resp.status_code != 200:
raise ProviderError(
f"FMP {endpoint} error for {ticker}: HTTP {resp.status_code}"
)
@staticmethod
def _safe_float(value: object) -> float | None:
"""Convert a value to float, returning None on failure."""
if value is None:
return None
try:
return float(value)
except (TypeError, ValueError):
return None
app/providers/fundamentals_chain.py
-354
View File
@@ -1,354 +0,0 @@
"""Chained fundamentals provider with fallback adapters.
Order:
1) FMP (if configured)
2) Finnhub (if configured)
3) Alpha Vantage (if configured)
"""
from __future__ import annotations
import logging
import os
from datetime import date, datetime, timedelta, timezone
from pathlib import Path
import httpx
from app.config import settings
from app.exceptions import ProviderError, RateLimitError
from app.providers.fmp import FMPFundamentalProvider
from app.providers.protocol import FundamentalData, FundamentalProvider
logger = logging.getLogger(__name__)
_CA_BUNDLE = os.environ.get("SSL_CERT_FILE", "")
if not _CA_BUNDLE or not Path(_CA_BUNDLE).exists():
_CA_BUNDLE_PATH: str | bool = True
else:
_CA_BUNDLE_PATH = _CA_BUNDLE
def _safe_float(value: object) -> float | None:
if value is None:
return None
try:
return float(value)
except (TypeError, ValueError):
return None
def _to_api_symbol(symbol: str) -> str:
"""Convert internal symbol format (BRK-B) to API format (BRK.B).
Finnhub and Alpha Vantage use dot-separated share class notation.
"""
return symbol.replace("-", ".")
class FinnhubFundamentalProvider:
"""Fundamentals provider backed by Finnhub free endpoints."""
def __init__(self, api_key: str) -> None:
if not api_key:
raise ProviderError("Finnhub API key is required")
self._api_key = api_key
self._base_url = "https://finnhub.io/api/v1"
async def fetch_fundamentals(self, ticker: str) -> FundamentalData:
unavailable: dict[str, str] = {}
api_symbol = _to_api_symbol(ticker)
today = date.today()
async with httpx.AsyncClient(timeout=30.0, verify=_CA_BUNDLE_PATH) as client:
profile_resp = await client.get(
f"{self._base_url}/stock/profile2",
params={"symbol": api_symbol, "token": self._api_key},
)
metric_resp = await client.get(
f"{self._base_url}/stock/metric",
params={"symbol": api_symbol, "metric": "all", "token": self._api_key},
)
earnings_resp = await client.get(
f"{self._base_url}/stock/earnings",
params={"symbol": api_symbol, "limit": 1, "token": self._api_key},
)
calendar_resp = await client.get(
f"{self._base_url}/calendar/earnings",
params={
"symbol": api_symbol,
"from": today.isoformat(),
"to": (today + timedelta(days=120)).isoformat(),
"token": self._api_key,
},
)
for resp, endpoint in (
(profile_resp, "profile2"),
(metric_resp, "stock/metric"),
(earnings_resp, "stock/earnings"),
(calendar_resp, "calendar/earnings"),
):
if resp.status_code == 429:
raise RateLimitError(f"Finnhub rate limit hit for {ticker} ({endpoint})")
if resp.status_code in (401, 403):
raise ProviderError(f"Finnhub access denied for {ticker} ({endpoint}): HTTP {resp.status_code}")
if resp.status_code != 200:
raise ProviderError(f"Finnhub error for {ticker} ({endpoint}): HTTP {resp.status_code}")
profile_payload = profile_resp.json() if profile_resp.text else {}
metric_payload = metric_resp.json() if metric_resp.text else {}
earnings_payload = earnings_resp.json() if earnings_resp.text else []
metrics = metric_payload.get("metric", {}) if isinstance(metric_payload, dict) else {}
# Finnhub profile2 marketCapitalization is in millions of USD.
# Normalize to absolute dollars so cap bands / formatters match FMP & Alpha Vantage.
market_cap_millions = _safe_float((profile_payload or {}).get("marketCapitalization"))
market_cap = market_cap_millions * 1_000_000.0 if market_cap_millions is not None else None
pe_ratio = _safe_float(metrics.get("peTTM") or metrics.get("peNormalizedAnnual"))
revenue_growth = _safe_float(metrics.get("revenueGrowthTTMYoy") or metrics.get("revenueGrowth5Y"))
earnings_surprise = None
if isinstance(earnings_payload, list) and earnings_payload:
first = earnings_payload[0] if isinstance(earnings_payload[0], dict) else {}
earnings_surprise = _safe_float(first.get("surprisePercent"))
next_earnings_date = self._next_earnings(calendar_resp)
if pe_ratio is None:
unavailable["pe_ratio"] = "not available from provider payload"
if revenue_growth is None:
unavailable["revenue_growth"] = "not available from provider payload"
if earnings_surprise is None:
unavailable["earnings_surprise"] = "not available from provider payload"
if market_cap is None:
unavailable["market_cap"] = "not available from provider payload"
return FundamentalData(
ticker=ticker,
pe_ratio=pe_ratio,
revenue_growth=revenue_growth,
earnings_surprise=earnings_surprise,
market_cap=market_cap,
fetched_at=datetime.now(timezone.utc),
next_earnings_date=next_earnings_date,
unavailable_fields=unavailable,
)
@staticmethod
def _next_earnings(resp: httpx.Response) -> date | None:
"""Earliest upcoming earnings date from Finnhub's calendar payload."""
try:
payload = resp.json() if resp.text else {}
except ValueError:
return None
entries = payload.get("earningsCalendar", []) if isinstance(payload, dict) else []
dates: list[date] = []
today = date.today()
for entry in entries if isinstance(entries, list) else []:
raw = entry.get("date") if isinstance(entry, dict) else None
if not raw:
continue
try:
parsed = date.fromisoformat(raw)
except ValueError:
continue
if parsed >= today:
dates.append(parsed)
return min(dates) if dates else None
class AlphaVantageFundamentalProvider:
"""Fundamentals provider backed by Alpha Vantage free endpoints."""
def __init__(self, api_key: str) -> None:
if not api_key:
raise ProviderError("Alpha Vantage API key is required")
self._api_key = api_key
self._base_url = "https://www.alphavantage.co/query"
async def fetch_fundamentals(self, ticker: str) -> FundamentalData:
unavailable: dict[str, str] = {}
api_symbol = _to_api_symbol(ticker)
async with httpx.AsyncClient(timeout=30.0, verify=_CA_BUNDLE_PATH) as client:
overview_resp = await client.get(
self._base_url,
params={"function": "OVERVIEW", "symbol": api_symbol, "apikey": self._api_key},
)
earnings_resp = await client.get(
self._base_url,
params={"function": "EARNINGS", "symbol": api_symbol, "apikey": self._api_key},
)
income_resp = await client.get(
self._base_url,
params={"function": "INCOME_STATEMENT", "symbol": api_symbol, "apikey": self._api_key},
)
for resp, endpoint in (
(overview_resp, "OVERVIEW"),
(earnings_resp, "EARNINGS"),
(income_resp, "INCOME_STATEMENT"),
):
if resp.status_code == 429:
raise RateLimitError(f"Alpha Vantage rate limit hit for {ticker} ({endpoint})")
if resp.status_code != 200:
raise ProviderError(f"Alpha Vantage error for {ticker} ({endpoint}): HTTP {resp.status_code}")
overview = overview_resp.json() if overview_resp.text else {}
earnings = earnings_resp.json() if earnings_resp.text else {}
income = income_resp.json() if income_resp.text else {}
if isinstance(overview, dict) and overview.get("Information"):
raise ProviderError(f"Alpha Vantage unavailable for {ticker}: {overview.get('Information')}")
if isinstance(overview, dict) and overview.get("Note"):
raise RateLimitError(f"Alpha Vantage rate limit for {ticker}: {overview.get('Note')}")
pe_ratio = _safe_float((overview or {}).get("PERatio"))
market_cap = _safe_float((overview or {}).get("MarketCapitalization"))
earnings_surprise = None
quarterly = earnings.get("quarterlyEarnings", []) if isinstance(earnings, dict) else []
if isinstance(quarterly, list) and quarterly:
first = quarterly[0] if isinstance(quarterly[0], dict) else {}
earnings_surprise = _safe_float(first.get("surprisePercentage"))
revenue_growth = None
annual = income.get("annualReports", []) if isinstance(income, dict) else []
if isinstance(annual, list) and len(annual) >= 2:
curr = _safe_float((annual[0] or {}).get("totalRevenue"))
prev = _safe_float((annual[1] or {}).get("totalRevenue"))
if curr is not None and prev not in (None, 0):
revenue_growth = ((curr - prev) / abs(prev)) * 100.0
if pe_ratio is None:
unavailable["pe_ratio"] = "not available from provider payload"
if revenue_growth is None:
unavailable["revenue_growth"] = "not available from provider payload"
if earnings_surprise is None:
unavailable["earnings_surprise"] = "not available from provider payload"
if market_cap is None:
unavailable["market_cap"] = "not available from provider payload"
return FundamentalData(
ticker=ticker,
pe_ratio=pe_ratio,
revenue_growth=revenue_growth,
earnings_surprise=earnings_surprise,
market_cap=market_cap,
fetched_at=datetime.now(timezone.utc),
unavailable_fields=unavailable,
)
_FUNDAMENTAL_FIELDS = ("pe_ratio", "revenue_growth", "earnings_surprise", "market_cap")
class ChainedFundamentalProvider:
"""Merge fundamentals across providers, filling gaps from later sources.
A single provider rarely covers everything on free tiers — FMP's free plan,
for example, returns only market cap (the ratios/growth/earnings endpoints
402). Rather than stop at the first provider with *any* field, we take each
field from the first provider that supplies it, so FMP's market cap is
combined with Finnhub's P/E and earnings surprise.
"""
def __init__(self, providers: list[tuple[str, FundamentalProvider]]) -> None:
if not providers:
raise ProviderError("No fundamental providers configured")
self._providers = providers
async def fetch_fundamentals(self, ticker: str, allow_partial: bool = False) -> FundamentalData:
"""Merge fundamentals across providers.
``allow_partial`` controls behaviour when a fallback provider is *rate
limited* and we end up with missing fields. By default we raise
RateLimitError so the caller (the bulk collector) can back off and retry
the ticker once the window frees — otherwise a transient 429 on Finnhub
would be silently stored as market-cap-only. Pass ``allow_partial=True``
(manual single fetches, or the collector's final give-up attempt) to
accept whatever was gathered instead of raising.
"""
merged: dict[str, float | None] = {f: None for f in _FUNDAMENTAL_FIELDS}
field_source: dict[str, str] = {}
errors: list[str] = []
rate_limited = False
next_earnings_date = None
for provider_name, provider in self._providers:
if all(merged[f] is not None for f in _FUNDAMENTAL_FIELDS) and next_earnings_date:
break
try:
data = await provider.fetch_fundamentals(ticker)
except RateLimitError as exc:
rate_limited = True
errors.append(f"{provider_name}: RateLimitError: {exc}")
continue
except Exception as exc:
errors.append(f"{provider_name}: {type(exc).__name__}: {exc}")
continue
if next_earnings_date is None and data.next_earnings_date is not None:
next_earnings_date = data.next_earnings_date
for field in _FUNDAMENTAL_FIELDS:
if merged[field] is None:
value = getattr(data, field)
if value is not None:
merged[field] = value
field_source[field] = provider_name
missing = [f for f in _FUNDAMENTAL_FIELDS if merged[f] is None]
# A rate limit left data incomplete: signal it (unless partial is OK) so
# the collector backs off rather than persisting a degraded record.
if rate_limited and missing and not allow_partial:
attempts = "; ".join(errors[:6])
raise RateLimitError(
f"Fundamentals incomplete for {ticker} due to provider rate limits "
f"(missing {', '.join(missing)}). Attempts: {attempts}"
)
if all(merged[f] is None for f in _FUNDAMENTAL_FIELDS):
attempts = "; ".join(errors[:6]) if errors else "no usable metrics from any provider"
raise ProviderError(f"All fundamentals providers failed for {ticker}. Attempts: {attempts}")
unavailable: dict[str, str] = {
field: "not available from any configured provider"
for field in _FUNDAMENTAL_FIELDS
if merged[field] is None
}
# Record which provider supplied each field for transparency.
for field, src in field_source.items():
unavailable[f"source_{field}"] = src
return FundamentalData(
ticker=ticker,
pe_ratio=merged["pe_ratio"],
revenue_growth=merged["revenue_growth"],
earnings_surprise=merged["earnings_surprise"],
market_cap=merged["market_cap"],
fetched_at=datetime.now(timezone.utc),
next_earnings_date=next_earnings_date,
unavailable_fields=unavailable,
)
def build_fundamental_provider_chain() -> FundamentalProvider:
providers: list[tuple[str, FundamentalProvider]] = []
if settings.fmp_api_key:
providers.append(("fmp", FMPFundamentalProvider(settings.fmp_api_key)))
if settings.finnhub_api_key:
providers.append(("finnhub", FinnhubFundamentalProvider(settings.finnhub_api_key)))
if settings.alpha_vantage_api_key:
providers.append(("alpha_vantage", AlphaVantageFundamentalProvider(settings.alpha_vantage_api_key)))
if not providers:
raise ProviderError(
"No fundamentals provider configured. Set one of FMP_API_KEY, FINNHUB_API_KEY, ALPHA_VANTAGE_API_KEY"
)
logger.info("Fundamentals provider chain configured: %s", [name for name, _ in providers])
return ChainedFundamentalProvider(providers)
app/providers/protocol.py
+2 -20
View File
@@ -44,20 +44,6 @@ class SentimentData:
recommendation: str | None = None # "buy" | "hold" | "avoid" — actionable LLM view
@dataclass(frozen=True, slots=True)
class FundamentalData:
"""Fundamental metrics returned by fundamental providers."""
ticker: str
pe_ratio: float | None
revenue_growth: float | None
earnings_surprise: float | None
market_cap: float | None
fetched_at: datetime
next_earnings_date: date | None = None
unavailable_fields: dict[str, str] = field(default_factory=dict)
# ---------------------------------------------------------------------------
# Provider Protocols
# ---------------------------------------------------------------------------
@@ -81,9 +67,5 @@ class SentimentProvider(Protocol):
...
class FundamentalProvider(Protocol):
"""Protocol for fundamental data providers."""
async def fetch_fundamentals(self, ticker: str) -> FundamentalData:
"""Fetch fundamental data for a ticker."""
...
# No fundamentals provider protocol: since A6 fundamentals come only from the
# batch SEC/Dolt imports, never from a request-time provider call.
app/routers/admin.py
-52
View File
@@ -13,7 +13,6 @@ from app.schemas.admin import (
AlertConfigUpdate,
CreateUserRequest,
DataCleanupRequest,
FundamentalsCutoverConfigUpdate,
JobTriggerRequest,
JobToggle,
RecommendationConfigUpdate,
@@ -138,27 +137,6 @@ async def list_settings(
)
@router.get("/admin/settings/fundamentals-cutover", response_model=APIEnvelope)
async def get_fundamentals_cutover_settings(
_admin: User = Depends(require_admin),
db: AsyncSession = Depends(get_db),
):
config = await admin_service.get_fundamentals_cutover_config(db)
return APIEnvelope(status="success", data=config)
@router.put("/admin/settings/fundamentals-cutover", response_model=APIEnvelope)
async def update_fundamentals_cutover_settings(
body: FundamentalsCutoverConfigUpdate,
_admin: User = Depends(require_admin),
db: AsyncSession = Depends(get_db),
):
config = await admin_service.update_fundamentals_cutover_config(
db, body.enabled
)
return APIEnvelope(status="success", data=config)
@router.get("/admin/settings/recommendations", response_model=APIEnvelope)
async def get_recommendation_settings(
_admin: User = Depends(require_admin),
@@ -475,36 +453,6 @@ async def toggle_job(
)
@router.get("/admin/fundamentals-parity", response_model=APIEnvelope)
async def get_fundamentals_parity_report(
_admin: User = Depends(require_admin),
):
"""Latest read-only A5 source/score comparison, or null before first run."""
return APIEnvelope(
status="success", data=admin_service.get_fundamentals_parity_report()
)
@router.get("/admin/fundamentals-parity/csv", response_model=APIEnvelope)
async def get_fundamentals_parity_csv(
_admin: User = Depends(require_admin),
):
"""Latest flattened A5 report for an authenticated browser download."""
artifact = admin_service.get_fundamentals_parity_csv()
data = None if artifact is None else {"filename": artifact[0], "content": artifact[1]}
return APIEnvelope(status="success", data=data)
@router.get("/admin/fundamentals-parity/json", response_model=APIEnvelope)
async def get_fundamentals_parity_json(
_admin: User = Depends(require_admin),
):
"""Canonical A5 JSON artifact for an authenticated browser download."""
artifact = admin_service.get_fundamentals_parity_json()
data = None if artifact is None else {"filename": artifact[0], "content": artifact[1]}
return APIEnvelope(status="success", data=data)
# ---------------------------------------------------------------------------
# System events (operational warnings / errors)
# ---------------------------------------------------------------------------
app/routers/ingestion.py
+4 -26
View File
@@ -23,7 +23,6 @@ from app.models.sr_level import SRLevel
from app.models.ticker import Ticker
from app.models.user import User
from app.providers.alpaca import AlpacaOHLCVProvider
from app.providers.fundamentals_chain import build_fundamental_provider_chain
from app.services.rr_scanner_service import (
resolve_activation_ranks_for_symbol,
scan_ticker,
@@ -31,7 +30,6 @@ from app.services.rr_scanner_service import (
from app.services.sentiment_provider_service import build_sentiment_provider
from app.schemas.common import APIEnvelope
from app.services import (
fundamental_service,
ingestion_service,
scoring_service,
sentiment_service,
@@ -185,33 +183,13 @@ async def fetch_symbol(
sources_out["sentiment"] = {"status": "error", "message": str(exc)}
# --- Fundamentals ---
# No per-ticker fetch exists any more: fundamental_data is rebuilt for the
# whole universe by the nightly SEC + Dolt imports, from local PostgreSQL.
# The source key is still accepted so older clients get a truthful answer.
if "fundamentals" in requested:
if settings.fmp_api_key or settings.finnhub_api_key or settings.alpha_vantage_api_key:
try:
fundamentals_provider = build_fundamental_provider_chain()
# Manual single fetch: take whatever we can get (a lone 429 on a
# fallback shouldn't fail the whole refresh).
fdata = await fundamentals_provider.fetch_fundamentals(
symbol_upper, allow_partial=True
)
await fundamental_service.store_fundamental(
db,
symbol=symbol_upper,
pe_ratio=fdata.pe_ratio,
revenue_growth=fdata.revenue_growth,
earnings_surprise=fdata.earnings_surprise,
market_cap=fdata.market_cap,
next_earnings_date=fdata.next_earnings_date,
unavailable_fields=fdata.unavailable_fields,
)
sources_out["fundamentals"] = {"status": "ok", "message": None}
except Exception as exc:
logger.error("Fundamentals fetch failed for %s: %s", symbol_upper, exc)
sources_out["fundamentals"] = {"status": "error", "message": str(exc)}
else:
sources_out["fundamentals"] = {
"status": "skipped",
"message": "No fundamentals provider key configured",
"message": "Fundamentals refresh nightly from the SEC + Dolt imports",
}
# --- Derived pipeline: S/R levels (free, always) ---
app/routers/tickers.py
+33 -1
View File
@@ -6,7 +6,7 @@ from sqlalchemy.ext.asyncio import AsyncSession
from app.dependencies import get_db, require_access
from app.models.user import User
from app.schemas.common import APIEnvelope
from app.schemas.ticker import TickerCreate, TickerResponse
from app.schemas.ticker import TickerCreate, TickerDelistingUpdate, TickerResponse
from app.services import ticker_service
router = APIRouter(tags=["tickers"])
@@ -51,3 +51,35 @@ async def delete_ticker(
"""Delete a ticker and all associated data."""
await ticker_service.delete_ticker(db, symbol)
return APIEnvelope(status="success", data=None)
@router.post("/tickers/{symbol}/delisting", response_model=APIEnvelope)
async def mark_ticker_delisted(
symbol: str,
body: TickerDelistingUpdate,
_user: User = Depends(require_access),
db: AsyncSession = Depends(get_db),
):
"""Retire a symbol: excluded from signals, price history kept.
The non-destructive alternative to DELETE, which cascades the history away.
"""
changed = await ticker_service.mark_delisted(
db, symbol, delisted_on=body.delisted_on, reason=ticker_service.REASON_MANUAL
)
return APIEnvelope(status="success", data={"changed": changed})
@router.delete("/tickers/{symbol}/delisting", response_model=APIEnvelope)
async def clear_ticker_delisting(
symbol: str,
_user: User = Depends(require_access),
db: AsyncSession = Depends(get_db),
):
"""Un-retire a symbol wrongly marked delisted.
Automatic marking is only defensible because this exists: a false positive
costs one row update rather than the price history a delete would take.
"""
changed = await ticker_service.clear_delisted(db, symbol)
return APIEnvelope(status="success", data={"changed": changed})
app/scheduler.py
+225 -343
View File
@@ -1,9 +1,9 @@
"""APScheduler job definitions and FastAPI lifespan integration.
Defines four scheduled jobs:
Defines the scheduled jobs, among them:
- Data Collector (OHLCV fetch for all tickers)
- Sentiment Collector (sentiment for all tickers)
- Fundamental Collector (fundamentals for all tickers)
- Dolt Earnings / SEC Fundamentals imports (bulk fundamentals sources)
- R:R Scanner (trade setup scan for all tickers)
Each job processes tickers independently, logs errors as structured JSON,
@@ -18,29 +18,28 @@ import logging
import asyncio
from datetime import date, datetime, timedelta, timezone
from apscheduler.events import EVENT_JOB_ERROR, EVENT_JOB_EXECUTED
from apscheduler.schedulers.asyncio import AsyncIOScheduler
from apscheduler.triggers.cron import CronTrigger
from sqlalchemy import and_, case, func, or_, select
from sqlalchemy.ext.asyncio import AsyncSession
from app import job_catalog
from app.config import settings
from app.database import async_session_factory
from app.models.fundamental import FundamentalData
from app.models.ohlcv import OHLCVRecord
from app.models.sentiment import SentimentScore
from app.models.ticker import Ticker
from app.exceptions import ProviderError
from app.providers.alpaca import AlpacaOHLCVProvider
from app.providers.fundamentals_chain import build_fundamental_provider_chain
from app.providers.protocol import SentimentData
from app.services import job_run_store
from app.services import (
fundamental_service,
ingestion_service,
pipeline_run,
sentiment_service,
settings_store,
shadow_book_service,
fundamentals_parity_service,
fundamental_data_refresh_service,
)
from app.services.data_import import (
@@ -67,6 +66,7 @@ from app.services.event_study_service import run_and_store as run_event_study_an
from app.services.outcome_service import evaluate_pending_setups
from app.services.rr_scanner_service import scan_all_tickers
from app.services.sentiment_provider_service import build_sentiment_provider
from app.services import ticker_service
from app.services.ticker_universe_service import bootstrap_universe
logger = logging.getLogger(__name__)
@@ -88,36 +88,58 @@ scheduler = AsyncIOScheduler(
}
)
def _on_job_finished(event: object) -> None:
"""Persist the run, then re-pause the job if it only runs on demand.
Covers every job APScheduler fires itself, including manual triggers.
Pipeline *steps* are invoked as plain coroutines and emit no events, so
``_run_pipeline`` persists those directly.
"""
job_id = getattr(event, "job_id", None)
if job_id:
_schedule_persist(job_id)
_repause_after_manual_run(event)
def _repause_after_manual_run(event: object) -> None:
"""Re-pause a job that only ever runs on demand, once its run finishes.
Pipeline steps and manual jobs are registered with a 520-week interval and
``next_run_time=None`` as a backstop. Triggering one sets next_run_time=now,
and APScheduler then re-arms that backstop -- so Admin → Jobs would show a
"next run" ten years out. Guarding on category means the six cron jobs and
the real interval jobs are never touched.
Registered at module level, not inside ``configure_scheduler``: that function
is called more than once (idempotency test) and ``add_listener`` does not
deduplicate.
"""
job_id = getattr(event, "job_id", None)
if job_catalog.JOB_CATEGORY.get(job_id) not in (
job_catalog.CATEGORY_STEP,
job_catalog.CATEGORY_MANUAL,
):
return
try:
scheduler.modify_job(job_id, next_run_time=None)
except Exception: # job gone, scheduler stopped — nothing to re-pause
logger.debug("Could not re-pause %s after its run", job_id, exc_info=True)
scheduler.add_listener(_on_job_finished, EVENT_JOB_EXECUTED | EVENT_JOB_ERROR)
# Track last successful ticker per job for rate-limit resume
_last_successful: dict[str, str | None] = {
"data_collector": None,
"data_backfill": None,
"sentiment_collector": None,
"fundamental_collector": None,
}
# Jobs whose per-run progress is surfaced to Admin → Jobs. (outcome_evaluator is
# created lazily on first run via _runtime_start.)
_JOB_NAMES = [
"data_collector",
"data_backfill",
"sentiment_collector",
"fundamental_collector",
"dolt_earnings_import",
"sec_fundamentals_import",
"fundamentals_parity_report",
"rr_scanner",
"ticker_universe_sync",
"alerts",
"market_regime",
"regime_monitor",
"event_study",
"backtest",
"daily_pipeline", # morning: OHLCV/sentiment/regime — no qualifying scan
"near_close_pipeline", # OHLCV fetch → R:R scan → Telegram alerts
"after_close_pipeline", # OHLCV fetch → outcome eval (final bar)
"intraday_pipeline",
]
# Seeded from the catalog rather than a private list. The old literal held 16 of
# the 19 jobs -- benchmark_collector, outcome_evaluator and shadow_book were
# missing, so they had no runtime row (and so no "last run" line in Admin → Jobs)
# until their first run in a given process.
def _idle_runtime() -> dict[str, object]:
@@ -134,7 +156,9 @@ def _idle_runtime() -> dict[str, object]:
}
_job_runtime: dict[str, dict[str, object]] = {name: _idle_runtime() for name in _JOB_NAMES}
_job_runtime: dict[str, dict[str, object]] = {
name: _idle_runtime() for name in sorted(job_catalog.VALID_JOB_NAMES)
}
_next_backtest_target_model = PRODUCTION_GTL_TARGET_MODEL
_next_backtest_cadence = DEFAULT_BACKTEST_CADENCE
@@ -261,7 +285,14 @@ def _runtime_finish(
processed: int,
total: int | None,
message: str | None = None,
emit_event: bool = True,
) -> None:
"""Finalize a job's runtime row, optionally raising a durable event.
``emit_event=False`` is for a *re-finalize* that only rewords an outcome an
earlier call already reported. The dedup key includes the message, so a
reworded error would otherwise land in Admin → System Events twice.
"""
runtime = _job_runtime.get(job_name, {})
runtime.update({
"running": False,
@@ -275,7 +306,7 @@ def _runtime_finish(
})
_job_runtime[job_name] = runtime
# Durable event for error / rate-limit finishes (badge + Admin → Jobs panel).
if status in ("error", "rate_limited"):
if emit_event and status in ("error", "rate_limited"):
severity = "error" if status == "error" else "warning"
try:
loop = asyncio.get_running_loop()
@@ -292,6 +323,67 @@ def _runtime_finish(
pass
async def _persist_job_run(job_name: str) -> None:
"""Write a job's finished runtime row to the durable last-run table.
Never raises: a persistence failure must not break the pipeline that was
otherwise successful. The in-memory row stays authoritative for live state.
"""
runtime = _job_runtime.get(job_name)
if not runtime or runtime.get("running") or not runtime.get("finished_at"):
return
try:
async with async_session_factory() as db:
await job_run_store.record_finish(db, job_name, runtime)
await db.commit()
except Exception:
logger.exception("Could not persist last-run state for %s", job_name)
# Detached persists are kept referenced: a bare create_task result can be
# garbage-collected mid-flight, and the shutdown drain needs something to await.
_persist_tasks: set[asyncio.Task] = set()
def _schedule_persist(job_name: str) -> None:
try:
task = asyncio.get_running_loop().create_task(_persist_job_run(job_name))
except RuntimeError: # no loop (sync context / tests) — nothing to persist
return
_persist_tasks.add(task)
task.add_done_callback(_persist_tasks.discard)
async def flush_job_run_persists(timeout: float = 5.0, settle: float = 0.05) -> None:
"""Drain last-run writes, including ones queued while we are draining.
``scheduler.shutdown(wait=False)`` returns before APScheduler has dispatched
its job-completion events, and those events are what create persist tasks. A
single snapshot of the set therefore misses writes still to be queued, and
``engine.dispose()`` could then close the pool underneath them. So: give the
loop a moment for pending callbacks to land, then keep draining until the
set stays empty or the deadline passes.
"""
loop = asyncio.get_running_loop()
deadline = loop.time() + timeout
# Bounded settle so callbacks dispatched by shutdown get to queue their work
# before the first emptiness check decides there is nothing to wait for.
await asyncio.sleep(min(settle, timeout))
while True:
pending = {task for task in _persist_tasks if not task.done()}
if not pending:
return
remaining = deadline - loop.time()
if remaining <= 0:
logger.warning(
"Timed out draining %d last-run write(s); some may be lost", len(pending)
)
return
await asyncio.wait(pending, timeout=remaining)
# Loop rather than return: a completion callback may have queued another.
await asyncio.sleep(0)
def get_job_runtime_snapshot(job_name: str | None = None) -> dict[str, dict[str, object]] | dict[str, object]:
if job_name is not None:
return dict(_job_runtime.get(job_name, {}))
@@ -305,8 +397,10 @@ async def _is_job_enabled(db: AsyncSession, job_name: str) -> bool:
async def _get_all_tickers(db: AsyncSession) -> list[str]:
"""Return all tracked ticker symbols sorted alphabetically."""
result = await db.execute(select(Ticker.symbol).order_by(Ticker.symbol))
"""Return all actively-traded ticker symbols sorted alphabetically."""
result = await db.execute(
ticker_service.active_only(select(Ticker.symbol).order_by(Ticker.symbol))
)
return list(result.scalars().all())
@@ -321,8 +415,10 @@ async def _get_ohlcv_priority_tickers(db: AsyncSession) -> list[str]:
latest_date = func.max(OHLCVRecord.date)
missing_first = case((latest_date.is_(None), 0), else_=1)
result = await db.execute(
ticker_service.active_only(
select(Ticker.symbol)
.outerjoin(OHLCVRecord, OHLCVRecord.ticker_id == Ticker.id)
)
.group_by(Ticker.id, Ticker.symbol)
.order_by(missing_first.asc(), latest_date.asc(), Ticker.symbol.asc())
)
@@ -466,23 +562,6 @@ async def _get_sentiment_priority_tickers(db: AsyncSession) -> list[str]:
return priority_syms + filler_syms
async def _get_fundamental_priority_tickers(db: AsyncSession) -> list[str]:
"""Return symbols prioritized for fundamentals refresh.
Priority:
1) Tickers with no fundamentals snapshot yet
2) Tickers with existing fundamentals, oldest fetched_at first
3) Alphabetical tiebreaker
"""
missing_first = case((FundamentalData.fetched_at.is_(None), 0), else_=1)
result = await db.execute(
select(Ticker.symbol)
.outerjoin(FundamentalData, FundamentalData.ticker_id == Ticker.id)
.order_by(missing_first.asc(), FundamentalData.fetched_at.asc(), Ticker.symbol.asc())
)
return list(result.scalars().all())
def _resume_tickers(symbols: list[str], job_name: str) -> list[str]:
"""Reorder tickers to resume after the last successful one (rate-limit resume).
@@ -588,6 +667,26 @@ async def collect_ohlcv(
_runtime_progress(job_name, processed=processed, total=total, current_ticker=symbol)
_log_event(logging.INFO, "ticker_collected", job=job_name, ticker=symbol, status=result.status, records=result.records_ingested)
if result.status == "stale":
# "No new bars" cannot distinguish a delisting from a halt
# or a rename, so ask SEC before warning again. A confirmed
# delisting retires the symbol (keeping its history) and
# ends the alert; anything unproven keeps warning.
delisted_on = await ticker_service.confirm_delisting(
db, symbol, last_bar=result.last_date
)
if delisted_on is not None:
await _record_system_event(
severity="info",
source=job_name,
code="ticker_delisted",
message=(
f"{symbol} delisted on {delisted_on} (SEC Form 25/15). "
"Retired from signals; price history retained."
),
symbol=symbol,
dedup_key=f"ticker_delisted:{symbol}",
)
else:
await _record_system_event(
severity="warning",
source=job_name,
@@ -816,149 +915,16 @@ async def collect_sentiment() -> None:
# ---------------------------------------------------------------------------
# Job: Fundamental Collector
# Jobs: bulk fundamentals source imports
# ---------------------------------------------------------------------------
async def collect_fundamentals() -> None:
"""Fetch fundamentals for all tracked tickers via FMP.
Processes each ticker independently. On rate limit, records last
successful ticker for resume.
"""
job_name = "fundamental_collector"
_log_event(logging.INFO, "job_start", job=job_name)
_runtime_start(job_name)
processed = 0
total: int | None = None
try:
async with async_session_factory() as db:
if not await _is_job_enabled(db, job_name):
_log_event(logging.INFO, "job_skipped", job=job_name, reason="disabled")
_runtime_finish(job_name, "skipped", processed=0, total=0, message="Disabled")
return
if await fundamental_data_refresh_service.is_enabled(db):
message = "SEC + Dolt fundamentals cutover is active"
_log_event(
logging.INFO,
"job_skipped",
job=job_name,
reason="sec_dolt_cutover_active",
)
_runtime_finish(
job_name,
"skipped",
processed=0,
total=0,
message=message,
)
return
symbols = await _get_fundamental_priority_tickers(db)
if not symbols:
_log_event(logging.INFO, "job_complete", job=job_name, tickers=0)
_runtime_finish(job_name, "completed", processed=0, total=0, message="No tickers")
return
total = len(symbols)
_runtime_progress(job_name, processed=0, total=total)
if not (settings.fmp_api_key or settings.finnhub_api_key or settings.alpha_vantage_api_key):
_log_event(logging.WARNING, "job_skipped", job=job_name, reason="no fundamentals provider keys configured")
_runtime_finish(job_name, "skipped", processed=0, total=total, message="No fundamentals provider keys configured")
return
try:
provider = build_fundamental_provider_chain()
except Exception as exc:
_log_event(logging.ERROR, "job_error", job=job_name, error_type=type(exc).__name__, message=str(exc))
_runtime_finish(job_name, "error", processed=0, total=total, message=str(exc))
return
max_retries = max(0, settings.fundamental_rate_limit_retries)
base_backoff = max(1, settings.fundamental_rate_limit_backoff_seconds)
spacing = max(0.0, settings.fundamental_request_spacing_seconds)
async def _store(symbol: str, data) -> None:
async with async_session_factory() as db:
await fundamental_service.store_fundamental(
db,
symbol=symbol,
pe_ratio=data.pe_ratio,
revenue_growth=data.revenue_growth,
earnings_surprise=data.earnings_surprise,
market_cap=data.market_cap,
next_earnings_date=data.next_earnings_date,
unavailable_fields=data.unavailable_fields,
)
for symbol in symbols:
_runtime_progress(job_name, processed=processed, total=total, current_ticker=symbol)
attempt = 0
while True:
try:
data = await provider.fetch_fundamentals(symbol)
await _store(symbol, data)
_last_successful[job_name] = symbol
processed += 1
_runtime_progress(job_name, processed=processed, total=total, current_ticker=symbol)
_log_event(logging.INFO, "ticker_collected", job=job_name, ticker=symbol)
break
except Exception as exc:
msg = str(exc).lower()
if "rate" in msg or "429" in msg:
if attempt < max_retries:
wait_seconds = base_backoff * (2 ** attempt)
attempt += 1
_log_event(logging.WARNING, "rate_limited_retry", job=job_name, ticker=symbol, attempt=attempt, max_retries=max_retries, wait_seconds=wait_seconds, processed=processed)
_runtime_progress(
job_name,
processed=processed,
total=total,
current_ticker=symbol,
message=f"Rate-limited at {symbol}; retry {attempt}/{max_retries} in {wait_seconds}s",
)
await asyncio.sleep(wait_seconds)
continue
# Retries exhausted: store whatever partial data we can
# still get (e.g. FMP market cap) and move on, rather than
# aborting the whole run and leaving every later ticker
# untouched.
_log_event(logging.WARNING, "rate_limited_partial", job=job_name, ticker=symbol, processed=processed)
try:
data = await provider.fetch_fundamentals(symbol, allow_partial=True)
await _store(symbol, data)
processed += 1
except Exception as exc2:
_log_job_error(job_name, symbol, exc2)
break
_log_job_error(job_name, symbol, exc)
break
if spacing:
await asyncio.sleep(spacing)
_last_successful[job_name] = None
_log_event(logging.INFO, "job_complete", job=job_name, tickers=processed)
_runtime_finish(job_name, "completed", processed=processed, total=total, message=f"Processed {processed} tickers")
except Exception as exc:
_log_event(logging.ERROR, "job_error", job=job_name, error_type=type(exc).__name__, message=str(exc))
_runtime_finish(job_name, "error", processed=processed, total=total, message=str(exc))
# ---------------------------------------------------------------------------
# Jobs: shadow fundamentals sources
# ---------------------------------------------------------------------------
async def _run_shadow_import(job_name: str, importer: SourceImporter) -> bool:
async def _run_source_import(job_name: str, importer: SourceImporter) -> bool:
"""Run an importer and return whether its scheduled job was enabled.
The SEC wrapper uses the return value to run its activated local cache step
after deferred, failed, no-op, promoted, or source-locked attempts while honoring
the job-level disable switch.
The SEC wrapper uses the return value only to word its runtime message: its
local cache step runs after deferred, failed, no-op, promoted, source-locked
and disabled attempts alike.
"""
_log_event(logging.INFO, "job_start", job=job_name)
_runtime_start(job_name, total=1)
@@ -968,7 +934,7 @@ async def _run_shadow_import(job_name: str, importer: SourceImporter) -> bool:
if not await _is_job_enabled(db, job_name):
_log_event(logging.INFO, "job_skipped", job=job_name, reason="disabled")
_runtime_finish(job_name, "skipped", processed=0, total=1, message="Disabled")
return
return False
run = await run_import(importer)
if run is None:
@@ -1015,25 +981,26 @@ async def _run_shadow_import(job_name: str, importer: SourceImporter) -> bool:
async def run_dolt_earnings_import() -> None:
"""Pull and import the Dolt earnings calendar/results feed in shadow."""
await _run_shadow_import("dolt_earnings_import", DoltEarningsImporter())
"""Pull and import the Dolt earnings calendar/results feed."""
await _run_source_import("dolt_earnings_import", DoltEarningsImporter())
async def run_sec_fundamentals_import() -> None:
"""Import SEC facts, then run the activated local compat-cache refresh.
"""Import SEC facts, then refresh the local compat cache.
The refresh is deliberately separate from the network import result. Once
activated it therefore still runs from stored snapshots/earnings/prices when
SEC is unavailable, unchanged, or another SEC import owns the source lock.
The refresh is deliberately independent of the network import: it reads only
stored snapshots, earnings events and closes, so it runs identically when SEC
is unavailable, unchanged, or owned by another import — and also when the
job's ingestion is switched off in Admin → Jobs. Disabling the job stops
SEC network access, not the cache; prices and earnings move daily even when
no filing does, and `fundamental_data` feeds scoring.
"""
job_name = "sec_fundamentals_import"
job_enabled = await _run_shadow_import(job_name, SecFundamentalsImporter())
if not job_enabled:
return
import_ran = await _run_source_import(job_name, SecFundamentalsImporter())
try:
async with async_session_factory() as db:
summary = await fundamental_data_refresh_service.refresh_if_enabled(db)
summary = await fundamental_data_refresh_service.refresh(db)
except asyncio.CancelledError:
_runtime_finish(
job_name, "error", processed=0, total=1, message="Cancelled"
@@ -1051,78 +1018,37 @@ async def run_sec_fundamentals_import() -> None:
_runtime_finish(job_name, "error", processed=0, total=1, message=message)
return
if not summary["enabled"]:
_log_event(
logging.INFO,
"fundamental_data_refresh_skipped",
job=job_name,
reason="cutover_disabled",
setting=fundamental_data_refresh_service.ACTIVATION_KEY,
)
return
_log_event(
logging.INFO,
"fundamental_data_refresh_complete",
job=job_name,
**summary,
)
runtime = get_job_runtime_snapshot(job_name)
if runtime.get("status") == "completed":
import_message = runtime.get("message") or "import completed"
cache_message = (
f"cache {summary['refreshed']} · "
f"{summary['score_inputs_changed']} score inputs changed"
)
# Every outcome carries the cache summary — including deferred, failed and
# source-locked ones. The import status is what varies; the refresh always
# happened, and Admin → Jobs is the only place an operator sees that.
#
# This only rewords what _run_source_import already finalized, so it must not
# emit a second durable event: the dedup key includes the message, and a
# failure would otherwise show up twice in Admin → System Events.
runtime = get_job_runtime_snapshot(job_name)
if import_ran:
status = str(runtime.get("status") or "completed")
import_message = runtime.get("message") or "import completed"
processed = 1 if status == "completed" else 0
else:
status, import_message, processed = "completed", "Import disabled", 1
_runtime_finish(
job_name,
"completed",
processed=1,
status,
processed=processed,
total=1,
message=f"{import_message} · {cache_message}",
)
async def run_fundamentals_parity_report() -> None:
"""Generate the A5 comparison bundle without mutating live fundamentals/scores."""
job_name = "fundamentals_parity_report"
_log_event(logging.INFO, "job_start", job=job_name)
_runtime_start(job_name, total=1)
try:
async with async_session_factory() as db:
if not await _is_job_enabled(db, job_name):
_runtime_finish(
job_name, "skipped", processed=0, total=1, message="Disabled"
)
return
report, artifacts = await fundamentals_parity_service.generate_and_store(
db, settings.fundamentals_parity_report_dir
)
summary = report["summary"]
message = (
f"{summary['universe_count']} tickers · "
f"{summary['fundamental_score_material_changes']} material score changes"
)
_runtime_finish(job_name, "completed", processed=1, total=1, message=message)
_log_event(
logging.INFO,
"job_complete",
job=job_name,
generated_at=report["generated_at"],
json_path=artifacts["json"],
csv_path=artifacts["csv"],
)
except asyncio.CancelledError:
_runtime_finish(job_name, "error", processed=0, total=1, message="Cancelled")
raise
except Exception as exc:
_runtime_finish(job_name, "error", processed=0, total=1, message=str(exc))
_log_event(
logging.ERROR,
"job_error",
job=job_name,
error_type=type(exc).__name__,
message=str(exc),
emit_event=False,
)
@@ -1247,7 +1173,7 @@ async def dispatch_alerts_job() -> None:
# ---------------------------------------------------------------------------
# Job: Market Regime
# Job: Market Trend (SPY)
# ---------------------------------------------------------------------------
@@ -1306,7 +1232,7 @@ async def collect_benchmark() -> None:
# ---------------------------------------------------------------------------
# Job: Regime Monitor
# Job: AI/Tech Risk Monitor
# ---------------------------------------------------------------------------
@@ -1490,54 +1416,14 @@ async def sync_ticker_universe() -> None:
# the intraday partial one (covers a long weekend / holiday gap).
_FINAL_REFETCH_DAYS = 5
_DAILY_PIPELINE_STEPS = [
("data_collector", "collect_ohlcv"),
("benchmark_collector", "collect_benchmark"),
("sentiment_collector", "collect_sentiment"),
("market_regime", "compute_market_regime"),
# Observational only — display/alerts; not trade selection.
("regime_monitor", "compute_regime_monitor"),
# Alerts after regime so quadrant changes reach Telegram in the morning.
# Dispatcher is change-driven; quiet days stay quiet. Setup alerts still
# fire on the near-close pipeline after the qualifying scan.
("alerts", "dispatch_alerts_job"),
]
# Near-close (~15:30 ET Mon–Fri): refresh in-progress day-t bars (incremental
# ingestion overlaps the latest stored session), then the only daily
# qualifying R:R scan, then Telegram immediately so manual fills can still hit
# MOC cutoffs (~15:50/15:55). Under a 15-minute delayed SIP feed a 15:30 scan
# may see ~15:15 prices — immaterial for a 12-1 momentum signal.
#
# US early-close days (~3/year, 13:00 ET close): this job runs post-close and
# entries behave like stale_close (still acceptable per execution-recovery matrix).
# No exchange calendar dependency.
_NEAR_CLOSE_PIPELINE_STEPS = [
# Must land today's in-progress bar (~20 min behind live), or the scan falls
# back to the previous close and execution degrades to the stale_close floor.
("data_collector", "collect_ohlcv_for_scan"),
("rr_scanner", "scan_rr"),
# Straight after the scan so shadow entries mark at the same near-close
# prices the discretionary book is looking at.
("shadow_book", "run_shadow_book"),
("alerts", "dispatch_alerts_job"),
]
# After close (~16:45 ET Mon–Fri): fresh OHLCV fetch so outcomes resolve on the
# final bar, not the near-close partial bar, then outcome/paper close.
_AFTER_CLOSE_PIPELINE_STEPS = [
("data_collector", "collect_ohlcv_final"),
("outcome_evaluator", "evaluate_outcomes"),
]
# Intraday (light): keep prices current and resolve outcomes through the day,
# without the expensive scan/sentiment. The dashboard recomputes live R:R from
# the latest price, so refreshing OHLCV is enough to stop prices lagging; the
# outcome step also closes paper trades that hit their stop/target intraday.
_INTRADAY_PIPELINE_STEPS = [
("data_collector", "collect_ohlcv"),
("outcome_evaluator", "evaluate_outcomes"),
]
# Step lists live in app.job_catalog so the runner, the admin API's pipeline
# membership and the UI's grouping all read one definition. Re-exported here
# under their original names: _run_pipeline and the scheduler_configured log
# payload refer to them directly.
_DAILY_PIPELINE_STEPS = job_catalog._DAILY_PIPELINE_STEPS
_NEAR_CLOSE_PIPELINE_STEPS = job_catalog._NEAR_CLOSE_PIPELINE_STEPS
_AFTER_CLOSE_PIPELINE_STEPS = job_catalog._AFTER_CLOSE_PIPELINE_STEPS
_INTRADAY_PIPELINE_STEPS = job_catalog._INTRADAY_PIPELINE_STEPS
# Warn if near-close fetch+scan+alert drifts past this — entries leave the close
# and the stale_close floor quietly becomes the ceiling.
@@ -1560,6 +1446,7 @@ async def _run_pipeline(job_name: str, steps: list[tuple[str, str]]) -> None:
if not await _is_job_enabled(db, job_name):
_log_event(logging.INFO, "job_skipped", job=job_name, reason="disabled")
_runtime_finish(job_name, "skipped", processed=0, total=0, message="Disabled")
await _persist_job_run(job_name)
return
total = len(steps)
@@ -1575,6 +1462,11 @@ async def _run_pipeline(job_name: str, steps: list[tuple[str, str]]) -> None:
await funcs[func_name]()
except Exception:
logger.exception("%s step %s failed", job_name, step_name)
# Outside the except on purpose: the step's own _runtime_finish has
# already recorded its outcome, so persisting here captures failures
# too. Steps are plain coroutine calls and fire no scheduler events,
# so the listener cannot see them -- this is their only write path.
await _persist_job_run(step_name)
done += 1
_runtime_finish(job_name, "completed", processed=done, total=total, message="Pipeline complete")
_log_event(logging.INFO, "job_complete", job=job_name)
@@ -1583,10 +1475,11 @@ async def _run_pipeline(job_name: str, steps: list[tuple[str, str]]) -> None:
_log_event(logging.ERROR, "job_error", job=job_name, error_type=type(exc).__name__, message=str(exc))
finally:
pipeline_run.release(token)
await _persist_job_run(job_name)
async def run_daily_pipeline() -> None:
"""Morning flow: OHLCV → benchmark → sentiment → market regime (no scan)."""
"""Morning flow: OHLCV → benchmark → sentiment → trend/risk (no scan)."""
await _run_pipeline("daily_pipeline", _DAILY_PIPELINE_STEPS)
@@ -1666,19 +1559,22 @@ SCHEDULE_DEFAULTS: dict[str, str] = {
"schedule_timezone": "America/New_York",
# Morning data/display refresh (no qualifying R:R scan).
"schedule_daily_pipeline_cron": "0 2 * * *",
# Bulk source imports. The SEC job writes the legacy compat cache only after
# the explicit, default-off A5 cutover setting is enabled.
# Bulk source imports. The SEC job also refreshes the fundamental_data compat
# cache that scoring reads — locally, from stored snapshots/earnings/closes.
"schedule_dolt_earnings_cron": "30 2 * * *",
"schedule_sec_fundamentals_cron": "0 4 * * *",
"schedule_fundamentals_parity_cron": "30 5 * * *",
# Fetch in-progress bars → scan → Telegram (manual MOC window).
"schedule_near_close_pipeline_cron": "30 15 * * mon-fri",
# Fetch final bars → outcome eval (must not run on the partial near-close bar).
"schedule_after_close_pipeline_cron": "45 16 * * mon-fri",
# Hourly mid-session price + outcome (10:00–15:00 ET Mon–Fri).
"schedule_intraday_pipeline_cron": "0 10-15 * * mon-fri",
# Weekly fundamentals early Monday NY.
"schedule_fundamentals_cron": "0 1 * * mon",
# Both were interval jobs until 2026-08-08 and hit exactly the pitfall
# described above: configure_scheduler calls remove_all_jobs() on every
# startup, so an interval countdown restarts from zero each deploy. A 168h
# backtest needed a week of uninterrupted uptime to fire even once.
"schedule_backtest_cron": "0 3 * * sun",
"schedule_ticker_universe_cron": "0 1 * * *",
}
# job id -> schedule setting key
@@ -1686,11 +1582,11 @@ _CRON_JOBS: dict[str, str] = {
"daily_pipeline": "schedule_daily_pipeline_cron",
"dolt_earnings_import": "schedule_dolt_earnings_cron",
"sec_fundamentals_import": "schedule_sec_fundamentals_cron",
"fundamentals_parity_report": "schedule_fundamentals_parity_cron",
"near_close_pipeline": "schedule_near_close_pipeline_cron",
"after_close_pipeline": "schedule_after_close_pipeline_cron",
"intraday_pipeline": "schedule_intraday_pipeline_cron",
"fundamental_collector": "schedule_fundamentals_cron",
"backtest": "schedule_backtest_cron",
"ticker_universe_sync": "schedule_ticker_universe_cron",
}
@@ -1756,8 +1652,12 @@ def configure_scheduler(schedule_config: dict[str, str] | None = None) -> None:
(scan_rr, "rr_scanner", "R:R Scanner"),
(run_shadow_book, "shadow_book", "Shadow Book (auto-traded strategy)"),
(evaluate_outcomes, "outcome_evaluator", "Outcome Evaluator"),
(compute_market_regime, "market_regime", "Market Regime"),
(compute_regime_monitor, "regime_monitor", "Regime Monitor"),
# Labels only -- the ids are persisted (pipeline steps, cron config, run
# history), so they stay. "Market Regime"/"Regime Monitor" read as the
# same job and had it backwards besides: the SPY guard is the one that
# changes what a setup shows, while the monitor is observational.
(compute_market_regime, "market_regime", "Market Trend (SPY)"),
(compute_regime_monitor, "regime_monitor", "AI/Tech Risk Monitor"),
]
for fn, job_id, job_name in _members:
scheduler.add_job(
@@ -1779,7 +1679,7 @@ def configure_scheduler(schedule_config: dict[str, str] | None = None) -> None:
"schedule_dolt_earnings_cron",
),
id="dolt_earnings_import",
name="Dolt Earnings Import (shadow)",
name="Dolt Earnings Import",
replace_existing=True,
)
scheduler.add_job(
@@ -1793,17 +1693,6 @@ def configure_scheduler(schedule_config: dict[str, str] | None = None) -> None:
name="SEC Fundamentals Import",
replace_existing=True,
)
scheduler.add_job(
run_fundamentals_parity_report,
_cron_trigger(
cfg["schedule_fundamentals_parity_cron"],
tz,
"schedule_fundamentals_parity_cron",
),
id="fundamentals_parity_report",
name="Fundamentals Parity Report (read-only)",
replace_existing=True,
)
scheduler.add_job(
run_near_close_pipeline,
_cron_trigger(
@@ -1831,17 +1720,13 @@ def configure_scheduler(schedule_config: dict[str, str] | None = None) -> None:
_cron_trigger(cfg["schedule_intraday_pipeline_cron"], tz, "schedule_intraday_pipeline_cron"),
id="intraday_pipeline", name="Intraday Pipeline", replace_existing=True,
)
# Fundamentals — quarterly-ish data; weekly by default (conserves API quota).
# Its own early cron so the slow, rate-limited fetch finishes before the day.
scheduler.add_job(
collect_fundamentals,
_cron_trigger(cfg["schedule_fundamentals_cron"], tz, "schedule_fundamentals_cron"),
id="fundamental_collector", name="Fundamental Collector", replace_existing=True,
)
# Independent interval jobs (own cadence, no ordering dependency)
# Independent jobs (own cadence, no ordering dependency). Cron, not interval,
# for the reason documented at SCHEDULE_DEFAULTS: an interval countdown
# restarts on every deploy, so these could be deferred indefinitely.
scheduler.add_job(
sync_ticker_universe, "interval", hours=24,
sync_ticker_universe,
_cron_trigger(cfg["schedule_ticker_universe_cron"], tz, "schedule_ticker_universe_cron"),
id="ticker_universe_sync", name="Ticker Universe Sync", replace_existing=True,
)
# Alerts auto-fire only via near_close_pipeline (scan → alert before MOC).
@@ -1852,7 +1737,8 @@ def configure_scheduler(schedule_config: dict[str, str] | None = None) -> None:
replace_existing=True, next_run_time=None,
)
scheduler.add_job(
run_backtest_job, "interval", hours=168,
run_backtest_job,
_cron_trigger(cfg["schedule_backtest_cron"], tz, "schedule_backtest_cron"),
id="backtest", name="Backtest", replace_existing=True,
)
# Deep history backfill: manual only (never auto-fires); triggered from
@@ -1879,9 +1765,6 @@ def configure_scheduler(schedule_config: dict[str, str] | None = None) -> None:
},
dolt_earnings_import={"cron": cfg["schedule_dolt_earnings_cron"]},
sec_fundamentals_import={"cron": cfg["schedule_sec_fundamentals_cron"]},
fundamentals_parity_report={
"cron": cfg["schedule_fundamentals_parity_cron"]
},
near_close_pipeline={
"cron": cfg["schedule_near_close_pipeline_cron"],
"steps": [name for name, _ in _NEAR_CLOSE_PIPELINE_STEPS],
@@ -1894,7 +1777,6 @@ def configure_scheduler(schedule_config: dict[str, str] | None = None) -> None:
"cron": cfg["schedule_intraday_pipeline_cron"],
"steps": [name for name, _ in _INTRADAY_PIPELINE_STEPS],
},
fundamental_collector={"cron": cfg["schedule_fundamentals_cron"]},
independent=["ticker_universe_sync", "backtest"],
manual_only=["alerts", "data_backfill", "event_study"],
)
app/schemas/admin.py
+2 -7
View File
@@ -73,11 +73,6 @@ class ActivationConfigUpdate(BaseModel):
exclude_neutral: bool | None = None
class FundamentalsCutoverConfigUpdate(BaseModel):
"""Switch the legacy fundamentals cache from quota APIs to SEC/Dolt."""
enabled: bool
class ScheduleConfigUpdate(BaseModel):
"""Cron schedule for the pipelines + fundamentals. Crons are 5-field
(min hour dom month dow); timezone is an IANA name (e.g. America/New_York)."""
@@ -85,11 +80,11 @@ class ScheduleConfigUpdate(BaseModel):
schedule_daily_pipeline_cron: str | None = Field(default=None, max_length=120)
schedule_dolt_earnings_cron: str | None = Field(default=None, max_length=120)
schedule_sec_fundamentals_cron: str | None = Field(default=None, max_length=120)
schedule_fundamentals_parity_cron: str | None = Field(default=None, max_length=120)
schedule_near_close_pipeline_cron: str | None = Field(default=None, max_length=120)
schedule_after_close_pipeline_cron: str | None = Field(default=None, max_length=120)
schedule_intraday_pipeline_cron: str | None = Field(default=None, max_length=120)
schedule_fundamentals_cron: str | None = Field(default=None, max_length=120)
schedule_backtest_cron: str | None = Field(default=None, max_length=120)
schedule_ticker_universe_cron: str | None = Field(default=None, max_length=120)
class PerformanceConfigUpdate(BaseModel):
app/schemas/ticker.py
+12 -1
View File
@@ -1,6 +1,6 @@
"""Ticker request/response schemas."""
from datetime import datetime
from datetime import date, datetime
from pydantic import BaseModel, Field
@@ -14,5 +14,16 @@ class TickerResponse(BaseModel):
symbol: str
name: str | None = None
created_at: datetime
# NULL == actively traded. Delisted symbols stay in the registry with their
# history and are excluded from signals — the date is what makes that
# visible instead of the row silently disappearing.
delisted_on: date | None = None
delisted_reason: str | None = None
model_config = {"from_attributes": True}
class TickerDelistingUpdate(BaseModel):
delisted_on: date = Field(
..., description="Effective date the symbol stopped trading"
)
app/services/admin_service.py
+98 -121
View File
@@ -7,6 +7,7 @@ from passlib.hash import bcrypt
from sqlalchemy import delete, func, select
from sqlalchemy.ext.asyncio import AsyncSession
from app import job_catalog
from app.exceptions import DuplicateError, NotFoundError, ValidationError
from app.models.fundamental import FundamentalData
from app.models.ohlcv import OHLCVRecord
@@ -17,7 +18,7 @@ from app.models.settings import SystemSetting
from app.models.ticker import Ticker
from app.models.trade_setup import TradeSetup
from app.models.user import User
from app.services import fundamental_data_refresh_service, settings_store
from app.services import job_run_store, settings_store
logger = logging.getLogger(__name__)
@@ -159,28 +160,6 @@ async def update_setting(db: AsyncSession, key: str, value: str) -> SystemSettin
return setting
# ---------------------------------------------------------------------------
# Fundamentals source cutover
# ---------------------------------------------------------------------------
async def get_fundamentals_cutover_config(db: AsyncSession) -> dict[str, bool]:
"""Return the explicit A5 cache-cutover switch (default off)."""
return {"enabled": await fundamental_data_refresh_service.is_enabled(db)}
async def update_fundamentals_cutover_config(
db: AsyncSession, enabled: bool
) -> dict[str, bool]:
"""Activate or pause SEC/Dolt writes to the legacy fundamentals cache."""
await settings_store.upsert_setting(
db,
fundamental_data_refresh_service.ACTIVATION_KEY,
"true" if enabled else "false",
)
await db.commit()
return await get_fundamentals_cutover_config(db)
# ---------------------------------------------------------------------------
# Activation thresholds
# ---------------------------------------------------------------------------
@@ -628,94 +607,110 @@ async def get_pipeline_readiness(db: AsyncSession) -> list[dict]:
# Job control (placeholder — scheduler is Task 12.1)
# ---------------------------------------------------------------------------
VALID_JOB_NAMES = {
"data_collector",
"data_backfill",
"benchmark_collector",
"sentiment_collector",
"fundamental_collector",
"dolt_earnings_import",
"sec_fundamentals_import",
"fundamentals_parity_report",
"rr_scanner",
"ticker_universe_sync",
"outcome_evaluator",
"alerts",
"market_regime",
"regime_monitor",
"event_study",
"backtest",
"daily_pipeline",
"near_close_pipeline",
"after_close_pipeline",
"intraday_pipeline",
"shadow_book",
}
# Job identity, labels and pipeline membership now live in app.job_catalog, which
# derives PIPELINE_MEMBERS from the pipeline step lists instead of restating them.
# Re-exported here because callers (routers, tests) import them from this module.
VALID_JOB_NAMES = job_catalog.VALID_JOB_NAMES
JOB_LABELS = job_catalog.JOB_LABELS
PIPELINE_MEMBERS = job_catalog.PIPELINE_MEMBERS
JOB_LABELS = {
"data_collector": "Data Collector (OHLCV)",
"data_backfill": "Data Backfill (deep history)",
"benchmark_collector": "Benchmark Collector",
"sentiment_collector": "Sentiment Collector",
"fundamental_collector": "Fundamental Collector",
"dolt_earnings_import": "Dolt Earnings Import (shadow)",
"sec_fundamentals_import": "SEC Fundamentals Import",
"fundamentals_parity_report": "Fundamentals Parity Report (read-only)",
"rr_scanner": "R:R Scanner",
"ticker_universe_sync": "Ticker Universe Sync",
"outcome_evaluator": "Outcome Evaluator",
"alerts": "Alerts Dispatcher",
"market_regime": "Market Regime",
"regime_monitor": "Regime Monitor",
"event_study": "Event Study",
"backtest": "Backtest",
"daily_pipeline": "Morning Pipeline",
"near_close_pipeline": "Near-Close Pipeline (scan+alert)",
"after_close_pipeline": "After-Close Pipeline (outcome)",
"intraday_pipeline": "Intraday Pipeline",
"shadow_book": "Shadow Book (auto-traded strategy)",
}
# Anything further out than this is a parked backstop, not a schedule: pipeline
# steps and manual jobs are registered on a 520-week interval, and triggering one
# re-arms it. Belt-and-braces behind the category rule in _next_run_fields.
_NEXT_RUN_HORIZON_DAYS = 365
# Jobs driven by a pipeline (in order) rather than their own auto timer.
PIPELINE_MEMBERS = {
"data_collector",
"benchmark_collector",
"sentiment_collector",
"rr_scanner",
"outcome_evaluator",
"alerts",
"market_regime",
"regime_monitor",
"shadow_book",
def _visible_next_run(next_run: datetime | None) -> datetime | None:
"""Drop a next-run that is really the parked backstop."""
if next_run is None:
return None
horizon = datetime.now(next_run.tzinfo) + timedelta(days=_NEXT_RUN_HORIZON_DAYS)
return None if next_run > horizon else next_run
def _own_next_run(scheduler, name: str) -> datetime | None:
# getattr: APScheduler only sets next_run_time once the scheduler is running,
# so a job registered but not yet started has no such attribute at all.
job = scheduler.get_job(name)
return _visible_next_run(getattr(job, "next_run_time", None)) if job else None
def _next_run_fields(scheduler, name: str, enabled_map: dict[str, bool]) -> dict:
"""Where this job's next run comes from, decided by category not by clock.
A pipeline step has no meaningful schedule of its own, so reporting one is
the bug: its parent's timer is the answer. Manual jobs have no answer at all,
and saying so beats rendering a parked backstop as a date.
"""
category = job_catalog.JOB_CATEGORY.get(name)
if category == job_catalog.CATEGORY_STEP:
parents = job_catalog.PIPELINES_BY_MEMBER.get(name, ())
soonest: datetime | None = None
via: str | None = None
for parent in parents:
if not enabled_map.get(parent, True):
continue
candidate = _own_next_run(scheduler, parent)
if candidate is not None and (soonest is None or candidate < soonest):
soonest, via = candidate, parent
return {
"next_run_at": None,
"next_run_source": "via_pipeline",
"via_next_run_at": soonest.isoformat() if soonest else None,
"via_next_run_job": via,
}
if category == job_catalog.CATEGORY_MANUAL:
return {
"next_run_at": None,
"next_run_source": "manual_only",
"via_next_run_at": None,
"via_next_run_job": None,
}
own = _own_next_run(scheduler, name)
return {
"next_run_at": own.isoformat() if own else None,
"next_run_source": "own_schedule",
"via_next_run_at": None,
"via_next_run_job": None,
}
async def list_jobs(db: AsyncSession) -> list[dict]:
"""Return status of all scheduled jobs."""
"""Return status of all scheduled jobs, grouped and ordered by category."""
from app.scheduler import get_job_runtime_snapshot, scheduler
visible = sorted(VALID_JOB_NAMES - job_catalog.HIDDEN_JOBS, key=job_catalog.sort_order)
# One query for every flag instead of one per job. Parents are read too, since
# a step reports its parent's next run only while that parent is enabled.
flags = await settings_store.get_map(
db, [f"job_{name}_enabled" for name in VALID_JOB_NAMES]
)
enabled_map = {
name: flags.get(f"job_{name}_enabled", "true") == "true"
for name in VALID_JOB_NAMES
}
last_runs = await job_run_store.get_map(db, visible)
jobs_out = []
for name in sorted(VALID_JOB_NAMES):
# Check enabled setting
setting = await settings_store.get_setting(db, f"job_{name}_enabled")
enabled = setting.value == "true" if setting else True # default enabled
# Get scheduler job info
for name in visible:
job = scheduler.get_job(name)
next_run = None
if job and job.next_run_time:
next_run = job.next_run_time.isoformat()
runtime = get_job_runtime_snapshot(name)
last = last_runs.get(name)
jobs_out.append({
"name": name,
"label": JOB_LABELS.get(name, name),
"enabled": enabled,
"next_run_at": next_run,
"via_pipeline": name in PIPELINE_MEMBERS,
"enabled": enabled_map.get(name, True),
"category": job_catalog.JOB_CATEGORY.get(name),
"sort_order": job_catalog.sort_order(name),
# Parent pipelines for a step; the steps themselves for a pipeline.
"pipelines": list(job_catalog.PIPELINES_BY_MEMBER.get(name, ())),
"steps": [step for step, _ in job_catalog.PIPELINE_STEPS.get(name, ())],
"registered": job is not None,
"running": bool(runtime.get("running", False)),
# runtime_* are strictly live in-memory state. Persisted history is
# reported separately as last_run_*, so a stale error cannot pin the
# status chip or the rate-limit banner.
"runtime_status": runtime.get("status"),
"runtime_processed": runtime.get("processed"),
"runtime_total": runtime.get("total"),
@@ -724,6 +719,15 @@ async def list_jobs(db: AsyncSession) -> list[dict]:
"runtime_started_at": runtime.get("started_at"),
"runtime_finished_at": runtime.get("finished_at"),
"runtime_message": runtime.get("message"),
# Survives restarts, unlike runtime_*. Reported separately so the
# status chip keeps meaning "state now" rather than "last outcome,
# forever" -- an error a week ago must not read as Inactive today.
"last_run_at": last.finished_at.isoformat() if last else None,
"last_run_status": last.status if last else None,
"last_run_message": last.message if last else None,
"last_run_processed": last.processed if last else None,
"last_run_total": last.total if last else None,
**_next_run_fields(scheduler, name, enabled_map),
})
return jobs_out
@@ -799,30 +803,3 @@ async def toggle_job(db: AsyncSession, job_name: str, enabled: bool) -> SystemSe
key = f"job_{job_name}_enabled"
return await update_setting(db, key, str(enabled).lower())
def get_fundamentals_parity_report() -> dict | None:
"""Return the latest compact A5 summary, if the job has run."""
from app.config import settings
from app.services.fundamentals_parity_service import load_latest
report = load_latest(settings.fundamentals_parity_report_dir)
if report is not None:
report.pop("rows", None) # full per-ticker data is download-only
return report
def get_fundamentals_parity_csv() -> tuple[str, str] | None:
"""Return the latest A5 CSV filename and content for authenticated download."""
from app.config import settings
from app.services.fundamentals_parity_service import load_latest_csv
return load_latest_csv(settings.fundamentals_parity_report_dir)
def get_fundamentals_parity_json() -> tuple[str, str] | None:
"""Return the canonical A5 JSON artifact for authenticated download."""
from app.config import settings
from app.services.fundamentals_parity_service import load_latest_json
return load_latest_json(settings.fundamentals_parity_report_dir)
app/services/alert_service.py
+1 -1
View File
@@ -860,7 +860,7 @@ async def _collect_regime_quadrant(db: AsyncSession) -> list[tuple[str, str]]:
else:
metrics = f"State {x:.0f} · Warning {y:.0f}"
text = (
f"🧭 <b>Regime quadrant change</b>\n"
f"🧭 <b>AI/Tech risk quadrant change</b>\n"
f"{QUAD_LABELS.get(prev, prev)} → {QUAD_LABELS.get(new_q, new_q)}\n"
f"{metrics}\n"
f"coverage: state {state.get('coverage'):.0f}% / warning {warning.get('coverage'):.0f}%\n"
app/services/backtest_service.py
+83 -448
View File
@@ -1320,7 +1320,6 @@ def _replay_candidates_for_period(
cadence: str = DEFAULT_BACKTEST_CADENCE,
include_short_candidates: bool = False,
include_universe_rank_observations: bool = False,
outcome_horizon_sessions: int = HORIZON,
) -> list[dict]:
"""Slim picklable replay used by local event studies.
@@ -1344,13 +1343,10 @@ def _replay_candidates_for_period(
)
]
cadence = validate_backtest_cadence(cadence)
replay_horizon = int(outcome_horizon_sessions)
if replay_horizon < 0:
raise ValueError('outcome_horizon_sessions must be non-negative')
candidates: list[dict] = []
for i in range(
MIN_LOOKBACK - 1,
len(bars) - replay_horizon,
len(bars) - HORIZON,
backtest_step_sessions(cadence),
):
if bars[i].date < start_date:
@@ -1464,6 +1460,21 @@ def _mp_context():
return None
async def _rollback_quietly(db: AsyncSession, context: str) -> None:
"""Discard a failed unit of work so later statements on this session survive.
Every DB call in ``run_backtest`` is best-effort — one unreadable ticker must
not abort the whole replay. But swallowing the exception alone leaves asyncpg
in "current transaction is aborted": every later statement then fails the same
way until the first unguarded one (the report write) surfaces it as the job
error, long after the real cause. Same guard as ``price_service``.
"""
try:
await db.rollback()
except Exception:
logger.exception("Session rollback after %s also failed", context)
async def _fetch_columns(db: AsyncSession, symbol: str) -> tuple | None:
"""Read one ticker's OHLCV and detach it to primitive column arrays in the
event loop (safe ORM access), ready to hand to a worker. None if no data."""
@@ -1705,7 +1716,14 @@ def _gate_ablation(candidates: list[dict], activation: dict, threshold: float) -
# the QUALIFIED setups at their detection close, best momentum first while
# slots and cash allow.
SIM_STARTING_CAPITAL = 10_000.0
SIM_MAX_POSITIONS = 10
# Headroom, not a target: the count cap should never bind. The capacity study
# (reports/portfolio-construction-prod505-capacity-bracket-daily-v1) showed a book
# that never hits the count cap earns +1.1pp CAGR over the old 10 (51 cohorts of 175
# better, 2 worse) at unchanged drawdown, because the blocked entries were as good as
# the taken ones — capacity costs trade COUNT, not trade quality. The real ceiling is
# cash plus SIM_NOTIONAL_CAP, which saturates the book near 12 positions, so 15/20/None
# are the same experiment. Judge any future change here on CAGR, never on EV per trade.
SIM_MAX_POSITIONS = 15
SIM_RISK_PER_TRADE = 0.01 # fraction of equity risked per position (entry→stop)
SIM_NOTIONAL_CAP = 0.20 # max fraction of equity per position (no margin)
_EULER_MASCHERONI = 0.5772156649015329
@@ -1946,7 +1964,6 @@ def _make_gate_reset_reentry_fn(
cadence: str,
qualified_fn: Callable[[dict], bool] | None = None,
ranking_key: str = PRODUCTION_PERCENTILE_KEY,
evaluation_horizon_sessions: int = HORIZON,
) -> Callable[[str, int, dict, Any], dict | None]:
"""Build the production post-stop gate-reset callback.
@@ -1964,18 +1981,11 @@ def _make_gate_reset_reentry_fn(
evaluation_ords: dict[str, set[int]] = {}
step_sessions = backtest_step_sessions(cadence)
evaluation_horizon = int(evaluation_horizon_sessions)
if evaluation_horizon < 0:
raise ValueError('evaluation_horizon_sessions must be non-negative')
for symbol, columns in prices.items():
ordinals = columns[0]
evaluation_ords[symbol] = {
int(ordinals[index])
for index in range(
MIN_LOOKBACK - 1,
len(ordinals) - evaluation_horizon,
step_sessions,
)
for index in range(MIN_LOOKBACK - 1, len(ordinals) - HORIZON, step_sessions)
}
qualified_by_symbol_date: dict[tuple[str, int], dict] = {}
@@ -2022,7 +2032,7 @@ def _simulate_portfolio(
*,
qualified_fn: Callable[[dict], bool] | None = None,
ranking_key: str = PRODUCTION_PERCENTILE_KEY,
max_positions: int | None = SIM_MAX_POSITIONS,
max_positions: int = SIM_MAX_POSITIONS,
risk_per_trade: float = SIM_RISK_PER_TRADE,
atr_trail_multiplier: float = ATR_TRAIL_MULTIPLIER,
cost_per_side: float = COST_PER_SIDE,
@@ -2046,12 +2056,6 @@ def _simulate_portfolio(
corr_lookback: int = 120,
corr_action: str = "skip",
corr_min_overlap: int = 60,
min_initial_risk_fraction: float | None = None,
weekly_top_n_rebalance: bool = False,
daily_rank_map: dict[tuple[str, str], dict[str, float | None]] | None = None,
measurement_start_date: date | None = None,
hard_end_date: date | None = None,
include_capacity_diagnostics: bool = False,
) -> dict | None:
"""Replay the qualified setups as ONE capital-constrained book and report
portfolio economics from the daily equity curve (return, CAGR, drawdown,
@@ -2101,20 +2105,6 @@ def _simulate_portfolio(
raise ValueError("corr_action must be 'skip' or 'half_size'")
if vol_target is not None and vol_target <= 0:
raise ValueError("vol_target must be positive when set")
if max_positions is not None and int(max_positions) <= 0:
raise ValueError("max_positions must be positive or None")
if min_initial_risk_fraction is not None and not (
0.0 < float(min_initial_risk_fraction) < 1.0
):
raise ValueError("min_initial_risk_fraction must be between 0 and 1")
if weekly_top_n_rebalance and (
max_positions is None or daily_rank_map is None
):
raise ValueError(
"weekly_top_n_rebalance requires max_positions and daily_rank_map"
)
if weekly_top_n_rebalance and fill_mode != FILL_MODE_CLOSE:
raise ValueError("weekly_top_n_rebalance requires fill_mode=close")
clamp_lo, clamp_hi = float(vol_clamp[0]), float(vol_clamp[1])
if clamp_lo <= 0 or clamp_hi < clamp_lo:
raise ValueError("vol_clamp must satisfy 0 < lo <= hi")
@@ -2126,26 +2116,8 @@ def _simulate_portfolio(
entries_by_ord: dict[int, list[dict]] = defaultdict(list)
start_ord = start_date.toordinal() if start_date is not None else None
measurement_start_ord = (
measurement_start_date.toordinal()
if measurement_start_date is not None
else start_ord
)
hard_end_ord = hard_end_date.toordinal() if hard_end_date is not None else None
# Explicit simulator/holdout end dates are exclusive split boundaries.
end_ord = end_date.toordinal() if end_date is not None else None
if (
start_ord is not None
and measurement_start_ord is not None
and measurement_start_ord < start_ord
):
raise ValueError("measurement_start_date cannot precede start_date")
if (
hard_end_ord is not None
and measurement_start_ord is not None
and hard_end_ord <= measurement_start_ord
):
raise ValueError("hard_end_date must follow measurement_start_date")
for c in candidates:
if not qualified_fn(c) or c.get("direction") != "long":
continue
@@ -2154,8 +2126,6 @@ def _simulate_portfolio(
continue
if end_ord is not None and entry_ord >= end_ord:
continue # holdout/validation: entries strictly before the split
if hard_end_ord is not None and entry_ord >= hard_end_ord:
continue
if not c.get("entry") or not c.get("stop"):
continue
entries_by_ord[entry_ord].append(c)
@@ -2168,12 +2138,7 @@ def _simulate_portfolio(
}
first_ord = start_ord if start_ord is not None else min(entries_by_ord)
full_calendar = sorted({o for cols in prices.values() for o in cols[0]})
calendar = [
o
for o in full_calendar
if o >= first_ord and (hard_end_ord is None or o < hard_end_ord)
]
calendar = sorted({o for cols in prices.values() for o in cols[0] if o >= first_ord})
if not calendar:
return None
@@ -2181,7 +2146,6 @@ def _simulate_portfolio(
# fill lag). Prevents trailing flat-cash after the last resolvable entry —
# the clear-air train-window bug — for train, validation, and full-period
# books alike (including max-hold sweeps out to 90 days).
if hard_end_ord is None:
last_signal_ord = max(entries_by_ord)
resolve_pad = hold_days + (1 if fill_mode in DELAYED_FILL_MODES else 0)
cut = bisect.bisect_left(calendar, last_signal_ord) + resolve_pad + 1
@@ -2189,31 +2153,13 @@ def _simulate_portfolio(
if not calendar:
return None
weekly_rebalance_ords: set[int] = set()
for index, session_ord in enumerate(full_calendar):
session_date = date.fromordinal(session_ord)
iso = session_date.isocalendar()
if index + 1 < len(full_calendar):
next_iso = date.fromordinal(full_calendar[index + 1]).isocalendar()
if (iso.year, iso.week) != (next_iso.year, next_iso.week):
weekly_rebalance_ords.add(session_ord)
elif session_date.weekday() == 4:
weekly_rebalance_ords.add(session_ord)
cash = SIM_STARTING_CAPITAL
positions: dict[str, dict] = {}
curve: list[tuple[int, float]] = []
trades: list[dict] = []
skipped_full = 0
measurement_skipped_full = 0
skipped_cooldown = 0
skipped_corr = 0
skipped_min_initial_risk = 0
measurement_skipped_min_initial_risk = 0
opened_positions = 0
measurement_opened_positions = 0
weekly_rank_rejected_entries = 0
measurement_weekly_rank_rejected_entries = 0
skipped_missing_fill = 0
skipped_gap_cap = 0
cooldown_until_index: dict[str, int] = {}
@@ -2228,12 +2174,6 @@ def _simulate_portfolio(
vol_scalars: list[float] = []
overnight_slippage_pct: list[float] = []
pending_delayed: list[dict] = []
measurement_start_equity: float | None = None
measurement_start_position_count: int | None = None
capacity_samples: list[dict[str, float | int]] = []
weekly_rebalance_events: list[dict] = []
rebalance_exit_index: dict[str, tuple[int, int]] = {}
rebalance_reentry_events: list[dict] = []
def _bar(sym: str, o: int):
idx = index_of.get(sym, {}).get(o)
@@ -2303,13 +2243,6 @@ def _simulate_portfolio(
cost = proceeds * cost_rate
cash += proceeds - cost
risk = pos["entry"] - pos["initial_stop"]
initial_risk_dollars = pos["shares"] * risk
net_pnl = (
proceeds
- pos["shares"] * pos["entry"]
- cost
- pos["entry_cost"]
)
trades.append({
"symbol": sym,
"entry_ord": pos["entry_ord"],
@@ -2318,13 +2251,8 @@ def _simulate_portfolio(
"initial_stop": pos["initial_stop"],
"active_stop": pos["stop"],
"fill": fill,
"shares": pos["shares"],
"initial_risk_dollars": initial_risk_dollars,
"pnl": net_pnl,
"pnl": proceeds - pos["shares"] * pos["entry"] - cost - pos["entry_cost"],
"r": (fill - pos["entry"]) / risk if risk > 0 else 0.0,
"net_r": net_pnl / initial_risk_dollars
if initial_risk_dollars > 0
else 0.0,
"hold": pos["bars_held"],
"reason": reason,
"stop_refreshes": pos["stop_refreshes"],
@@ -2339,13 +2267,6 @@ def _simulate_portfolio(
cooldown_sessions = max(0, int(reentry_cooldown_sessions))
for calendar_index, o in enumerate(calendar):
in_measurement = (
measurement_start_ord is None or o >= measurement_start_ord
)
if in_measurement and measurement_start_equity is None:
measurement_start_equity = _marked_equity()
measurement_start_position_count = len(positions)
# 1) exits on today's bars (stop intraday, target intraday, time at close)
for sym in list(positions):
pos = positions[sym]
@@ -2459,82 +2380,6 @@ def _simulate_portfolio(
reverse=True,
)
weekly_selected_entries: list[dict] | None = None
if weekly_top_n_rebalance and o in weekly_rebalance_ords:
assert max_positions is not None
assert daily_rank_map is not None
asof = date.fromordinal(o).isoformat()
protected: set[str] = set()
ranked_pool: list[tuple[float, int, str, dict | None]] = []
for sym in positions:
rank_row = daily_rank_map.get((sym, asof))
current_rank = (
rank_row.get("strategy_rank") if rank_row is not None else None
)
if current_rank is None or _bar(sym, o) is None:
protected.add(sym)
continue
ranked_pool.append((float(current_rank), 0, sym, None))
entrants_by_symbol: dict[str, dict] = {}
for candidate in signal_todays:
sym = str(candidate["symbol"])
if sym in positions or sym in entrants_by_symbol:
continue
entrants_by_symbol[sym] = candidate
eligible_entrants = 0
for sym, candidate in entrants_by_symbol.items():
rank_row = daily_rank_map.get((sym, asof))
current_rank = (
rank_row.get("strategy_rank") if rank_row is not None else None
)
if current_rank is None:
continue
eligible_entrants += 1
ranked_pool.append((float(current_rank), 1, sym, candidate))
available_slots = max(0, int(max_positions) - len(protected))
ranked_pool.sort(key=lambda row: (-row[0], row[1], row[2]))
selected = ranked_pool[:available_slots]
selected_holding_symbols = {
sym for _rank, kind, sym, _candidate in selected if kind == 0
}
weekly_selected_entries = [
candidate
for _rank, kind, _sym, candidate in selected
if kind == 1 and candidate is not None
]
selected_entrant_symbols = {
str(candidate["symbol"]) for candidate in weekly_selected_entries
}
rejected_now = max(0, eligible_entrants - len(selected_entrant_symbols))
weekly_rank_rejected_entries += rejected_now
if in_measurement:
measurement_weekly_rank_rejected_entries += rejected_now
exited_symbols: list[str] = []
for sym in list(positions):
if sym in protected or sym in selected_holding_symbols:
continue
bar = _bar(sym, o)
if bar is None:
continue
_close_trade(sym, float(bar.close), "weekly_rebalance")
rebalance_exit_index[sym] = (calendar_index, o)
exited_symbols.append(sym)
weekly_rebalance_events.append({
"ord": o,
"fresh_entrant_pool": len(entrants_by_symbol),
"rank_eligible_entrant_pool": eligible_entrants,
"selected_entrants": len(selected_entrant_symbols),
"replacements": len(exited_symbols),
"exited_symbols": sorted(exited_symbols),
"selected_entrant_symbols": sorted(selected_entrant_symbols),
"measurement": in_measurement,
})
equity = _marked_equity()
if fill_mode in DELAYED_FILL_MODES:
fill_candidates = sorted(
pending_delayed,
@@ -2543,11 +2388,7 @@ def _simulate_portfolio(
)
pending_delayed = []
else:
fill_candidates = (
weekly_selected_entries
if weekly_selected_entries is not None
else signal_todays
)
fill_candidates = signal_todays
def _corr_scale_for(sym: str, asof_idx: int) -> float | None:
"""1.0 ok, 0.5 half-size, None = skip. Missing history → uncorrelated."""
@@ -2592,21 +2433,15 @@ def _simulate_portfolio(
corr_scale: float,
fill_bar: Any | None,
) -> None:
nonlocal cash, equity, skipped_full, measurement_skipped_full
nonlocal skipped_cooldown, post_stop_events
nonlocal skipped_min_initial_risk
nonlocal measurement_skipped_min_initial_risk
nonlocal opened_positions, measurement_opened_positions
nonlocal cash, equity, skipped_full, skipped_cooldown, post_stop_events
sym = c["symbol"]
if sym in positions:
return
if calendar_index < cooldown_until_index.get(sym, -1):
skipped_cooldown += 1
return
if max_positions is not None and len(positions) >= max_positions:
if len(positions) >= max_positions:
skipped_full += 1
if in_measurement:
measurement_skipped_full += 1
return
risk_ps = entry - stop
if risk_ps <= 0 or entry <= 0:
@@ -2623,16 +2458,6 @@ def _simulate_portfolio(
(equity * SIM_NOTIONAL_CAP) / entry,
max(cash, 0.0) / (entry * (1.0 + cost_rate)),
)
initial_risk_dollars = shares * risk_ps
if (
min_initial_risk_fraction is not None
and initial_risk_dollars
< equity * float(min_initial_risk_fraction)
):
skipped_min_initial_risk += 1
if in_measurement:
measurement_skipped_min_initial_risk += 1
return
if shares * entry < 1.0:
return
entry_cost = shares * entry * cost_rate
@@ -2672,21 +2497,6 @@ def _simulate_portfolio(
"vol_scalar": scalar,
"corr_scale": corr_scale,
}
opened_positions += 1
if in_measurement:
measurement_opened_positions += 1
prior_rebalance_exit = rebalance_exit_index.pop(sym, None)
if prior_rebalance_exit is not None:
prior_exit_index, prior_exit_ord = prior_rebalance_exit
rebalance_reentry_events.append({
"symbol": sym,
"exit_ord": prior_exit_ord,
"exit_calendar_index": prior_exit_index,
"reentry_calendar_index": calendar_index,
"wait_sessions": calendar_index - prior_exit_index,
"reentry_ord": entry_ord,
"measurement": in_measurement,
})
# next_open only: fill is at the open, so the rest of the bar can stop out.
# stale_close fills at the close — same-day stop after entry does not apply.
# bars_held stays 0 on the fill day (matches close-fill cadence).
@@ -2788,25 +2598,7 @@ def _simulate_portfolio(
# Queue today's signals for the next session's fill.
pending_delayed.extend(signal_todays)
marked_equity = _marked_equity()
if in_measurement and include_capacity_diagnostics:
gross_notional = sum(
pos["shares"] * pos["last_close"] for pos in positions.values()
)
capacity_samples.append({
"positions": len(positions),
"cash_pct": cash / marked_equity * 100.0
if marked_equity > 0
else 0.0,
"gross_exposure_pct": gross_notional / marked_equity * 100.0
if marked_equity > 0
else 0.0,
"at_capacity": int(
max_positions is not None
and len(positions) >= max_positions
),
})
curve.append((o, marked_equity))
curve.append((o, _marked_equity()))
# Close whatever is still open at its last mark so final equity is realized.
for sym in list(positions):
@@ -2814,57 +2606,32 @@ def _simulate_portfolio(
final_equity = cash
curve[-1] = (calendar[-1], final_equity)
metric_start_ord = (
measurement_start_ord if measurement_start_ord is not None else calendar[0]
)
metric_curve = [(day_ord, eq) for day_ord, eq in curve if day_ord >= metric_start_ord]
if not metric_curve:
return None
metric_base_equity = (
measurement_start_equity
if measurement_start_date is not None and measurement_start_equity is not None
else SIM_STARTING_CAPITAL
)
total_return_pct = (final_equity / metric_base_equity - 1.0) * 100.0
years = (calendar[-1] - metric_start_ord) / 365.25
total_return_pct = (final_equity / SIM_STARTING_CAPITAL - 1.0) * 100.0
years = (calendar[-1] - calendar[0]) / 365.25
cagr_pct = (
((final_equity / metric_base_equity) ** (1.0 / years) - 1.0) * 100.0
((final_equity / SIM_STARTING_CAPITAL) ** (1.0 / years) - 1.0) * 100.0
if years > 0.25 and final_equity > 0
else None
)
peak = float("-inf")
max_dd = 0.0
drawdown_equities = (
[metric_base_equity, *(eq for _, eq in metric_curve)]
if measurement_start_date is not None
else [eq for _, eq in metric_curve]
)
for eq in drawdown_equities:
for _, eq in curve:
peak = max(peak, eq)
if peak > 0:
max_dd = max(max_dd, (peak - eq) / peak)
return_equities = (
[metric_base_equity, *(eq for _, eq in metric_curve)]
if measurement_start_date is not None
else [eq for _, eq in metric_curve]
)
rets = [
b / a - 1.0
for a, b in zip(return_equities, return_equities[1:])
if a > 0
]
rets = [b / a - 1.0 for (_, a), (_, b) in zip(curve, curve[1:]) if a > 0]
diag = sharpe_diagnostics(rets)
sharpe = diag["sharpe"]
# Per-calendar-year returns off the equity curve — shows whether every year
# contributed or one exceptional stretch carried the result.
yearly: list[dict] = []
year_start_eq = metric_base_equity
cur_year = date.fromordinal(metric_start_ord).year
last_eq = metric_base_equity
for o, eq in metric_curve:
year_start_eq = curve[0][1]
cur_year = date.fromordinal(curve[0][0]).year
last_eq = curve[0][1]
for o, eq in curve:
y = date.fromordinal(o).year
if y != cur_year:
yearly.append({
@@ -2883,29 +2650,24 @@ def _simulate_portfolio(
),
})
metric_trades = [
trade for trade in trades if trade["entry_ord"] >= metric_start_ord
]
pnls = [t["pnl"] for t in metric_trades]
pnls = [t["pnl"] for t in trades]
wins = sum(1 for p in pnls if p > 0)
reason_counts = {
reason: sum(1 for t in metric_trades if t["reason"] == reason)
for reason in sorted({t["reason"] for t in metric_trades})
reason: sum(1 for t in trades if t["reason"] == reason)
for reason in sorted({t["reason"] for t in trades})
}
spy_pct = None
if spy_closes:
from app.services.benchmark_service import benchmark_return_pct
spy_pct = benchmark_return_pct(
spy_closes,
date.fromordinal(metric_start_ord),
date.fromordinal(calendar[-1]),
spy_closes, date.fromordinal(calendar[0]), date.fromordinal(calendar[-1])
)
curve_payload: list[dict] | None = None
benchmark_payload: list[dict] | None = None
if include_curve:
curve_base = metric_base_equity
curve_base = curve[0][1] if curve else SIM_STARTING_CAPITAL
curve_payload = [
{
"date": date.fromordinal(o).isoformat(),
@@ -2914,12 +2676,12 @@ def _simulate_portfolio(
if curve_base > 0
else None,
}
for o, eq in metric_curve
for o, eq in curve
]
if spy_closes:
benchmark_payload = []
base_spy = None
for o, _ in metric_curve:
for o, _ in curve:
d = date.fromordinal(o)
close = spy_closes.get(d)
if close is None or close <= 0:
@@ -2938,8 +2700,6 @@ def _simulate_portfolio(
calmar = float(cagr_pct) / max_dd_pct
result = {
"starting_capital": SIM_STARTING_CAPITAL,
"measurement_start_equity": round(metric_base_equity, 2),
"measurement_start_positions": measurement_start_position_count or 0,
"cost_per_side_pct": round(cost_rate * 100.0, 3),
"fill_mode": fill_mode,
"final_equity": round(final_equity, 2),
@@ -2953,161 +2713,23 @@ def _simulate_portfolio(
"n_returns": diag["n_returns"],
"return_skew": diag["return_skew"],
"return_kurtosis": diag["return_kurtosis"],
"trades": len(metric_trades),
"win_rate": (
round(wins / len(metric_trades) * 100.0, 1)
if metric_trades
else None
),
"trades": len(trades),
"win_rate": round(wins / len(trades) * 100.0, 1) if trades else None,
"avg_trade_pnl": round(sum(pnls) / len(pnls), 2) if pnls else None,
"best_trade_r": (
round(max(t["r"] for t in metric_trades), 2)
if metric_trades
else None
),
"worst_trade_r": (
round(min(t["r"] for t in metric_trades), 2)
if metric_trades
else None
),
"best_trade_r": round(max(t["r"] for t in trades), 2) if trades else None,
"worst_trade_r": round(min(t["r"] for t in trades), 2) if trades else None,
"best_trade_pnl": round(max(pnls), 2) if pnls else None,
"worst_trade_pnl": round(min(pnls), 2) if pnls else None,
"avg_hold_days": (
round(
sum(t["hold"] for t in metric_trades) / len(metric_trades),
1,
)
if metric_trades
else None
round(sum(t["hold"] for t in trades) / len(trades), 1) if trades else None
),
"exit_reasons": reason_counts,
"skipped_book_full": skipped_full,
"spy_return_pct": round(spy_pct, 1) if spy_pct is not None else None,
"yearly_returns": yearly,
"start_date": date.fromordinal(metric_start_ord).isoformat(),
"start_date": date.fromordinal(calendar[0]).isoformat(),
"end_date": date.fromordinal(calendar[-1]).isoformat(),
}
if measurement_start_date is not None:
result["simulation_start_date"] = date.fromordinal(calendar[0]).isoformat()
if hard_end_date is not None:
result["hard_end_date_exclusive"] = hard_end_date.isoformat()
if measurement_start_date is not None:
result["measurement_skipped_book_full"] = measurement_skipped_full
result["measurement_opened_positions"] = measurement_opened_positions
if min_initial_risk_fraction is not None:
result["min_initial_risk_fraction"] = float(min_initial_risk_fraction)
result["skipped_min_initial_risk"] = skipped_min_initial_risk
result["measurement_skipped_min_initial_risk"] = (
measurement_skipped_min_initial_risk
)
if include_capacity_diagnostics:
measured_opened = (
measurement_opened_positions
if measurement_start_date is not None
else opened_positions
)
measured_full = (
measurement_skipped_full
if measurement_start_date is not None
else skipped_full
)
capacity_opportunities = measured_opened + measured_full
result["opened_positions"] = measured_opened
result["capacity_opportunities"] = capacity_opportunities
result["blocked_fraction"] = (
round(measured_full / capacity_opportunities, 6)
if capacity_opportunities
else 0.0
)
result["avg_positions"] = (
round(
sum(float(sample["positions"]) for sample in capacity_samples)
/ len(capacity_samples),
4,
)
if capacity_samples
else 0.0
)
result["peak_positions"] = (
max(int(sample["positions"]) for sample in capacity_samples)
if capacity_samples
else 0
)
result["sessions_at_capacity"] = sum(
int(sample["at_capacity"]) for sample in capacity_samples
)
result["sessions_measured"] = len(capacity_samples)
result["avg_cash_pct"] = (
round(
sum(float(sample["cash_pct"]) for sample in capacity_samples)
/ len(capacity_samples),
4,
)
if capacity_samples
else None
)
result["avg_gross_exposure_pct"] = (
round(
sum(
float(sample["gross_exposure_pct"])
for sample in capacity_samples
)
/ len(capacity_samples),
4,
)
if capacity_samples
else None
)
if weekly_top_n_rebalance:
measured_events = [
event for event in weekly_rebalance_events if event["measurement"]
]
measured_reentries = [
event for event in rebalance_reentry_events if event["measurement"]
]
result["weekly_rank_rejected_entries"] = (
measurement_weekly_rank_rejected_entries
if measurement_start_date is not None
else weekly_rank_rejected_entries
)
result["weekly_rebalance_events"] = [
{
**{
key: value
for key, value in event.items()
if key not in {"ord", "measurement"}
},
"date": date.fromordinal(event["ord"]).isoformat(),
}
for event in measured_events
]
result["rebalance_reentry_events"] = [
{
**{
key: value
for key, value in event.items()
if key
not in {
"exit_ord",
"reentry_ord",
"measurement",
"exit_calendar_index",
"reentry_calendar_index",
}
},
"exit_date": date.fromordinal(event["exit_ord"]).isoformat(),
"reentry_date": date.fromordinal(
event["reentry_ord"]
).isoformat(),
}
for event in measured_reentries
]
for session_limit in (5, 10, 20):
result[f"rebalance_reentries_within_{session_limit}_sessions"] = sum(
1
for event in measured_reentries
if int(event["wait_sessions"]) <= session_limit
)
if vol_target is not None:
result["vol_target"] = vol_target
result["vol_lookback"] = int(vol_lookback)
@@ -3182,7 +2804,7 @@ def _simulate_portfolio(
"entry_date": date.fromordinal(trade["entry_ord"]).isoformat(),
"exit_date": date.fromordinal(trade["exit_ord"]).isoformat(),
}
for trade in metric_trades
for trade in trades
]
return result
@@ -4430,9 +4052,12 @@ async def run_backtest(
config = await get_recommendation_config(db)
activation = await get_activation_config(db)
result = await db.execute(select(Ticker).order_by(Ticker.symbol))
tickers = list(result.scalars().all())
total = len(tickers)
# Plain strings, not Ticker instances: the rollbacks below expire any ORM
# objects held across them, and touching an expired attribute afterwards
# triggers sync lazy-loading, which raises on an AsyncSession.
result = await db.execute(select(Ticker.symbol).order_by(Ticker.symbol))
symbols = list(result.scalars().all())
total = len(symbols)
rank_only_symbols = await _load_research_rank_only_symbols(db)
if rank_only_symbols:
logger.info(json.dumps({
@@ -4456,6 +4081,7 @@ async def run_backtest(
)
except Exception:
logger.exception("Benchmark load for residual momentum failed")
await _rollback_quietly(db, "benchmark load")
def _merge(result: tuple[list[dict], dict]) -> None:
cands, series = result
@@ -4487,26 +4113,27 @@ async def run_backtest(
done = 0
with pool:
for start in range(0, total, chunk):
batch = tickers[start : start + chunk]
batch = symbols[start : start + chunk]
futures = []
for ticker in batch:
for symbol in batch:
try:
columns = await _fetch_columns(db, ticker.symbol)
columns = await _fetch_columns(db, symbol)
except Exception:
logger.exception("Backtest fetch failed for %s", ticker.symbol)
logger.exception("Backtest fetch failed for %s", symbol)
await _rollback_quietly(db, f"fetch for {symbol}")
continue
if columns is not None:
futures.append(loop.run_in_executor(
pool,
_replay_and_signals,
ticker.symbol,
symbol,
columns,
config,
activation,
benchmark_closes,
target_model,
cadence,
ticker.symbol in rank_only_symbols,
symbol in rank_only_symbols,
))
for result in await asyncio.gather(*futures, return_exceptions=True):
if isinstance(result, Exception):
@@ -4519,25 +4146,26 @@ async def run_backtest(
else:
# Sequential fallback (Windows / 1 worker): run each replay in a worker
# thread so the event loop — and the API server — stays responsive.
for index, ticker in enumerate(tickers):
for index, symbol in enumerate(symbols):
if progress_cb is not None:
progress_cb(index, total, ticker.symbol)
progress_cb(index, total, symbol)
try:
columns = await _fetch_columns(db, ticker.symbol)
columns = await _fetch_columns(db, symbol)
if columns is not None:
_merge(await asyncio.to_thread(
_replay_and_signals,
ticker.symbol,
symbol,
columns,
config,
activation,
benchmark_closes,
target_model,
cadence,
ticker.symbol in rank_only_symbols,
symbol in rank_only_symbols,
))
except Exception:
logger.exception("Backtest replay failed for %s", ticker.symbol)
logger.exception("Backtest replay failed for %s", symbol)
await _rollback_quietly(db, f"replay for {symbol}")
if progress_cb is not None and total:
progress_cb(total, total, "")
@@ -4602,6 +4230,7 @@ async def run_backtest(
)
except Exception:
logger.exception("Benchmark load for the portfolio sim failed")
await _rollback_quietly(db, "portfolio-sim benchmark load")
for policy in ("target", "hold"):
sim = _simulate_portfolio(
@@ -4622,6 +4251,7 @@ async def run_backtest(
live_exit_policy = await get_exit_policy(db)
except Exception:
logger.exception("Live exit policy load failed; monitor uses defaults")
await _rollback_quietly(db, "exit policy load")
portfolio_monitor_report = _portfolio_monitor(
candidates, price_columns, spy_closes, hold_horizon,
live_exit_policy=live_exit_policy,
@@ -4641,6 +4271,11 @@ async def run_backtest(
)
except Exception:
logger.exception("Portfolio simulation failed")
# Catches the price_columns fetch loop, which has no handler of its
# own. The inner handlers above may already have rolled back; a
# rollback on a clean session is a no-op, so this stays safe as the
# backstop for whichever DB call actually failed.
await _rollback_quietly(db, "portfolio simulation")
report = {
"generated_at": datetime.now(timezone.utc).isoformat(),
app/services/breadth_service.py
+2 -9
View File
@@ -25,6 +25,7 @@ from sqlalchemy import select
from sqlalchemy.ext.asyncio import AsyncSession
from app.models.ticker import Ticker
from app.services import ticker_service
from app.services.price_service import query_ohlcv
logger = logging.getLogger(__name__)
@@ -112,7 +113,7 @@ def compute_divergence_series(
async def _load_universe_closes(
db: AsyncSession, symbols: list[str] | None = None
) -> dict[str, Series]:
stmt = select(Ticker).order_by(Ticker.symbol)
stmt = ticker_service.active_only(select(Ticker).order_by(Ticker.symbol))
if symbols is not None:
stmt = stmt.where(Ticker.symbol.in_(symbols))
result = await db.execute(stmt)
@@ -148,11 +149,3 @@ async def compute_breadth_details(
"""Breadth values plus the qualifying-member count for snapshot metadata."""
closes_by_symbol = await _load_universe_closes(db, symbols)
return _breadth_with_counts(closes_by_symbol, window, min_tickers)
async def compute_breadth_today(db: AsyncSession) -> float | None:
"""Latest breadth reading (thin wrapper, for future live use)."""
series = await compute_breadth_series(db)
if not series:
return None
return series[max(series)]
app/services/dolt_earnings_importer.py
+6 -2
View File
@@ -32,7 +32,7 @@ from app.config import settings
from app.database import insert_for_session
from app.models.earnings_event import EarningsEvent
from app.models.ticker import Ticker
from app.services import dolt_client, earnings_alignment
from app.services import dolt_client, earnings_alignment, ticker_service
from app.services.data_import import ValidationResult
logger = logging.getLogger(__name__)
@@ -289,7 +289,11 @@ class DoltEarningsImporter:
# -- helpers -----------------------------------------------------------
async def _load_universe(self, db) -> dict[str, int]:
rows = (await db.execute(select(Ticker.id, Ticker.symbol))).all()
rows = (
await db.execute(
ticker_service.active_only(select(Ticker.id, Ticker.symbol))
)
).all()
return {
earnings_alignment.normalise_symbol(symbol): tid
for tid, symbol in rows
app/services/event_study_service.py
+1 -1
View File
@@ -1,4 +1,4 @@
"""Compact chronological validation for the Regime Monitor warning score.
"""Compact chronological validation for the AI/Tech Risk Monitor warning score.
The study calls its outcome a 10% correction, uses the first 70% of sessions to
freeze an 80th-percentile warning threshold, and reports alarm episodes only on
app/services/fundamental_data_refresh_service.py
+5 -30
View File
@@ -1,4 +1,7 @@
"""A5 activation: refresh the legacy fundamentals cache from local bulk data."""
"""Refresh the fundamentals compat cache from local SEC/Dolt bulk data.
``fundamental_data`` is the table scoring reads. This is its only writer.
"""
from __future__ import annotations
@@ -12,38 +15,11 @@ from sqlalchemy.ext.asyncio import AsyncSession
from app.database import insert_for_session
from app.models.fundamental import FundamentalData
from app.models.score import CompositeScore, DimensionScore
from app.services import fundamentals_candidate_service, settings_store
from app.services import fundamentals_candidate_service
# Absence is deliberately false. Production activation therefore requires one
# explicit, durable SystemSetting change after the A5 evidence is approved.
ACTIVATION_KEY = "fundamental_data_sec_dolt_cutover_enabled"
_SCORE_FIELDS = ("pe_ratio", "revenue_growth", "earnings_surprise")
async def is_enabled(db: AsyncSession) -> bool:
raw = await settings_store.get_value(db, ACTIVATION_KEY, "false")
return str(raw).strip().lower() == "true"
async def refresh_if_enabled(
db: AsyncSession,
*,
now: datetime | None = None,
today: date | None = None,
) -> dict[str, Any]:
"""Refresh atomically when activated; otherwise perform no writes."""
if not await is_enabled(db):
return {
"enabled": False,
"refreshed": 0,
"score_inputs_changed": 0,
"dimension_scores_staled": 0,
"composite_scores_staled": 0,
}
return await refresh(db, now=now, today=today)
async def refresh(
db: AsyncSession,
*,
@@ -117,7 +93,6 @@ async def refresh(
await db.commit()
return {
"enabled": True,
"refreshed": len(candidates),
"score_inputs_changed": len(changed_ids),
"dimension_scores_staled": len(dimension_ids),
app/services/fundamental_service.py
+5 -67
View File
@@ -1,22 +1,19 @@
"""Fundamental data service.
"""Fundamental data read access.
Stores fundamental data (P/E, revenue growth, earnings surprise, market cap)
and marks the fundamental dimension score as stale on new data.
``fundamental_data`` is the compat cache scoring reads. It is written solely by
``fundamental_data_refresh_service`` from SEC snapshots, Dolt earnings events and
stored closes; nothing fetches it per ticker.
"""
from __future__ import annotations
import json
import logging
from datetime import datetime, timezone
from sqlalchemy import select, update
from sqlalchemy import select
from sqlalchemy.ext.asyncio import AsyncSession
from app.database import insert_for_session
from app.exceptions import NotFoundError
from app.models.fundamental import FundamentalData
from app.models.score import DimensionScore
from app.models.ticker import Ticker
logger = logging.getLogger(__name__)
@@ -32,65 +29,6 @@ async def _get_ticker(db: AsyncSession, symbol: str) -> Ticker:
return ticker
async def store_fundamental(
db: AsyncSession,
symbol: str,
pe_ratio: float | None = None,
revenue_growth: float | None = None,
earnings_surprise: float | None = None,
market_cap: float | None = None,
next_earnings_date=None,
unavailable_fields: dict[str, str] | None = None,
) -> FundamentalData:
"""Store or update fundamental data for a ticker.
Keeps a single latest snapshot per ticker. On new data, marks the
fundamental dimension score as stale (if one exists).
"""
ticker = await _get_ticker(db, symbol)
now = datetime.now(timezone.utc)
unavailable_fields_json = json.dumps(unavailable_fields or {})
stmt = insert_for_session(db, FundamentalData).values(
ticker_id=ticker.id,
pe_ratio=pe_ratio,
revenue_growth=revenue_growth,
earnings_surprise=earnings_surprise,
market_cap=market_cap,
next_earnings_date=next_earnings_date,
fetched_at=now,
unavailable_fields_json=unavailable_fields_json,
)
stmt = stmt.on_conflict_do_update(
index_elements=["ticker_id"],
set_={
"pe_ratio": stmt.excluded.pe_ratio,
"revenue_growth": stmt.excluded.revenue_growth,
"earnings_surprise": stmt.excluded.earnings_surprise,
"market_cap": stmt.excluded.market_cap,
"next_earnings_date": stmt.excluded.next_earnings_date,
"fetched_at": stmt.excluded.fetched_at,
"unavailable_fields_json": stmt.excluded.unavailable_fields_json,
},
).returning(FundamentalData)
record = (await db.execute(stmt)).scalar_one()
# Mark fundamental dimension score as stale if it exists
# TODO: Use DimensionScore service when built
await db.execute(
update(DimensionScore)
.where(
DimensionScore.ticker_id == ticker.id,
DimensionScore.dimension == "fundamental",
)
.values(is_stale=True)
)
await db.commit()
return record
async def get_fundamental(
db: AsyncSession,
symbol: str,
app/services/fundamentals_candidate_service.py
+10 -6
View File
@@ -1,9 +1,8 @@
"""Local SEC/Dolt candidate values for the legacy fundamentals cache.
"""Local SEC/Dolt candidate values for the fundamentals compat cache.
This is the single read path shared by the A5 parity report and the activated
``fundamental_data`` refresh. It never contacts SEC or Dolt: every input comes
from PostgreSQL, so price- and earnings-driven values can still refresh when an
upstream import is unchanged or unavailable.
This is the read path behind the ``fundamental_data`` refresh. It never contacts
SEC or Dolt: every input comes from PostgreSQL, so price- and earnings-driven
values can still refresh when an upstream import is unchanged or unavailable.
"""
from __future__ import annotations
@@ -23,6 +22,7 @@ from app.models.fundamental_snapshot import FundamentalSnapshot
from app.models.ohlcv import OHLCVRecord
from app.models.ticker import Ticker
from app.services import fundamentals_derivation as deriv
from app.services import ticker_service
@dataclass(frozen=True)
@@ -47,7 +47,11 @@ async def build_candidates(
"""Derive current cache candidates using only already-stored data."""
today = today or datetime.now(ZoneInfo("America/New_York")).date()
tickers = list(
(await db.execute(select(Ticker).order_by(Ticker.symbol))).scalars()
(
await db.execute(
ticker_service.active_only(select(Ticker).order_by(Ticker.symbol))
)
).scalars()
)
if not tickers:
return []
app/services/fundamentals_parity_service.py
-498
View File
@@ -1,498 +0,0 @@
"""Read-only A5 comparison of legacy and SEC/Dolt fundamental inputs.
The report deliberately does not write ``fundamental_data`` or score tables.
It reconstructs the current legacy and candidate fundamental scores, projects
their composite-score/rank effect with the active weights, and archives a
timestamped JSON + CSV bundle for explicit human approval.
"""
from __future__ import annotations
import csv
import io
import json
import math
import os
import statistics
from datetime import date, datetime, timezone
from pathlib import Path
from typing import Any, Iterable
from zoneinfo import ZoneInfo
from sqlalchemy import select, text
from sqlalchemy.ext.asyncio import AsyncSession
from app.models.data_import_run import DataImportRun
from app.models.fundamental import FundamentalData
from app.services import fundamentals_candidate_service as candidate_service
REPORT_VERSION = 1
APPROVAL_STATUS = "pending_explicit_approval"
FIELD_KEYS = ("pe_ratio", "revenue_growth", "earnings_surprise")
MIN_SCORE_METRICS = 2
# Materiality is a review aid, never an automatic cutover verdict. Definition
# changes remain visible even when a delta falls inside these bands.
FIELD_TOLERANCES = {
"pe_ratio": {"absolute": 1.0, "relative_pct": 10.0},
"revenue_growth": {"absolute": 2.0, "relative_pct": None},
"earnings_surprise": {"absolute": 2.0, "relative_pct": None},
}
DEFINITION_NOTES = {
"pe_ratio": (
"Legacy provider P/E convention versus latest close divided by "
"SEC-derived TTM diluted EPS."
),
"revenue_growth": (
"Legacy provider growth convention versus SEC-derived TTM revenue YoY."
),
"earnings_surprise": (
"Legacy provider latest surprise versus latest completed Dolt earnings "
"event with actual and estimate."
),
}
def fundamental_score(
pe_ratio: float | None,
revenue_growth: float | None,
earnings_surprise: float | None,
) -> float | None:
"""Match the production fundamental-dimension formula without persistence."""
scores: list[float] = []
if _finite(pe_ratio) and pe_ratio > 0:
scores.append(max(0.0, min(100.0, 100.0 - (pe_ratio - 15.0) * (100.0 / 30.0))))
if _finite(revenue_growth):
scores.append(max(0.0, min(100.0, 50.0 + revenue_growth * 2.5)))
if _finite(earnings_surprise):
scores.append(max(0.0, min(100.0, 50.0 + earnings_surprise * 5.0)))
return sum(scores) / len(scores) if len(scores) >= MIN_SCORE_METRICS else None
async def build_report(
db: AsyncSession,
*,
generated_at: datetime | None = None,
today: date | None = None,
) -> dict[str, Any]:
"""Build a point-in-time parity report from one database session."""
generated_at = generated_at or datetime.now(timezone.utc)
today = today or datetime.now(ZoneInfo("America/New_York")).date()
# A report must not mix rows from before and after a concurrent import
# promotion. The scheduled job provides a fresh session, so establish the
# production snapshot before its first query and have Postgres enforce the
# no-write contract as well. SQLite tests retain their normal transaction.
if db.get_bind().dialect.name == "postgresql":
connection = await db.connection(
execution_options={"isolation_level": "REPEATABLE READ"}
)
await connection.execute(text("SET TRANSACTION READ ONLY"))
candidates = await candidate_service.build_candidates(db, today=today)
ticker_ids = [candidate.ticker_id for candidate in candidates]
legacy_by_ticker = await _legacy_values(db, ticker_ids)
source_runs = await _source_runs(db)
rows: list[dict[str, Any]] = []
for candidate in candidates:
legacy = legacy_by_ticker.get(candidate.ticker_id)
candidate_values = {
"pe_ratio": candidate.pe_ratio,
"revenue_growth": candidate.revenue_growth,
"earnings_surprise": candidate.earnings_surprise,
}
legacy_values = {
"pe_ratio": legacy.pe_ratio if legacy else None,
"revenue_growth": legacy.revenue_growth if legacy else None,
"earnings_surprise": legacy.earnings_surprise if legacy else None,
}
fields = {
key: _field_comparison(key, legacy_values[key], candidate_values[key])
for key in FIELD_KEYS
}
legacy_score = fundamental_score(**legacy_values)
candidate_score = fundamental_score(**candidate_values)
rows.append(
{
"symbol": candidate.symbol,
"cik": candidate.cik,
"legacy_fetched_at": _iso(legacy.fetched_at) if legacy else None,
"price_date": _iso(candidate.price_date),
"fields": fields,
"scores": {
"legacy_fundamental": _round(legacy_score),
"candidate_fundamental": _round(candidate_score),
"fundamental_delta": _delta(legacy_score, candidate_score),
"legacy_fundamental_rank": None,
"candidate_fundamental_rank": None,
"fundamental_rank_change": None,
},
}
)
_attach_ranks(rows, "legacy_fundamental", "legacy_fundamental_rank")
_attach_ranks(rows, "candidate_fundamental", "candidate_fundamental_rank")
for row in rows:
scores = row["scores"]
scores["fundamental_rank_change"] = _rank_change(
scores["legacy_fundamental_rank"], scores["candidate_fundamental_rank"]
)
return {
"report_version": REPORT_VERSION,
"generated_at": generated_at.isoformat(),
"as_of_date": today.isoformat(),
"approval_status": APPROVAL_STATUS,
"read_only": True,
"fundamental_score_formula": (
"Equal-weighted mean of 2+ available sub-scores: P/E = "
"clamp(100-(pe-15)*(100/30)); revenue growth = "
"clamp(50+growth*2.5); earnings surprise = "
"clamp(50+surprise*5)."
),
"source_runs": source_runs,
"definition_notes": DEFINITION_NOTES,
"materiality_notes": {
"fields": FIELD_TOLERANCES,
"fundamental_score_absolute": 5.0,
"automatic_cutover": False,
},
"summary": _summary(rows),
"rows": rows,
}
def store_report(report: dict[str, Any], report_dir: str | Path) -> dict[str, str]:
"""Atomically archive JSON/CSV artifacts and update the latest manifest."""
directory = Path(report_dir).expanduser().resolve()
directory.mkdir(parents=True, exist_ok=True)
stamp = _artifact_stamp(report["generated_at"])
json_name = f"fundamentals-parity-{stamp}.json"
csv_name = f"fundamentals-parity-{stamp}.csv"
json_path = directory / json_name
csv_path = directory / csv_name
_atomic_write(json_path, json.dumps(report, indent=2, sort_keys=True) + "\n")
_atomic_write(csv_path, report_csv(report))
manifest = {
"generated_at": report["generated_at"],
"json_file": json_name,
"csv_file": csv_name,
}
_atomic_write(
directory / "latest.json",
json.dumps(manifest, indent=2, sort_keys=True) + "\n",
)
return {
"json": str(json_path),
"csv": str(csv_path),
"manifest": str(directory / "latest.json"),
}
async def generate_and_store(
db: AsyncSession,
report_dir: str | Path,
*,
generated_at: datetime | None = None,
today: date | None = None,
) -> tuple[dict[str, Any], dict[str, str]]:
report = await build_report(db, generated_at=generated_at, today=today)
return report, store_report(report, report_dir)
def load_latest(report_dir: str | Path) -> dict[str, Any] | None:
manifest = _load_manifest(report_dir)
if manifest is None:
return None
try:
path = _manifest_artifact(report_dir, manifest, "json_file")
loaded = json.loads(path.read_text(encoding="utf-8"))
except (OSError, json.JSONDecodeError, TypeError, ValueError):
return None
return loaded if isinstance(loaded, dict) else None
def load_latest_csv(report_dir: str | Path) -> tuple[str, str] | None:
return _load_latest_text_artifact(report_dir, "csv_file")
def load_latest_json(report_dir: str | Path) -> tuple[str, str] | None:
return _load_latest_text_artifact(report_dir, "json_file")
def _load_latest_text_artifact(
report_dir: str | Path, manifest_key: str
) -> tuple[str, str] | None:
manifest = _load_manifest(report_dir)
if manifest is None:
return None
try:
path = _manifest_artifact(report_dir, manifest, manifest_key)
return path.name, path.read_text(encoding="utf-8")
except (OSError, TypeError, ValueError):
return None
def report_csv(report: dict[str, Any]) -> str:
output = io.StringIO(newline="")
columns = [
"symbol",
"cik",
"legacy_fetched_at",
"price_date",
*(
f"{field}_{suffix}"
for field in FIELD_KEYS
for suffix in ("legacy", "candidate", "absolute_delta", "relative_delta_pct", "material")
),
"legacy_fundamental",
"candidate_fundamental",
"fundamental_delta",
"legacy_fundamental_rank",
"candidate_fundamental_rank",
"fundamental_rank_change",
]
writer = csv.DictWriter(output, fieldnames=columns)
writer.writeheader()
for row in report.get("rows", []):
flat = {
"symbol": row["symbol"],
"cik": row.get("cik"),
"legacy_fetched_at": row.get("legacy_fetched_at"),
"price_date": row.get("price_date"),
**row["scores"],
}
for field in FIELD_KEYS:
comparison = row["fields"][field]
for suffix in (
"legacy",
"candidate",
"absolute_delta",
"relative_delta_pct",
"material",
):
flat[f"{field}_{suffix}"] = comparison.get(suffix)
writer.writerow(flat)
return output.getvalue()
async def _legacy_values(
db: AsyncSession, ticker_ids: list[int]
) -> dict[int, FundamentalData]:
if not ticker_ids:
return {}
rows = (
await db.execute(
select(FundamentalData).where(FundamentalData.ticker_id.in_(ticker_ids))
)
).scalars()
return {row.ticker_id: row for row in rows}
async def _source_runs(db: AsyncSession) -> dict[str, dict[str, Any] | None]:
sources = ("sec_facts", "dolt_earnings")
rows = (
await db.execute(
select(DataImportRun)
.where(
DataImportRun.source.in_(sources),
DataImportRun.status.in_(("promoted", "no_op")),
)
.order_by(DataImportRun.id.desc())
)
).scalars()
latest: dict[str, dict[str, Any] | None] = {source: None for source in sources}
for row in rows:
if latest[row.source] is None:
latest[row.source] = {
"run_id": row.id,
"status": row.status,
"revision": row.revision,
"source_max_date": _iso(row.source_max_date),
"completed_at": _iso(row.completed_at),
}
return latest
def _field_comparison(
key: str, legacy: float | None, candidate: float | None
) -> dict[str, Any]:
legacy = float(legacy) if _finite(legacy) else None
candidate = float(candidate) if _finite(candidate) else None
absolute = _delta(legacy, candidate)
relative = (
None
if absolute is None or legacy in (None, 0)
else round(absolute / abs(legacy) * 100.0, 4)
)
tolerance = FIELD_TOLERANCES[key]
material = False
if absolute is not None:
material = abs(absolute) > tolerance["absolute"]
relative_limit = tolerance["relative_pct"]
if relative_limit is not None:
material = material and relative is not None and abs(relative) > relative_limit
return {
"legacy": _round(legacy),
"candidate": _round(candidate),
"absolute_delta": absolute,
"relative_delta_pct": relative,
"material": material,
"definition_changed": True,
}
def _attach_ranks(rows: list[dict[str, Any]], value_key: str, rank_key: str) -> None:
values = [
row["scores"][value_key]
for row in rows
if _finite(row["scores"][value_key])
]
for row in rows:
value = row["scores"][value_key]
row["scores"][rank_key] = (
1 + sum(other > value for other in values) if _finite(value) else None
)
def _summary(rows: list[dict[str, Any]]) -> dict[str, Any]:
field_stats = {}
for key in FIELD_KEYS:
comparisons = [row["fields"][key] for row in rows]
deltas = [
abs(item["absolute_delta"])
for item in comparisons
if item["absolute_delta"] is not None
]
field_stats[key] = {
"legacy_available": sum(item["legacy"] is not None for item in comparisons),
"candidate_available": sum(
item["candidate"] is not None for item in comparisons
),
"both_available": len(deltas),
"material_differences": sum(item["material"] for item in comparisons),
"median_absolute_delta": _round(statistics.median(deltas) if deltas else None),
"p95_absolute_delta": _round(_percentile(deltas, 0.95)),
"max_absolute_delta": _round(max(deltas) if deltas else None),
}
fundamental_deltas = _score_deltas(rows, "fundamental_delta")
changed_rows = sorted(
(
{
"symbol": row["symbol"],
"fundamental_delta": row["scores"]["fundamental_delta"],
"fundamental_rank_change": row["scores"]["fundamental_rank_change"],
}
for row in rows
if row["scores"]["fundamental_delta"] is not None
),
key=lambda item: (
abs(item["fundamental_delta"] or 0),
),
reverse=True,
)[:20]
return {
"universe_count": len(rows),
"legacy_fundamental_score_available": _count_score(
rows, "legacy_fundamental"
),
"candidate_fundamental_score_available": _count_score(
rows, "candidate_fundamental"
),
"fundamental_scores_compared": len(fundamental_deltas),
"fundamental_score_material_changes": sum(
abs(delta) > 5.0 for delta in fundamental_deltas
),
"fundamental_rank_changes": _rank_change_count(
rows, "fundamental_rank_change"
),
"field_stats": field_stats,
"largest_changes": changed_rows,
}
def _score_deltas(rows: Iterable[dict[str, Any]], key: str) -> list[float]:
return [
row["scores"][key]
for row in rows
if row["scores"][key] is not None
]
def _count_score(rows: Iterable[dict[str, Any]], key: str) -> int:
return sum(row["scores"][key] is not None for row in rows)
def _rank_change_count(rows: Iterable[dict[str, Any]], key: str) -> int:
return sum(
row["scores"][key] not in (None, 0)
for row in rows
)
def _rank_change(legacy: int | None, candidate: int | None) -> int | None:
# Positive means the candidate improved its rank.
return legacy - candidate if legacy is not None and candidate is not None else None
def _delta(legacy: float | None, candidate: float | None) -> float | None:
if not _finite(legacy) or not _finite(candidate):
return None
return round(candidate - legacy, 4)
def _round(value: float | None, digits: int = 4) -> float | None:
return round(float(value), digits) if _finite(value) else None
def _percentile(values: list[float], quantile: float) -> float | None:
if not values:
return None
ordered = sorted(values)
index = max(0, math.ceil(quantile * len(ordered)) - 1)
return ordered[index]
def _finite(value: Any) -> bool:
return (
isinstance(value, (int, float))
and not isinstance(value, bool)
and math.isfinite(value)
)
def _iso(value: Any) -> str | None:
return value.isoformat() if value is not None else None
def _artifact_stamp(raw: str) -> str:
parsed = datetime.fromisoformat(raw.replace("Z", "+00:00"))
return parsed.astimezone(timezone.utc).strftime("%Y%m%dT%H%M%S%fZ")
def _atomic_write(path: Path, content: str) -> None:
temp = path.with_name(f".{path.name}.{os.getpid()}.tmp")
temp.write_text(content, encoding="utf-8", newline="")
os.replace(temp, path)
def _load_manifest(report_dir: str | Path) -> dict[str, Any] | None:
path = Path(report_dir).expanduser().resolve() / "latest.json"
try:
loaded = json.loads(path.read_text(encoding="utf-8"))
except (OSError, json.JSONDecodeError, TypeError, ValueError):
return None
return loaded if isinstance(loaded, dict) else None
def _manifest_artifact(
report_dir: str | Path, manifest: dict[str, Any], key: str
) -> Path:
directory = Path(report_dir).expanduser().resolve()
name = Path(str(manifest.get(key, ""))).name
if not name:
raise ValueError(f"Latest parity manifest has no {key}")
return directory / name
app/services/fundamentals_quality_service.py
-3
View File
@@ -12,7 +12,6 @@ from app.models.data_import_run import DataImportRun
from app.models.fundamental_snapshot import FundamentalSnapshot
from app.models.sec_filing_gap import SecFilingGap
from app.models.ticker import Ticker
from app.services import fundamental_data_refresh_service
_SEC_FORMS = ("10-K", "10-Q", "10-K/A", "10-Q/A")
@@ -78,8 +77,6 @@ async def blocked_reasons_by_cik(
ciks: set[str] | None = None,
) -> dict[str, str]:
"""Current SEC blocker code by CIK; no historical audit scan."""
if not await fundamental_data_refresh_service.is_enabled(db):
return {}
if ciks is not None and not ciks:
return {}
app/services/job_run_store.py
+91
View File
@@ -0,0 +1,91 @@
"""Single source for JobRunState reads/writes.
Mirrors ``settings_store``: ``record_finish`` never commits — the caller owns
the transaction — and reads are batched so the admin listing stays one query.
"""
from __future__ import annotations
import logging
from collections.abc import Iterable
from datetime import datetime, timezone
from sqlalchemy import select
from sqlalchemy.dialects.postgresql import insert as pg_insert
from sqlalchemy.dialects.sqlite import insert as sqlite_insert
from sqlalchemy.ext.asyncio import AsyncSession
from app.models.job_run_state import JobRunState
logger = logging.getLogger(__name__)
def _as_datetime(value: object) -> datetime | None:
"""Runtime snapshots carry ISO strings; the column wants a datetime."""
if isinstance(value, datetime):
return value
if isinstance(value, str) and value:
try:
return datetime.fromisoformat(value)
except ValueError:
return None
return None
async def get_map(db: AsyncSession, job_names: Iterable[str]) -> dict[str, JobRunState]:
"""Return {job_name: row} for the given jobs that have ever finished.
``populate_existing`` because rows are written by core upserts, which leave
any previously-loaded ORM instance in the identity map stale.
"""
result = await db.execute(
select(JobRunState)
.where(JobRunState.job_name.in_(list(job_names)))
.execution_options(populate_existing=True)
)
return {row.job_name: row for row in result.scalars().all()}
def _insert_for(db: AsyncSession):
"""ON CONFLICT is dialect-specific; prod is Postgres, tests are SQLite."""
dialect = db.get_bind().dialect.name
return pg_insert if dialect == "postgresql" else sqlite_insert
async def record_finish(db: AsyncSession, job_name: str, runtime: dict) -> None:
"""Upsert the last-run row from a scheduler runtime snapshot.
Atomic, and newer-wins. Select-then-insert loses races that really happen
here: pipelines are separate scheduler jobs that can overlap, and they share
step ids -- data_collector belongs to all four. Two of them finishing that
step together would both see no row and both insert, and the loser's
IntegrityError is swallowed by the caller, so the run silently vanishes.
The ``where`` guard is the other half: without it a slower pipeline
finishing an *older* run last would rewind finished_at and the status with
it, so the panel would report a stale outcome as the latest one.
"""
finished_at = _as_datetime(runtime.get("finished_at")) or datetime.now(timezone.utc)
message = runtime.get("message")
now = datetime.now(timezone.utc)
values = {
"job_name": job_name,
"status": str(runtime.get("status") or "completed"),
"started_at": _as_datetime(runtime.get("started_at")),
"finished_at": finished_at,
"processed": runtime.get("processed"),
"total": runtime.get("total"),
"message": str(message)[:4000] if message else None,
# Set explicitly: the model's onupdate hook does not fire for a core
# INSERT ... ON CONFLICT DO UPDATE.
"updated_at": now,
}
statement = _insert_for(db)(JobRunState).values(**values)
await db.execute(
statement.on_conflict_do_update(
index_elements=[JobRunState.job_name],
set_={key: statement.excluded[key] for key in values if key != "job_name"},
where=JobRunState.finished_at < statement.excluded.finished_at,
)
)
app/services/momentum_service.py
+4 -1
View File
@@ -18,6 +18,7 @@ from sqlalchemy import select
from sqlalchemy.ext.asyncio import AsyncSession
from app.models.ticker import Ticker
from app.services import ticker_service
from app.services.price_service import query_ohlcv
logger = logging.getLogger(__name__)
@@ -169,7 +170,9 @@ async def compute_activation_ranks(db: AsyncSession) -> dict[str, dict[str, floa
before scanning; the research backtest ranked each weekly setup-candidate
cross-section, so this is the deliberate production approximation.
"""
result = await db.execute(select(Ticker).order_by(Ticker.symbol))
result = await db.execute(
ticker_service.active_only(select(Ticker).order_by(Ticker.symbol))
)
tickers = list(result.scalars().all())
benchmark_closes = await _load_activation_benchmark(db)
app/services/regime_monitor_service.py
+194 -37
View File
@@ -1,4 +1,4 @@
"""AI/Tech Regime Monitor v3.
"""AI/Tech Risk Monitor v4.
The monitor is a risk thermometer, not a probability or trading rule. It keeps
two deliberately separate outputs:
@@ -8,13 +8,13 @@ two deliberately separate outputs:
relative strength, credit impulse).
Both scores are quantitative and daily. The sourced hyperscaler capex and
earnings-reaction observations are a qualitative *overlay* in v3 rather than
earnings-reaction observations are a qualitative *overlay* since v3 rather than
weighted sensors: at a combined 20 points they could not reach the event
study's alarm threshold even when both pegged, so refreshing them appeared to
do nothing. They are reported next to the scores instead of inside them.
Daily snapshots are the point-in-time record. The first run under a new
``METHODOLOGY`` rewrites the latest ``REBUILD_SESSIONS`` trading sessions once;
``METHODOLOGY`` rewrites every session inside ``REBUILD_LOOKBACK_DAYS`` once;
ordinary runs thereafter only upsert the latest trading date. The overlay is
still gated by its effective date so a rebuild cannot stamp today's observation
onto historical snapshots.
@@ -48,11 +48,26 @@ _CA_BUNDLE = os.environ.get("SSL_CERT_FILE", "")
KEY_CONFIG = "regime_monitor_config"
KEY_FUNDAMENTALS = "regime_fundamental_overrides"
METHODOLOGY = "v3"
METHODOLOGY = "v4"
# Snapshots are reseeded on a methodology bump, but fundamental observations are
# collected by hand/LLM and carried across it when the format is compatible.
CATEGORICAL_FUNDAMENTAL_METHODOLOGIES = frozenset({"v2", "v3"})
REBUILD_SESSIONS = 400
# EVERY methodology sharing the categorical format must be listed: this is checked
# against the *stored* blob, so omitting the current one discards the observation
# on its first write, which leaves fetched_at null and locked false -- and then
# update_regime_monitor refreshes it via the LLM on every single run, forever.
CATEGORICAL_FUNDAMENTAL_METHODOLOGIES = frozenset({"v2", "v3", "v4"})
# Bumped when a fix changes what historical rows *should* contain without
# changing the live formula, so stored history needs one reseed. Deliberately
# not METHODOLOGY: that partitions the history API and discards the cached event
# study, neither of which is warranted here -- the study recomputes its Warning
# series from source rather than reading snapshots, so a reseed cannot stale it.
# Snapshots written before this marker existed carry no key and read as 1.
# Deliberately NOT bumped for v4: a METHODOLOGY change already forces a full
# reseed (every stored row fails _parse_snapshot, so _latest_snapshot_row returns
# None and rebuilding is True). Bumping both would imply the reseed was
# revision-driven.
SENSOR_REVISION = 2
MIN_COVERAGE = 75.0
SOURCE_MAX_LAG_DAYS = 7
@@ -61,9 +76,18 @@ SOURCE_MAX_LAG_DAYS = 7
# exceeded 64.9 in 408 sessions while State reached 91.2). Thresholds are round
# numbers chosen so each band covers a sane share of history, not percentile
# fits -- percentile-derived bands would drift on every rebuild and silently
# rewrite what past snapshots meant. Realized shares over the 408 sessions to
# 2026-07-24: State 73/15/8/3%, Warning 69/20/8/3%.
STATE_BANDS = (20.0, 50.0, 80.0)
# rewrite what past snapshots meant.
#
# v4 moved State's top band 80 -> 65, and only that one. With credit calm it
# scores 0.0 (not None) and still holds its full 20 points, so price + breadth +
# volatility at *literal maximum* summed to exactly 80.0 -- the old threshold, to
# the decimal, with nothing to spare. A 2022-style AI/tech drawdown with calm
# credit computes to 70.3-74.0 depending on whether a death cross has formed, so
# at 80 the case this monitor exists to measure could not print the top band.
# 65 clears it under either assumption. Realized shares over the 408 sessions to
# 2026-07-24, reported not fitted: State 78.9/13.0/4.7/3.4%, Warning 69/20/8/3%.
# The v4 breaking share (3.4%) matches v3's, which was arrived at independently.
STATE_BANDS = (20.0, 50.0, 65.0)
WARNING_BANDS = (20.0, 40.0, 60.0)
QUADRANT_STATE_DIVIDER = 50.0
@@ -81,7 +105,24 @@ HY_OAS_STRESSED = 7.0
# of stress at 3.5 -- the level these anchors call "mild". The anchors already
# encode the long-run distribution, so the credit *level* is now purely anchored
# and credit *dynamics* live in W3 on the Warning axis where they belong.
HY_OAS_WINDOW_DAYS = 400 # only W3's lookback plus slack is needed now
# Calendar days, and it must cover the oldest date a rebuild replays -- not just
# W3's lookback. REBUILD_SESSIONS is 400 *trading* sessions (~579 calendar
# days), so a 400-calendar-day fetch left the oldest ~180 days of a rebuild with
# no OAS at all: C1 and W3 both returned None, State landed at 80% coverage and
# Warning at exactly MIN_COVERAGE, and *both still published bands* -- a series
# that looks homogeneous while its oldest rows were scored without credit.
# Widening only prepends older observations; C1 reads [-1] and W3 reads [-21], so
# live scores are unchanged and this needs no methodology bump. Stays under
# ICE's ~3-year cap so FRED still honours the request.
HY_OAS_WINDOW_DAYS = 700
# A rebuild replays every session inside this window. Bounded by calendar days
# rather than a session count because the binding constraint is the OAS fetch:
# each replayed row needs W3's 20-business-day lookback (~28 calendar days)
# inside HY_OAS_WINDOW_DAYS, so replaying further back would recreate the exact
# credit gap a reseed exists to close. 672 days is ~464 trading sessions, which
# comfortably covers the 400-session series the v3 cutover wrote.
REBUILD_LOOKBACK_DAYS = HY_OAS_WINDOW_DAYS - 28
W3_OAS_LOOKBACK = 20
W3_OAS_FULL_SCALE_PCT = 35.0
@@ -94,6 +135,27 @@ P3_DRAWDOWN_ANCHORS = (
(0.0, 0.0), (4.0, 10.0), (8.0, 25.0), (16.0, 50.0), (28.0, 78.0), (40.0, 100.0),
)
# Trend-break depth (% below the 200-DMA, stress score). v4; see _under_200 for
# why the crossing gets a floor of 20 rather than starting at 0. Calibrated to
# sit alongside P3 rather than swamp it -- the 200-DMA lags, so a 20% drawdown
# typically coincides with ~10% below the average, where this reads ~61 against
# P3's ~59. On the population the P1_SCORE_CAP rule actually names -- sessions
# with State >= 40 -- P1 is the sole price argmax on 17 of 47 (36.2%), against
# P2's 16 and P3's 14, so it informs the pillar without owning it and no cap
# was needed.
P1_TREND_BREAK_ANCHORS = (
(0.0, 20.0), (3.0, 35.0), (8.0, 55.0), (15.0, 75.0), (25.0, 100.0),
)
# VIX level anchors (v4). Full scale at 55 rather than at 2020's ~82: anchoring
# the top at a once-in-a-generation print would make VIX 50 -- a genuine crisis
# -- read only ~70. A typical correction (25-35) now reads 38-67 where v3 read
# 66.7-100. The anchors encode the long-run distribution as constants, the same
# argument the credit level uses.
P5_VIX_ANCHORS = (
(15.0, 0.0), (20.0, 20.0), (25.0, 38.0), (30.0, 55.0), (40.0, 80.0), (55.0, 100.0),
)
STATE_WEIGHTS = {
"price": 40.0,
"breadth": 25.0,
@@ -195,10 +257,24 @@ def band_for(score: float, bands: tuple[float, float, float] = STATE_BANDS) -> s
def _under_200(closes: list[float]) -> float | None:
"""Trend break graded by depth below the 200-DMA, not a bare yes/no.
Through v3 this returned 0 or 100, so P1 printed 100 the moment SMH and QQQ
were both under their average -- and because the price pillar takes
``max(P1, P2, P3)``, that pinned the pillar and stopped P3's anchored ladder
resolving anything for the whole of a selloff. It pegged on 46 of the 408
sessions to 2026-07-24; under this table, none.
The step at the crossing (0 -> 20) is deliberate: the break itself is a
genuine binary event and deserves a floor. Only the depth past it is graded.
"""
sma200 = _sma(closes, 200)
if sma200 is None:
if sma200 is None or sma200 <= 0:
return None
return 100.0 if closes[-1] < sma200 else 0.0
pct_below = (sma200 - closes[-1]) / sma200 * 100.0
if pct_below <= 0:
return 0.0
return _clamp(_interpolate(pct_below, P1_TREND_BREAK_ANCHORS))
def p1_trend_break(smh: list[float], qqq: list[float], leader_weight: float = 2.0) -> float | None:
@@ -264,9 +340,17 @@ def p4_relative_strength(smh: list[float], spy: list[float], lookback: int = 60)
def p5_volatility(vix: float | None) -> float | None:
"""VIX level against named anchors, so it keeps resolving past a 30 print.
v3 used ``(vix - 15) / 15``, which reached 100 at VIX 30 -- the same
saturation v3 itself had just removed from P3. VIX 30 is a bad week, 50 is a
crisis and 82 was March 2020, and all three scored identically. In the 408
sessions to 2026-07-24 that flattened five distinct April-2025 prints
(52.33, 46.98, 45.31, 40.72, 38.57) into a single 100.
"""
if vix is None:
return None
return _clamp((vix - 15.0) / 15.0 * 100.0)
return _clamp(_interpolate(vix, P5_VIX_ANCHORS))
def breadth_level_score(pct_above_200: float | None) -> float | None:
@@ -477,17 +561,29 @@ def _fundamental_effective_date(overrides: dict) -> date | None:
return _next_weekday(fetched) if fetched else None
def fundamental_overlay(overrides: dict, config: dict, as_of: date) -> dict:
"""Point-in-time qualitative overlay. Never feeds State or Warning in v3.
The effective-date gate stays even though nothing is scored from this: the
400-session rebuild replays historical dates, and stamping today's LLM read
onto 2024 snapshots would be plain lookahead in the stored record.
"""
def _overlay_timing(
overrides: dict, config: dict, as_of: date
) -> tuple[date | None, bool, int | None, bool]:
"""Shared effective-date arithmetic: (effective, pending, age_days, stale)."""
effective = _fundamental_effective_date(overrides)
pending = effective is None or as_of < effective
age = None if pending else (as_of - effective).days
stale = bool(age is not None and age > int(config.get("fundamental_staleness_days", 80)))
return effective, pending, age, stale
def fundamental_overlay(overrides: dict, config: dict, as_of: date) -> dict:
"""Point-in-time qualitative overlay. Never feeds State or Warning since v3.
The effective-date gate stays even though nothing is scored from this: the
400-session rebuild replays historical dates, and stamping today's LLM read
onto 2024 snapshots would be plain lookahead in the stored record.
This is the *record*. For "what do we know right now", use
``current_observation`` -- do not add a bypass flag here, because this runs
for every replayed date during a rebuild.
"""
effective, pending, age, stale = _overlay_timing(overrides, config, as_of)
return {
"available": not pending and not stale,
"pending": pending,
@@ -504,6 +600,43 @@ def fundamental_overlay(overrides: dict, config: dict, as_of: date) -> dict:
}
def current_observation(overrides: dict, config: dict, as_of: date) -> dict:
"""The observation as it stands now, for the live reading only.
Same shape as ``fundamental_overlay``, but the effective date is *reported*
rather than used to blank the content. A refresh stamps
``_next_weekday(today)``, so gating the live card hid a just-collected read
for one day -- three over a weekend -- and refreshing appeared to do
nothing. Nothing here is scored, so showing it early cannot leak into a
published number; the stored snapshot keeps the gate.
"""
effective, pending, age, stale = _overlay_timing(overrides, config, as_of)
# The default override carries "unknown"/"mixed" placeholders for every
# hyperscaler. Those are the absence of an observation, not an observation
# of absence, and must never be presented as collected. ``fetched_at`` is
# the collection timestamp and is the only field written on every path that
# produces real content (LLM refresh and manual save both stamp it).
observed = bool(overrides.get("fetched_at"))
return {
"observed": observed,
# Live availability is about usefulness, not effectiveness: a pending
# observation is the freshest thing we have -- but nothing collected is
# never available.
"available": observed and not stale,
"pending": pending,
"stale": stale,
"effective_date": effective.isoformat() if effective else None,
"age_days": age,
"capex": overrides.get("capex") if observed else None,
"good_news_stock_down": overrides.get("good_news_stock_down") if observed else None,
"capex_stress": overrides.get("f1_score") if observed else None,
"earnings_stress": overrides.get("f3_score") if observed else None,
"reasoning": overrides.get("reasoning") if observed else None,
"source": overrides.get("source"),
"fetched_at": overrides.get("fetched_at"),
}
def _basket_hash(symbols: list[str]) -> str:
canonical = ",".join(sorted({s.strip().upper() for s in symbols if s.strip()}))
return hashlib.sha256(canonical.encode("utf-8")).hexdigest()[:12]
@@ -524,7 +657,7 @@ def _compute_index(
divergence_series: Series | None = None,
breadth_counts: dict[date, int] | None = None,
) -> dict:
"""Compute the complete v2 State/Warning snapshot as of one trading date."""
"""Compute the complete State/Warning snapshot as of one trading date."""
tickers = config["tickers"]
smh = _closes_asof(prices.get(tickers["leaders"][0], []), as_of)
qqq = _closes_asof(prices.get(tickers["confirm"][0], []), as_of)
@@ -630,6 +763,8 @@ def _compute_index(
return {
"methodology": METHODOLOGY,
# Not part of the history filter -- only the reseed trigger.
"sensor_revision": SENSOR_REVISION,
"date": as_of.isoformat(),
"state": state,
"warning": warning,
@@ -697,7 +832,7 @@ async def get_regime_config(db: AsyncSession) -> dict:
if stored.get("fundamental_staleness_days") is not None:
cfg["fundamental_staleness_days"] = int(stored["fundamental_staleness_days"])
except (TypeError, ValueError, ValidationError):
logger.warning("Corrupt %s; using v2 defaults", KEY_CONFIG)
logger.warning("Corrupt %s; using defaults", KEY_CONFIG)
return cfg
@@ -843,7 +978,7 @@ async def _fetch_prices(config: dict, start: date, end: date) -> dict[str, Serie
bars = await provider.fetch_ohlcv(symbol, start, end)
out[symbol] = sorted(((b.date, float(b.close)) for b in bars), key=lambda item: item[0])
except Exception as exc:
logger.warning("Regime monitor: price fetch failed for %s: %s", symbol, exc)
logger.warning("Risk monitor: price fetch failed for %s: %s", symbol, exc)
return out
@@ -866,7 +1001,7 @@ async def _fetch_fred_series(series_id: str, start: date, end: date) -> Series |
response.raise_for_status()
payload = response.json()
except Exception as exc:
logger.warning("Regime monitor: FRED fetch failed for %s: %s", series_id, exc)
logger.warning("Risk monitor: FRED fetch failed for %s: %s", series_id, exc)
return None
out: Series = []
@@ -889,7 +1024,7 @@ async def _upsert_snapshot(
db: AsyncSession,
result: dict,
*,
rewrite_existing_v2: bool,
rewrite_existing: bool,
) -> tuple[bool, dict]:
snapshot_date = date.fromisoformat(result["date"])
existing = await db.execute(select(RegimeSnapshot).where(RegimeSnapshot.date == snapshot_date))
@@ -906,15 +1041,23 @@ async def _upsert_snapshot(
created_at=datetime.now(timezone.utc),
))
else:
existing_v2 = _parse_snapshot(row.breakdown_json)
if existing_v2 is not None and not rewrite_existing_v2:
return False, existing_v2
existing_parsed = _parse_snapshot(row.breakdown_json)
if existing_parsed is not None and not rewrite_existing:
return False, existing_parsed
row.total_score = float(state_score or 0.0)
row.band = state_band or "unavailable"
row.breakdown_json = payload
return True, result
def _snapshot_revision(snapshot: dict) -> int:
"""Sensor revision of a stored snapshot; pre-marker rows read as 1."""
try:
return int(snapshot.get("sensor_revision") or 1)
except (TypeError, ValueError):
return 1
def _parse_snapshot(raw: str) -> dict | None:
try:
parsed = json.loads(raw)
@@ -934,14 +1077,16 @@ async def _latest_snapshot_row(db: AsyncSession) -> tuple[RegimeSnapshot, dict]
return None
async def update_regime_monitor(db: AsyncSession, rebuild_sessions: int = REBUILD_SESSIONS) -> dict:
async def update_regime_monitor(
db: AsyncSession, rebuild_lookback_days: int = REBUILD_LOOKBACK_DAYS
) -> dict:
config = await get_regime_config(db)
overrides = await get_fundamental_overrides(db)
if _fundamentals_stale(overrides, config) and not overrides.get("locked"):
try:
overrides = await refresh_fundamental_overrides(db, config=config)
except Exception as exc:
logger.warning("Regime monitor: fundamentals refresh skipped: %s", exc)
logger.warning("Risk monitor: fundamentals refresh skipped: %s", exc)
end = date.today()
prices = await _fetch_prices(config, end - timedelta(days=1200), end)
@@ -965,13 +1110,21 @@ async def update_regime_monitor(db: AsyncSession, rebuild_sessions: int = REBUIL
)
divergence = breadth_service.compute_divergence_series(breadth, leader_series)
except Exception as exc:
logger.warning("Regime monitor: fixed-basket breadth skipped: %s", exc)
logger.warning("Risk monitor: fixed-basket breadth skipped: %s", exc)
breadth, breadth_counts, divergence = {}, {}, {}
latest_v2 = await _latest_snapshot_row(db)
rebuilding = latest_v2 is None and bool(leader_series)
latest_snapshot = await _latest_snapshot_row(db)
# A stored series written under an older sensor revision is reseeded once.
# Without this, raising HY_OAS_WINDOW_DAYS would only ever reach newly
# computed rows: routine runs touch the latest date alone, so every older row
# would keep the credit gap indefinitely.
rebuilding = bool(leader_series) and (
latest_snapshot is None
or _snapshot_revision(latest_snapshot[1]) < SENSOR_REVISION
)
if rebuilding:
dates = [d for d, _ in leader_series[-max(1, rebuild_sessions):]]
floor = end - timedelta(days=rebuild_lookback_days)
dates = [d for d, _ in leader_series if d >= floor] or [latest_date]
else:
# Routine PIT rule: only the latest trading date may be inserted/updated.
dates = [latest_date]
@@ -995,7 +1148,9 @@ async def update_regime_monitor(db: AsyncSession, rebuild_sessions: int = REBUIL
written, latest_result = await _upsert_snapshot(
db,
computed,
rewrite_existing_v2=rebuilding or snapshot_date == latest_date,
# True for *every* replayed date on a reseed, or it would write one
# row and leave the rest at the old revision.
rewrite_existing=rebuilding or snapshot_date == latest_date,
)
snapshots_written += int(written)
await db.commit()
@@ -1042,7 +1197,7 @@ def _delta(current: dict, previous: dict | None) -> float | None:
async def get_regime_monitor(db: AsyncSession) -> dict:
latest = await _latest_snapshot_row(db)
if latest is None:
return {"available": False, "reason": "v2 not computed yet"}
return {"available": False, "reason": "not computed yet"}
row, result = latest
basket_hash = (result.get("basket") or {}).get("hash")
previous_7 = await _result_at_or_before(
@@ -1071,7 +1226,9 @@ async def get_regime_monitor(db: AsyncSession) -> dict:
# session, because otherwise refreshing it looks like it did nothing.
config = await get_regime_config(db)
overrides = await get_fundamental_overrides(db)
live = fundamental_overlay(overrides, config, date.today())
live = current_observation(overrides, config, date.today())
# Deliberately reads the *snapshot's* overlay, not the live one: this is how
# the reader tells "shown here" from "in the stored record".
live["observed_in_snapshot"] = bool((result.get("fundamental_overlay") or {}).get("available"))
result["fundamental_context"] = live
result["available"] = True
app/services/rr_scanner_service.py
+6 -2
View File
@@ -31,7 +31,7 @@ from app.services import fundamentals_quality_service, system_event_service
from app.services.price_service import query_ohlcv
from app.services.qualification import setup_qualifies
from app.services.sr_service import detect_gate_target_ladder
from app.services import settings_store
from app.services import settings_store, ticker_service
from app.services.trade_policy import (
MANUAL_BOOK,
SHADOW_BOOK,
@@ -735,7 +735,11 @@ async def scan_all_tickers(
# Plain ids/strings, not Ticker instances: the rollbacks below expire any
# ORM objects held across them, and touching an expired attribute afterwards
# triggers sync lazy-loading, which raises on an AsyncSession.
result = await db.execute(select(Ticker.id, Ticker.symbol).order_by(Ticker.symbol))
result = await db.execute(
ticker_service.active_only(
select(Ticker.id, Ticker.symbol).order_by(Ticker.symbol)
)
)
ticker_rows = [(int(ticker_id), symbol) for ticker_id, symbol in result.all()]
total = len(ticker_rows)
app/services/scoring_service.py
+9 -5
View File
@@ -20,7 +20,7 @@ from app.database import insert_for_session
from app.exceptions import NotFoundError, ValidationError
from app.models.score import CompositeScore, DimensionScore
from app.models.ticker import Ticker
from app.services import settings_store
from app.services import settings_store, ticker_service
logger = logging.getLogger(__name__)
@@ -497,8 +497,8 @@ async def _compute_fundamental_score(
"reason": "Earnings surprise data not available",
})
# Require at least two real metrics — a single available metric (e.g. only
# market cap is free on FMP) does not make a meaningful fundamental score.
# Require at least two real metrics — a single available metric (e.g. an
# issuer with only a market cap) does not make a meaningful fundamental score.
MIN_METRICS = 2
if len(scores) < MIN_METRICS:
unavailable.append({
@@ -883,7 +883,11 @@ async def get_rankings(db: AsyncSession) -> dict:
Returns dict suitable for RankingResponse.
"""
weights = await _get_weights(db)
tickers = (await db.execute(select(Ticker).order_by(Ticker.symbol))).scalars().all()
tickers = (
await db.execute(
ticker_service.active_only(select(Ticker).order_by(Ticker.symbol))
)
).scalars().all()
async def _load_scores() -> tuple[dict[int, CompositeScore], dict[int, dict[str, DimensionScore]]]:
comps = {
@@ -947,7 +951,7 @@ async def update_weights(
await _save_weights(db, full_weights)
# Recompute all composite scores
result = await db.execute(select(Ticker))
result = await db.execute(ticker_service.active_only(select(Ticker)))
tickers = list(result.scalars().all())
for ticker in tickers:
app/services/sec_client.py
+113 -1
View File
@@ -39,12 +39,39 @@ logger = logging.getLogger(__name__)
_WWW = "https://www.sec.gov"
_DATA = "https://data.sec.gov"
# Resolve CA bundle for explicit httpx verify (matches app/providers/fmp.py).
# Resolve CA bundle for explicit httpx verify (matches app/providers/alpaca.py).
_CA = os.environ.get("SSL_CERT_FILE", "")
_CA_VERIFY: str | bool = _CA if _CA and Path(_CA).exists() else True
_FORMS_10 = frozenset({"10-K", "10-Q", "10-K/A", "10-Q/A"})
# Notification of removal from listing. "25" is issuer-filed, "25-NSE" exchange-
# filed. The Form 15 family is deliberately absent: it ends a *reporting*
# obligation and does not mean the security stopped trading.
_DELISTING_FORMS = frozenset({"25", "25-NSE"})
# ``descriptionClassSecurity`` is free text ("Common Stock", "Class A Common
# Stock, $0.01 par value", "6.25% Notes due 2030", "Warrants", "Depositary
# Shares"). Only a common-equity class means the ticker itself stopped trading.
_NON_COMMON_CLASS = re.compile(
r"\b(note|bond|debenture|preferred|warrant|right|unit|depositary|"
r"subordinated|debt|trust)s?\b",
re.IGNORECASE,
)
def _is_common_stock(description: str) -> bool:
"""Does this Form 25 security class describe common equity?
Requires an explicit common-stock match AND no debt/preferred/warrant marker,
so "Depositary Shares each representing 1/1000th of Preferred" cannot pass on
the word "shares" alone. Unrecognised text is rejected — a symbol is retired
on this answer, so ambiguity must not read as yes.
"""
if _NON_COMMON_CLASS.search(description):
return False
return re.search(r"\bcommon\s+(stock|share)", description, re.IGNORECASE) is not None
class SecError(ProviderError):
"""SEC request failed (403, exhausted 429/5xx, timeout, transport, parse)."""
@@ -253,6 +280,91 @@ class SecClient:
"filings": filings,
}
async def delisting_filing(
self, cik: int | str, *, not_before: date | None = None
) -> dict[str, Any] | None:
"""Newest Form 25 removing this issuer's COMMON stock from listing.
Deliberately narrow, because the caller retires a symbol on the answer:
- **Form 25 only.** The Form 15 family terminates a reporting obligation
(often just a class falling under the holder threshold) and is no
evidence that trading stopped.
- **Class-checked.** Form 25 is filed per security class — an issuer
delisting its notes, preferred, warrants or an ADR class while the
common keeps trading files one too. The filing's own
``descriptionClassSecurity`` is what separates those, so the primary
document is fetched and read rather than trusting the form type.
- **``not_before``** rejects a historical filing for some long-gone
class. Without it a 2019 Form 25 would retire a symbol whose bars
stopped in 2026, and stamp 2019 as the date.
Anything unreadable — no primary document (pre-2009 filings have none),
malformed XML, unrecognised class — returns ``None``. Fail closed: the
caller keeps warning instead of retiring on a guess.
Reads ``filings.recent`` directly; ``submissions()`` keeps only the
10-K/10-Q family, so Form 25 never survives its parser.
"""
base = await self.get_json(f"{_DATA}/submissions/CIK{cik10(cik)}.json")
arrays = (base.get("filings") or {}).get("recent") or {}
forms = arrays.get("form") or []
dates = arrays.get("filingDate") or []
accessions = arrays.get("accessionNumber") or []
docs = arrays.get("primaryDocument") or []
candidates: list[tuple[date, str, str, str]] = []
for i, form in enumerate(forms):
if form not in _DELISTING_FORMS or i >= len(dates) or not dates[i]:
continue
try:
filed = date.fromisoformat(dates[i])
except ValueError:
continue
if not_before is not None and filed < not_before:
continue
if i >= len(accessions) or not accessions[i]:
continue
candidates.append((filed, form, accessions[i], docs[i] if i < len(docs) else ""))
for filed, form, accession, _doc in sorted(candidates, reverse=True):
security = await self._form25_security_class(cik, accession)
if security is None:
continue
if not _is_common_stock(security):
continue
return {
"form": form,
"filing_date": filed,
"security_class": security,
}
return None
async def _form25_security_class(
self, cik: int | str, accession: str
) -> str | None:
"""``descriptionClassSecurity`` from a Form 25's primary XML, or None.
The rendered ``primaryDocument`` is an XSL view of this file; the raw
``primary_doc.xml`` beside it is the structured original.
"""
folder = accession.replace("-", "")
url = (
f"{_WWW}/Archives/edgar/data/{int(cik)}/{folder}/primary_doc.xml"
)
try:
body = await self.get_text(url)
except SecNotFoundError:
return None
match = re.search(
r"<descriptionClassSecurity>(.*?)</descriptionClassSecurity>",
body,
re.IGNORECASE | re.DOTALL,
)
if match is None:
return None
return " ".join(match.group(1).split()) or None
async def companyfacts(self, cik: int | str) -> dict[str, Any]:
"""Raw companyfacts JSON ({cik, entityName, facts})."""
return await self.get_json(f"{_DATA}/api/xbrl/companyfacts/CIK{cik10(cik)}.json")
app/services/sec_fundamentals_importer.py
+97 -2
View File
@@ -51,10 +51,10 @@ import json
import logging
from collections import Counter, defaultdict
from dataclasses import dataclass, field, replace
from datetime import date, datetime, timedelta, timezone
from datetime import date, datetime, time, timedelta, timezone
from typing import Any, Callable
from sqlalchemy import delete, select, update
from sqlalchemy import delete, exists, select, update
from app.database import insert_for_session
from app.models.data_import_run import DataImportRun
@@ -81,6 +81,26 @@ MIN_BACKFILL_COVERAGE = 0.5
# three); past that it is misfiled, not late, and blocking forever costs more
# than the missing filing does — see the unresolved-filing guardrail below.
MISSING_XBRL_RETRY_DAYS = 3
# Aggregate ceiling on deferral. MISSING_XBRL_RETRY_DAYS bounds how long ONE
# filing blocks; it does not bound how long the import as a whole can stay
# deferred. Those differ because a blocking filing is only queued by promote(),
# which a deferred run never reaches — so during a rolling supply of
# unresolvable filings (earnings season, when SEC's Company-Facts aggregation is
# furthest behind) each new arrival restarts the clock before the previous one
# clears, and nothing is written at all: not the good rows, not the gap rows
# that would stop those filings blocking again.
#
# Once promotions have been stale this long, every unresolved filing is treated
# as past the window. promote() then queues them all (see the _past_retry_window
# call there), source_max_date advances, and _missing() forces queued rows
# aged-out on later runs so they never block again — the import self-heals
# through the paths that already exist.
#
# Well above MISSING_XBRL_RETRY_DAYS so ordinary overlapping blocks never trip
# it. Affected symbols stay barred from setups either way: setup_blocked_ciks is
# built from every missing filing regardless of window.
PROMOTION_CEILING_DAYS = 7
FILING_GAP_ESCALATE_DAYS = 14
# Share-count band a co-registrant-recovered row must land in, relative to the
# issuer's own last snapshot. Wide enough for buybacks/issuance, nowhere near
@@ -157,6 +177,9 @@ class SecFundamentalsImporter:
self._retry_rows: list[dict[str, Any]] = []
self._latest_index_date: date | None = None
self._backfill = False
# Set by validate() when the aggregate ceiling forced the block open;
# read by promote() to alert that it did.
self._ceiling_tripped: dict[str, Any] | None = None
# -- SourceImporter protocol -------------------------------------------
@@ -421,6 +444,24 @@ class SecFundamentalsImporter:
# reconstructible by re-walking the index.
blocking = _within_retry_window(staged.missing_xbrl)
aged_out = _past_retry_window(staged.missing_xbrl)
# ...unless promotions have been stale past the aggregate ceiling, in
# which case the deferral has cost more than the filings it withholds.
# Ageing them here (not just locally) is deliberate: promote() re-derives
# the queue from the same list, so this is what gets them queued.
self._ceiling_tripped = None
if blocking and db is not None and await self._promotions_stale(db):
for item in staged.missing_xbrl:
item["age_days"] = max(
item.get("age_days", 0), MISSING_XBRL_RETRY_DAYS + 1
)
self._ceiling_tripped = {
"forced": len(blocking),
"unresolved": len(staged.missing_xbrl),
}
blocking = _within_retry_window(staged.missing_xbrl)
aged_out = _past_retry_window(staged.missing_xbrl)
if blocking:
messages.append(
f"{len(blocking)} tracked XBRL filing(s) unresolved within the "
@@ -464,6 +505,9 @@ class SecFundamentalsImporter:
"missing_xbrl": staged.missing_xbrl[:50],
"missing_xbrl_count": len(staged.missing_xbrl),
"missing_xbrl_blocking": len(blocking),
# Present only when the aggregate ceiling forced this run through, so
# a promoted run that carries known-unresolved filings says so.
"promotion_ceiling_tripped": self._ceiling_tripped,
"recovered_from_coregistrant": staged.recovered[:50],
"recovered_count": len(staged.recovered),
# Complete compact gate input; detailed audit lists above stay capped.
@@ -616,6 +660,25 @@ class SecFundamentalsImporter:
created_at=_now(),
))
# A ceiling-forced promotion is the safety valve firing — it must be
# visible, or the import silently starts carrying known-unresolved
# filings. The affected symbols stay barred from setups regardless.
if self._ceiling_tripped:
db.add(SystemEvent(
severity="warning",
source="sec_facts",
code="promotion_ceiling_forced",
message=(
f"Promoted with {self._ceiling_tripped['unresolved']} unresolved "
f"filing(s) — {self._ceiling_tripped['forced']} still inside the "
f"{MISSING_XBRL_RETRY_DAYS}-day retry window — because nothing had "
f"promoted in {PROMOTION_CEILING_DAYS} days. They are queued for "
"retry and their symbols remain blocked from setups."
)[:4000],
dedup_key=f"sec_facts:promotion_ceiling_forced:{run_id}",
created_at=now,
))
# Persistent current gaps get one actionable escalation rather than a
# daily warning. The nullable marker makes this durable and noise-free.
escalation_cutoff = now - timedelta(days=FILING_GAP_ESCALATE_DAYS)
@@ -757,6 +820,38 @@ class SecFundamentalsImporter:
if accession not in resolved
]
async def _promotions_stale(self, db) -> bool:
"""Has nothing promoted within ``PROMOTION_CEILING_DAYS``?
Only true for a source that HAS promoted before. A never-promoted import
is initial setup, not a wedge: forcing its first promotion through would
mask a misconfiguration rather than recover from a transient SEC gap.
Measured from ``self.today`` rather than the wall clock, so the ceiling
honors the same injected date that ages the filings it releases.
"""
cutoff = datetime.combine(
self.today - timedelta(days=PROMOTION_CEILING_DAYS),
time.min,
tzinfo=timezone.utc,
)
ever, recent = (
await db.execute(
select(
exists().where(
DataImportRun.source == SOURCE,
DataImportRun.status == STATUS_PROMOTED,
),
exists().where(
DataImportRun.source == SOURCE,
DataImportRun.status == STATUS_PROMOTED,
DataImportRun.started_at >= cutoff,
),
)
)
).one()
return bool(ever) and not bool(recent)
async def _last_processed_index_date(self, db) -> date | None:
return (
await db.execute(
app/services/sec_universe.py
+6 -2
View File
@@ -24,7 +24,7 @@ from typing import Iterable
from sqlalchemy import select, update
from app.models.ticker import Ticker
from app.services import settings_store
from app.services import settings_store, ticker_service
from app.services.earnings_alignment import normalise_symbol
from app.services.sec_client import SecClient
@@ -55,7 +55,11 @@ async def resolve_ciks(db, client: SecClient) -> ResolvedUniverse:
returns the mapping + proposed `tickers.cik` writes; mutates nothing."""
ticker_to_cik = await client.company_tickers()
overrides = await cik_overrides(db)
rows = (await db.execute(select(Ticker.id, Ticker.symbol, Ticker.cik))).all()
rows = (
await db.execute(
ticker_service.active_only(select(Ticker.id, Ticker.symbol, Ticker.cik))
)
).all()
result = ResolvedUniverse()
for tid, symbol, current_cik in rows:
app/services/shadow_book_service.py
+6 -4
View File
@@ -40,10 +40,12 @@ KEY_CAPACITY = "shadow_book_capacity"
KEY_RISK_PCT = "shadow_book_risk_pct"
KEY_START_EQUITY = "shadow_book_start_equity"
# Matches the validated configuration: 10-position book, 1% fixed-fractional
# risk. Start equity is only a sizing base — comparisons are drawn in percent
# and R-multiples, never in raw currency.
DEFAULT_CAPACITY = 10
# Matches the validated configuration: 1% fixed-fractional risk, and a count cap
# set as headroom rather than a target — see backtest_service.SIM_MAX_POSITIONS,
# which this must track. NOTIONAL_CAP below saturates the book near 12 positions,
# so the count cap should simply never bind. Start equity is only a sizing base —
# comparisons are drawn in percent and R-multiples, never in raw currency.
DEFAULT_CAPACITY = 15
DEFAULT_RISK_PCT = 1.0
DEFAULT_START_EQUITY = 100_000.0
app/services/ticker_service.py
+199 -3
View File
@@ -1,13 +1,65 @@
"""Ticker Registry service: add, delete, and list tracked tickers."""
"""Ticker Registry service: add, delete, list, and retire tracked tickers."""
import logging
import re
from datetime import date, timedelta
from sqlalchemy import select
from sqlalchemy import func, or_, select, update
from sqlalchemy.ext.asyncio import AsyncSession
from app.exceptions import DuplicateError, NotFoundError, ValidationError
from app.models.ticker import Ticker
logger = logging.getLogger(__name__)
# Reasons a symbol may be marked delisted, narrowest first.
REASON_FORM_25 = "form_25" # SEC Form 25/25-NSE/15 confirmed the exchange exit
REASON_MANUAL = "manual" # an operator decided
# How long a symbol must be without bars before we spend an SEC request asking
# whether it delisted. Guards against a market-data outage probing the whole
# universe at once; a real delisting is still stale days later.
MIN_STALE_DAYS_BEFORE_PROBE = 3
# Rule 12d2-2: a Form 25 removal takes effect ten days after filing, so the
# filing date is not the date the security stopped trading.
FORM_25_EFFECTIVE_DAYS = 10
# How far before the last bar a Form 25 may be filed and still explain this gap.
# An exchange can file shortly before trading actually stops; anything older
# concerns a class that was already gone while the symbol kept printing bars.
FILING_LOOKBACK_DAYS = 30
def _sec_client_factory():
"""Build the SEC client for a delisting probe (patched in tests).
Imported lazily so the SEC/httpx stack stays off the import path of every
module that only wants ``active_only``.
"""
from app.services.sec_client import SecClient
return SecClient()
def active_only(stmt, *, as_of: date | None = None):
"""Restrict a Ticker query to symbols that still trade.
Opt-in on purpose rather than folded into a shared getter: list and admin
views deliberately keep delisted rows so the delisting is *visible*, which a
silent default would undo. Apply this on the live signal path — scanning,
ranking, scoring, breadth, ingestion — and nowhere else.
``delisted_on`` is an *effective* date, and a Form 25 is known ten days
before it takes effect, so a future date must not drop the symbol yet — it
is still trading and still worth scanning and ingesting. Compared in SQL
against the database's own date; ``as_of`` overrides it for tests.
"""
cutoff = func.current_date() if as_of is None else as_of
return stmt.where(
or_(Ticker.delisted_on.is_(None), Ticker.delisted_on > cutoff)
)
async def add_ticker(db: AsyncSession, symbol: str) -> Ticker:
"""Add a new ticker after validation.
@@ -52,6 +104,150 @@ async def delete_ticker(db: AsyncSession, symbol: str) -> None:
async def list_tickers(db: AsyncSession) -> list[Ticker]:
"""Return all tracked tickers sorted alphabetically by symbol."""
"""Return all tracked tickers sorted alphabetically by symbol.
Delisted symbols are included and carry ``delisted_on`` — the registry is
where an operator needs to *see* that a symbol retired, not where it should
quietly disappear.
"""
result = await db.execute(select(Ticker).order_by(Ticker.symbol.asc()))
return list(result.scalars().all())
async def mark_delisted(
db: AsyncSession,
symbol: str,
*,
delisted_on: date,
reason: str = REASON_MANUAL,
) -> bool:
"""Record that a symbol stopped trading. True if this changed anything.
Idempotent, so the staleness path can call it every run without churning the
row: re-marking is a no-op. The one exception is an SEC confirmation landing
on a row an operator marked by hand — Form 25 carries the real effective
date, so it replaces the operator's estimate. Nothing downgrades a confirmed
row back to a manual one.
"""
normalised = symbol.strip().upper()
result = await db.execute(select(Ticker).where(Ticker.symbol == normalised))
ticker = result.scalar_one_or_none()
if ticker is None:
raise NotFoundError(f"Ticker not found: {normalised}")
if ticker.delisted_on is not None:
upgrading = (
reason == REASON_FORM_25 and ticker.delisted_reason != REASON_FORM_25
)
if not upgrading:
return False
await db.execute(
update(Ticker)
.where(Ticker.id == ticker.id)
.values(delisted_on=delisted_on, delisted_reason=reason)
)
await db.commit()
logger.info(
"ticker %s marked delisted on %s (%s)", normalised, delisted_on, reason
)
return True
async def confirm_delisting(
db: AsyncSession,
symbol: str,
*,
last_bar: date | None,
today: date | None = None,
) -> date | None:
"""Ask SEC whether ``symbol`` actually delisted; mark it if so.
Called when OHLCV goes stale, because "no new bars" alone cannot tell a
delisting from a halt or a rename. Returns the effective date whenever the
symbol is known to have delisted — whether this call established that or an
earlier one did — and ``None`` while it remains unproven, so the caller warns
only about gaps that still have no explanation.
Returning the already-known date matters between filing and effect: trading
usually stops before the ten-day Rule 12d2-2 delay expires, so the symbol is
correctly still active (see ``active_only``) while producing no bars. Without
this the staleness warning would fire daily across that window — the exact
noise the delisting flow exists to remove.
Deliberately driven by staleness rather than by the SEC fundamentals import:
that importer stalls for days at a time on unrelated Company-Facts gaps, and
detection wired into it would stall with it.
The probe waits for ``MIN_STALE_DAYS_BEFORE_PROBE``. A delisted symbol stays
stale forever, so the delay costs nothing, and it keeps a broad market-data
outage — where every tracked symbol reports stale at once — from turning into
one SEC request per symbol per run.
"""
from app.services.sec_client import SecError
normalised = symbol.strip().upper()
result = await db.execute(select(Ticker).where(Ticker.symbol == normalised))
ticker = result.scalar_one_or_none()
if ticker is None:
return None
known = ticker.delisted_on
# Already confirmed by SEC — nothing left to learn, but the caller still
# needs the date to know this gap is explained. A row an operator marked by
# hand is worth probing: Form 25 upgrades the estimated date.
if ticker.delisted_reason == REASON_FORM_25:
return known
if not ticker.cik:
return known
# No bars at all is an ingestion problem, not evidence of a delisting.
if last_bar is None:
return known
if ((today or date.today()) - last_bar).days < MIN_STALE_DAYS_BEFORE_PROBE:
return known
try:
async with _sec_client_factory() as client:
# Only a Form 25 filed around or after the last bar can explain THIS
# gap. An older one belongs to a class that stopped trading before
# the symbol was still printing bars, and must not retire it.
filing = await client.delisting_filing(
ticker.cik, not_before=last_bar - timedelta(days=FILING_LOOKBACK_DAYS)
)
except SecError:
# Never let a probe failure escalate a routine staleness warning.
logger.warning("delisting probe failed for %s", normalised, exc_info=True)
return known
if filing is None:
return known
# Removal takes effect ten days after filing, so the filing date is not the
# date the symbol stopped trading.
effective = filing["filing_date"] + timedelta(days=FORM_25_EFFECTIVE_DAYS)
if await mark_delisted(
db, normalised, delisted_on=effective, reason=REASON_FORM_25
):
return effective
return known
async def clear_delisted(db: AsyncSession, symbol: str) -> bool:
"""Un-retire a symbol. True if it had been marked.
The counterpart that makes automatic marking acceptable: a false positive
costs one row update, where a delete would have cost the price history.
"""
normalised = symbol.strip().upper()
result = await db.execute(select(Ticker).where(Ticker.symbol == normalised))
ticker = result.scalar_one_or_none()
if ticker is None:
raise NotFoundError(f"Ticker not found: {normalised}")
if ticker.delisted_on is None:
return False
await db.execute(
update(Ticker)
.where(Ticker.id == ticker.id)
.values(delisted_on=None, delisted_reason=None)
)
await db.commit()
logger.info("ticker %s un-marked as delisted", normalised)
return True
app/services/ticker_universe_service.py
+30 -125
View File
@@ -113,116 +113,6 @@ def _normalise_symbols(symbols: Iterable[str]) -> list[str]:
return sorted(deduped)
def _extract_symbols_from_fmp_payload(payload: object) -> list[str]:
if not isinstance(payload, list):
return []
symbols: list[str] = []
for item in payload:
if not isinstance(item, dict):
continue
candidate = item.get("symbol") or item.get("ticker")
if isinstance(candidate, str):
symbols.append(candidate)
return symbols
async def _try_fmp_urls(
client: httpx.AsyncClient,
urls: list[str],
) -> tuple[list[str], list[str]]:
failures: list[str] = []
for url in urls:
endpoint = url.split("?")[0]
try:
response = await client.get(url)
except httpx.HTTPError as exc:
failures.append(f"{endpoint}: network error ({type(exc).__name__}: {exc})")
continue
if response.status_code != 200:
failures.append(f"{endpoint}: HTTP {response.status_code}")
continue
try:
payload = response.json()
except ValueError:
failures.append(f"{endpoint}: invalid JSON payload")
continue
symbols = _extract_symbols_from_fmp_payload(payload)
if symbols:
return symbols, failures
failures.append(f"{endpoint}: empty/unsupported payload")
return [], failures
async def _fetch_universe_symbols_from_fmp(universe: str) -> list[str]:
if not settings.fmp_api_key:
raise ValidationError(
"FMP API key is required for universe bootstrap (set FMP_API_KEY)"
)
api_key = settings.fmp_api_key
stable_base = "https://financialmodelingprep.com/stable"
legacy_base = "https://financialmodelingprep.com/api/v3"
stable_candidates: dict[str, list[str]] = {
"sp500": [
f"{stable_base}/sp500-constituent?apikey={api_key}",
f"{stable_base}/sp500-constituents?apikey={api_key}",
],
"nasdaq100": [
f"{stable_base}/nasdaq-100-constituent?apikey={api_key}",
f"{stable_base}/nasdaq100-constituent?apikey={api_key}",
f"{stable_base}/nasdaq-100-constituents?apikey={api_key}",
],
"nasdaq_all": [
f"{stable_base}/stock-screener?exchange=NASDAQ&isEtf=false&limit=10000&apikey={api_key}",
f"{stable_base}/available-traded/list?apikey={api_key}",
],
}
legacy_candidates: dict[str, list[str]] = {
"sp500": [
f"{legacy_base}/sp500_constituent?apikey={api_key}",
f"{legacy_base}/sp500_constituent",
],
"nasdaq100": [
f"{legacy_base}/nasdaq_constituent?apikey={api_key}",
f"{legacy_base}/nasdaq_constituent",
],
"nasdaq_all": [
f"{legacy_base}/stock-screener?exchange=NASDAQ&isEtf=false&limit=10000&apikey={api_key}",
],
}
failures: list[str] = []
async with httpx.AsyncClient(timeout=30.0, verify=_CA_BUNDLE_PATH) as client:
stable_symbols, stable_failures = await _try_fmp_urls(client, stable_candidates[universe])
failures.extend(stable_failures)
if stable_symbols:
return stable_symbols
legacy_symbols, legacy_failures = await _try_fmp_urls(client, legacy_candidates[universe])
failures.extend(legacy_failures)
if legacy_symbols:
return legacy_symbols
if failures:
reason = "; ".join(failures[:6])
logger.warning("FMP universe fetch failed for %s: %s", universe, reason)
raise ProviderError(
f"Failed to fetch universe symbols from FMP for '{universe}'. Attempts: {reason}"
)
raise ProviderError(f"Failed to fetch universe symbols from FMP for '{universe}'")
async def _fetch_wiki_constituent_symbols(
client: httpx.AsyncClient,
url: str,
@@ -351,13 +241,16 @@ async def fetch_universe_symbols(
Fallback order:
1) Free public sources (Wikipedia/NASDAQ trader)
2) FMP endpoints (if available)
3) Cached snapshot in SystemSetting
4) Built-in seed symbols
2) Cached snapshot in SystemSetting
3) Built-in seed symbols
Returns ``(symbols, source_label)`` so bootstrap UI can show where the
list came from (important when Wikipedia/FMP fail and a stale cache still
lists BK instead of BNY).
list came from (important when the public source fails and a stale cache
still lists BK instead of BNY).
The seeds are representative, not complete, so a *fresh* install whose
public source is down bootstraps a partial universe. A warm instance is
unaffected — it falls through to its cached snapshot.
"""
normalised_universe = _validate_universe(universe)
failures: list[str] = []
@@ -369,15 +262,6 @@ async def fetch_universe_symbols(
await _write_cached_symbols(db, normalised_universe, cleaned_public, public_source or "public")
return cleaned_public, public_source or "public"
try:
fmp_symbols = await _fetch_universe_symbols_from_fmp(normalised_universe)
cleaned_fmp = _normalise_symbols(fmp_symbols)
if cleaned_fmp:
await _write_cached_symbols(db, normalised_universe, cleaned_fmp, "fmp")
return cleaned_fmp, "fmp"
except (ProviderError, ValidationError) as exc:
failures.append(str(exc))
cached_symbols = await _read_cached_symbols(db, normalised_universe)
if cached_symbols:
logger.warning(
@@ -473,8 +357,25 @@ async def bootstrap_universe(
db.add(Ticker(symbol=symbol))
deleted_count = 0
skipped_delisted: list[str] = []
if symbols_to_delete:
result = await db.execute(delete(Ticker).where(Ticker.symbol.in_(symbols_to_delete)))
# A delisted row was retained on purpose — its price history is exactly
# what a survivorship-honest backtest needs, and the delete cascades it
# away. Pruning must not undo that. (Pruning a symbol that is merely no
# longer an index constituent still destroys history; that needs a
# tracked/membership state separate from delisting.)
protected = (
await db.execute(
select(Ticker.symbol).where(
Ticker.symbol.in_(symbols_to_delete),
Ticker.delisted_on.is_not(None),
)
)
).scalars().all()
skipped_delisted = sorted(protected)
deletable = [s for s in symbols_to_delete if s not in set(protected)]
if deletable:
result = await db.execute(delete(Ticker).where(Ticker.symbol.in_(deletable)))
deleted_count = int(result.rowcount or 0)
await db.commit()
@@ -494,4 +395,8 @@ async def bootstrap_universe(
"already_tracked": len(target_symbols & existing_symbols),
"deleted": deleted_count,
"added_symbols": symbols_to_add[:50],
# Delisted rows a prune declined to destroy, so the caller can see the
# count did not match what they asked to remove.
"kept_delisted": skipped_delisted[:50],
"kept_delisted_count": len(skipped_delisted),
}
deploy/provision_fundamentals.sh
-13
View File
@@ -15,7 +15,6 @@ MIN_FREE_GB="${DOLT_MIN_FREE_DISK_GB:-5}"
EARNINGS_DIR="${DOLT_DATA_DIR}/${DOLT_EARNINGS_SUBDIR}"
DOLT_IDENTITY_NAME="${DOLT_IDENTITY_NAME:-Signal Platform}"
DOLT_IDENTITY_EMAIL="${DOLT_IDENTITY_EMAIL:-signal-platform@localhost}"
FUNDAMENTALS_PARITY_REPORT_DIR="${FUNDAMENTALS_PARITY_REPORT_DIR:-/var/lib/signal-platform/reports/fundamentals-parity}"
fail() {
echo "ERROR: $*" >&2
@@ -80,8 +79,6 @@ check_env() {
|| fail "set DOLT_EARNINGS_SUBDIR=$DOLT_EARNINGS_SUBDIR in $ENV_FILE"
grep -Eq '^SEC_USER_AGENT=.*@.*' "$ENV_FILE" \
|| fail "SEC_USER_AGENT in $ENV_FILE must contain a real contact email"
grep -Fqx "FUNDAMENTALS_PARITY_REPORT_DIR=$FUNDAMENTALS_PARITY_REPORT_DIR" "$ENV_FILE" \
|| fail "set FUNDAMENTALS_PARITY_REPORT_DIR=$FUNDAMENTALS_PARITY_REPORT_DIR in $ENV_FILE"
}
check_all() {
@@ -104,15 +101,6 @@ check_all() {
identity_email="$(repo_config_value user.email 2>/dev/null || true)"
[[ -n "$identity_name" ]] || fail "missing Dolt user.name for $EARNINGS_DIR"
[[ -n "$identity_email" ]] || fail "missing Dolt user.email for $EARNINGS_DIR"
[[ -d "$FUNDAMENTALS_PARITY_REPORT_DIR" ]] \
|| fail "missing parity report directory: $FUNDAMENTALS_PARITY_REPORT_DIR"
if [[ "$(id -un)" == "$APP_USER" ]]; then
[[ -w "$FUNDAMENTALS_PARITY_REPORT_DIR" ]] \
|| fail "parity report directory is not writable by $APP_USER"
else
runuser -u "$APP_USER" -- test -w "$FUNDAMENTALS_PARITY_REPORT_DIR" \
|| fail "parity report directory is not writable by $APP_USER"
fi
check_free_space
check_env
echo "OK: Dolt $DOLT_VERSION and earnings clone are provisioned"
@@ -139,7 +127,6 @@ fi
version_ok || fail "Dolt $DOLT_VERSION installation failed"
install -d -o "$APP_USER" -g "$APP_GROUP" -m 0750 "$DOLT_DATA_DIR"
install -d -o "$APP_USER" -g "$APP_GROUP" -m 0750 "$FUNDAMENTALS_PARITY_REPORT_DIR"
check_free_space
if [[ ! -d "$EARNINGS_DIR/.dolt" ]]; then
docs/dolt-integration-plan.md
+90 -43
View File
@@ -1,15 +1,18 @@
# Dolt bulk-data integration — implementation plan
Status: approved 2026-07-21, revised through four review rounds; direction: KISS
backend, UI value first. Hand-off document for the implementing agent;
self-contained.
Status: **workstream A complete and deployed** (A0–A6, last step 2026-08-07);
**workstream B dropped 2026-08-07** — see § Why B was dropped. Approved 2026-07-21,
revised through five review rounds; direction: KISS backend, UI value first.
Originally a hand-off document for the implementing agent; now the design record.
Current operations live in `docs/fundamentals-deployment.md`.
## Objective
Replace the free-tier fundamentals APIs (FMP, Finnhub, Alpha Vantage) with bulk
data: SEC Company Facts for fundamentals, the DoltHub earnings repo for the
earnings calendar/history, and — later, independently — the DoltHub stocks repo for
historical OHLCV. PostgreSQL stays the production system of record.
data: SEC Company Facts for fundamentals and the DoltHub earnings repo for the
earnings calendar/history. PostgreSQL stays the production system of record.
(A third source — the DoltHub stocks repo for historical OHLCV — was planned as
workstream B and dropped; Alpaca remains the price source.)
**Delivery order: two independent workstreams.**
@@ -17,9 +20,9 @@ historical OHLCV. PostgreSQL stays the production system of record.
FundamentalsPanel + decommission FMP/Finnhub/Alpha Vantage. Valuation uses the
existing Alpaca closes already in `ohlcv_records`. This alone achieves the goal
(killing the quota-limited APIs) and delivers all the UI value.
- **Workstream B (later, optional until needed):** replace historical OHLCV with
the Dolt stocks repo. The most complex machinery (4.7 GB clone, split
adjustment, source-bar table, reconciliation) lives here and blocks nothing in A.
- **Workstream B — DROPPED 2026-08-07, see below.** Would have replaced historical
OHLCV with the Dolt stocks repo. Its design is retained further down as a record,
not as a backlog item.
**Guiding principle: KISS.** Plain daily importers with staging and atomic
promotion — no forensic replay, no permanent archive store, no conflict tables, no
@@ -70,8 +73,9 @@ notes (retain a CC BY-SA 4.0 reference + attribution to `post-no-preference/earn
and a note of the transformations applied — e.g. in a repo `NOTICE`/attribution file
and the importer module); **no public API, bulk export, or redistribution** of the
data; re-review licensing before any public or commercial access. The
`post-no-preference/stocks` repo (workstream B) is **not** covered here and will be
reviewed separately if B begins.
`post-no-preference/stocks` repo (workstream B) is **not** covered here. B was
dropped before any licensing review, so that repo has never been assessed — any
future use of it starts that review from scratch.
## Schema
@@ -119,7 +123,9 @@ reviewed separately if B begins.
cache, repopulated by the daily SEC job — but only after the phase-A5 parity
gate.
**Migration 027 (workstream B, written when B starts):**
**Migration 027 (workstream B — NEVER WRITTEN; B was dropped, and `027` was
subsequently used for `fundamental_snapshots.weighted_avg_diluted_shares`). The
design below is a record only:**
- `ohlcv_source_bars` — source-truth bar table, required because `ohlcv_records`
allows one row per (ticker_id, date) (`app/models/ohlcv.py:12`) and Alpaca
@@ -219,7 +225,7 @@ Workstream A:
**The new API valuation object is not stored anywhere** — it is computed at
request time (below). No valuation cache or table exists.
Workstream B:
Workstream B (dropped — never built):
- Dolt OHLCV+splits pull/import: `0 2 * * tue-sat` ET. If source_max_date is not
fresh, retry hourly until ~06:00, then give up quietly. After a successful
@@ -430,16 +436,55 @@ workstream B — Alpaca remains the price source throughout.
approval** — see the handoff section below. Step (c) is implemented behind the
default-off `fundamental_data_sec_dolt_cutover_enabled` SystemSetting; the
remaining production action is flipping that switch on and observing it.
- A6. Remove FMP/Finnhub/Alpha Vantage; keep monitoring + manual fallback.
- A6. **DONE 2026-08-07.** FMP/Finnhub/Alpha Vantage removed, along with the
weekly `fundamental_collector` job, the A5 cutover toggle (SEC+Dolt is now the
unconditional path) and the parity report. Migration `029` tombstoned the two
behavior-bearing settings rows for the rollback window and `030` dropped them
once the deploy was confirmed healthy; the archived parity bundles stay as the
A5 evidence trail.
**Workstream B (independent, start when wanted):**
**Workstream B — DROPPED 2026-08-07.** The phases below are recorded for anyone
who revisits the decision; none of them are scheduled work.
- B0. Stocks clone (~4.7 GB) provisioned; migration 027.
- B1. OHLCV + split adjustment in shadow (writes `ohlcv_source_bars` only; Alpaca
keeps owning `ohlcv_records`); historical backfill.
- B2. Reconciliation window (≥ 2 weeks) vs Alpaca; review validation summaries.
- B3. Promote Dolt as historical OHLCV source (canonical rebuilt from raw source
bars + splits); morning pipeline → 03:00.
- ~~B0. Stocks clone (~4.7 GB) provisioned; migration 027.~~
- ~~B1. OHLCV + split adjustment in shadow (writes `ohlcv_source_bars` only; Alpaca
keeps owning `ohlcv_records`); historical backfill.~~
- ~~B2. Reconciliation window (≥ 2 weeks) vs Alpaca; review validation summaries.~~
- ~~B3. Promote Dolt as historical OHLCV source (canonical rebuilt from raw source
bars + splits); morning pipeline → 03:00.~~
### Why B was dropped
Reviewed after A6 shipped. Four reasons, in order of weight:
1. **Its motivation no longer exists.** B was scoped inside a plan whose goal was
killing the quota-limited free-tier APIs. Alpaca was never one of them, and the
plan always said so (§ Decommissioning: "Alpaca remains the price source
throughout"). A6 achieved the goal. What remained was swapping one working
price source for another.
2. **Its only concrete benefit is reachable far more cheaply.** The prize was
`corporate_actions`, the documented fix for the KLAC-class post-filing split
(TTM EPS pre-split vs a post-split price → P/E 6.19 instead of ~13, invisible to
snapshots). That needs *split events*, not 4.7 GB of bars — and the Alpaca SDK
already in the venv exposes them via
`alpaca.data.historical.corporate_actions.CorporateActionsClient.get_corporate_actions`
with `CorporateActionsRequest` / `CorporateActionsType`. See the follow-up below.
3. **The benefit is small.** Fundamentals carry 20% of the composite, P/E is one of
three fundamental inputs, and only names that split between their last 10-Q and
today are affected — a handful at a time, self-correcting at the next filing.
4. **B would add a risk the current setup does not carry.** By design a newly
published split rewrites a symbol's entire adjusted history. A backtest↔prod
parity guard exists precisely because changed history invalidates comparisons;
B makes history mutable as a routine event. It also needs its own license
review — the A0 CC BY-SA decision covers only `post-no-preference/earnings`.
**Optional follow-up, not scheduled:** a small `corporate_actions` table populated
from Alpaca, used to null or correct P/E when a split post-dates the newest
snapshot. Roughly a day's work; captures essentially all of B's value with no
clone, no `ohlcv_source_bars`, no split-adjustment pipeline and no reconciliation
window. Worth doing only if the wart starts costing something — it has been visible
and harmless since July 2026. Note that migration numbering has moved on: head is
`030`, so any such table would be `031+`, not the `027` named below.
## Test plan
@@ -464,8 +509,8 @@ workstream B — Alpaca remains the price source throughout.
falls back from P/E to FCF yield for the valuation segment when P/E is null.
- Peer comparison disappears below 5 peer issuers; favorable-percentile direction
correct for both polarities.
- Workstream B: split-adjusted OHLCV matches Alpaca on representative normal /
split / reverse-split symbols.
- ~~Workstream B: split-adjusted OHLCV matches Alpaca on representative normal /
split / reverse-split symbols.~~ (dropped)
- UI states: positive, adverse, neutral, insufficient history, insufficient
peers; mobile layout; non-color accessibility.
- Unit, integration, scheduler and frontend suites pass.
@@ -492,30 +537,32 @@ Post-fix: candidate scores 504 of 511 vs legacy's 507 (gap = PSKY/Q new registra
FITB, all explained); revenue-growth agreement 0.0038 median abs delta where both exist.
Dennis reviewed the evidence 2026-07-24 and directed proceeding to cutover.
**Task 1 — A5 activation (IMPLEMENTED 2026-07-24; production switch remains).** The
post-activation local refresh of `fundamental_data` derives `pe_ratio` and
`market_cap` from newest valid snapshots × latest PostgreSQL close, `revenue_growth`
from snapshots, `earnings_surprise`/`next_earnings_date` from `earnings_events`; mark
affected cached fundamental scores stale; must run identically when SEC is unreachable.
**Task 1 — A5 activation: DONE.** Implemented 2026-07-24, switched on and observed
in production, and made unconditional by A6 (2026-08-07) — there is no longer a
switch, an Admin card, or a weekly legacy collector to skip. The local refresh of
`fundamental_data` derives `pe_ratio` and `market_cap` from newest valid snapshots ×
latest PostgreSQL close, `revenue_growth` from snapshots, and
`earnings_surprise`/`next_earnings_date` from `earnings_events`; it marks affected
cached fundamental scores stale and runs identically when SEC is unreachable.
It consumes `fundamentals_derivation.derive()` outputs, NOT raw snapshot fields —
that path carries the split guard (`ttm_diluted_eps`
nulls when contaminated, with `ttm_diluted_eps_caveat`) and the multi-class share
fallback (`shares_outstanding` + `shares_outstanding_estimated`). Parity and activation
share the same candidate builder. Activation is the explicit
`fundamental_data_sec_dolt_cutover_enabled` SystemSetting and defaults off. It is
managed by the **Fundamentals data source** card in Admin → Settings; while active,
the weekly legacy collector skips itself so it cannot overwrite the SEC/Dolt cache.
See `docs/fundamentals-deployment.md` for the production flip and rollback procedure.
that path carries the split guard (`ttm_diluted_eps` nulls when contaminated, with
`ttm_diluted_eps_caveat`) and the multi-class share fallback (`shares_outstanding` +
`shares_outstanding_estimated`). See `docs/fundamentals-deployment.md` for current
operations and rollback.
**Task 2 — A6 decommissioning.** After a short observation window: remove
FMP/Finnhub/Alpha Vantage providers, config and env keys; keep monitoring + manual
fallback. Gated by the acceptance criteria above — especially forward-calendar
timeliness from `dolt_earnings` (its `source_max_date` ran ~5 weeks ahead as of
2026-07-23, which passes).
**Task 2 — A6 decommissioning: DONE 2026-08-07.** The cutover ran on and was
observed in production, so the legacy providers, their config/env keys, the weekly
collector job and the parity report were all removed. Two consequences to carry:
(1) `fundamental_data` now has no provider fallback — recovery is restore-from-backup;
(2) disabling **SEC Fundamentals Import** stops the SEC fetch only, because the local
cache refresh was deliberately moved outside the job-enable check. No follow-ups
remain: migration `030` dropped the tombstone rows after the deploy was verified.
**Known caveats to carry (documented in the findings report, not bugs to fix):**
- KLAC-class post-filing splits: P/E wrong until the next 10-Q; undetectable from
snapshots. Workstream B's `corporate_actions` table is the natural future fix.
snapshots. Still open and still harmless. The fix, if ever wanted, is a small
`corporate_actions` table fed from Alpaca — **not** workstream B, which was
dropped; see § Why B was dropped.
- BRK-B: no share count exists anywhere in companyfacts → no market cap, correctly.
- FITB: unscored (split guard + no taggable revenue) — the one name that lost its
score relative to legacy; composite renormalises.
@@ -528,7 +575,7 @@ timeliness from `dolt_earnings` (its `source_max_date` ran ~5 weeks ahead as of
## Deferred (explicitly, until a concrete need appears)
- Workstream B itself is deferred relative to A and blocks nothing in A.
- Workstream B: **dropped** 2026-08-07, not deferred — see § Why B was dropped.
- Exact byte-level source replay of historical imports; permanent archive store.
- Point-in-time backtest enforcement (`accepted_at` is stored now; derivation and
backtest visibility rules are built only when fundamentals enter
docs/fundamentals-deployment.md
+47 -89
View File
@@ -1,17 +1,21 @@
# Fundamentals production deployment
This is the one-time production setup for the Dolt earnings and SEC fundamentals
imports. The A5 scoring cutover was approved on 2026-07-24; the compat-cache write
path is still default-off until the explicit production switch below is set. Do
not add OS cron entries: the application scheduler owns both jobs.
imports. Since A6 (2026-08) these are the *only* fundamentals sources — the
FMP/Finnhub/Alpha Vantage providers, the weekly legacy collector and the A5 parity
report are gone, and the cache write path is unconditional. Do not add OS cron
entries: the application scheduler owns both jobs.
## What the deployment adds
- `Dolt Earnings Import (shadow)` runs daily at 02:30 America/New_York.
- `SEC Fundamentals Import` runs daily at 04:00 America/New_York. Its local
`fundamental_data` refresh runs only when the A5 switch is enabled.
- `Fundamentals Parity Report (read-only)` runs daily at 05:30 America/New_York.
- `Dolt Earnings Import` runs daily at 02:30 America/New_York.
- `SEC Fundamentals Import` runs daily at 04:00 America/New_York, then refreshes
`fundamental_data` — the compat cache scoring reads — from stored snapshots,
earnings events and closes.
- Both jobs are visible, toggleable, and manually triggerable in Admin → Jobs.
**Disabling the SEC job stops its SEC network fetch only**; the local cache
refresh still runs, because prices and earnings move daily even when no filing
does.
- Cron expressions are editable in Admin → Schedule.
- Every attempt is recorded in `data_import_runs`; failures also create a system
event. A failed validation does not promote partial data.
@@ -36,14 +40,16 @@ DOLT_EARNINGS_SUBDIR=earnings
DOLT_MIN_FREE_DISK_GB=5.0
SEC_USER_AGENT=signal-platform/1.0 (contact: real-address@example.com)
SEC_REQUEST_SPACING_SECONDS=0.2
FUNDAMENTALS_PARITY_REPORT_DIR=/var/lib/signal-platform/reports/fundamentals-parity
```
Use a real monitored contact address. Keep at least 5 GB free at the Dolt data
path; 8–10 GB gives comfortable growth headroom. The data directory must stay
outside `/opt/signalplatform`, because deployments use `rsync --delete` there.
The parity-report directory is also persistent and owned by the service user;
its small timestamped JSON/CSV bundles form the temporary A5 review trail.
`FMP_API_KEY`, `FINNHUB_API_KEY` and `ALPHA_VANTAGE_API_KEY` must be **removed**
from this file. Nothing reads them any more, and leaving them installed is the
one thing that would let a rolled-back pre-A6 process resume the legacy
collector and overwrite the SEC/Dolt cache.
## One-time provisioning
@@ -80,7 +86,7 @@ a reviewed change to `DOLT_VERSION`, followed by the same provision/check flow.
In Admin → Jobs, wait until no other job is running, then:
1. Trigger **Dolt Earnings Import (shadow)**. Expect `completed` with import
1. Trigger **Dolt Earnings Import**. Expect `completed` with import
status `promoted`; a repeat without an upstream change should report `no_op`.
2. Trigger **SEC Fundamentals Import**. The first run performs the
tracked-universe history backfill and can take materially longer than a daily
@@ -91,27 +97,7 @@ In Admin → Jobs, wait until no other job is running, then:
data and still handles partial/missing issuers cleanly. A ticker held by the
quality gate should show **New setups paused** with the specific SEC reason.
## A5 parity observation window
After both shadow imports are healthy, trigger **Fundamentals Parity Report
(read-only)** once in Admin → Jobs. The **A5 Fundamentals Parity** card above
the jobs shows the latest coverage/delta summary and provides authenticated JSON
and CSV downloads. The canonical server-side bundles are archived at:
```text
/var/lib/signal-platform/reports/fundamentals-parity/
```
The scheduler then generates one report daily at 05:30 New York time, after the
02:30 Dolt and 04:00 SEC jobs. Review 5–7 consecutive reports before making the
cutover decision. A report never writes `fundamental_data`, dimension/composite
scores, rankings, qualification state, or an approval flag. Materiality bands
only highlight rows for review; A5 still requires explicit approval.
Each bundle contains legacy and candidate P/E, revenue growth, and earnings
surprise; definition notes; source revisions and price dates; recomputed legacy
and candidate fundamental scores; and per-universe fundamental-rank changes.
Definition changes remain explicit even when numeric deltas are small.
## Verification
Optional database verification:
@@ -162,45 +148,24 @@ Expect `OK: source lock is busy`. This is the remaining live-PostgreSQL
mutual-exclusion check; SQLite unit tests cannot exercise PostgreSQL advisory
locks. A second Admin trigger should independently report the job as busy.
## A5 production activation (approved 2026-07-24)
## The fundamentals cache
The write path is controlled by the SystemSetting
`fundamental_data_sec_dolt_cutover_enabled`. An absent value, `false`, or any
value other than `true` leaves `fundamental_data` untouched. Before enabling it,
confirm the normal PostgreSQL backup containing `fundamental_data` is current.
`fundamental_data` is the compat cache scoring reads. The SEC Fundamentals
Import rebuilds it every run from data already in PostgreSQL: newest valid
snapshots x latest close for `pe_ratio` and `market_cap`, snapshots alone for
`revenue_growth`, and `earnings_events` for `earnings_surprise` and
`next_earnings_date`. It therefore also runs after an SEC network/validation
failure, a `no_op`, a source-lock skip, or with the job disabled — no network
access is involved. The job message appends the cache row count and the changed
score-input count.
In **Admin → Settings → Fundamentals data source**:
A refresh marks affected fundamental and composite score caches stale. The
normal 15:30 near-close scanner recomputes them before using the rankings; until
then, reads truthfully expose the stale state.
1. Turn on **Use SEC + Dolt for scoring inputs** and accept the confirmation.
2. Click **Run refresh now**. The SEC import may be `promoted` or `no_op`; either
result runs the local cache refresh.
The weekly legacy collector is automatically skipped while the switch is on, so
it cannot overwrite the activated cache. The switch remains visible even before
its SystemSetting row exists because the safe default is off.
If the Admin UI is unavailable, enable the cutover directly in PostgreSQL:
Verify the refreshed rows:
```sql
INSERT INTO system_settings (key, value, updated_at)
VALUES ('fundamental_data_sec_dolt_cutover_enabled', 'true', now())
ON CONFLICT (key) DO UPDATE
SET value = EXCLUDED.value, updated_at = now();
```
Then trigger **SEC Fundamentals Import** once in Admin → Jobs. Once enabled, the
same refresh also runs after an SEC network/validation failure or a source-lock
skip, because it reads only PostgreSQL snapshots, earnings events, and closes.
The job message appends the cache row count and changed score-input count when
the import itself completed successfully.
Verify the switch and refreshed rows:
```sql
SELECT key, value, updated_at
FROM system_settings
WHERE key = 'fundamental_data_sec_dolt_cutover_enabled';
SELECT count(*) AS rows,
max(fetched_at) AS refreshed_at,
count(pe_ratio) AS pe_available,
@@ -213,28 +178,26 @@ SELECT dimension, is_stale, count(*)
FROM dimension_scores
WHERE dimension = 'fundamental'
GROUP BY dimension, is_stale;
SELECT is_stale, count(*)
FROM composite_scores
GROUP BY is_stale;
```
The first refresh intentionally marks affected fundamental and composite score
caches stale. The normal 15:30 near-close scanner recomputes them before using
the rankings; until then, reads truthfully expose the stale state. Observe at
least several scheduled cycles before A6 removes the legacy providers.
## Failure and rollback
- To stop the A5 cache writes without stopping SEC snapshot ingestion, turn off
**Use SEC + Dolt for scoring inputs** in Admin → Settings. If the UI is
unavailable, set `fundamental_data_sec_dolt_cutover_enabled` back to `false`
with the SQL above (changing only the value). This prevents the next local
refresh but does not restore rows already replaced. Restore `fundamental_data`
from the pre-cutover database backup, or—before A6—manually run the legacy
Fundamental Collector if its provider keys and quota are still available.
- Disable a failing source-import job in Admin → Jobs only when ingestion itself
must stop. Existing promoted snapshots/events remain available.
- **There is no provider fallback any more, and no Admin switch that freezes the
cache.** Disabling **SEC Fundamentals Import** stops SEC network access only;
the 04:00 job still rebuilds `fundamental_data` from the stored snapshots,
earnings events and closes.
- Restoring `fundamental_data` from the PostgreSQL backup is therefore a
*temporary* fix on its own: if the bad values come from the snapshots or from
the derivation code, the next scheduled run reproduces them. Fix the cause —
restore or repair `fundamental_snapshots` / `earnings_events`, or revert the
parser change and re-run `scripts/reparse_fundamentals.py --apply`.
- To genuinely freeze the cache while you work, stop the service
(`sudo systemctl stop signalplatform.service`) — that stops the scheduler with
it. There is no finer-grained control, by design: a silently frozen scoring
input is worse than an obvious outage.
- Disable a failing source-import job in Admin → Jobs when SEC network access
itself must stop. Existing promoted snapshots and events remain available, and
the job's runtime message still reports the cache result.
- Inspect the job runtime, latest `data_import_runs.validation_json`, service
logs, and Admin → System Events before retrying.
- `unresolved_filing` is emitted once when a filing enters automatic retry. It
@@ -250,8 +213,3 @@ least several scheduled cycles before A6 removes the legacy providers.
- The Dolt clone is a reproducible cache and does not need a bespoke backup.
PostgreSQL (including `earnings_events`, `fundamental_snapshots`, and import
audit rows) must remain covered by the normal production database backup.
- Do not proceed to A6 until the activated cache has completed the observation
window and the forward earnings calendar remains timely.
- If report generation fails, inspect Admin → System Events and verify
`FUNDAMENTALS_PARITY_REPORT_DIR` exists and is writable by `deploy`. Existing
reports and all live data remain untouched.
docs/research/README.md
+29 -13
View File
@@ -25,7 +25,7 @@ score, Structural S/R, the Gate Target Ladder, sentiment, fundamentals) is
| 1.5× ATR initial stop | Real exit | Cuts losers fast |
| 3× ATR trailing stop, 30-day max hold | Real exit | Best Sharpe of every exit tested |
| Post-stop normal gate reset | Re-entry policy | Stop always closes; a later gate failure and subsequent fresh qualification define the next signal episode. The selected study arm reached Sharpe 1.77 / CAGR 48.3% at capacity 10; live scan-before-outcome timing is stricter (Sharpe 1.68 / CAGR 44.8% analogue). [Full study](post-stop-reentry.md) |
| Max 10 concurrent positions, 1% risk per trade | Sizing | The cap binds by signal count, but the focused bracket found negligible opportunity cost: cap 15 admitted every blocked setup and added only 0.0018 R/trade in affected paths. [Findings](portfolio-capacity-bracket-findings.md) |
| Max **15** concurrent positions, 1% risk per trade | Sizing | Raised from 10 (2026-08-05) so the count cap never binds: +1.075pp CAGR paired, 51 paths better / 2 worse, drawdown unchanged. Cash plus the 20% notional cap saturates the book near 12. [Findings](portfolio-capacity-bracket-findings.md#correction-2026-08-05-ev-per-trade-was-the-wrong-lens) |
| Structural S/R | Human-facing product context | Clean, capped zones for charts and alerts; not read by the scanner |
| Gate Target Ladder | Screening machinery | Volume-free transient proposals preserve the production candidate set exactly; never an exit |
@@ -61,7 +61,7 @@ invites overfitting.
|---|---|
| ATR trail multiple {1.5–4.0} | **Keep 3.0** — ≤2.0 whipsaws out the right tail; ≥2.5 is a plateau |
| Momentum lookback (6-1, 3-1, 12-7 Novy-Marx, composites) | **Keep residual 12-1** — the others have IC ≈ 0 or weaker t-stats |
| Selection cutoff {70…90} × book size {10, 15, 20} | **Keep 80 × 10** — the focused daily bracket found no meaningful gain from cap 15, while weekly rank replacement hurt. [Findings](portfolio-capacity-bracket-findings.md) |
| Selection cutoff {70…90} × book size {10, 15, 20} | **Keep cutoff 80; book size now 15** — the focused daily bracket found cap 15 worth +1.075pp CAGR (the weekly replay's contrary reading was EV-per-trade). Weekly rank replacement hurt. [Findings](portfolio-capacity-bracket-findings.md#correction-2026-08-05-ev-per-trade-was-the-wrong-lens) |
| Position sizing (equal-weight, inverse-vol, risk-% sweep) | **Keep 1% fixed-fractional** |
| Primary-target probability floor | **Keep 20%** — pruned lottery targets, 1,428 → 1,089 qualified, lifted Sharpe |
| Primary-target R:R selector | **Keep 1.5** — target choice is intentionally independent of the later 2.0 activation floor |
@@ -146,7 +146,7 @@ knobs.
| **Broader universe** | Composition changes factor signs (fip tug-of-war); vol-tilt on breadth is only a **directional hypothesis** (auth. −0.048 / t −1.36) | Any prod broaden must re-validate 80/20 tilt; offline research only; research.sqlite requires completion manifest |
| **Forward paper-trade record** | The only true out-of-sample evidence the snapshot cannot give | Time; mark entries at actual near-close fill once ops ships |
| **Better target model for clear-air names** | The return is demonstrably there (#2 wins on raw CAGR in *both* train and test); it's the *flat* 3× ATR target that makes it too expensive in risk | Needs a per-name model, not a constant k×ATR |
| **Minimum effective-risk floor** | In cap-never-bound paths, the confounded 0.5% floor arm removed about 8% of fills while EV rose from 0.328 to 0.399 R and PF from 1.60 to 1.75, with exposure nearly unchanged | Run the frozen single-variable cap-10 A/B. [Specification](effective-risk-floor-ab.md) / [capacity findings](portfolio-capacity-bracket-findings.md) |
| **Minimum effective-risk floor** | ⛔ CLOSED NEGATIVE, not run. The floor lifts EV/trade (+0.032) and PF (+0.073) *by deleting trades* — 11.4 fewer per path, never one more — and costs **−0.753pp CAGR**, −0.047 Sharpe, −0.051 Calmar | Do not run the A/B; its EV-based pass rule would have shipped it. [Withdrawn specification](effective-risk-floor-ab.md) / [findings](portfolio-capacity-bracket-findings.md#correction-2026-08-05-ev-per-trade-was-the-wrong-lens) |
---
@@ -198,15 +198,31 @@ qualification. The [daily re-entry matrix](post-stop-reentry.md) supports this
for the current 10-position book, but not as a universal rule for other
portfolio capacities.
Capacity is now closed as a negative result. The current daily Phase A control
does reject 519 qualified entries because the ten-slot book is full versus 472
admitted trades, so the older weekly “cap never binds” claim was stale. But the
clean cap-15 arm admitted every opportunity the strategy requested and added
only 0.0018 R/trade in paths where cap 10 bound. Weekly current-rank replacement
reduced mean EV and created substantial churn. Keep cap 10 and do not build the
replacement policy. See the [frozen specification](portfolio-capacity-bracket.md)
and the separate [capacity findings](portfolio-capacity-bracket-findings.md).
The only open follow-up from that run is the
[frozen confound-free 0.5% minimum effective-risk-floor A/B](effective-risk-floor-ab.md).
Capacity is closed **positive**: the count cap was raised 10 → 15 so it no longer
binds, worth **+1.075pp CAGR** paired across 175 paths (51 better, 2 worse) at
unchanged drawdown. Fifteen is headroom, not a target — cap15 peaked at 12 with
zero full-book skips, so cash plus the 20% notional cap is the real ceiling.
An earlier reading of this run concluded "keep cap 10, added only 0.0018 R/trade."
That was **EV per trade**, which is the wrong metric for a treatment that changes
trade *count*: flat EV/trade means the blocked entries were as good as the taken
ones, so refusing them cost their whole contribution to return. Weekly
current-rank replacement remains rejected (−0.043 EV R, 24% churn). The 0.5%
effective-risk-floor A/B is **closed negative** without being run — it costs
0.75pp of CAGR while raising EV/trade, and its frozen pass rule would have shipped
it. See the [frozen specification](portfolio-capacity-bracket.md) and the
[capacity findings](portfolio-capacity-bracket-findings.md#correction-2026-08-05-ev-per-trade-was-the-wrong-lens).
The next real evidence is **forward**, not backward: the live paper-trade record.
## AI/Tech Risk Monitor
An observational risk thermometer (State + Warning) shown on the Risk page. It
gates nothing — no entries, exits, sizing or ranking — so it is not a strategy
document, but its calibration follows the same rules as one.
- [Methodology, v4](regime-monitor-v4.md) — sensors, weights, bands, and the
reasoning behind each cut from v2 onward.
- Reproduce any number in it with `scripts/run_regime_monitor_calibration.py`,
which replays the series offline and refuses to report unless it first
reproduces the published v2 and v3 figures.
docs/research/effective-risk-floor-ab.md
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@@ -1,8 +1,27 @@
# Effective initial-risk floor A/B - frozen specification
> ## ⛔ CLOSED 2026-08-05 — NEGATIVE. DO NOT RUN.
>
> This A/B was never executed because the capacity-bracket run already contains
> it. `cap15_incumbent` (peak 12, zero blocked, no floor) and `cash_unbounded`
> (peak 12, floor) have the same effective capacity and differ essentially only
> by `min_initial_risk_fraction`. Paired over 175 paths, the 0.5% floor gives
> **EV/trade +0.032 and profit factor +0.073, but CAGR −0.753pp, total return
> −0.765pp, Sharpe −0.047, Calmar −0.051**, and it removes 11.4 trades per path
> while never adding one (174 worse / 0 better).
>
> **The pass rule below is unsafe.** It promotes on paired EV, and the floor
> raises EV per trade *precisely by deleting trades* that were net positive
> contributors — so this specification would have shipped a change costing
> 0.75pp of CAGR. Any successor study must decide on CAGR/total return and treat
> EV per trade as a diagnostic.
>
> See [portfolio-capacity-bracket-findings.md](portfolio-capacity-bracket-findings.md#correction-2026-08-05-ev-per-trade-was-the-wrong-lens).
> Retained as a record of what was specified and why it was withdrawn.
Date frozen: 2026-08-05
Branch: research/portfolio-capacity-rebalancing
Runner: scripts/run_portfolio_construction_matrix.py
Branch: research/portfolio-capacity-rebalancing (deleted; tag `research/portfolio-capacity-final`)
Runner: scripts/run_portfolio_construction_matrix.py (not on main; see tag)
Study ID: risk-floor-ab
## Question
docs/research/phase-a-matrix.md
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@@ -32,8 +32,13 @@ Mechanics guards confirmed before reading results: calendar truncation asserted
skipped_book_full = 519 versus 472 admitted trades, so the ten-slot book
refuses 52.4% of admitted+blocked qualified opportunities. The older weekly
claim that the cap never bound is stale and does not apply to this daily
gate-reset configuration. Capacity is now isolated in the
[focused bracket study](portfolio-capacity-bracket.md).
gate-reset configuration. Capacity was isolated in the
[focused bracket study](portfolio-capacity-bracket.md) and **resolved: the count
cap was raised 10 → 15 so it no longer binds (+1.075pp CAGR paired, 51 paths
better / 2 worse, drawdown unchanged).** Note that the blocked *count* was a poor
guide in both directions — one path had 244 blocked entries and relieving all of
them moved CAGR by −0.1pp. See the
[findings correction](portfolio-capacity-bracket-findings.md#correction-2026-08-05-ev-per-trade-was-the-wrong-lens).
Validation SE ≈ 0.72 — almost no arm clears a 1-SE delta.
docs/research/portfolio-capacity-bracket-findings.md
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@@ -2,8 +2,16 @@
Date interpreted: 2026-08-05
Status: **capacity and weekly replacement closed as negative results; the
minimum effective-risk floor remains an open single-variable follow-up.**
Status: **SUPERSEDED IN PART — see [Correction](#correction-2026-08-05-ev-per-trade-was-the-wrong-lens)
at the foot of this document before acting on anything here.** Weekly replacement
is closed as a negative result and that still holds. The capacity decision below
("keep cap 10") and the recommendation to run the effective-risk-floor A/B were
both reached on EV per trade and are **reversed** by the correction: the count cap
was raised so it no longer binds, and the floor A/B is closed as negative.
> The runner (`scripts/run_portfolio_construction_matrix.py`), the research
> simulator hooks, and the study's unit tests were deliberately not merged to
> main. They live at tag `research/portfolio-capacity-final`.
This document interprets the frozen v2 run without modifying its generated
outputs:
@@ -116,9 +124,96 @@ start-date evidence, but they necessarily mix initialization with market regime.
## Final decisions
1. Keep cap 10; its measured opportunity cost is negligible.
2. Reject weekly rank replacement.
1. ~~Keep cap 10; its measured opportunity cost is negligible.~~ **REVERSED —
see the correction below.**
2. Reject weekly rank replacement. *(Stands.)*
3. Do not interpret the `cash_unbounded` improvement as a capacity effect.
4. Run only the focused cap-10 effective-risk-floor A/B next.
*(Stands — and it is not a floor effect worth having either; see below.)*
4. ~~Run only the focused cap-10 effective-risk-floor A/B next.~~ **REVERSED —
that A/B is answered and negative; do not run it.**
5. Report means, inert fractions, and absolute dispersion beside medians and
ratios in future sparse-treatment studies.
ratios in future sparse-treatment studies. *(Stands, and see below — the
metric itself matters as much as the summary statistic.)*
## Correction 2026-08-05: EV per trade was the wrong lens
Everything above judged the arms on **mean paired net EV per trade**. That is the
wrong metric for any treatment that changes how many trades the book takes.
Capacity does not change trade *quality*; it changes trade *count*. A flat EV/trade
delta therefore does not mean "no benefit" — it means the blocked entries were
**just as good** as the taken ones, so refusing them cost their entire
contribution to return. Re-running the same paired comparison on CAGR inverts two
conclusions.
### Capacity: raise the cap (reverses decision 1)
`cap15_incumbent` versus `cap10_incumbent`, paired, all 175 paths, 0.10% per fill:
| Metric | Mean Δ | Worse / better |
|---|---:|---:|
| Trades | +1.00 | **0 / 76** (never fewer) |
| **CAGR pp** | **+1.075** | 2 / 51 |
| Total return pp | +1.079 | 1 / 51 |
| Max drawdown pp | +0.007 | 1 / 2 |
| Calmar | +0.062 | **1 / 51** |
| Sharpe | +0.022 | 10 / 28 |
| Net EV R/trade | +0.001 | 47 / 29 |
Restricted to the 105 paths where the cap actually bound: **+1.791pp CAGR**.
The honest tail: exactly one path was materially hurt — `empty-2023-04`, CAGR
87.2 → 81.2 (−6.0pp), drawdown 13.0 → 14.3, from two extra trades. Second-worst
was −0.1pp. The best paths (+6.6/+6.7/+6.9pp) came with *identical* drawdown. Best
and worst magnitudes are symmetric at roughly ±6pp, but the frequency is 51:1.
Blocked count is not lost value in either direction: `empty-2021-05` had **244**
blocked entries under cap 10, and relieving every one of them moved CAGR by
−0.1pp.
**Shipped:** `SIM_MAX_POSITIONS` and `shadow_book_service.DEFAULT_CAPACITY` raised
10 → 15. Fifteen is headroom, not a target — cap15 peaked at 12 with zero
full-book skips, so cash plus the 20% notional cap is the real ceiling and
15/20/None are the same experiment.
### Effective-risk floor: closed negative (reverses decision 4)
The floor A/B does not need running — this study already contains it.
`cap15_incumbent` (peak 12, zero blocked, no floor) and `cash_unbounded` (peak 12,
floor) have the same effective capacity and differ essentially only by
`min_initial_risk_fraction`. Paired, n=175, 0.10% per fill, floor minus no-floor:
| Metric | Mean Δ | Worse / better |
|---|---:|---:|
| Net EV R/trade | **+0.032** | 53 / 121 |
| Profit factor | **+0.073** | 46 / 128 |
| Trades | **−11.4** | **174 / 0** (never adds one) |
| **CAGR pp** | **−0.753** | 105 / 68 |
| Total return pp | −0.765 | 105 / 68 |
| Sharpe | −0.047 | 108 / 65 |
| Calmar | −0.051 | 103 / 71 |
| Max drawdown pp | +0.333 (worse) | — |
The same trap, mirrored: the floor raises per-trade quality *precisely by deleting
trades*, and the deleted trades were net positive contributors. The frozen
specification in [effective-risk-floor-ab.md](effective-risk-floor-ab.md) would
have passed it on paired EV and shipped a change costing 0.75pp of CAGR.
Genuinely open, low priority: 0.005 clearly over-cuts, but the sizing code's real
floor is a **$1** minimum, which is no floor at all. Whether something near 0.001
strips true dust without cutting real trades is untested, and only worth revisiting
if live broker order minimums force it.
### Start-date sensitivity is real but not a capacity artifact
Within-year spread of EV across monthly start dates is ~0.672 R and is
*identical* for `cap10` (0.672), `cap15` (0.672) and `cash_unbounded` (0.677). It
is small-sample noise — roughly 84 trades per 252-session window drawn from a
fat-tailed R distribution gives an EV standard error near 0.15–0.25 R — not a
queueing artifact. No construction policy reduces it.
### Rule for future studies
Choose the metric from the treatment's mechanism before reading any table. If a
treatment changes trade count, CAGR and total return are the decision metrics and
EV per trade is a diagnostic. The generated report's headline tables lead with
ΔEV net R, which is what made this error easy to make twice.
docs/research/regime-monitor-v3.md
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# Regime Monitor v3 methodology
# Moved
The Regime Monitor is an observational AI/Tech risk thermometer. It does not
gate entries, exits, position size, ranking, or alerts about individual setups.
The methodology doc now lives at [regime-monitor-v4.md](regime-monitor-v4.md).
v3 supersedes v2. Every parameter below was calibrated against the 408 v2
sessions ending 2026-07-24, reproduced offline from the same Alpaca and FRED
inputs the live job uses; the reproduction matched the stored prod distribution
exactly (State avg 22.6/22.7, p80 35.1, max 91.2, P3 pegged 39, W1 live 108).
## What changed and why
**Fundamentals left the score.** F1 (capex) and F3 (good-news-stock-down)
carried 12 + 8 of 100 Warning points. Pegged at maximum stress they produced a
Warning of exactly 20.0 — below the event study's 25.3 alarm threshold, and
still inside the "stable" band. The sourced observation could not change any
published conclusion, so refreshing it looked like it did nothing. They are now
a qualitative overlay reported beside the scores. Capex also stopped scoring
`raising` and `holding` identically at 0: `holding` is the deceleration case and
now scores 50, so a boom no longer reads the same as a stall.
**The drawdown sensor stopped saturating.** v2 used `dd_pct * 5`, reaching 100 at
a 20% drawdown — the 90th percentile of the observed distribution. 39 of 408
sessions sat at exactly 100 with no resolution left, and the price pillar showed
the top band on 13.5% of sessions. v3 uses named anchors with headroom past the
observed 36% maximum, and blends leader/confirm 2:1 as P1 and P2 already did
instead of taking `max()`. P3's realized share of State falls from 65% to 40%,
matching its nominal weight.
**Warning gained a sensor with range.** The HY OAS *level* is pinned at zero
below the 3.5 mild anchor (2.77 at the cutover), so credit contributed nothing
in a calm tape. Its 20-session rate of change still does, and spread widening is
a classic lead.
**The credit percentile leg was removed.** Its reference window silently shrank
from 10 years to 3 when ICE restricted the upstream series in April 2026, after
which it scored 20 points of stress at a spread the same sensor's anchors call
"mild". See Calibration below.
**Breadth loss counts during declines.** v2's divergence gate was
`price_ret >= 0`, so the sensor zeroed during every selloff. On 2026-07-24 the
basket shed 10 points of participation in 20 sessions while SMH fell 11.9% and
Warning printed exactly 0. v3 tapers to a floor instead: deterioration counts
fully when price masks it (true divergence, the dangerous pre-top case) and at
35% when price confirms it. Breadth *level* lives in State, but breadth
*velocity* appears nowhere else, so this is not double counting.
**Bands are per axis.** v2 Warning never exceeded 64.9 in 408 sessions while
State reached 91.2, yet both used 30/60/80 with quadrant dividers at 60. The
upper half of the Warning axis was unreachable.
## Outputs
**State** — current structural stress:
- Price structure, 40%: `max(P1, P2, P3)`, one capped vote for correlated reads.
- Fixed-basket breadth level, 25%.
- HY option-adjusted credit spread level, 20%.
- VIX level, 15%.
**Warning** — deterioration and divergence:
- Fixed-basket breadth divergence, 45%.
- 60-session SMH/SPY relative-strength deterioration, 30%.
- HY OAS 20-session widening, 25%.
Combined, RSP/SPY (former F4), and the NVDA canary (former P6) do not enter v3.
## Calibration
P3 drawdown anchors, as (drawdown %, score): 0→0, 4→10, 8→25, 16→50, 28→78,
40→100, flat outside. Credit impulse is relative (+35% over 20 sessions = 100)
rather than absolute, because +0.5pp means something very different at an OAS of
2.7 than at 8.0.
Bands are round, meaning-anchored numbers, not percentile fits — percentile
thresholds would drift on every rebuild and silently rewrite what past snapshots
meant. Realized shares over the calibration window:
| Axis | stable | watch | elevated | breaking | thresholds |
|------|--------|-------|----------|----------|------------|
| State | 73.3% | 15.0% | 8.3% | 3.4% | 20 / 50 / 80 |
| Warning | 69.4% | 19.6% | 7.6% | 3.4% | 20 / 40 / 60 |
Quadrant dividers sit at each axis's watch/elevated boundary: State 50,
Warning 40.
Scores renormalize over available fixed weights, but a band is published only at
75% or greater coverage. Trend deltas are suppressed when the participating
pillar set changes. Zero means ordinary/healthy; only stress contributes.
Credit level is the named HY OAS anchors alone: 3.5 mild, 5.0 elevated, 7.0
stressed, linear between, and nothing else. v2 blended those anchors at 70% with
a 30% upper-tail percentile over a nominally 10-year window.
That leg was removed rather than repaired. ICE restricted FRED to a rolling
3-year window for `BAMLH0A0HYM2` in April 2026 — the series metadata states it
outright ("Starting in April 2026, this series will only include 3 years of
observations"), and an unbounded request returns the same 795 observations as a
30-year one. The v2 percentile therefore ranked the current spread against three
uniformly tight years (range 2.59–4.61 over the calibration window), which made
it fire early and saturate absurdly: at an OAS of 3.50 — the level the anchors
call *mild*, scoring zero stress — the blended sensor read 20.1, and the
percentile leg pegged at 100 by an OAS of 4.5. Across the 408 sessions it
roughly tripled the credit sensor's average (2.70 vs 1.00) and more than doubled
its nonzero days (60 vs 27).
The anchors already encode the long-run distribution as constants, so the
percentile was a second, noisier estimate of the same thing. What it was
genuinely reaching for — "unusual versus recent history" — is now W3 on the
Warning axis, computed as a rate of change, which is where deterioration
belongs. Removing it moved State's average by −0.4 and its maximum by −3.8, left
Warning bit-identical, and did not shift any band threshold.
A long-history alternative (`BAA10Y`, Fed-published, 7,712 observations back to
1997) was considered and rejected: ranking an HY spread against investment-grade
history is not a coherent statistic, and it would rescue a leg that is redundant
anyway.
Every snapshot now records `data_quality.credit_history_days` and
`vix_history_days`. This defect was invisible for roughly three months because
nothing asserted the window the code claimed; the spans make a future upstream
truncation show up in the record instead of quietly reshaping a sensor.
**Survivorship caveat.** The basket was frozen 2026-07-15 but the calibration
window reaches back to 2024, so names were partly selected for having done well.
Every distribution above inherits that bias. It is the same bias v2 carried, so
the v2/v3 comparison is like-for-like, but the absolute band shares are
optimistic.
## Point-in-time record
The first run under a new `METHODOLOGY` rebuilds the latest 400 trading sessions
with sufficient sensor warm-up; routine runs thereafter insert/update only the
latest trading date. The history API and main chart show only snapshots matching
the current methodology, so a bump reseeds the series rather than splicing two
formulas into one line.
The fundamental overlay keeps its effective date (normally the next session after
collection) and is never replayed backward, so a rebuild cannot stamp today's
observation onto historical snapshots. Because the observation is stored in a
single slot, a refresh replaces the previously effective record: the snapshot
therefore reports the overlay as `pending` until the new effective date, and the
live reading additionally carries `fundamental_context` so a just-collected
observation is visible immediately rather than appearing to have done nothing.
Each snapshot stores the fixed basket symbols, hash, and freeze date.
Reconstructed history before that freeze date is retrospective/exploratory.
## Warning study
The study calls the outcome a **10% correction**, not a regime break. The first
70% of sessions freezes the 80th-percentile warning threshold; alarm episodes are
measured on the final 30%. Because v3 dropped fundamentals from the score, the
study now measures exactly the live Warning score rather than a technical-only
approximation of it, and both are computed from one shared sensor definition
(`warning_sensor_scores`) so they cannot drift apart.
A cached report is discarded when its methodology no longer matches, so the panel
reverts to "not run yet" after a bump rather than showing stale numbers. **Re-run
the Event Study job after cutting over to v3.**
### Reading the result
The report carries a `reliability` block and the UI renders its warnings, because
the headline numbers invite over-reading in two specific ways.
**The holdout is thin.** The study detects 11 corrections across 5 years but the
70/30 split leaves only 4 in the test period. Recall is therefore one event away
from a materially different headline, and in practice the event that flips is
decided by where the frozen threshold happens to land rather than by whether the
score saw anything. The v3 cutover run illustrates it: v3 scored 2/4 against v2's
3/4, but "v3 without the credit sensor" scores 3/4 at a *higher* threshold
(35.5) than shipped v3 misses it at (32.3) — because the alarm rule needs a
rising edge, and a lower threshold can mean the alarm already fired outside the
20-session horizon and never reset below. Below `MIN_EVENTS_FOR_CONFIDENCE`
holdout events the report says so explicitly.
Some events carry no information at all for comparison: in that run every
variant caught 2026-03-06, every variant missed 2026-06-05, and every variant
"caught" 2025-11-20 with a 1-session lead, which is coincident rather than a
warning.
**Sensor coverage can straddle the split.** The score renormalises over available
sensors, so a training window predating a sensor's history freezes the threshold
on a different construct than the holdout is measured against. At the v3 cutover
only 39% of training sessions had all three Warning sensors versus 100% of the
test period, because credit history begins 2023-07-25.
Restricting the threshold to sensor-matched training sessions was tried and is
*not* the fix: those sessions are a calm recent stretch, so the threshold drops
from 32.3 to 22.5 and false alarms rise from 3.3 to 8.6 per year. It trades a
coverage bias for a regime-selection bias. The honest position is that the
threshold is hypersensitive to window choice at this sample size; the report
states its limits rather than pretending to a precision it does not have.
## Operator rule
Quadrant alerts default off for new/reset configurations. When enabled they
require fresh inputs, at least 75% coverage on both axes, two consecutive daily
confirmations, hysteresis, and cooldown. Every alert states: **Risk thermometer —
not a trade signal.**
v3's text is in git history (`git log --follow docs/research/regime-monitor-v4.md`).
This stub exists because commit messages up to 2026-08-08 cite the old path.
docs/research/regime-monitor-v4.md
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# AI/Tech Risk Monitor v4 methodology
Named "Regime Monitor" until 2026-08-07; the filename's `regime` stem, the
`regime_monitor` job id, the `/regime` route and the `METHODOLOGY`/snapshot
fields keep the old word, because those are persisted or externally linked.
The AI/Tech Risk Monitor is an observational risk thermometer. It does not
gate entries, exits, position size, ranking, or alerts about individual setups.
**v4 supersedes v3** (2026-08-08). Unlike v3, whose calibration was ad-hoc and
never landed, every number below is reproducible:
```
.venv/Scripts/python.exe scripts/run_regime_monitor_calibration.py --methodology v2_reconstruction,v2_reconstruction_oas400,v3,v4,v4-vix-only,v4-p1-only --cache-dir .calib-cache
```
`v3` and `v4` are mandatory — the row-wise `state_v4 <= state_v3` invariant is
a hard gate and needs both — and the replayed **start** date is asserted
against the published window. The session *count* alone proves nothing, since
the harness slices the tail of the price series to whatever was asked for.
The harness replays the 408 sessions ending 2026-07-24 from the live inputs
(Alpaca for all 33 symbols, FRED for VIX and HY OAS) with no database, and
reproduces the published v2 and v3 figures before it will emit anything:
| figure | published | replayed |
|---|---|---|
| v2 State avg | 22.6 | 22.68 |
| v2 State p80 | 35.1 | **35.1** |
| v2 State max | 91.2 | **91.2** |
| v2 P3 pegged | 39 | **39** |
| v2 W1 live | 108 | **108** |
| v3 State max | 87.4 | **87.4** |
| v3 band shares | 73.3 / 15.0 / 8.3 / 3.4 | 73.0 / 15.4 / 8.1 / 3.4 |
It refuses to emit a band recommendation, and exits non-zero, unless every hard
gate passes — 33 symbols fetched with full warm-up, the whole basket on every
session, the calendar anchors, 100% coverage on every row, and a row-wise
`state_v4 <= state_v3` invariant. Reading a calibration result out of a run whose
pipeline did not validate is meant to be structurally impossible.
## What changed in v4
**V1 stopped saturating at VIX 30.** `(vix - 15) / 15` reached 100 at VIX 30 —
the same defect v3 had *just* removed from P3, left in place one sensor over. VIX
30 is a bad week, 50 is a crisis and 82 was March 2020, and all three scored
identically. In the calibration window this flattened five distinct April-2025
prints (52.33, 46.98, 45.31, 40.72, 38.57) into a single 100. It pegged on 14 of
408 sessions; under the anchors below, none.
**The trend break is graded by depth, not a yes/no.** `_under_200` returned a
bare 0/100, so P1 printed 100 the moment SMH and QQQ were both under their
average — and because the price pillar takes `max(P1, P2, P3)`, that pinned the
pillar and stopped P3's anchored ladder resolving anything for the whole of a
selloff. It pegged on 46 of 408 sessions; now none. A 2% break reads ~30 where it
used to read 100.
`max()` was **kept**. The defect was the step function feeding it, not the vote
itself, and v3's "one capped vote for correlated reads" rationale still holds.
The `P1_SCORE_CAP` fallback drafted during design was to fire if P1 became the
sole price argmax on **more than 80% of sessions with State ≥ 40** — i.e. if it
had quietly become a second drawdown sensor. Measured on that population: 47
qualifying sessions, P1 sole argmax on **17 of them (36.2%)**, against P2's 16
and P3's 14. Well under the threshold, so the cap is not shipped.
**The top State band moved 80 → 65.** See Calibration; this is the one change
that is about the band rather than a sensor.
**Scope.** All three are State-side. `WARNING_BANDS`, `WARNING_WEIGHTS`,
`QUADRANT_WARNING_DIVIDER` and the event study's frozen threshold are untouched.
`QUADRANT_STATE_DIVIDER` stays 50 because only `breaking` moved.
## What changed in v3
**Fundamentals left the score.** F1 (capex) and F3 (good-news-stock-down)
carried 12 + 8 of 100 Warning points. Pegged at maximum stress they produced a
Warning of exactly 20.0 — below the event study's 25.3 alarm threshold, and
still inside the "stable" band. The sourced observation could not change any
published conclusion, so refreshing it looked like it did nothing. They are now
a qualitative overlay reported beside the scores. Capex also stopped scoring
`raising` and `holding` identically at 0: `holding` is the deceleration case and
now scores 50, so a boom no longer reads the same as a stall.
**The drawdown sensor stopped saturating.** v2 used `dd_pct * 5`, reaching 100 at
a 20% drawdown — the 90th percentile of the observed distribution. 39 of 408
sessions sat at exactly 100 with no resolution left, and the price pillar showed
the top band on 13.5% of sessions. v3 uses named anchors with headroom past the
observed 36% maximum, and blends leader/confirm 2:1 as P1 and P2 already did
instead of taking `max()`. P3's realized share of State falls from 65% to 40%,
matching its nominal weight.
**Warning gained a sensor with range.** The HY OAS *level* is pinned at zero
below the 3.5 mild anchor (2.77 at the cutover), so credit contributed nothing
in a calm tape. Its 20-session rate of change still does, and spread widening is
a classic lead.
**The credit percentile leg was removed.** Its reference window silently shrank
from 10 years to 3 when ICE restricted the upstream series in April 2026, after
which it scored 20 points of stress at a spread the same sensor's anchors call
"mild". See Calibration below.
**Breadth loss counts during declines.** v2's divergence gate was
`price_ret >= 0`, so the sensor zeroed during every selloff. On 2026-07-24 the
basket shed 10 points of participation in 20 sessions while SMH fell 11.9% and
Warning printed exactly 0. v3 tapers to a floor instead: deterioration counts
fully when price masks it (true divergence, the dangerous pre-top case) and at
35% when price confirms it. Breadth *level* lives in State, but breadth
*velocity* appears nowhere else, so this is not double counting.
**Bands are per axis.** v2 Warning never exceeded 64.9 in 408 sessions while
State reached 91.2, yet both used 30/60/80 with quadrant dividers at 60. The
upper half of the Warning axis was unreachable.
## Outputs
**State** — current structural stress:
- Price structure, 40%: `max(P1, P2, P3)`, one capped vote for correlated reads.
- Fixed-basket breadth level, 25%.
- HY option-adjusted credit spread level, 20%.
- VIX level, 15%.
**Warning** — deterioration and divergence:
- Fixed-basket breadth divergence, 45%.
- 60-session SMH/SPY relative-strength deterioration, 30%.
- HY OAS 20-session widening, 25%.
Combined, RSP/SPY (former F4), and the NVDA canary (former P6) do not enter v3 or v4.
## Calibration
### Interpolated sensor tables
All three are `(x, stress score)` pairs read by `_interpolate`, flat outside the
first and last anchor.
| sensor | anchors |
|---|---|
| P3 drawdown (% below the 52w high) | 0→0, 4→10, 8→25, 16→50, 28→78, 40→100 |
| **P1 trend break** (% below the 200-DMA) | 0→**20**, 3→35, 8→55, 15→75, 25→100 |
| **V1 volatility** (VIX level) | 15→0, 20→20, 25→38, 30→55, 40→80, 55→100 |
P1's floor of 20 at the crossing is deliberate: the break itself is a genuine
binary event and deserves a floor; only the depth past it is graded. P1 is
calibrated to sit alongside P3 rather than swamp it — the 200-DMA lags, so a 20%
drawdown typically coincides with ~10% below the average, where P1 reads ~61
against P3's ~59.
V1 reaches full scale at 55 rather than at 2020's ~82: anchoring the top at a
once-in-a-generation print would make VIX 50 — a genuine crisis — read only ~70.
The anchors encode the long-run distribution as constants, the same argument the
credit level uses. Unlike P1 and V1, whose slopes ease off monotonically, P3's do
not (2.5, 3.75, 3.125, 2.33, 1.83) — its gentle onset is intentional and the
monotone-slope test excludes it.
Credit impulse is relative (+35% over 20 sessions = 100) rather than absolute,
because +0.5pp means something very different at an OAS of 2.7 than at 8.0.
### Bands
Round, meaning-anchored numbers, **not** percentile fits — those would drift on
every rebuild and silently rewrite what past snapshots meant.
**Why `breaking` moved 80 → 65.** With credit calm, `f2_credit_spreads` returns
`0.0` (not `None`), so it keeps its full 20 points pinned at zero. Price, breadth
and volatility at *literal maximum* therefore sum to:
(100×40 + 100×25 + 0×20 + 100×15) / 100 = 80.0 exactly
`band_for` uses `>=`, so v3's top band was reachable only by touching its floor
to the decimal, with nothing above it. The band was fit on v2, when credit's
since-removed percentile leg still contributed regularly; the sensor is not
wrong — a calm-credit selloff genuinely *is* less stressed than one with credit
contagion — the threshold was stale.
Chosen by scenario arithmetic on unchanged weights (`_scenarios` in the harness
computes these, so they are machine-checked, not prose):
| scenario | price | breadth | C1 | V1 | State |
|---|---|---|---|---|---|
| Ordinary tape (3% dd, breadth 65%, VIX 16, OAS 2.8) | 7.5 | 0 | 0 | 4.0 | **3.6** |
| 10% correction, calm credit (2% below, breadth 35%, VIX 24) | 31.2 | 62.5 | 0 | 34.4 | **33.3** |
| **2022-style drawdown, calm credit, no death cross** | 90.8 | 100 | 0 | 60.0 | **70.3** |
| **same, with death cross** (P2 pegged) | 100 | 100 | 0 | 60.0 | **74.0** |
| Credit event on top (OAS 6.0, VIX 45) | 100 | 100 | 75.0 | 86.7 | **93.0** |
| March 2020 (everything pegged) | 100 | 100 | 100 | 100 | **100** |
Rows 3 and 4 are the case this monitor exists to measure, and they must print
`breaking`. At 80 they do not. **65** clears them under either P2 assumption,
which matters because P2 is set by the 50/200-DMA gap and no drawdown figure
implies it; 70 would have left 0.33 points of headroom in row 3, reproducing the
defect being fixed.
Realized shares, **reported not fitted**, over the 408 sessions to 2026-07-24:
| Axis | stable | watch | elevated | breaking | thresholds |
|------|--------|-------|----------|----------|------------|
| State (v4) | 78.9% | 13.0% | 4.7% | **3.4%** | 20 / 50 / **65** |
| Warning | 69.4% | 19.6% | 7.6% | 3.4% | 20 / 40 / 60 |
The v4 `breaking` share lands on 3.4% — the same as v3's — having been chosen by
scenario reasoning rather than aimed at that number. Sensitivity: 60 gives 5.1%,
70 gives 1.2%.
Quadrant dividers sit at each axis's watch/elevated boundary: State 50,
Warning 40. Only `breaking` moved in v4, so the dividers and every alert
threshold are unchanged. `test_quadrant_dividers_match_the_band_boundaries` now
enforces that relationship, which nothing did before.
Scores renormalize over available fixed weights, but a band is published only at
75% or greater coverage. Trend deltas are suppressed when the participating
pillar set changes. Zero means ordinary/healthy; only stress contributes.
Credit level is the named HY OAS anchors alone: 3.5 mild, 5.0 elevated, 7.0
stressed, linear between, and nothing else. v2 blended those anchors at 70% with
a 30% upper-tail percentile over a nominally 10-year window.
That leg was removed rather than repaired. ICE restricted FRED to a rolling
3-year window for `BAMLH0A0HYM2` in April 2026 — the series metadata states it
outright ("Starting in April 2026, this series will only include 3 years of
observations"), and an unbounded request returns the same 795 observations as a
30-year one. The v2 percentile therefore ranked the current spread against three
uniformly tight years (range 2.59–4.61 over the calibration window), which made
it fire early and saturate absurdly: at an OAS of 3.50 — the level the anchors
call *mild*, scoring zero stress — the blended sensor read 20.1, and the
percentile leg pegged at 100 by an OAS of 4.5. Across the 408 sessions it
roughly tripled the credit sensor's average (2.70 vs 1.00) and more than doubled
its nonzero days (60 vs 27).
The anchors already encode the long-run distribution as constants, so the
percentile was a second, noisier estimate of the same thing. What it was
genuinely reaching for — "unusual versus recent history" — is now W3 on the
Warning axis, computed as a rate of change, which is where deterioration
belongs. Removing it moved State's average by −0.4 and its maximum by −3.8, left
Warning bit-identical, and did not shift any band threshold.
A long-history alternative (`BAA10Y`, Fed-published, 7,712 observations back to
1997) was considered and rejected: ranking an HY spread against investment-grade
history is not a coherent statistic, and it would rescue a leg that is redundant
anyway.
Every snapshot now records `data_quality.credit_history_days` and
`vix_history_days`. This defect was invisible for roughly three months because
nothing asserted the window the code claimed; the spans make a future upstream
truncation show up in the record instead of quietly reshaping a sensor.
**Survivorship caveat.** The basket was frozen 2026-07-15 but the calibration
window reaches back to 2024, so names were partly selected for having done well.
Every distribution above inherits that bias. It is the same bias v2 carried, so
the v2/v3 comparison is like-for-like, but the absolute band shares are
optimistic.
**Which OAS window the published v2 figures used.** v2 requested 13 years of HY
OAS and sliced `HY_OAS_REFERENCE_YEARS = 10.0` per session; ICE serves only ~3
years (778 observations from 2023-08-08), so the effective window was that. But
production v2 also fetched only 400 *calendar* days at one point — the bug fixed
2026-08-07 — and whether the published numbers predate that was not recoverable
from the text. Settled by replay rather than assumed: the
`v2_reconstruction_oas400` variant truncates the OAS **source series** to 400
days (patching the per-session window cannot simulate data that was simply
absent) and yields avg 26.54, p80 42.52, max **100.00**, against published
22.6 / 35.1 / 91.2. Full coverage reproduces all three. So the published figures
correspond to the untruncated fetch.
**The top VIX anchors are exercised, not just asserted.** The window contains a
52.33 close (2025-04-08), so the 40 → 80 → 55 → 100 segment is fed by real data
rather than justified from long-run history alone.
## Point-in-time record
The first run under a new `METHODOLOGY` rebuilds every session inside
`REBUILD_LOOKBACK_DAYS` — 672 calendar days, roughly 464 trading sessions;
routine runs thereafter insert/update only the latest trading date. The bound is
in calendar days rather than a session count because the binding constraint is
the OAS fetch: each replayed row needs W3's lookback inside
`HY_OAS_WINDOW_DAYS`, so replaying further back would recreate the credit gap a
reseed exists to close. The history API and main chart show only snapshots matching
the current methodology, so a bump reseeds the series rather than splicing two
formulas into one line.
The fundamental overlay keeps its effective date (normally the next session after
collection) and is never replayed backward, so a rebuild cannot stamp today's
observation onto historical snapshots. Because the observation is stored in a
single slot, a refresh replaces the previously effective record: the snapshot
therefore reports the overlay as `pending` until the new effective date.
Two functions, deliberately: `fundamental_overlay` is the **record** and keeps
the gate — it runs for every replayed date during a rebuild, so it must never
grow a bypass flag. `current_observation` is the **live reading** behind
`fundamental_context`, and *reports* the effective date instead of blanking the
content.
Until 2026-08-07 the live reading called the gated function, so a just-collected
observation stayed hidden until the next weekday — three days over a weekend —
and refreshing appeared to do nothing. That was the opposite of what this section
already claimed. Showing it early cannot leak into a published number, because
nothing in the overlay is scored (see "Fundamentals left the score").
`current_observation` gates on `observed` (a non-null `fetched_at`, the one field
every path writing real content stamps). Without it, the default override —
`unknown` for every hyperscaler and `mixed` for the reaction — was reported as a
live observation with `available: true`, so the card presented placeholders as a
collected reading. Those are the absence of an observation, not an observation of
absence. `fundamental_overlay` never had this problem: no observation means no
effective date, which means `pending`, which already blanks the content.
Each snapshot stores the fixed basket symbols, hash, and freeze date.
Reconstructed history before that freeze date is retrospective/exploratory.
## Presentation
The page is deliberately thin: two gauges, one chart card, one pillar table, the
overlay, and a provenance strip. Time and Path are two projections of the same
snapshot series and share one card and one query key — they were previously two
panels, which read as two datasets. Methodology rationale lives in this document,
not on the page; page text is limited to what changes how the reader interprets
today's number. The quadrant dividers rendered in Path view come from
`quadrant_config` and are the same constants the alert path consumes
(`alert_service`), so the chart cannot drift from what actually fires.
## Warning study
The study calls the outcome a **10% correction**, not a regime break. The first
70% of sessions freezes the 80th-percentile warning threshold; alarm episodes are
measured on the final 30%. Because v3 dropped fundamentals from the score, the
study now measures exactly the live Warning score rather than a technical-only
approximation of it, and both are computed from one shared sensor definition
(`warning_sensor_scores`) so they cannot drift apart.
A cached report is discarded when its methodology no longer matches, so the panel
reverts to "not run yet" after a bump rather than showing stale numbers. **Re-run
the Event Study job after cutting over to v4.**
### Reading the result
The report carries a `reliability` block and the UI renders its warnings, because
the headline numbers invite over-reading in two specific ways.
**The holdout is thin.** The study detects 11 corrections across 5 years but the
70/30 split leaves only 4 in the test period. Recall is therefore one event away
from a materially different headline, and in practice the event that flips is
decided by where the frozen threshold happens to land rather than by whether the
score saw anything. The v3 cutover run illustrates it: v3 scored 2/4 against v2's
3/4, but "v3 without the credit sensor" scores 3/4 at a *higher* threshold
(35.5) than shipped v3 misses it at (32.3) — because the alarm rule needs a
rising edge, and a lower threshold can mean the alarm already fired outside the
20-session horizon and never reset below. Below `MIN_EVENTS_FOR_CONFIDENCE`
holdout events the report says so explicitly.
Some events carry no information at all for comparison: in that run every
variant caught 2026-03-06, every variant missed 2026-06-05, and every variant
"caught" 2025-11-20 with a 1-session lead, which is coincident rather than a
warning.
**Sensor coverage can straddle the split.** The score renormalises over available
sensors, so a training window predating a sensor's history freezes the threshold
on a different construct than the holdout is measured against. At the v3 cutover
only 39% of training sessions had all three Warning sensors versus 100% of the
test period, because credit history begins 2023-07-25.
Restricting the threshold to sensor-matched training sessions was tried and is
*not* the fix: those sessions are a calm recent stretch, so the threshold drops
from 32.3 to 22.5 and false alarms rise from 3.3 to 8.6 per year. It trades a
coverage bias for a regime-selection bias. The honest position is that the
threshold is hypersensitive to window choice at this sample size; the report
states its limits rather than pretending to a precision it does not have.
## Resolved in v4 (raised 2026-08-07, shipped 2026-08-08)
The three questions this section used to hold are now answered. Kept here
because the reasoning that resolved them is not obvious from the code.
**1. `breaking` had zero headroom — resolved by moving the band, not the sensor.**
`f2_credit_spreads` returns `0.0`, not `None`, below the 3.5 mild anchor, so
credit stays *available* at weight 20 and is pinned at zero on roughly 93% of
sessions rather than being renormalized out. Price + breadth + volatility at
literal maximum therefore summed to exactly 80.0 — v3's threshold, to the
decimal.
The sensor is **deliberately unchanged**. A calm-credit selloff genuinely is less
stressed than one with credit contagion, so scoring it lower is correct; what was
stale was `STATE_BANDS`, fit on v2 while credit's since-removed percentile leg
still contributed. Making credit `None` when calm was considered and rejected: it
would leave State on 80% coverage, which still publishes, but consumes the whole
buffer — any *second* missing pillar would then suppress the band, and the 7d/30d
trend deltas would null out every time OAS crossed 3.5, because `_delta`
suppresses on a change of participating pillars. See Calibration for the
scenario arithmetic behind 65.
**2. V1 saturated at VIX 30 — resolved with an anchor table.** See "What changed
in v4".
**3. `max(P1, P2, P3)` defeated P3's anchoring — resolved by grading `_under_200`,
keeping `max()`.** The `max` was deliberate ("one capped vote for correlated
reads") and survives; the binary step feeding it was the defect.
**Its limit, stated precisely.** `_death_cross` is `clamp(-gap_pct * 20)`, so P2
pegs at a −5% 50/200-DMA gap — routine in a real downtrend. In a *deep* selloff
the price pillar therefore still reaches 100 via P2 even with P1 graded. What v4
repairs is the shallow-to-moderate break, which is where resolution was most
obviously missing: a 10% correction 2% below the average now scores 31 where v3
scored 100. It would be wrong to claim "the price pillar no longer pegs".
P2 did not peg once in the 408-session calibration window, so this is a property
of the sensor rather than an observed problem. Grading P2 the same way is the
natural next item if it starts binding; the replay reports a P2-pegged census
alongside P3 and V1 so the evidence accumulates.
## Fixed 2026-08-07: the OAS fetch window did not cover a rebuild
`HY_OAS_WINDOW_DAYS` was 400 **calendar** days, but a rebuild replays
`leader_series[-REBUILD_SESSIONS:]` — 400 **trading** sessions, about 579
calendar days. The oldest ~180 calendar days of any rebuild therefore got no OAS
data at all, so `f2_credit_spreads` and `w3_credit_impulse` both returned `None`.
Verified: State then lands at 80% coverage and Warning at exactly 75.0% —
`MIN_COVERAGE` — so **both still publish bands**. The rebuilt series would look
homogeneous while its oldest rows had been scored without credit, the tell being
a null `data_quality.credit_history_days` on exactly those rows.
The window is now 700 days: it must cover the oldest replayed date (~579) plus
W3's lookback and slack, while staying under ICE's ~3-year cap so FRED still
honours the request. This required **no methodology bump** — C1 reads
`oas_values[-1]` and W3 reads `oas_values[-21]`, both indexed from the end, so
widening only prepends older observations and every live score is bit-identical.
Confirmed by evaluating both windows against a varying synthetic series: today's
C1/W3 match exactly, while the oldest rebuild row goes from `None`/`None` to real
values.
Expect `credit_history_days` on new snapshots to rise from ~400 to ~700. That is
the widened request, not new upstream history — and it makes the chip a better
truncation canary, since a 700-day request returning ~1095 days' worth is now
the visible ceiling.
**Widening the window alone does not repair stored history.** Routine runs
recompute only the latest trading date, and `rebuilding` was keyed on "no v3
snapshot exists at all" — which is false once the cutover has run — so every row
already written would have kept its credit gap indefinitely. `SENSOR_REVISION`
fixes that: it is stamped into each snapshot, snapshots predating it read as 1,
and a stored revision below the current one triggers exactly one reseed.
It is deliberately not `METHODOLOGY`. That constant partitions the history API
and discards the cached event study; neither is warranted here, because the study
recomputes its Warning series from source (`_warning_series` calls
`warning_sensor_scores` against freshly fetched prices and OAS) rather than
reading snapshots, so a reseed cannot stale it.
The reseed is bounded by `REBUILD_LOOKBACK_DAYS` in calendar days rather than a
session count, because the binding constraint is the OAS fetch: each replayed row
needs W3's 20-business-day lookback inside `HY_OAS_WINDOW_DAYS`. At 672 days the
replay reaches ~464 sessions, W3's oldest requirement lands exactly on the first
fetched OAS day, and the ~400-session series the v3 cutover wrote is fully
covered. A test asserts that relationship so the two constants cannot drift into
recreating the gap.
The fix was sequenced deliberately: acting on items 1–3 above bumped
`METHODOLOGY`, which fires `rebuilding`, which would have baked the credit-less
rows into the fresh series. Fixing the window first meant the v4 reseed replayed
a clean window; doing it the other way round would have meant reseeding twice.
## Operator rule
Quadrant alerts default off for new/reset configurations. When enabled they
require fresh inputs, at least 75% coverage on both axes, two consecutive daily
confirmations, hysteresis, and cooldown. Every alert states: **Risk thermometer —
not a trade signal.**
frontend/harness.html
-18
View File
@@ -1,18 +0,0 @@
<!doctype html>
<html lang="en" class="dark">
<head>
<meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>FundamentalsPanel harness</title>
<link rel="preconnect" href="https://fonts.googleapis.com" />
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin />
<link
href="https://fonts.googleapis.com/css2?family=Space+Grotesk:wght@400;500;600;700&family=Instrument+Sans:wght@400;500;600;700&family=IBM+Plex+Mono:wght@400;500;600&display=swap"
rel="stylesheet"
/>
</head>
<body class="bg-[#0a0b11] text-gray-100 font-sans">
<div id="root"></div>
<script type="module" src="/src/dev/harness.tsx"></script>
</body>
</html>
frontend/src/api/admin.ts
+23 -67
View File
@@ -4,7 +4,6 @@ import type {
AdminUser,
AlertConfig,
AlertTestResult,
FundamentalsCutoverConfig,
PipelineReadiness,
RecommendationConfig,
ScheduleConfig,
@@ -57,18 +56,6 @@ export function updateSetting(key: string, value: string) {
.then((r) => r.data);
}
export function getFundamentalsCutoverSettings() {
return apiClient
.get<FundamentalsCutoverConfig>('admin/settings/fundamentals-cutover')
.then((r) => r.data);
}
export function updateFundamentalsCutoverSettings(enabled: boolean) {
return apiClient
.put<FundamentalsCutoverConfig>('admin/settings/fundamentals-cutover', { enabled })
.then((r) => r.data);
}
export function getRecommendationSettings() {
return apiClient
.get<RecommendationConfig>('admin/settings/recommendations')
@@ -220,14 +207,28 @@ export function backfillTickerNames() {
}
// Jobs
export type JobCategory = 'pipeline' | 'pipeline_step' | 'scheduled' | 'manual';
export type NextRunSource = 'own_schedule' | 'via_pipeline' | 'manual_only';
export interface JobStatus {
name: string;
label: string;
enabled: boolean;
next_run_at: string | null;
via_pipeline?: boolean;
registered: boolean;
category?: JobCategory;
/** Server-assigned ordering; the payload already arrives grouped by it. */
sort_order?: [number, number];
/** Parent pipelines for a step. Many-to-many: data_collector runs in all four. */
pipelines?: string[];
/** Step names, for a pipeline row. */
steps?: string[];
next_run_at: string | null;
next_run_source?: NextRunSource;
/** For a step: the soonest enabled parent's next run, and which parent. */
via_next_run_at?: string | null;
via_next_run_job?: string | null;
running?: boolean;
/** runtime_* is live, in-memory state only — it resets when the app restarts. */
runtime_status?: string | null;
runtime_processed?: number | null;
runtime_total?: number | null;
@@ -236,6 +237,13 @@ export interface JobStatus {
runtime_started_at?: string | null;
runtime_finished_at?: string | null;
runtime_message?: string | null;
/** last_run_* is persisted and survives restarts. Kept separate from
* runtime_* so a stale error cannot pin the status chip or the banner. */
last_run_at?: string | null;
last_run_status?: string | null;
last_run_message?: string | null;
last_run_processed?: number | null;
last_run_total?: number | null;
}
export interface TriggerJobResponse {
@@ -246,40 +254,6 @@ export interface TriggerJobResponse {
cadence?: BacktestCadence;
}
export interface ParityFieldStats {
legacy_available: number;
candidate_available: number;
both_available: number;
material_differences: number;
median_absolute_delta: number | null;
p95_absolute_delta: number | null;
max_absolute_delta: number | null;
}
export interface FundamentalsParityReport {
report_version: number;
generated_at: string;
as_of_date: string;
approval_status: string;
read_only: boolean;
summary: {
universe_count: number;
legacy_fundamental_score_available: number;
candidate_fundamental_score_available: number;
fundamental_scores_compared: number;
fundamental_score_material_changes: number;
fundamental_rank_changes: number;
field_stats: Record<string, ParityFieldStats>;
};
source_runs: Record<string, {
run_id: number;
status: string;
revision: string | null;
source_max_date: string | null;
completed_at: string | null;
} | null>;
}
export type BacktestTargetModel = 'production_gtl' | 'structural_sr';
export type BacktestCadence = 'weekly' | 'daily';
@@ -306,24 +280,6 @@ export function triggerJob(
.then((r) => r.data);
}
export function getFundamentalsParityReport() {
return apiClient
.get<FundamentalsParityReport | null>('admin/fundamentals-parity')
.then((r) => r.data);
}
export function getFundamentalsParityCsv() {
return apiClient
.get<{ filename: string; content: string } | null>('admin/fundamentals-parity/csv')
.then((r) => r.data);
}
export function getFundamentalsParityJson() {
return apiClient
.get<{ filename: string; content: string } | null>('admin/fundamentals-parity/json')
.then((r) => r.data);
}
// System events (operational warnings / errors)
export interface SystemEvent {
id: number;
frontend/src/api/client.ts
+1 -1
View File
@@ -6,7 +6,7 @@ import { useAuthStore } from '../stores/authStore';
* Typed error class for API errors, providing structured error handling
* across the application.
*/
export class ApiError extends Error {
class ApiError extends Error {
constructor(message: string) {
super(message);
this.name = 'ApiError';
frontend/src/api/ingestion.ts
+1 -1
View File
@@ -14,7 +14,7 @@ export interface FetchDataResult {
}
/** Provider sources that cost an API call/quota. */
export type FetchSource = 'ohlcv' | 'sentiment' | 'fundamentals';
export type FetchSource = 'ohlcv' | 'sentiment';
/** Source selector: omit → fetch all; array → those providers; 'recompute' → derived only (free). */
export type FetchSelector = FetchSource[] | 'recompute';
frontend/src/api/paperTrades.ts
-4
View File
@@ -34,10 +34,6 @@ export interface EquityPoint {
benchmark_pnl: number;
}
export function getEquityCurve() {
return apiClient.get<EquityPoint[]>('paper-trades/equity-curve').then((r) => r.data);
}
export interface PerfPoint {
date: string;
manual_pnl: number;
frontend/src/components/admin/AlertSettings.tsx
+1 -1
View File
@@ -21,7 +21,7 @@ const TRIGGERS: { key: TriggerKey; label: string; hint: string }[] = [
{ key: 'sr_proximity_enabled', label: 'Watchlist S/R proximity', hint: 'a watched ticker nears a strong support/resistance' },
{ key: 'score_drop_enabled', label: 'Score deterioration', hint: 'a watched ticker’s composite drops sharply' },
{ key: 'digest_enabled', label: 'Daily digest', hint: 'end-of-day summary incl. open trades + trailing stops' },
{ key: 'regime_quadrant_enabled', label: 'Regime quadrant change', hint: 'the regime monitor shifts quadrant (hysteresis + cooldown)' },
{ key: 'regime_quadrant_enabled', label: 'Risk quadrant change', hint: 'the AI/Tech risk monitor shifts quadrant (hysteresis + cooldown)' },
{ key: 'trade_closed_enabled', label: 'Trade closed', hint: 'a paper trade auto-closes (trailing/target/stop) — incl. losses' },
];
frontend/src/components/admin/FundamentalsCutoverSettings.tsx
-176
View File
@@ -1,176 +0,0 @@
import {
useFundamentalsCutoverSettings,
useJobs,
useTriggerJob,
useUpdateFundamentalsCutoverSettings,
} from '../../hooks/useAdmin';
import { SkeletonCard } from '../ui/Skeleton';
const SEC_JOB = 'sec_fundamentals_import';
function formatRun(iso: string | null | undefined): string {
if (!iso) return 'not run in this process';
const minutes = Math.floor((Date.now() - new Date(iso).getTime()) / 60_000);
if (minutes < 1) return 'just now';
if (minutes < 60) return `${minutes}m ago`;
const hours = Math.floor(minutes / 60);
return hours < 24 ? `${hours}h ago` : `${Math.floor(hours / 24)}d ago`;
}
export function FundamentalsCutoverSettings() {
const cutover = useFundamentalsCutoverSettings();
const update = useUpdateFundamentalsCutoverSettings();
const trigger = useTriggerJob();
const { data: jobs } = useJobs();
if (cutover.isLoading) return <SkeletonCard />;
if (cutover.isError || !cutover.data) {
return (
<p className="text-sm text-red-400">
{(cutover.error as Error)?.message || 'Failed to load fundamentals data source'}
</p>
);
}
const enabled = cutover.data.enabled;
const secJob = jobs?.find((job) => job.name === SEC_JOB);
const runningJob = jobs?.find((job) => job.running);
const refreshBlocked = Boolean(runningJob && runningJob.name !== SEC_JOB);
const changeSource = () => {
const next = !enabled;
const confirmed = window.confirm(
next
? 'Activate SEC + Dolt fundamentals? The next SEC import will replace the legacy cache and mark affected scores stale.'
: 'Pause SEC + Dolt cache refreshes? Existing cache values will stay in place; legacy values are not restored automatically.',
);
if (confirmed) update.mutate(next);
};
return (
<section className="glass overflow-hidden" aria-labelledby="fundamentals-source-title">
<div className={`h-0.5 ${enabled ? 'bg-gradient-to-r from-sky-500 via-cyan-300 to-emerald-400' : 'bg-white/[0.06]'}`} />
<div className="space-y-5 p-5">
<div className="flex flex-wrap items-start justify-between gap-3">
<div>
<div className="flex items-center gap-2">
<h3 id="fundamentals-source-title" className="text-sm font-semibold text-gray-200">
Fundamentals data source
</h3>
<span
className={`rounded-full border px-2 py-0.5 text-[10px] font-semibold uppercase tracking-[0.14em] ${
enabled
? 'border-cyan-400/25 bg-cyan-400/10 text-cyan-300'
: 'border-white/10 bg-white/[0.04] text-gray-500'
}`}
>
{enabled ? 'SEC + Dolt active' : 'Legacy cache'}
</span>
</div>
<p className="mt-1 max-w-3xl text-xs leading-relaxed text-gray-500">
Controls what repopulates <span className="num text-gray-400">fundamental_data</span>, the
compatibility cache used by scoring. SEC filings supply P/E, growth and estimated market
cap; Dolt supplies earnings dates and surprises. Everything is derived locally from PostgreSQL.
</p>
</div>
</div>
<div className="grid grid-cols-[minmax(0,1fr)_5rem_minmax(0,1fr)] items-center gap-3 rounded-xl border border-white/[0.06] bg-black/10 px-4 py-3">
<div className={enabled ? 'text-gray-600' : 'text-amber-200/90'}>
<div className="num text-[10px] uppercase tracking-[0.16em]">Legacy APIs</div>
<div className="mt-0.5 text-[11px]">FMP / Finnhub / Alpha Vantage</div>
</div>
<div className="relative h-px bg-white/10" aria-hidden="true">
<span
className={`absolute top-1/2 h-2.5 w-2.5 -translate-y-1/2 rounded-full border-2 border-[#0e120f] transition-all duration-300 ${
enabled
? 'right-0 bg-cyan-300 shadow-[0_0_12px_rgba(103,232,249,0.55)]'
: 'left-0 bg-amber-300'
}`}
/>
</div>
<div className={`text-right ${enabled ? 'text-cyan-200' : 'text-gray-600'}`}>
<div className="num text-[10px] uppercase tracking-[0.16em]">SEC + Dolt</div>
<div className="mt-0.5 text-[11px]">Bulk imports → PostgreSQL cache</div>
</div>
</div>
<div className="grid gap-4 border-t border-white/[0.06] pt-4 md:grid-cols-2">
<div className="flex items-start justify-between gap-4 rounded-xl bg-white/[0.025] p-3.5">
<div>
<div className="num text-[10px] uppercase tracking-[0.14em] text-gray-600">1 · Source</div>
<div className="mt-1 text-sm text-gray-200">Use SEC + Dolt for scoring inputs</div>
<p className="mt-1 text-[11px] leading-relaxed text-gray-500">
While active, the weekly legacy collector is skipped so it cannot overwrite the new cache.
</p>
</div>
<button
type="button"
role="switch"
aria-checked={enabled}
aria-label="Use SEC and Dolt fundamentals"
onClick={changeSource}
disabled={update.isPending}
className={`relative mt-1 inline-flex h-6 w-11 shrink-0 rounded-full border-2 border-transparent transition-colors focus:outline-none focus:ring-2 focus:ring-cyan-400/70 focus:ring-offset-2 focus:ring-offset-[#0e120f] disabled:cursor-wait disabled:opacity-50 ${
enabled ? 'bg-gradient-to-r from-sky-500 to-cyan-400' : 'bg-white/10'
}`}
>
<span
className={`pointer-events-none inline-block h-5 w-5 rounded-full bg-white shadow transition-transform ${
enabled ? 'translate-x-5' : 'translate-x-0'
}`}
/>
</button>
</div>
<div className="rounded-xl bg-white/[0.025] p-3.5">
<div className="num text-[10px] uppercase tracking-[0.14em] text-gray-600">2 · Refresh</div>
<div className="mt-1 flex flex-wrap items-center justify-between gap-3">
<div>
<div className="text-sm text-gray-200">Apply the source now</div>
<p className="mt-1 text-[11px] text-gray-500">
{secJob?.running
? 'SEC import and cache refresh are running.'
: secJob?.runtime_message || `Last SEC run: ${formatRun(secJob?.runtime_finished_at)}`}
</p>
</div>
<button
type="button"
onClick={() => trigger.mutate(SEC_JOB)}
disabled={
!enabled ||
trigger.isPending ||
Boolean(secJob?.running) ||
refreshBlocked ||
secJob?.enabled === false
}
className="btn-primary px-3 py-2 text-xs disabled:cursor-not-allowed disabled:opacity-40"
>
<span>
{secJob?.running
? 'Refreshing…'
: trigger.isPending
? 'Starting…'
: refreshBlocked
? 'Another job is running'
: 'Run refresh now'}
</span>
</button>
</div>
{!enabled && (
<p className="mt-2 text-[11px] text-amber-300/70">Activate the source before running the refresh.</p>
)}
{enabled && secJob?.enabled === false && (
<p className="mt-2 text-[11px] text-amber-300/70">Enable the SEC Fundamentals job on the Jobs tab first.</p>
)}
</div>
</div>
<p className="text-[11px] leading-relaxed text-gray-600">
Rollback pauses future writes only. To restore pre-cutover values, use the database backup or
pause this source and manually run the legacy collector while its provider keys remain installed.
</p>
</div>
</section>
);
}
frontend/src/components/admin/FundamentalsParityPanel.tsx
-156
View File
@@ -1,156 +0,0 @@
import { useState } from 'react';
import {
getFundamentalsParityCsv,
getFundamentalsParityJson,
} from '../../api/admin';
import { useFundamentalsParityReport } from '../../hooks/useAdmin';
import { SkeletonTable } from '../ui/Skeleton';
const FIELD_LABELS: Record<string, string> = {
pe_ratio: 'P/E',
revenue_growth: 'Revenue growth',
earnings_surprise: 'Earnings surprise',
};
function downloadText(filename: string, content: string, type: string) {
const blob = new Blob([content], { type });
const url = URL.createObjectURL(blob);
const anchor = document.createElement('a');
anchor.href = url;
anchor.download = filename;
anchor.click();
URL.revokeObjectURL(url);
}
export function FundamentalsParityPanel() {
const { data: report, isLoading, isError, error } = useFundamentalsParityReport();
const [downloading, setDownloading] = useState(false);
if (isLoading) return <SkeletonTable rows={2} cols={4} />;
if (isError) {
return <p className="text-sm text-red-400">{(error as Error).message}</p>;
}
if (!report) {
return (
<div className="glass p-5">
<h3 className="text-sm font-semibold text-gray-200">A5 Fundamentals Parity</h3>
<p className="mt-1 text-xs text-gray-500">
No report yet. Trigger “Fundamentals Parity Report (read-only)” below.
</p>
</div>
);
}
const summary = report.summary;
const generated = new Date(report.generated_at).toLocaleString();
async function downloadCsv() {
setDownloading(true);
try {
const artifact = await getFundamentalsParityCsv();
if (artifact) downloadText(artifact.filename, artifact.content, 'text/csv;charset=utf-8');
} finally {
setDownloading(false);
}
}
async function downloadJson() {
setDownloading(true);
try {
const artifact = await getFundamentalsParityJson();
if (artifact) downloadText(artifact.filename, artifact.content, 'application/json;charset=utf-8');
} finally {
setDownloading(false);
}
}
return (
<div className="glass p-5 space-y-4">
<div className="flex flex-wrap items-start justify-between gap-3">
<div>
<div className="flex flex-wrap items-center gap-2">
<h3 className="text-sm font-semibold text-gray-200">A5 Fundamentals Parity</h3>
<span className="rounded-full border border-amber-400/20 bg-amber-400/10 px-2 py-0.5 text-[10px] uppercase tracking-wide text-amber-300">
approval pending
</span>
<span className="rounded-full border border-cyan-400/20 bg-cyan-400/10 px-2 py-0.5 text-[10px] uppercase tracking-wide text-cyan-300">
read-only
</span>
</div>
<p className="mt-1 text-xs text-gray-500">
Generated {generated} · as of {report.as_of_date} · {summary.universe_count} tracked tickers
</p>
</div>
<div className="flex gap-2">
<button
type="button"
className="rounded border border-white/10 px-3 py-1.5 text-xs text-gray-300 hover:text-white"
onClick={downloadJson}
disabled={downloading}
>
Download JSON
</button>
<button
type="button"
className="rounded border border-white/10 px-3 py-1.5 text-xs text-gray-300 hover:text-white disabled:opacity-50"
onClick={downloadCsv}
disabled={downloading}
>
{downloading ? 'Preparing…' : 'Download CSV'}
</button>
</div>
</div>
<div className="grid gap-3 sm:grid-cols-2 lg:grid-cols-4">
<Summary label="Candidate score coverage" value={`${summary.candidate_fundamental_score_available}/${summary.universe_count}`} />
<Summary label="Scores compared" value={summary.fundamental_scores_compared} />
<Summary label="Material score moves" value={summary.fundamental_score_material_changes} />
<Summary label="Fundamental rank moves" value={summary.fundamental_rank_changes} />
</div>
<div className="overflow-x-auto">
<table className="w-full text-left text-xs">
<thead className="text-[10px] uppercase tracking-wider text-gray-500">
<tr>
<th className="pb-2 pr-4 font-medium">Field</th>
<th className="pb-2 px-3 font-medium">Legacy</th>
<th className="pb-2 px-3 font-medium">Candidate</th>
<th className="pb-2 px-3 font-medium">Compared</th>
<th className="pb-2 px-3 font-medium">Material</th>
<th className="pb-2 pl-3 font-medium">Median |Δ|</th>
</tr>
</thead>
<tbody className="divide-y divide-white/[0.06] text-gray-300">
{Object.entries(summary.field_stats).map(([key, stats]) => (
<tr key={key}>
<td className="py-2.5 pr-4">{FIELD_LABELS[key] ?? key}</td>
<td className="py-2.5 px-3 num">{stats.legacy_available}</td>
<td className="py-2.5 px-3 num">{stats.candidate_available}</td>
<td className="py-2.5 px-3 num">{stats.both_available}</td>
<td className="py-2.5 px-3 num">{stats.material_differences}</td>
<td className="py-2.5 pl-3 num">
{stats.median_absolute_delta == null ? 'n/a' : stats.median_absolute_delta.toFixed(2)}
</td>
</tr>
))}
</tbody>
</table>
</div>
<p className="text-[11px] leading-relaxed text-gray-500">
Materiality bands highlight review candidates only. They do not approve a cutover or write fundamentals,
scores, rankings, or qualification state.
</p>
</div>
);
}
function Summary({ label, value }: { label: string; value: string | number }) {
return (
<div className="rounded-lg border border-white/[0.07] bg-white/[0.025] px-3 py-2.5">
<div className="text-[10px] uppercase tracking-wider text-gray-500">{label}</div>
<div className="mt-1 num text-lg text-gray-200">{value}</div>
</div>
);
}
frontend/src/components/admin/JobControls.tsx
+228 -96
View File
@@ -1,4 +1,5 @@
import { useJobs, useToggleJob, useTriggerJob } from '../../hooks/useAdmin';
import type { JobCategory, JobStatus } from '../../api/admin';
import { SkeletonTable } from '../ui/Skeleton';
function formatNextRun(iso: string | null): string {
@@ -10,7 +11,8 @@ function formatNextRun(iso: string | null): string {
const mins = Math.round(diffMs / 60_000);
if (mins < 60) return `in ${mins}m`;
const hrs = Math.round(mins / 60);
return `in ${hrs}h`;
if (hrs < 48) return `in ${hrs}h`;
return `in ${Math.round(hrs / 24)}d`;
}
function formatAgo(iso: string | null | undefined): string {
@@ -29,85 +31,95 @@ function lastRunColor(status: string | null | undefined): string {
return 'text-gray-500';
}
export function JobControls() {
const { data: jobs, isLoading } = useJobs();
const toggleJob = useToggleJob();
const triggerJob = useTriggerJob();
const anyJobRunning = (jobs ?? []).some((job) => job.running);
const runningJob = jobs?.find((job) => job.running);
const pausedJob = jobs?.find((job) => !job.running && job.runtime_status === 'rate_limited');
const runningJobLabel = runningJob?.label;
if (isLoading) return <SkeletonTable rows={4} cols={3} />;
/** The four kinds of job, in the order the API already sorts them. A job whose
* category the client does not recognise still renders, under "Other" — better
* a stray section than a job that silently vanishes from the admin page. */
const SECTIONS: { key: JobCategory; title: string; hint: string }[] = [
{
key: 'pipeline',
title: 'Pipelines',
hint: 'own schedule · run their steps in order',
},
{
key: 'pipeline_step',
title: 'Pipeline steps',
hint: 'no timer of their own · still triggerable individually',
},
{
key: 'scheduled',
title: 'Standalone scheduled',
hint: 'own schedule · independent of any pipeline',
},
{ key: 'manual', title: 'Manual only', hint: 'never fires on its own' },
];
/** One consistent answer per job: its own timer, its parent's, or "manual only".
* A step has no schedule of its own, so reporting one was the original bug. */
function NextRun({ job, labels }: { job: JobStatus; labels: Record<string, string> }) {
const muted = 'text-[11px] text-gray-500';
if (job.next_run_source === 'manual_only') {
return <span className={muted}>manual only</span>;
}
if (job.next_run_source === 'via_pipeline') {
if (!job.via_next_run_at || !job.via_next_run_job) {
return <span className={muted}>runs via pipeline</span>;
}
return (
<div className="space-y-3">
{runningJob && (
<div className="rounded-xl border border-blue-400/30 bg-blue-500/10 px-4 py-3">
<div className="flex flex-wrap items-center justify-between gap-3">
<div>
<div className="text-xs font-semibold text-blue-300">
Active job: {runningJob.label}
</div>
<div className="mt-0.5 text-[11px] text-blue-100/80">
Manual triggers are blocked until this run finishes.
</div>
</div>
<div className="text-[11px] text-blue-200">
{runningJob.runtime_processed ?? 0}
{typeof runningJob.runtime_total === 'number'
? ` / ${runningJob.runtime_total}`
: ''}
</div>
</div>
<div className="mt-2 h-1.5 w-full rounded-full bg-slate-700/80 overflow-hidden">
<div
className="h-full bg-blue-400 transition-all duration-500"
style={{
width: `${
typeof runningJob.runtime_progress_pct === 'number'
? Math.max(5, Math.min(100, runningJob.runtime_progress_pct))
: 30
}%`,
}}
/>
</div>
{runningJob.runtime_current_ticker && (
<div className="mt-1 text-[11px] text-blue-100/80">
Current: {runningJob.runtime_current_ticker}
</div>
)}
{runningJob.runtime_message && (
<div className="mt-1 text-[11px] text-blue-100/80">
{runningJob.runtime_message}
</div>
)}
</div>
)}
<span className={muted}>
Next via {labels[job.via_next_run_job] ?? job.via_next_run_job}{' '}
{formatNextRun(job.via_next_run_at)}
</span>
);
}
if (!job.next_run_at) return null;
return <span className={muted}>Next run {formatNextRun(job.next_run_at)}</span>;
}
{!runningJob && pausedJob && (
<div className="rounded-xl border border-amber-400/30 bg-amber-500/10 px-4 py-3">
<div className="flex flex-wrap items-center justify-between gap-3">
<div>
<div className="text-xs font-semibold text-amber-300">
Last run paused: {pausedJob.label}
/** Membership, shown rather than nested: a step can belong to several pipelines
* (data_collector is in all four), so duplicating rows under each parent would
* render Trigger buttons that are not distinct actions. */
function Membership({ job, labels }: { job: JobStatus; labels: Record<string, string> }) {
const name = (id: string) => labels[id] ?? id;
if (job.category === 'pipeline' && job.steps?.length) {
return (
<div className="mt-1 text-[11px] leading-relaxed text-gray-600">
{job.steps.map(name).join(' → ')}
</div>
<div className="mt-0.5 text-[11px] text-amber-100/90">
{pausedJob.runtime_message || 'Rate limit hit. The collector stopped early and will resume from last progress on the next run.'}
);
}
if (job.category === 'pipeline_step' && job.pipelines?.length) {
return (
<div className="mt-1 text-[11px] leading-relaxed text-gray-600">
runs in: {job.pipelines.map(name).join(', ')}
</div>
</div>
<div className="text-[11px] text-amber-200">
{pausedJob.runtime_processed ?? 0}
{typeof pausedJob.runtime_total === 'number'
? ` / ${pausedJob.runtime_total}`
: ''}
</div>
</div>
</div>
)}
);
}
return null;
}
{jobs?.map((job) => (
<div key={job.name} className="glass p-4 glass-hover">
interface JobCardProps {
job: JobStatus;
labels: Record<string, string>;
anyJobRunning: boolean;
runningJobLabel?: string;
onToggle: (job: JobStatus) => void;
onTrigger: (job: JobStatus) => void;
togglePending: boolean;
triggerPending: boolean;
}
function JobCard({
job,
labels,
anyJobRunning,
runningJobLabel,
onToggle,
onTrigger,
togglePending,
triggerPending,
}: JobCardProps) {
return (
<div className="glass p-4 glass-hover">
<div className="flex flex-wrap items-center justify-between gap-4">
<div className="flex items-center gap-3">
{/* Status dot */}
@@ -122,7 +134,9 @@ export function JobControls() {
/>
<div>
<span className="text-sm font-medium text-gray-200">{job.label}</span>
<div className="flex items-center gap-3 mt-0.5">
<div className="mt-0.5 flex flex-wrap items-center gap-3">
{/* Live state only — a persisted error must not read as the
current status forever, so this never consults last_run_*. */}
<span
className={`text-[11px] font-medium ${
job.running
@@ -148,26 +162,23 @@ export function JobControls() {
? 'Active'
: 'Inactive'}
</span>
{job.via_pipeline ? (
<span className="text-[11px] text-gray-500">runs via pipeline</span>
) : (
job.enabled && job.next_run_at && (
<span className="text-[11px] text-gray-500">
Next run {formatNextRun(job.next_run_at)}
</span>
)
)}
{job.enabled && <NextRun job={job} labels={labels} />}
{!job.registered && (
<span className="text-[11px] text-red-400">Not registered</span>
)}
</div>
{!job.running && job.runtime_finished_at && (
<div className={`mt-1 text-[11px] ${lastRunColor(job.runtime_status)}`}>
Last run {formatAgo(job.runtime_finished_at)}
{job.runtime_status ? ` · ${job.runtime_status}` : ''}
{job.runtime_message ? ` — ${job.runtime_message}` : ''}
<Membership job={job} labels={labels} />
{/* Persisted, so this survives a deploy — unlike runtime_* above. */}
{!job.running && job.last_run_at && (
<div className={`mt-1 text-[11px] ${lastRunColor(job.last_run_status)}`}>
Last run {formatAgo(job.last_run_at)}
{job.last_run_status ? ` · ${job.last_run_status}` : ''}
{job.last_run_message ? ` — ${job.last_run_message}` : ''}
</div>
)}
{!job.running && !job.last_run_at && (
<div className="mt-1 text-[11px] text-gray-600">No run recorded yet</div>
)}
{job.running && (
<div className="mt-2 space-y-1.5">
<div className="flex items-center justify-between text-[11px] text-gray-400">
@@ -180,7 +191,7 @@ export function JobControls() {
<span>{Math.max(0, Math.min(100, job.runtime_progress_pct)).toFixed(0)}%</span>
)}
</div>
<div className="h-1.5 w-56 rounded-full bg-slate-700/80 overflow-hidden">
<div className="h-1.5 w-56 overflow-hidden rounded-full bg-slate-700/80">
<div
className="h-full bg-blue-400 transition-all duration-500"
style={{
@@ -203,8 +214,8 @@ export function JobControls() {
<div className="flex items-center gap-2">
<button
type="button"
onClick={() => toggleJob.mutate({ jobName: job.name, enabled: !job.enabled })}
disabled={toggleJob.isPending}
onClick={() => onToggle(job)}
disabled={togglePending}
className={`rounded-lg border px-3 py-1.5 text-xs transition-all duration-200 disabled:opacity-50 ${
job.enabled
? 'border-red-500/20 bg-red-500/10 text-red-400 hover:bg-red-500/20'
@@ -215,14 +226,14 @@ export function JobControls() {
</button>
<button
type="button"
onClick={() => triggerJob.mutate(job.name)}
disabled={triggerJob.isPending || !job.enabled || anyJobRunning}
className="btn-primary px-3 py-1.5 text-xs disabled:opacity-50 disabled:cursor-not-allowed"
onClick={() => onTrigger(job)}
disabled={triggerPending || !job.enabled || anyJobRunning}
className="btn-primary px-3 py-1.5 text-xs disabled:cursor-not-allowed disabled:opacity-50"
>
<span>
{job.running
? 'Running…'
: triggerJob.isPending
: triggerPending
? 'Triggering…'
: anyJobRunning
? 'Blocked'
@@ -237,7 +248,128 @@ export function JobControls() {
</div>
)}
</div>
);
}
export function JobControls() {
const { data: jobs, isLoading } = useJobs();
const toggleJob = useToggleJob();
const triggerJob = useTriggerJob();
const all = jobs ?? [];
// Job id -> display label, so a step can name its parent pipeline.
const labels = Object.fromEntries(all.map((job) => [job.name, job.label]));
const anyJobRunning = all.some((job) => job.running);
const runningJob = all.find((job) => job.running);
const pausedJob = all.find((job) => !job.running && job.runtime_status === 'rate_limited');
if (isLoading) return <SkeletonTable rows={4} cols={3} />;
const known = new Set<string>(SECTIONS.map((s) => s.key));
const groups: { key: string; title: string; hint: string; jobs: JobStatus[] }[] = [
...SECTIONS.map((section) => ({
...section,
jobs: all.filter((job) => job.category === section.key),
})),
{
key: 'other',
title: 'Other',
hint: 'uncategorised',
jobs: all.filter((job) => !job.category || !known.has(job.category)),
},
];
const cardProps = {
labels,
anyJobRunning,
runningJobLabel: runningJob?.label,
onToggle: (job: JobStatus) =>
toggleJob.mutate({ jobName: job.name, enabled: !job.enabled }),
onTrigger: (job: JobStatus) => triggerJob.mutate(job.name),
togglePending: toggleJob.isPending,
triggerPending: triggerJob.isPending,
};
return (
<div className="space-y-6">
{runningJob && (
<div className="rounded-xl border border-blue-400/30 bg-blue-500/10 px-4 py-3">
<div className="flex flex-wrap items-center justify-between gap-3">
<div>
<div className="text-xs font-semibold text-blue-300">
Active job: {runningJob.label}
</div>
<div className="mt-0.5 text-[11px] text-blue-100/80">
Manual triggers are blocked until this run finishes.
</div>
</div>
<div className="text-[11px] text-blue-200">
{runningJob.runtime_processed ?? 0}
{typeof runningJob.runtime_total === 'number'
? ` / ${runningJob.runtime_total}`
: ''}
</div>
</div>
<div className="mt-2 h-1.5 w-full overflow-hidden rounded-full bg-slate-700/80">
<div
className="h-full bg-blue-400 transition-all duration-500"
style={{
width: `${
typeof runningJob.runtime_progress_pct === 'number'
? Math.max(5, Math.min(100, runningJob.runtime_progress_pct))
: 30
}%`,
}}
/>
</div>
{runningJob.runtime_current_ticker && (
<div className="mt-1 text-[11px] text-blue-100/80">
Current: {runningJob.runtime_current_ticker}
</div>
)}
{runningJob.runtime_message && (
<div className="mt-1 text-[11px] text-blue-100/80">{runningJob.runtime_message}</div>
)}
</div>
)}
{!runningJob && pausedJob && (
<div className="rounded-xl border border-amber-400/30 bg-amber-500/10 px-4 py-3">
<div className="flex flex-wrap items-center justify-between gap-3">
<div>
<div className="text-xs font-semibold text-amber-300">
Last run paused: {pausedJob.label}
</div>
<div className="mt-0.5 text-[11px] text-amber-100/90">
{pausedJob.runtime_message || 'Rate limit hit. The collector stopped early and will resume from last progress on the next run.'}
</div>
</div>
<div className="text-[11px] text-amber-200">
{pausedJob.runtime_processed ?? 0}
{typeof pausedJob.runtime_total === 'number'
? ` / ${pausedJob.runtime_total}`
: ''}
</div>
</div>
</div>
)}
{groups.map(
(group) =>
group.jobs.length > 0 && (
<section key={group.key} className="space-y-3">
<h3 className="text-xs font-medium uppercase tracking-widest text-gray-500">
{group.title}
<span className="ml-2 num text-gray-600">{group.jobs.length}</span>
<span className="ml-2 normal-case tracking-normal text-gray-600">
{group.hint}
</span>
</h3>
{group.jobs.map((job) => (
<JobCard key={job.name} job={job} {...cardProps} />
))}
</section>
),
)}
</div>
);
}
frontend/src/components/admin/ScheduleSettings.tsx
+14 -14
View File
@@ -8,11 +8,11 @@ const DEFAULTS: ScheduleConfig = {
schedule_daily_pipeline_cron: '0 2 * * *',
schedule_dolt_earnings_cron: '30 2 * * *',
schedule_sec_fundamentals_cron: '0 4 * * *',
schedule_fundamentals_parity_cron: '30 5 * * *',
schedule_near_close_pipeline_cron: '30 15 * * mon-fri',
schedule_after_close_pipeline_cron: '45 16 * * mon-fri',
schedule_intraday_pipeline_cron: '0 10-15 * * mon-fri',
schedule_fundamentals_cron: '0 1 * * mon',
schedule_backtest_cron: '0 3 * * sun',
schedule_ticker_universe_cron: '0 1 * * *',
};
const FIELDS: { key: keyof ScheduleConfig; label: string; hint: string; mono?: boolean }[] = [
@@ -24,25 +24,19 @@ const FIELDS: { key: keyof ScheduleConfig; label: string; hint: string; mono?: b
{
key: 'schedule_daily_pipeline_cron',
label: 'Morning pipeline',
hint: 'OHLCV → benchmark → sentiment → regime → alerts (no R:R scan). Default 02:00 ET so regime-quadrant changes hit Telegram in the morning.',
hint: 'OHLCV → benchmark → sentiment → trend/risk → alerts (no R:R scan). Default 02:00 ET so risk-quadrant changes hit Telegram in the morning.',
mono: true,
},
{
key: 'schedule_dolt_earnings_cron',
label: 'Dolt earnings',
hint: 'Pull and import earnings dates/results daily at 02:30 ET. The activated cache refresh uses these local events.',
hint: 'Pull and import earnings dates/results daily at 02:30 ET. The fundamentals cache refresh uses these local events.',
mono: true,
},
{
key: 'schedule_sec_fundamentals_cron',
label: 'SEC fundamentals',
hint: 'Import tracked-universe SEC facts daily at 04:00 ET and refresh the scoring cache when the cutover is active.',
mono: true,
},
{
key: 'schedule_fundamentals_parity_cron',
label: 'Fundamentals parity report',
hint: 'Read-only legacy vs SEC/Dolt comparison daily at 05:30 ET, after the bulk imports.',
hint: 'Import tracked-universe SEC facts daily at 04:00 ET, then refresh the fundamentals cache scoring reads. Disabling the job stops the SEC fetch only — the local cache refresh still runs.',
mono: true,
},
{
@@ -64,9 +58,15 @@ const FIELDS: { key: keyof ScheduleConfig; label: string; hint: string; mono?: b
mono: true,
},
{
key: 'schedule_fundamentals_cron',
label: 'Legacy fundamentals (weekly)',
hint: 'Fallback provider chain. Automatically skipped while the SEC + Dolt cutover is active.',
key: 'schedule_backtest_cron',
label: 'Backtest',
hint: 'Replay history and refresh the Track Record report. Default Sunday 03:00 ET. Was a 168h interval, which restarted on every deploy and so could defer indefinitely.',
mono: true,
},
{
key: 'schedule_ticker_universe_cron',
label: 'Ticker universe sync',
hint: 'Refresh the tracked-symbol universe. Default 01:00 ET daily, before the morning pipeline.',
mono: true,
},
];
frontend/src/components/admin/SettingsForm.tsx
+1 -4
View File
@@ -3,8 +3,6 @@ import { useSettings, useUpdateSetting } from '../../hooks/useAdmin';
import { SkeletonTable } from '../ui/Skeleton';
import type { SystemSetting } from '../../lib/types';
const MANAGED_SETTINGS = new Set(['fundamental_data_sec_dolt_cutover_enabled']);
export function SettingsForm() {
const { data: settings, isLoading, isError, error } = useSettings();
const updateSetting = useUpdateSetting();
@@ -34,11 +32,10 @@ export function SettingsForm() {
if (isLoading) return <SkeletonTable rows={4} cols={2} />;
if (isError) return <p className="text-sm text-red-400">{(error as Error)?.message || 'Failed to load settings'}</p>;
if (!settings || settings.length === 0) return <p className="text-sm text-gray-500">No settings found.</p>;
const visibleSettings = settings.filter((setting) => !MANAGED_SETTINGS.has(setting.key));
return (
<div className="space-y-4">
{visibleSettings.map((setting) => (
{settings.map((setting) => (
<div key={setting.key} className="glass p-4 flex flex-wrap items-center gap-3 glass-hover">
<label className="min-w-[140px] text-sm font-medium text-gray-300">{setting.key}</label>
{setting.key === 'registration' ? (
frontend/src/components/layout/MobileNav.tsx
+1 -1
View File
@@ -7,7 +7,7 @@ const navItems = [
{ to: '/', label: 'Overview', end: true },
{ to: '/market', label: 'Market', end: false },
{ to: '/signals', label: 'Signals', end: false },
{ to: '/regime', label: 'Regime', end: false },
{ to: '/regime', label: 'Risk', end: false },
];
export default function MobileNav() {
frontend/src/components/layout/TopBar.tsx
+4 -3
View File
@@ -13,7 +13,8 @@ const navItems = [
{ to: '/', label: 'Overview', end: true },
{ to: '/market', label: 'Market', end: false },
{ to: '/signals', label: 'Signals', end: false },
{ to: '/regime', label: 'Regime', end: false },
// Route stays /regime so existing links keep working; only the label changes.
{ to: '/regime', label: 'Risk', end: false },
];
const linkClasses = (isActive: boolean) =>
@@ -84,7 +85,7 @@ export default function TopBar() {
</div>
<div className="ml-auto flex items-center gap-5">
{/* Market regime — ambient status; the full picture lives on /regime */}
{/* SPY trend — ambient status; the full picture lives on /regime */}
{regime.data && (
<NavLink
to="/regime"
@@ -99,7 +100,7 @@ export default function TopBar() {
>
<span className={`inline-block h-1.5 w-1.5 rounded-full ${regimeDot(regime.data.label)}`} />
<span className="text-[11px] capitalize text-gray-500 transition-colors group-hover:text-gray-300">
{regime.data.label} regime
{regime.data.label} trend
</span>
</NavLink>
)}
frontend/src/components/regime/RegimeChart.tsx
+308
View File
@@ -0,0 +1,308 @@
import { useMemo, useState } from 'react';
import { useQuery } from '@tanstack/react-query';
import {
CartesianGrid,
Cell,
Line,
LineChart,
ReferenceArea,
ReferenceLine,
ResponsiveContainer,
Scatter,
ScatterChart,
Tooltip,
XAxis,
YAxis,
ZAxis,
} from 'recharts';
import { getRegimeHistory, getRegimeMonitor } from '../../api/regime';
import { Callout } from '../ui/Callout';
import { SkeletonCard } from '../ui/Skeleton';
import { formatDate } from '../../lib/format';
// Lazy-loaded (see RegimePage) so recharts stays in the regime-tab chunk.
// Time and Path are two projections of one series, so they share a card and a
// query rather than sitting in two panels that look like different data.
const VIEWS = ['Time', 'Path'] as const;
type View = (typeof VIEWS)[number];
const RANGES = [
{ key: '1M', days: 30 },
{ key: '3M', days: 90 },
{ key: '6M', days: 182 },
{ key: 'All', days: Number.POSITIVE_INFINITY },
] as const;
type RangeKey = (typeof RANGES)[number]['key'];
/** Sessions drawn in Path view. The full series is unreadable as a path. */
const PATH_TRAIL = 60;
const STATE_COLOR = '#60a5fa';
const WARNING_COLOR = '#fb923c';
// Fall back to the shipped constants, not v2's shared 60/60, so a missing
// quadrant_config cannot draw dividers that disagree with the alert path.
const DEFAULT_STATE_DIVIDER = 50;
const DEFAULT_WARNING_DIVIDER = 40;
interface PathPoint {
x: number;
y: number;
date: string;
}
/** Centered moving average to de-noise the path; today (last) kept exact. */
function smoothTrail(points: PathPoint[], half = 2): PathPoint[] {
const n = points.length;
return points.map((p, i) => {
if (i === n - 1) return { ...p };
let sx = 0;
let sy = 0;
let c = 0;
for (let j = Math.max(0, i - half); j <= Math.min(n - 1, i + half); j++) {
sx += points[j].x;
sy += points[j].y;
c += 1;
}
return { x: sx / c, y: sy / c, date: p.date };
});
}
/** Recency gradient: 0 = oldest (muted slate), 1 = newest (bright blue). */
function recencyColor(t: number): string {
const lerp = (a: number, b: number) => Math.round(a + (b - a) * t);
return `rgba(${lerp(71, 96)}, ${lerp(85, 165)}, ${lerp(105, 250)}, ${(0.3 + 0.7 * t).toFixed(2)})`;
}
function SegmentedControl<T extends string>({
options,
value,
onChange,
label,
}: {
options: readonly T[];
value: T;
onChange: (next: T) => void;
label: string;
}) {
return (
<div className="flex gap-1" role="group" aria-label={label}>
{options.map((option) => (
<button
key={option}
type="button"
aria-pressed={value === option}
onClick={() => onChange(option)}
className={`rounded px-2 py-1 text-[11px] font-medium tabular-nums transition-colors ${
value === option ? 'bg-white/10 text-blue-300' : 'text-gray-500 hover:text-gray-300'
}`}
>
{option}
</button>
))}
</div>
);
}
function PathTip({ active, payload }: { active?: boolean; payload?: { payload: PathPoint }[] }) {
if (!active || !payload?.length) return null;
const p = payload[0].payload;
return (
<div className="glass px-2.5 py-1.5 text-[11px]">
<div className="text-gray-300">{formatDate(p.date)}</div>
<div className="text-gray-400">
State <span style={{ color: STATE_COLOR }}>{Math.round(p.x)}</span> · Warning{' '}
<span style={{ color: WARNING_COLOR }}>{Math.round(p.y)}</span>
</div>
</div>
);
}
export default function RegimeChart() {
const [view, setView] = useState<View>('Time');
const [range, setRange] = useState<RangeKey>('3M');
const history = useQuery({ queryKey: ['regime', 'history'], queryFn: () => getRegimeHistory(800) });
const monitor = useQuery({ queryKey: ['regime', 'monitor'], queryFn: getRegimeMonitor });
const xDiv = monitor.data?.quadrant_config?.state_divider ?? DEFAULT_STATE_DIVIDER;
const yDiv = monitor.data?.quadrant_config?.warning_divider ?? DEFAULT_WARNING_DIVIDER;
const basketAsOf = monitor.data?.basket?.basket_asof;
const series = useMemo(() => {
const data = history.data ?? [];
if (view === 'Path') {
return data
.filter((p) => p.state != null && p.warning != null)
.slice(-PATH_TRAIL);
}
const days = RANGES.find((r) => r.key === range)!.days;
if (!Number.isFinite(days)) return data;
const cutoff = new Date();
cutoff.setDate(cutoff.getDate() - days);
return data.filter((p) => new Date(p.date) >= cutoff);
}, [history.data, view, range]);
const pathPoints = useMemo<PathPoint[]>(
() => series.map((p) => ({ x: p.state as number, y: p.warning as number, date: p.date })),
[series],
);
const trail = useMemo(() => (view === 'Path' ? smoothTrail(pathPoints) : []), [pathPoints, view]);
const latest = view === 'Path' && pathPoints.length ? pathPoints[pathPoints.length - 1] : null;
// Only warn about pre-freeze history when the drawn window actually reaches
// back past the freeze date.
const crossesFreeze = Boolean(basketAsOf && series.length && series[0].date < basketAsOf);
const enoughData = view === 'Path' ? pathPoints.length > 0 : series.length >= 2;
return (
<div className="glass p-5">
<div className="flex flex-wrap items-center justify-between gap-3">
<div className="flex items-center gap-3">
<span className="text-[11px] uppercase tracking-wider text-gray-500">
{view === 'Time' ? 'State & Warning over time' : `State × Warning path · last ${PATH_TRAIL} sessions`}
</span>
<SegmentedControl options={VIEWS} value={view} onChange={setView} label="Chart view" />
</div>
{view === 'Time' ? (
<SegmentedControl options={RANGES.map((r) => r.key)} value={range} onChange={setRange} label="Time range" />
) : (
latest && (
<span className="text-[11px] text-gray-500">
now: State <span style={{ color: STATE_COLOR }}>{Math.round(latest.x)}</span> · Warning{' '}
<span style={{ color: WARNING_COLOR }}>{Math.round(latest.y)}</span>
</span>
)
)}
</div>
{history.isLoading ? (
<SkeletonCard className="mt-3 h-72" />
) : !enoughData ? (
<Callout variant="empty">Not enough coverage-qualified history yet — it accumulates as the daily job runs.</Callout>
) : (
<>
<div className="mt-3 h-72">
<ResponsiveContainer width="100%" height="100%">
{view === 'Time' ? (
<LineChart data={series} margin={{ top: 6, right: 8, left: 0, bottom: 0 }}>
<CartesianGrid stroke="rgba(255,255,255,0.05)" vertical={false} />
<XAxis
dataKey="date"
tick={{ fill: '#6b7280', fontSize: 10 }}
tickFormatter={(d) => formatDate(String(d))}
minTickGap={28}
tickLine={false}
axisLine={{ stroke: 'rgba(255,255,255,0.08)' }}
/>
{/* width must clear a 3-digit label: the old chart paired
width 28 with margin.left -18 and clipped every tick. */}
<YAxis
domain={[0, 100]}
ticks={[0, 25, 50, 75, 100]}
tick={{ fill: '#6b7280', fontSize: 10 }}
width={34}
tickLine={false}
axisLine={false}
/>
{/* The two axes have different thresholds, so each divider is
drawn in its series' colour rather than as shared gridlines. */}
<ReferenceLine y={xDiv} stroke={STATE_COLOR} strokeOpacity={0.25} strokeDasharray="4 4" />
<ReferenceLine y={yDiv} stroke={WARNING_COLOR} strokeOpacity={0.25} strokeDasharray="4 4" />
<Tooltip
contentStyle={{
background: 'rgba(17,24,39,0.95)',
border: '1px solid rgba(255,255,255,0.1)',
borderRadius: 8,
fontSize: 12,
}}
labelStyle={{ color: '#9ca3af' }}
labelFormatter={(l) => formatDate(String(l))}
formatter={(value) => (value == null ? '—' : Math.round(Number(value)))}
/>
<Line type="monotone" dataKey="state" name="State" stroke={STATE_COLOR} dot={false} strokeWidth={1.5} isAnimationActive={false} />
<Line type="monotone" dataKey="warning" name="Warning" stroke={WARNING_COLOR} dot={false} strokeWidth={1.5} isAnimationActive={false} />
</LineChart>
) : (
<ScatterChart margin={{ top: 10, right: 16, bottom: 22, left: 0 }}>
<ReferenceArea x1={0} x2={xDiv} y1={yDiv} y2={100} fill="#f59e0b" fillOpacity={0.07} stroke="none" />
<ReferenceArea x1={xDiv} x2={100} y1={yDiv} y2={100} fill="#f97316" fillOpacity={0.07} stroke="none" />
<ReferenceArea x1={0} x2={xDiv} y1={0} y2={yDiv} fill="#10b981" fillOpacity={0.07} stroke="none" />
<ReferenceArea x1={xDiv} x2={100} y1={0} y2={yDiv} fill="#ef4444" fillOpacity={0.08} stroke="none" />
<CartesianGrid stroke="rgba(255,255,255,0.04)" />
<ReferenceLine x={xDiv} stroke="rgba(255,255,255,0.12)" />
<ReferenceLine y={yDiv} stroke="rgba(255,255,255,0.12)" />
<XAxis
type="number"
dataKey="x"
domain={[0, 100]}
ticks={[0, 20, 40, 60, 80, 100]}
tick={{ fill: '#6b7280', fontSize: 10 }}
tickLine={false}
axisLine={{ stroke: 'rgba(255,255,255,0.08)' }}
label={{ value: 'State →', position: 'insideBottom', offset: -12, fill: '#6b7280', fontSize: 10 }}
/>
<YAxis
type="number"
dataKey="y"
domain={[0, 100]}
ticks={[0, 20, 40, 60, 80, 100]}
tick={{ fill: '#6b7280', fontSize: 10 }}
width={30}
tickLine={false}
axisLine={false}
label={{ value: 'Warning', angle: -90, position: 'insideLeft', fill: '#6b7280', fontSize: 10 }}
/>
<ZAxis range={[13, 13]} />
<Tooltip cursor={{ strokeDasharray: '3 3', stroke: 'rgba(255,255,255,0.2)' }} content={<PathTip />} />
<Scatter data={trail} line={{ stroke: 'rgba(96,165,250,0.18)', strokeWidth: 1.5 }} isAnimationActive={false}>
{trail.map((_, i) => (
<Cell key={i} fill={recencyColor(trail.length <= 1 ? 1 : i / (trail.length - 1))} />
))}
</Scatter>
{latest && (
<Scatter
data={[latest]}
isAnimationActive={false}
shape={(props: { cx?: number; cy?: number }) => (
<circle cx={props.cx} cy={props.cy} r={6} fill="#ffffff" stroke={STATE_COLOR} strokeWidth={2} />
)}
/>
)}
</ScatterChart>
)}
</ResponsiveContainer>
</div>
{view === 'Time' ? (
<div className="mt-2 flex flex-wrap items-center gap-4 text-[11px] text-gray-400">
<span className="flex items-center gap-1.5">
<span className="inline-block h-2 w-3 rounded-sm" style={{ background: STATE_COLOR }} />
State
</span>
<span className="flex items-center gap-1.5">
<span className="inline-block h-2 w-3 rounded-sm" style={{ background: WARNING_COLOR }} />
Warning
</span>
<span className="text-gray-600">dashed = each axis's elevated threshold ({xDiv} / {yDiv})</span>
</div>
) : (
<div className="mt-2 grid grid-cols-1 gap-x-4 gap-y-1 text-[11px] text-gray-500 sm:grid-cols-2">
<span><span className="text-amber-400">Early warning</span> — calm, fragility rising</span>
<span><span className="text-orange-400">Active stress</span> — damaged and deteriorating</span>
<span><span className="text-emerald-400">Healthy</span> — calm, broadly supported</span>
<span><span className="text-red-400">Stabilizing</span> — damage remains, warning lower</span>
<span className="text-gray-600 sm:col-span-2">White dot = today; trail brightens toward the present, smoothed.</span>
</div>
)}
{crossesFreeze && (
<p className="mt-2 text-[11px] text-gray-600">
History before {basketAsOf} is reconstructed against today's basket — retrospective, not a live record.
</p>
)}
</>
)}
</div>
);
}
frontend/src/components/regime/RegimeQuadrant.tsx
-184
View File
@@ -1,184 +0,0 @@
import { useMemo } from 'react';
import { useQuery } from '@tanstack/react-query';
import {
ScatterChart,
Scatter,
Cell,
XAxis,
YAxis,
ZAxis,
CartesianGrid,
Tooltip,
ResponsiveContainer,
ReferenceLine,
ReferenceArea,
} from 'recharts';
import { getRegimeHistory, getRegimeMonitor } from '../../api/regime';
import { Callout } from '../ui/Callout';
import { SkeletonCard } from '../ui/Skeleton';
// Lazy-loaded (see RegimePage) so recharts stays in the regime-tab chunk.
// Quadrant boundaries come from the backend v2 methodology response.
const TRAIL = 60; // sessions shown
interface QPoint {
x: number;
y: number;
date: string;
}
/** Centered moving average to de-noise the path; today (last) kept exact. */
function smoothTrail(points: QPoint[], half = 2): QPoint[] {
const n = points.length;
return points.map((p, i) => {
if (i === n - 1) return { ...p };
let sx = 0;
let sy = 0;
let c = 0;
for (let j = Math.max(0, i - half); j <= Math.min(n - 1, i + half); j++) {
sx += points[j].x;
sy += points[j].y;
c += 1;
}
return { x: sx / c, y: sy / c, date: p.date };
});
}
/** Recency gradient: 0 = oldest (muted slate), 1 = newest (bright blue). */
function recencyColor(t: number): string {
const lerp = (a: number, b: number) => Math.round(a + (b - a) * t);
const r = lerp(71, 96);
const g = lerp(85, 165);
const b = lerp(105, 250);
const alpha = (0.3 + 0.7 * t).toFixed(2);
return `rgba(${r}, ${g}, ${b}, ${alpha})`;
}
function QuadrantTip({ active, payload }: { active?: boolean; payload?: { payload: QPoint }[] }) {
if (!active || !payload?.length) return null;
const p = payload[0].payload;
return (
<div className="glass px-2.5 py-1.5 text-[11px]">
<div className="text-gray-300">{p.date}</div>
<div className="text-gray-400">
State <span className="text-blue-300">{Math.round(p.x)}</span> · Warning{' '}
<span className="text-orange-300">{Math.round(p.y)}</span>
</div>
</div>
);
}
export default function RegimeQuadrant() {
const history = useQuery({ queryKey: ['regime', 'history'], queryFn: () => getRegimeHistory(800) });
const monitor = useQuery({ queryKey: ['regime', 'monitor'], queryFn: getRegimeMonitor });
const xDiv = monitor.data?.quadrant_config?.state_divider ?? 60;
const yDiv = monitor.data?.quadrant_config?.warning_divider ?? 60;
const points = useMemo<QPoint[]>(() => {
const data = history.data ?? [];
return data
.filter((p) => p.state != null && p.warning != null)
.slice(-TRAIL)
.map((p) => ({ x: p.state as number, y: p.warning as number, date: p.date }));
}, [history.data]);
const trail = useMemo(() => smoothTrail(points), [points]);
const latest = points.length ? points[points.length - 1] : null;
return (
<div className="glass p-5">
<div className="flex flex-wrap items-center justify-between gap-2">
<div className="text-[11px] uppercase tracking-wider text-gray-500">
State × Warning quadrant — last {TRAIL} sessions
</div>
{latest && (
<div className="text-[11px] text-gray-500">
now: State <span className="text-blue-300">{Math.round(latest.x)}</span> · Warning{' '}
<span className="text-orange-300">{Math.round(latest.y)}</span>
</div>
)}
</div>
{history.isLoading ? (
<SkeletonCard className="mt-3 h-72" />
) : !points.length ? (
<Callout variant="empty">
Not enough coverage-qualified v2 history yet.
</Callout>
) : (
<>
<div className="mt-3 h-80">
<ResponsiveContainer width="100%" height="100%">
<ScatterChart margin={{ top: 10, right: 16, bottom: 22, left: 0 }}>
{/* Quadrant shading (drawn first, behind everything) */}
<ReferenceArea x1={0} x2={xDiv} y1={yDiv} y2={100} fill="#f59e0b" fillOpacity={0.07} stroke="none" />
<ReferenceArea x1={xDiv} x2={100} y1={yDiv} y2={100} fill="#f97316" fillOpacity={0.07} stroke="none" />
<ReferenceArea x1={0} x2={xDiv} y1={0} y2={yDiv} fill="#10b981" fillOpacity={0.07} stroke="none" />
<ReferenceArea x1={xDiv} x2={100} y1={0} y2={yDiv} fill="#ef4444" fillOpacity={0.08} stroke="none" />
<CartesianGrid stroke="rgba(255,255,255,0.04)" />
<ReferenceLine x={xDiv} stroke="rgba(255,255,255,0.12)" />
<ReferenceLine y={yDiv} stroke="rgba(255,255,255,0.12)" />
<XAxis
type="number"
dataKey="x"
domain={[0, 100]}
ticks={[0, 20, 40, 60, 80, 100]}
tick={{ fill: '#6b7280', fontSize: 10 }}
tickLine={false}
axisLine={{ stroke: 'rgba(255,255,255,0.08)' }}
label={{ value: 'State →', position: 'insideBottom', offset: -12, fill: '#6b7280', fontSize: 10 }}
/>
<YAxis
type="number"
dataKey="y"
domain={[0, 100]}
ticks={[0, 20, 40, 60, 80, 100]}
tick={{ fill: '#6b7280', fontSize: 10 }}
width={30}
tickLine={false}
axisLine={false}
label={{ value: 'Warning', angle: -90, position: 'insideLeft', fill: '#6b7280', fontSize: 10 }}
/>
<ZAxis range={[13, 13]} />
<Tooltip cursor={{ strokeDasharray: '3 3', stroke: 'rgba(255,255,255,0.2)' }} content={<QuadrantTip />} />
{/* Smoothed trail with a recency gradient (old → new) */}
<Scatter
data={trail}
line={{ stroke: 'rgba(96,165,250,0.18)', strokeWidth: 1.5 }}
isAnimationActive={false}
>
{trail.map((_, i) => (
<Cell key={i} fill={recencyColor(trail.length <= 1 ? 1 : i / (trail.length - 1))} />
))}
</Scatter>
{/* Today */}
{latest && (
<Scatter
data={[latest]}
isAnimationActive={false}
shape={(props: { cx?: number; cy?: number }) => (
<circle cx={props.cx} cy={props.cy} r={6} fill="#ffffff" stroke="#60a5fa" strokeWidth={2} />
)}
/>
)}
</ScatterChart>
</ResponsiveContainer>
</div>
<div className="mt-2 grid grid-cols-1 gap-x-4 gap-y-1 text-[11px] text-gray-500 sm:grid-cols-2">
<span><span className="text-amber-400">Early warning</span> — state calm, fragility rising</span>
<span><span className="text-orange-400">Active stress</span> — damaged and deteriorating</span>
<span><span className="text-emerald-400">Healthy</span> — calm and broadly supported</span>
<span><span className="text-red-400">Stressed / stabilizing</span> — damage remains, warning lower</span>
</div>
<p className="mt-2 text-[11px] leading-relaxed text-gray-600">
White dot = today; the trail fades from muted (older) to bright blue (newer) over the last {TRAIL}{' '}
sessions, smoothed. The path matters more than a single point. Risk thermometer — not an entry, exit,
or sizing signal.
</p>
</>
)}
</div>
);
}
frontend/src/components/regime/ScoreHistoryChart.tsx
-133
View File
@@ -1,133 +0,0 @@
import { useState, useMemo } from 'react';
import { useQuery } from '@tanstack/react-query';
import {
LineChart,
Line,
XAxis,
YAxis,
CartesianGrid,
Tooltip,
ResponsiveContainer,
ReferenceLine,
} from 'recharts';
import { getRegimeHistory } from '../../api/regime';
import { Callout } from '../ui/Callout';
import { SkeletonCard } from '../ui/Skeleton';
import { formatDate } from '../../lib/format';
// Lazy-loaded (see RegimePage) so recharts only ships in the regime-tab chunk.
const HISTORY_RANGES = [
{ key: '1M', days: 30 },
{ key: '3M', days: 90 },
{ key: '6M', days: 182 },
{ key: 'All', days: 100000 },
] as const;
type HistoryRange = (typeof HISTORY_RANGES)[number]['key'];
const HISTORY_SERIES = [
{ key: 'state', label: 'State', color: '#60a5fa' },
{ key: 'warning', label: 'Warning', color: '#fb923c' },
] as const;
export default function ScoreHistoryChart() {
const [range, setRange] = useState<HistoryRange>('3M');
const history = useQuery({ queryKey: ['regime', 'history'], queryFn: () => getRegimeHistory(800) });
const filtered = useMemo(() => {
const data = history.data ?? [];
const days = HISTORY_RANGES.find((r) => r.key === range)!.days;
if (range === 'All') return data;
const cutoff = new Date();
cutoff.setDate(cutoff.getDate() - days);
return data.filter((p) => new Date(p.date) >= cutoff);
}, [history.data, range]);
return (
<div className="glass p-5">
<div className="flex flex-wrap items-center justify-between gap-2">
<div className="text-[11px] uppercase tracking-wider text-gray-500">Score history</div>
<div className="flex gap-1">
{HISTORY_RANGES.map((r) => (
<button
key={r.key}
type="button"
onClick={() => setRange(r.key)}
className={`rounded px-2 py-1 text-[11px] font-medium tabular-nums transition-colors ${
range === r.key ? 'bg-white/10 text-blue-300' : 'text-gray-500 hover:text-gray-300'
}`}
>
{r.key}
</button>
))}
</div>
</div>
{history.isLoading ? (
<SkeletonCard className="mt-3 h-56" />
) : filtered.length < 2 ? (
<Callout variant="empty">Not enough history yet — it accumulates as the daily job runs.</Callout>
) : (
<>
<div className="mt-3 h-60">
<ResponsiveContainer width="100%" height="100%">
<LineChart data={filtered} margin={{ top: 6, right: 8, left: -18, bottom: 0 }}>
<CartesianGrid stroke="rgba(255,255,255,0.05)" vertical={false} />
<XAxis
dataKey="date"
tick={{ fill: '#6b7280', fontSize: 10 }}
tickFormatter={(d) => formatDate(String(d))}
minTickGap={28}
tickLine={false}
axisLine={{ stroke: 'rgba(255,255,255,0.08)' }}
/>
<YAxis
domain={[0, 100]}
ticks={[0, 30, 60, 80, 100]}
tick={{ fill: '#6b7280', fontSize: 10 }}
width={28}
tickLine={false}
axisLine={false}
/>
<ReferenceLine y={30} stroke="rgba(255,255,255,0.06)" />
<ReferenceLine y={60} stroke="rgba(255,255,255,0.06)" />
<ReferenceLine y={80} stroke="rgba(255,255,255,0.06)" />
<Tooltip
contentStyle={{
background: 'rgba(17,24,39,0.95)',
border: '1px solid rgba(255,255,255,0.1)',
borderRadius: 8,
fontSize: 12,
}}
labelStyle={{ color: '#9ca3af' }}
labelFormatter={(l) => formatDate(String(l))}
formatter={(value) => (value == null ? '—' : Math.round(Number(value)))}
/>
{HISTORY_SERIES.map((s) => (
<Line
key={s.key}
type="monotone"
dataKey={s.key}
name={s.label}
stroke={s.color}
dot={false}
strokeWidth={1.5}
isAnimationActive={false}
/>
))}
</LineChart>
</ResponsiveContainer>
</div>
<div className="mt-2 flex flex-wrap gap-4">
{HISTORY_SERIES.map((s) => (
<span key={s.key} className="flex items-center gap-1.5 text-[11px] text-gray-400">
<span className="inline-block h-2 w-3 rounded-sm" style={{ background: s.color }} />
{s.label}
</span>
))}
</div>
</>
)}
</div>
);
}
frontend/src/components/ui/Button.tsx
+1 -1
View File
@@ -16,7 +16,7 @@ const sizeClasses: Record<Size, string> = {
md: 'px-4 py-2 text-sm',
};
export function Spinner({ className = 'h-4 w-4' }: { className?: string }) {
function Spinner({ className = 'h-4 w-4' }: { className?: string }) {
return (
<svg className={`animate-spin ${className}`} viewBox="0 0 24 24" fill="none" aria-hidden="true">
<circle className="opacity-25" cx="12" cy="12" r="10" stroke="currentColor" strokeWidth="4" />
frontend/src/components/ui/Skeleton.tsx
-4
View File
@@ -1,9 +1,5 @@
const pulse = 'animate-pulse rounded-lg bg-white/[0.05]';
export function SkeletonLine({ className = '' }: { className?: string }) {
return <div className={`${pulse} h-4 w-full ${className}`} />;
}
export function SkeletonCard({ className = '' }: { className?: string }) {
return <div className={`${pulse} h-32 w-full ${className}`} />;
}
frontend/src/dev/harness.tsx
-146
View File
@@ -1,146 +0,0 @@
/* Dev-only visual harness for FundamentalsPanel. Served at /harness.html by
* `vite`. Not imported by the app. Renders the three key states so desktop and
* mobile can be eyeballed with representative fixtures. */
import { createRoot } from 'react-dom/client';
import '../styles/globals.css';
import { FundamentalsPanel } from '../components/ticker/FundamentalsPanel';
import type { FundamentalResponse, MetricItem } from '../lib/types';
function h(period: string, value: number | null) {
return { period_end: period, value };
}
const P = ['2025-06-30', '2025-09-30', '2025-12-31', '2026-03-28'];
function dateFromToday(days: number): string {
const date = new Date();
date.setHours(12, 0, 0, 0);
date.setDate(date.getDate() + days);
return [
date.getFullYear(),
String(date.getMonth() + 1).padStart(2, '0'),
String(date.getDate()).padStart(2, '0'),
].join('-');
}
function metric(key: string, value: number | null, hist: (number | null)[],
industry: MetricItem['industry'] = null,
caveat: string | null = null): MetricItem {
return {
key: key as MetricItem['key'], value,
history: hist.map((v, i) => h(P[i], v)),
industry, period_end: '2026-03-28', filed_date: '2026-05-01', caveat,
source: 'sec',
};
}
const ind = (median: number, favorable_percentile: number) =>
({ label: 'SIC 35 peers', median, favorable_percentile, peer_count: 12 });
const legacy = {
pe_ratio: null, revenue_growth: null, earnings_surprise: null, market_cap: null,
next_earnings_date: null, fetched_at: null, unavailable_fields: {},
setup_eligible: true, setup_block_code: null, setup_block_reason: null,
};
const full: FundamentalResponse = {
symbol: 'AAPL', ...legacy,
earnings: {
next: { date: dateFromToday(12), session: 'amc', days_until: 12 },
recent: [
{ announce_date: '2025-08-01', period_end: '2025-06-30', eps_estimate: 1.4, eps_actual: 1.6, surprise_pct: 14.3 },
{ announce_date: '2025-11-01', period_end: '2025-09-30', eps_estimate: 1.7, eps_actual: 1.9, surprise_pct: 11.8 },
{ announce_date: '2026-02-01', period_end: '2025-12-31', eps_estimate: 2.6, eps_actual: 2.4, surprise_pct: -7.7 },
{ announce_date: '2026-05-01', period_end: '2026-03-28', eps_estimate: 1.5, eps_actual: 1.65, surprise_pct: 10.0 },
],
},
metrics: [
metric('revenue_growth_yoy', 18, [8, 11, 15, 18], ind(11, 82)),
metric('eps_growth_yoy', 24, [10, 18, 22, 24], ind(15, 70)),
metric('operating_margin', 32, [30, 31, 31, 32], ind(22, 88)),
metric('fcf_margin', 28, [24, 25, 27, 28], ind(18, 80)),
metric('net_debt', 16.2e9, [46e9, 44e9, 24e9, 16.2e9], null),
metric('net_debt_to_ebitda', 1.4, [1.9, 1.7, 1.5, 1.4], ind(2.1, 68)),
metric('share_count_change_yoy', -1.7, [-2.4, -2.2, -2.3, -1.7], null),
],
valuation: {
pe: 29.2, fcf_yield: 3.8, market_cap_est: 3.2e12,
pe_industry: ind(23.5, 30), fcf_yield_industry: ind(3.1, 70), price_date: '2026-05-01',
},
reads: {
header: 'growth accelerating · margins improving · valuation priced above peers',
by_key: {
revenue_growth_yoy: 'accelerating', eps_growth_yoy: 'accelerating',
operating_margin: 'improving', fcf_margin: 'improving',
share_count_change_yoy: 'buying back', net_debt_to_ebitda: 'conservative leverage',
pe: 'priced above peers', fcf_yield: 'above peers', net_debt: null,
},
},
};
const partial: FundamentalResponse = {
symbol: 'NEWCO', ...legacy,
earnings: { next: { date: dateFromToday(0), session: 'unknown', days_until: 0 }, recent: [] },
metrics: [
metric('revenue_growth_yoy', 12, [null, 8, 10, 12], null),
metric(
'eps_growth_yoy',
null,
[null, null, null, null],
null,
'Not comparable: share count changed at least 25%; possible split or corporate action.',
),
metric('operating_margin', 25, [24, 24, 25, 25], null),
metric('fcf_margin', null, [null, null, null, null], null),
metric('net_debt', null, [], null),
metric('net_debt_to_ebitda', 1.9, [1.7, 1.8, 1.9, 1.9], null),
metric(
'share_count_change_yoy',
null,
[1.8, 2.0, 2.0, null],
null,
'Not comparable: share count changed at least 25%; possible split or corporate action.',
),
],
valuation: {
pe: 15.2, fcf_yield: null, market_cap_est: 5.4e8,
pe_industry: null, fcf_yield_industry: null, price_date: '2026-05-01',
},
reads: {
header: 'growth steady · margins stable',
by_key: {
revenue_growth_yoy: 'steady', operating_margin: 'stable',
share_count_change_yoy: '2.1% dilution', net_debt_to_ebitda: null,
pe: null, fcf_yield: null, eps_growth_yoy: null, fcf_margin: null, net_debt: null,
},
},
};
const empty: FundamentalResponse = {
symbol: 'ADR', ...legacy,
earnings: { next: null, recent: [] },
metrics: [
'revenue_growth_yoy', 'eps_growth_yoy', 'operating_margin', 'fcf_margin',
'net_debt', 'net_debt_to_ebitda', 'share_count_change_yoy',
].map((k) => metric(k, null, [])),
valuation: null,
reads: { header: null, by_key: {} },
};
function Case({ title, data }: { title: string; data: FundamentalResponse }) {
return (
<div>
<div className="mb-1.5 text-[11px] uppercase tracking-widest text-gray-500">{title}</div>
<FundamentalsPanel data={data} />
</div>
);
}
createRoot(document.getElementById('root')!).render(
<div className="mx-auto max-w-3xl space-y-8 p-6">
<p className="text-[11px] uppercase tracking-widest text-gray-500">
Desktop width (~768px, two columns). Resize the browser to ~390px to check mobile (single column).
</p>
<Case title="Full" data={full} />
<Case title="Partial · insufficient peers" data={partial} />
<Case title="Empty" data={empty} />
</div>,
);
frontend/src/hooks/useAdmin.ts
-38
View File
@@ -90,36 +90,6 @@ export function useUpdateSetting() {
});
}
export function useFundamentalsCutoverSettings() {
return useQuery({
queryKey: ['admin', 'fundamentals-cutover'],
queryFn: () => adminApi.getFundamentalsCutoverSettings(),
});
}
export function useUpdateFundamentalsCutoverSettings() {
const qc = useQueryClient();
const { addToast } = useToast();
return useMutation({
mutationFn: (enabled: boolean) =>
adminApi.updateFundamentalsCutoverSettings(enabled),
onSuccess: (config) => {
qc.setQueryData(['admin', 'fundamentals-cutover'], config);
qc.invalidateQueries({ queryKey: ['admin', 'settings'] });
addToast(
config.enabled ? 'success' : 'info',
config.enabled
? 'SEC + Dolt fundamentals activated'
: 'SEC + Dolt cache refresh paused',
);
},
onError: (error: Error) => {
addToast('error', error.message || 'Failed to update fundamentals data source');
},
});
}
export function useRecommendationSettings() {
return useQuery({
queryKey: ['admin', 'recommendation-settings'],
@@ -346,14 +316,6 @@ export function useJobs() {
});
}
export function useFundamentalsParityReport() {
return useQuery({
queryKey: ['admin', 'fundamentals-parity'],
queryFn: () => adminApi.getFundamentalsParityReport(),
refetchInterval: 15_000,
});
}
export function usePipelineReadiness() {
return useQuery({
queryKey: ['admin', 'pipeline-readiness'],
frontend/src/lib/exitPlan.ts
+1 -1
View File
@@ -21,7 +21,7 @@ import type { ExitPolicy, TradeSetup } from './types';
* Guarded by test_prod_strategy_parity.py so a backend change can't silently
* desync this.
*/
export const SETUP_STOP_ATR_MULTIPLIER = 1.5;
const SETUP_STOP_ATR_MULTIPLIER = 1.5;
export interface ExitPlan {
mode: ExitPolicy['mode'];
frontend/src/lib/fundamentals.ts
-112
View File
@@ -1,112 +0,0 @@
/**
* Fundamental dimension readouts for the Fundamentals tab.
*
* Scoring mirrors app/services/scoring_service.py _compute_fundamental_score:
* equal-weighted average of available P/E, revenue growth, and earnings
* surprise (need ≥2 metrics). Market cap is display-only, not scored.
*/
export interface FundamentalMetrics {
pe_ratio: number | null;
revenue_growth: number | null;
earnings_surprise: number | null;
market_cap?: number | null;
}
export interface StatusRead {
text: string;
tone: string;
}
const clamp = (v: number, lo = 0, hi = 100) => Math.max(lo, Math.min(hi, v));
/** P/E sub-score: lower is better. PE 15 → 100, 30 → 50, 45 → 0. */
export function peSubScore(pe: number): number {
return clamp(100 - (pe - 15) * (100 / 30));
}
/** Revenue growth sub-score: 0% → 50, +20% → 100, −20% → 0. */
export function revenueGrowthSubScore(growthPct: number): number {
return clamp(50 + growthPct * 2.5);
}
/** Earnings surprise sub-score: 0% → 50, +10% → 100, −10% → 0. */
export function earningsSurpriseSubScore(surprisePct: number): number {
return clamp(50 + surprisePct * 5);
}
/**
* Overall fundamental score, or null when fewer than 2 scored metrics.
* Matches backend MIN_METRICS = 2.
*/
export function fundamentalScore(m: FundamentalMetrics): number | null {
const parts: number[] = [];
if (m.pe_ratio != null && m.pe_ratio > 0) parts.push(peSubScore(m.pe_ratio));
if (m.revenue_growth != null) parts.push(revenueGrowthSubScore(m.revenue_growth));
if (m.earnings_surprise != null) parts.push(earningsSurpriseSubScore(m.earnings_surprise));
if (parts.length < 2) return null;
return parts.reduce((a, b) => a + b, 0) / parts.length;
}
export function overallFundamentalStatus(score: number | null): StatusRead {
if (score == null) {
return { text: 'incomplete data', tone: 'text-amber-300' };
}
if (score >= 70) return { text: 'strong fundamentals', tone: 'text-emerald-300' };
if (score >= 55) return { text: 'healthy', tone: 'text-emerald-300' };
if (score >= 45) return { text: 'mixed / average', tone: 'text-gray-400' };
if (score >= 30) return { text: 'soft', tone: 'text-amber-300' };
return { text: 'weak fundamentals', tone: 'text-red-300' };
}
export function peStatus(pe: number | null): StatusRead | null {
if (pe == null || !(pe > 0)) return null;
if (pe <= 15) return { text: 'cheap / attractive', tone: 'text-emerald-300' };
if (pe <= 25) return { text: 'fair', tone: 'text-gray-400' };
if (pe <= 35) return { text: 'expensive', tone: 'text-amber-300' };
return { text: 'rich', tone: 'text-red-300' };
}
export function revenueGrowthStatus(growthPct: number | null): StatusRead | null {
if (growthPct == null) return null;
if (growthPct >= 20) return { text: 'strong growth', tone: 'text-emerald-300' };
if (growthPct >= 5) return { text: 'solid growth', tone: 'text-emerald-300' };
if (growthPct >= -5) return { text: 'flat', tone: 'text-gray-400' };
if (growthPct >= -20) return { text: 'contracting', tone: 'text-amber-300' };
return { text: 'deep contraction', tone: 'text-red-300' };
}
export function earningsSurpriseStatus(surprisePct: number | null): StatusRead | null {
if (surprisePct == null) return null;
if (surprisePct >= 10) return { text: 'beat (large)', tone: 'text-emerald-300' };
if (surprisePct >= 2) return { text: 'beat', tone: 'text-emerald-300' };
if (surprisePct >= -2) return { text: 'in line', tone: 'text-gray-400' };
if (surprisePct >= -10) return { text: 'miss', tone: 'text-amber-300' };
return { text: 'miss (large)', tone: 'text-red-300' };
}
/** Size band only — not good/bad, not part of the score. */
export function marketCapStatus(marketCap: number | null): StatusRead | null {
if (marketCap == null || !(marketCap > 0)) return null;
if (marketCap >= 200e9) return { text: 'mega cap', tone: 'text-gray-400' };
if (marketCap >= 10e9) return { text: 'large cap', tone: 'text-gray-400' };
if (marketCap >= 2e9) return { text: 'mid cap', tone: 'text-gray-400' };
if (marketCap >= 300e6) return { text: 'small cap', tone: 'text-gray-400' };
return { text: 'micro cap', tone: 'text-gray-400' };
}
export function metricStatus(
key: 'pe_ratio' | 'revenue_growth' | 'earnings_surprise' | 'market_cap',
value: number | null,
): StatusRead | null {
switch (key) {
case 'pe_ratio':
return peStatus(value);
case 'revenue_growth':
return revenueGrowthStatus(value);
case 'earnings_surprise':
return earningsSurpriseStatus(value);
case 'market_cap':
return marketCapStatus(value);
}
}
frontend/src/lib/regime.ts
+1 -14
View File
@@ -1,18 +1,5 @@
import type { MarketRegime } from './types';
export function regimeColor(label: MarketRegime['label']): string {
switch (label) {
case 'bullish':
return 'text-emerald-400';
case 'bearish':
return 'text-red-400';
case 'neutral':
return 'text-amber-400';
default:
return 'text-gray-400';
}
}
export function regimeDot(label: MarketRegime['label']): string {
switch (label) {
case 'bullish':
@@ -36,7 +23,7 @@ export function regimeHeadline(r: MarketRegime): string {
return `${b} ${r.label}${pct}`;
}
/** Whether a setup direction fights the prevailing market regime. */
/** Whether a setup direction fights the prevailing SPY trend. */
export function isCounterTrend(direction: string, label: MarketRegime['label']): boolean {
if (label === 'bullish') return direction === 'short';
if (label === 'bearish') return direction === 'long';
frontend/src/lib/types.ts
+14 -8
View File
@@ -187,21 +187,17 @@ export interface ActivationConfig {
exclude_neutral: boolean;
}
export interface FundamentalsCutoverConfig {
enabled: boolean;
}
// Cron schedule for morning / near-close / after-close / intraday + fundamentals
export interface ScheduleConfig {
schedule_timezone: string;
schedule_daily_pipeline_cron: string;
schedule_dolt_earnings_cron: string;
schedule_sec_fundamentals_cron: string;
schedule_fundamentals_parity_cron: string;
schedule_near_close_pipeline_cron: string;
schedule_after_close_pipeline_cron: string;
schedule_intraday_pipeline_cron: string;
schedule_fundamentals_cron: string;
schedule_backtest_cron: string;
schedule_ticker_universe_cron: string;
}
// Runtime sentiment LLM configuration
@@ -506,6 +502,9 @@ export interface RegimeFundamentalOverlay {
reasoning: string | null;
source: string | null;
fetched_at: string | null;
/** Whether anything was actually collected. Live reading only; the snapshot's
* point-in-time overlay omits it. */
observed?: boolean;
observed_in_snapshot?: boolean;
}
@@ -555,12 +554,15 @@ export interface RegimeMonitor {
inputs_fresh: boolean;
snapshot_age_days?: number;
is_fresh?: boolean;
/** Upstream history spans, so a silently truncated series is visible. */
credit_history_days?: number | null;
vix_history_days?: number | null;
};
quadrant_config?: { state_divider: number; warning_divider: number; margin: number };
}
export interface RegimeFundamentals {
methodology: 'v3';
methodology: 'v4';
f1_score: number | null;
f3_score: number | null;
locked: boolean;
@@ -856,6 +858,10 @@ export interface Ticker {
symbol: string;
name: string | null;
created_at: string;
/** Set once the symbol stopped trading: excluded from signals, history kept. */
delisted_on: string | null;
/** How the delisting was learned: "form_25" (SEC confirmed) | "manual". */
delisted_reason: string | null;
}
// Admin
@@ -892,7 +898,7 @@ export interface TickerUniverseSetting {
export interface TickerUniverseBootstrapResult {
universe: TickerUniverse;
/** Where the member list came from: wikipedia_sp500 | fmp | cache | seed | … */
/** Where the member list came from: wikipedia_sp500 | nasdaq_trader | cache | seed | … */
source?: string;
total_universe_symbols: number;
added: number;
frontend/src/pages/AdminPage.tsx
-4
View File
@@ -5,8 +5,6 @@ import { AlertSettings } from '../components/admin/AlertSettings';
import { SentimentProviderSettings } from '../components/admin/SentimentProviderSettings';
import { DataCleanup } from '../components/admin/DataCleanup';
import { JobControls } from '../components/admin/JobControls';
import { FundamentalsParityPanel } from '../components/admin/FundamentalsParityPanel';
import { FundamentalsCutoverSettings } from '../components/admin/FundamentalsCutoverSettings';
import { PerformanceSettings } from '../components/admin/PerformanceSettings';
import { PipelineReadinessPanel } from '../components/admin/PipelineReadinessPanel';
import { SystemEventsPanel } from '../components/admin/SystemEventsPanel';
@@ -37,7 +35,6 @@ export default function AdminPage() {
{activeTab === 'Tickers' && <TickerManagement />}
{activeTab === 'Settings' && (
<div className="space-y-4">
<FundamentalsCutoverSettings />
<ActivationSettings />
<ExitPolicySettings />
<PerformanceSettings />
@@ -51,7 +48,6 @@ export default function AdminPage() {
{activeTab === 'Jobs' && (
<div className="space-y-4">
<ScheduleSettings />
<FundamentalsParityPanel />
<JobControls />
<PipelineReadinessPanel />
</div>
frontend/src/pages/RegimePage.tsx
+123 -70
View File
@@ -24,11 +24,11 @@ import type {
RegimeFundamentalOverlay,
RegimeFundamentals,
RegimeFundamentalsUpdate,
RegimeMonitor,
RegimeReading,
} from '../lib/types';
const ScoreHistoryChart = lazy(() => import('../components/regime/ScoreHistoryChart'));
const RegimeQuadrant = lazy(() => import('../components/regime/RegimeQuadrant'));
const RegimeChart = lazy(() => import('../components/regime/RegimeChart'));
const BAND_STYLES: Record<RegimeBand, { text: string; bar: string; ring: string; label: string }> = {
stable: { text: 'text-emerald-400', bar: 'bg-emerald-400', ring: 'border-emerald-400/30', label: 'Stable' },
@@ -53,12 +53,10 @@ function TrendChip({ label, delta }: { label: string; delta: number | null | und
function ScoreGauge({
label,
reading,
divider,
footnote,
}: {
label: string;
reading: RegimeReading | undefined;
divider?: number;
footnote: ReactNode;
}) {
const score = reading?.score;
@@ -66,7 +64,9 @@ function ScoreGauge({
const style = complete ? BAND_STYLES[reading.band as RegimeBand] : null;
const position = Math.min(100, Math.max(0, score ?? 0));
const bands = reading?.bands;
const ticks = bands ? [bands.watch, bands.elevated, bands.breaking] : [30, 60, 80];
// No fallback ticks: the two axes have different thresholds, so guessing a
// shared set would mislabel one of them. Render none rather than wrong ones.
const ticks = bands ? [bands.watch, bands.elevated, bands.breaking] : [];
return (
<div className={`glass border p-6 ${style?.ring ?? 'border-white/[0.06]'}`}>
<div className="flex flex-wrap items-end justify-between gap-3">
@@ -92,10 +92,10 @@ function ScoreGauge({
</div>
{score != null && (
<>
{/* The quadrant divider is each axis's watch/elevated boundary, so it
is already the middle tick below — drawing it again was two marks
for one threshold. */}
<div className="relative mt-5 h-2 rounded-full bg-gradient-to-r from-emerald-500/30 via-amber-500/30 to-red-500/40">
{divider != null && (
<div className="absolute -top-1 h-4 w-0.5 bg-gray-300/70" style={{ left: `${divider}%` }} />
)}
<div
className={`absolute -top-1.5 h-5 w-5 -translate-x-1/2 rounded-full border-2 border-white/70 ${style?.bar ?? 'bg-gray-500'}`}
style={{ left: `${position}%` }}
@@ -113,7 +113,7 @@ function ScoreGauge({
</div>
</>
)}
<p className="mt-4 text-xs leading-relaxed text-gray-500">{footnote}</p>
<p className="mt-4 text-xs text-gray-500">{footnote}</p>
</div>
);
}
@@ -125,30 +125,48 @@ const CAPEX_TONE: Record<CapexState, string> = {
unknown: 'text-gray-500',
};
const OVERLAY_TITLE = 'Fundamental overlay · context, not scored';
function FundamentalOverlayCard({ overlay }: { overlay: RegimeFundamentalOverlay }) {
const capex = overlay.capex ?? {};
const reaction = overlay.good_news_stock_down;
// Nothing collected: the stored default is "unknown" for every hyperscaler
// and "mixed" for the reaction, which are placeholders, not a reading.
if (overlay.observed === false) {
return (
<div className="glass border border-white/[0.06] p-5">
<div className="text-[11px] uppercase tracking-wider text-gray-500">{OVERLAY_TITLE}</div>
<p className="mt-3 text-xs text-gray-500">
No observation collected yet. An admin can collect one under Admin · Monitor settings. It is
context only — it never enters State or Warning.
</p>
</div>
);
}
return (
<div className="glass border border-white/[0.06] p-5">
<div className="flex flex-wrap items-baseline justify-between gap-2">
<div className="text-[11px] uppercase tracking-wider text-gray-500">
Fundamental overlay · context, not scored
</div>
<div className="text-[11px] uppercase tracking-wider text-gray-500">{OVERLAY_TITLE}</div>
<div className="flex flex-wrap items-center gap-2 text-[11px] text-gray-500">
{overlay.source && <span>{overlay.source}</span>}
{overlay.effective_date && <span>· effective {overlay.effective_date}</span>}
{/* When pending, the line below is the single carrier of this date. */}
{overlay.effective_date && !overlay.pending && <span>· effective {overlay.effective_date}</span>}
{overlay.pending && <Badge label="pending" variant="manual" />}
{overlay.stale && <Badge label="stale" variant="manual" />}
</div>
</div>
{overlay.pending ? (
<p className="mt-3 text-xs leading-relaxed text-amber-400/90">
A newer observation was collected but is not effective until {overlay.effective_date ?? 'the next session'}.
Observations are never backdated, so the reading below appears from that session onward.
{/* A pending observation is still shown — it is the freshest read we
have, and nothing here is scored. The date says when the stored
point-in-time record picks it up. */}
{overlay.pending && (
<p className="mt-3 text-xs text-amber-400/90">
Shown as collected. The point-in-time record picks it up{' '}
{overlay.effective_date ?? 'next session'} — observations are never backdated.
</p>
) : (
<>
)}
<div className="mt-4 grid gap-4 sm:grid-cols-2">
<div>
<div className="mb-2 flex items-baseline justify-between text-xs">
@@ -174,24 +192,20 @@ function FundamentalOverlayCard({ overlay }: { overlay: RegimeFundamentalOverlay
</div>
</div>
</div>
{overlay.reasoning && (
<p className="mt-4 text-xs leading-relaxed text-gray-400">{overlay.reasoning}</p>
)}
</>
)}
<p className="mt-4 text-[11px] leading-relaxed text-gray-600">
These observations are qualitative, refreshed roughly quarterly, and deliberately excluded from State and
Warning. In v2 they carried 20 of 100 Warning points — not enough to cross the study's alarm threshold even
when both were pegged — so they are reported here rather than diluted into a daily score.
</p>
{overlay.reasoning && <p className="mt-4 text-xs leading-relaxed text-gray-400">{overlay.reasoning}</p>}
</div>
);
}
function PillarBreakdown({ title, reading }: { title: string; reading: RegimeReading }) {
/** One table for both axes — they share a shape, and two panels invited
* comparing numbers that are not on the same scale. */
function PillarTable({ state, warning }: { state: RegimeReading; warning: RegimeReading }) {
const groups: { title: string; reading: RegimeReading }[] = [
{ title: 'State', reading: state },
{ title: 'Warning', reading: warning },
];
return (
<Disclosure summary={`${title} pillars · ${Math.round(reading.coverage)}% coverage`}>
<Disclosure summary="Pillars & sensors · what drives each score">
<div className="overflow-x-auto rounded-lg border border-white/[0.06]">
<table className="w-full text-sm">
<thead>
@@ -202,7 +216,16 @@ function PillarBreakdown({ title, reading }: { title: string; reading: RegimeRea
<th className="px-4 py-3 text-right font-medium">Contribution</th>
</tr>
</thead>
<tbody>
{groups.map(({ title, reading }) => (
<tbody key={title}>
<tr className="border-b border-white/[0.06] bg-white/[0.02]">
<td colSpan={4} className="px-4 py-2 text-[11px] uppercase tracking-wider text-gray-400">
{title}
<span className="ml-2 normal-case tracking-normal text-gray-600">
{reading.score ?? '—'} · {Math.round(reading.coverage)}% coverage
</span>
</td>
</tr>
{reading.pillars.map((pillar) => (
<tr key={pillar.id} className="border-b border-white/[0.04] align-top last:border-0">
<td className="px-4 py-3">
@@ -218,16 +241,53 @@ function PillarBreakdown({ title, reading }: { title: string; reading: RegimeRea
</td>
<td className="px-4 py-3 text-right num text-gray-300">{pillar.score ?? '—'}</td>
<td className="px-4 py-3 text-right num text-gray-400">{pillar.weight}</td>
<td className="px-4 py-3 text-right num text-gray-300">{pillar.available ? pillar.contribution.toFixed(1) : '—'}</td>
<td className="px-4 py-3 text-right num text-gray-300">
{pillar.available ? pillar.contribution.toFixed(1) : '—'}
</td>
</tr>
))}
</tbody>
))}
</table>
</div>
</Disclosure>
);
}
function MetaChip({ label, value, title }: { label: string; value: ReactNode; title?: string }) {
return (
<span className="rounded-lg bg-white/[0.03] px-2.5 py-1 text-[11px] text-gray-500" title={title}>
{label} <span className="num text-gray-400">{value}</span>
</span>
);
}
/** Provenance strip — replaces three separate prose blocks. */
function MetaStrip({ data }: { data: RegimeMonitor }) {
const quality = data.data_quality;
const basket = data.basket;
const days = (value: number | null | undefined) => (value == null ? '—' : `${value}d`);
return (
<div className="flex flex-wrap items-center gap-2">
<MetaChip label="as of" value={data.date ?? '—'} />
<MetaChip label="oldest input" value={days(quality?.oldest_market_input_age_days)} />
{basket && (
<MetaChip
label="basket"
value={`${basket.members_available ?? '—'}/${basket.members_expected} · frozen ${basket.basket_asof}`}
title={`hash ${basket.hash}`}
/>
)}
<MetaChip
label="credit history"
value={days(quality?.credit_history_days)}
title="Upstream span actually available. ICE caps the HY OAS series at 3 rolling years."
/>
<MetaChip label="VIX history" value={days(quality?.vix_history_days)} />
</div>
);
}
function EventStudyBody({ report }: { report: EventStudyReport }) {
const metrics = report.metrics;
return (
@@ -278,9 +338,7 @@ function EventStudyBody({ report }: { report: EventStudyReport }) {
<p>
<strong>Underpowered.</strong> Only {report.reliability.events_in_holdout} of{' '}
{report.reliability.events_detected} detected corrections fall in the test period (
{report.reliability.minimum_events}+ needed). Recall is one event away from a materially
different headline, and which events flip is usually decided by where the frozen threshold
lands rather than by what the score saw. Read the direction, not the ratio.
{report.reliability.minimum_events}+ needed). Read the direction, not the ratio.
</p>
)}
{report.reliability.sensor_coverage_mismatch && (
@@ -290,17 +348,12 @@ function EventStudyBody({ report }: { report: EventStudyReport }) {
{report.reliability.sensors_expected} Warning sensors versus{' '}
{report.reliability.holdout_full_sensor_share}% of test sessions
{report.params?.credit_sensor_from && ` — credit history begins ${report.params.credit_sensor_from}`}
. The score renormalises over what is available, so the threshold was frozen on a partly
different construct than it is measured against.
. The threshold was frozen on a partly different construct than it is measured against.
</p>
)}
</div>
</Callout>
)}
<p className="text-[11px] leading-relaxed text-gray-600">
The threshold is frozen on the training period and measured on the chronological test period. Reconstructed
pre-freeze basket history remains exploratory.
</p>
</div>
);
}
@@ -375,7 +428,7 @@ function FundamentalsEditor({
</label>
))}
</div>
<p className="mt-1.5 text-[11px] text-gray-600">Raising = 0, holding = 50, cutting = 100; at least three known names required. Display only — this does not enter Warning.</p>
<p className="mt-1.5 text-[11px] text-gray-600">Raising = 0, holding = 50, cutting = 100; at least three known names required.</p>
</div>
<label className="flex items-center justify-between gap-3 text-xs text-gray-400">
<span>
@@ -450,14 +503,17 @@ export default function RegimePage() {
const isAdmin = useAuthStore((state) => state.role) === 'admin';
const monitor = useQuery({ queryKey: ['regime', 'monitor'], queryFn: getRegimeMonitor });
const data = monitor.data;
const inputs = data?.inputs;
return (
<div className="space-y-6 animate-slide-up">
<PageHeader title="Regime Monitor" subtitle="AI/Tech risk thermometer · State and Warning · feeds no trades" />
<Callout variant="info"><strong>Risk thermometer — not an entry, exit, or sizing signal.</strong> State measures current stress; Warning measures deterioration and divergence.</Callout>
<PageHeader
title="AI/Tech Risk Monitor"
subtitle="AI/Tech risk thermometer — observational only, feeds no entry, exit, or sizing decision"
/>
{monitor.isLoading && <><SkeletonCard className="h-44" /><SkeletonTable rows={6} cols={4} /></>}
{monitor.isError && <Callout variant="error" onRetry={() => monitor.refetch()}>Failed to load: {(monitor.error as Error).message}</Callout>}
{data && !data.available && <Callout variant="empty">V2 is not computed yet — run “Regime Monitor” from Admin → Jobs or wait for the daily pipeline.</Callout>}
{data && !data.available && <Callout variant="empty">Not computed yet — run “AI/Tech Risk Monitor” from Admin → Jobs or wait for the daily pipeline.</Callout>}
{data?.available && data.state && data.warning && (
<>
@@ -467,39 +523,36 @@ export default function RegimePage() {
{data.data_quality?.stale_inputs?.length ? ` · stale: ${data.data_quality.stale_inputs.join(', ')}` : ''}.
</Callout>
)}
<div className="grid gap-4 lg:grid-cols-2">
<ScoreGauge
label="State · current structural stress"
label="State · stress right now"
reading={data.state}
divider={data.quadrant_config?.state_divider}
footnote={<>One capped price vote plus fixed-basket breadth, HY credit, and volatility. As of {data.date}. VIX {data.inputs?.vix ?? '—'} · HY OAS {data.inputs?.hy_oas ?? '—'}.</>}
footnote={
<>
Price, breadth, credit and volatility levels · VIX{' '}
<span className="num text-gray-400">{inputs?.vix ?? '—'}</span> · HY OAS{' '}
<span className="num text-gray-400">{inputs?.hy_oas ?? '—'}</span> · breadth{' '}
<span className="num text-gray-400">
{inputs?.breadth_pct_above_200 == null ? '—' : `${inputs.breadth_pct_above_200}%`}
</span>
</>
}
/>
<ScoreGauge
label="Warning · deterioration & divergence"
reading={data.warning}
divider={data.quadrant_config?.warning_divider}
footnote={<>Breadth divergence, SMH/SPY rollover, and HY credit impulse. Breadth loss counts fully when price masks it and partially when price confirms it. Missing sensors reduce coverage; they never default to 50.</>}
footnote="Breadth divergence, SMH/SPY rollover, and HY credit impulse. Missing sensors reduce coverage; they never default to 50."
/>
</div>
{data.fundamental_context && <FundamentalOverlayCard overlay={data.fundamental_context} />}
<p className="text-xs text-gray-600">
Data quality · oldest market input:{' '}
{data.data_quality?.oldest_market_input_age_days == null
? 'unavailable'
: `${data.data_quality.oldest_market_input_age_days}d`}
</p>
<Suspense fallback={<SkeletonCard className="h-80" />}><RegimeQuadrant /></Suspense>
<Suspense fallback={<SkeletonCard className="h-72" />}><ScoreHistoryChart /></Suspense>
<div className="grid gap-3 lg:grid-cols-2">
<PillarBreakdown title="State" reading={data.state} />
<PillarBreakdown title="Warning" reading={data.warning} />
</div>
{data.basket && (
<p className="text-xs leading-relaxed text-gray-600">
Fixed basket {data.basket.members_available ?? '—'}/{data.basket.members_expected} available · hash {data.basket.hash} · frozen {data.basket.basket_asof}. History reconstructed before the freeze date is retrospective/exploratory; readings after it form the trustworthy forward series.
</p>
)}
<Suspense fallback={<SkeletonCard className="h-80" />}><RegimeChart /></Suspense>
<PillarTable state={data.state} warning={data.warning} />
{data.fundamental_context && <FundamentalOverlayCard overlay={data.fundamental_context} />}
<MetaStrip data={data} />
</>
)}
frontend/src/pages/TickerDetailPage.tsx
+6 -3
View File
@@ -102,7 +102,7 @@ interface DataStatusItem {
available: boolean;
timestamp?: string | null;
timestampLabel?: string | null;
selector: FetchSelector; // what a refresh of this row fetches
selector?: FetchSelector; // what a refresh fetches; omit for rows with no manual refresh
paid?: boolean; // provider call that may cost money/quota
}
@@ -138,6 +138,7 @@ function DataFreshnessBar({
) : !item.available ? (
<span className="text-[10px] text-gray-600">no data</span>
) : null}
{item.selector && (
<button
onClick={() => onRefresh(item)}
disabled={busy}
@@ -146,6 +147,7 @@ function DataFreshnessBar({
>
<RefreshIcon spinning={pendingLabel === item.label} />
</button>
)}
{item.paid && <span className="text-[9px] text-amber-500/70" title="Uses a paid/quota provider call">$</span>}
</div>
))}
@@ -226,11 +228,11 @@ export default function TickerDetailPage() {
paid: true,
},
{
// Rebuilt for the whole universe by the nightly SEC + Dolt imports —
// there is no per-ticker fetch to offer here.
label: 'Fundamentals',
available: !!fundamentals.data && fundamentals.data.fetched_at !== null,
timestamp: fundamentals.data?.fetched_at,
selector: ['fundamentals'] as FetchSelector,
paid: true,
},
{
label: 'S/R Levels',
@@ -247,6 +249,7 @@ export default function TickerDetailPage() {
], [ohlcv.data, sentiment.data, fundamentals.data, srLevels.data, scores.data]);
const handleRefresh = (item: DataStatusItem) => {
if (!item.selector) return;
setRefreshingLabel(item.label);
ingestion.mutate(
{ symbol, sources: item.selector },
pyproject.toml
+18
View File
@@ -40,3 +40,21 @@ include = ["app*"]
[tool.pytest.ini_options]
asyncio_mode = "auto"
testpaths = ["tests"]
[tool.ruff]
target-version = "py312"
[tool.ruff.lint]
# Pinned explicitly rather than inherited. CI installs ruff unpinned, and the
# default rule set is not stable across releases: 0.16 broadened it so far that
# `ruff check app/` went from 0 findings to 376 -- 168 of them B008 flagging
# FastAPI's `Depends()` in a signature default, which is the framework's
# documented idiom and not a defect. An unpinned linter with drifting defaults
# fails the deploy pipeline on code nobody touched, so the rule set is the thing
# to pin; the ruff version can then float freely.
#
# E4 imports, E7 statements, E9 syntax/IO errors, F pyflakes. This is the set the
# tree was already clean under, now applied repo-wide instead of to app/ alone.
# Adding rules is welcome -- do it here, deliberately, with the fixes in the same
# commit.
select = ["E4", "E7", "E9", "F"]
reports/regime-monitor-v4-calibration-20260808-231644.json
+2094
View File
File diff suppressed because it is too large Load Diff
reports/regime-monitor-v4-calibration-20260808-231644.md
+88
View File
@@ -0,0 +1,88 @@
# Regime Monitor v4 calibration
Generated 2026-08-08T23:16:44 at `43ee619`, 2024-12-05 → 2026-07-24.
Source hashes (sha256, first 16):
- `app/services/regime_monitor_service.py` — `3b307e3c2045f35b`
- `app/services/breadth_service.py` — `b9ceb93d68d8f01b`
- `scripts/run_regime_monitor_calibration.py` — `9bc925256cc6f567`
## Hard gates
| gate | expected | measured | |
|---|---|---|---|
| symbols_fetched | 33 | 33 | ok |
| per_symbol_warmup_252_bars | all | 33 | ok |
| per_symbol_reaches_last_session | 2026-07-24 | 33 | ok |
| breadth_counts_full_basket | 30 | 408/408 sessions | ok |
| sessions_scored | 408 | 408 | ok |
| last_scored_date | 2026-07-24 | 2026-07-24 | ok |
| w1_available_every_session | 408 | 408 | ok |
| state_coverage_100_every_row | 0 | 0 | ok |
| no_stale_inputs | 0 | 0 | ok |
| first_scored_date | 2024-12-05 | 2024-12-05 | ok |
| state_v4_le_v3_every_row | 0 | 0 | ok |
## Distributions
| variant | avg | median | p80 | p90 | max |
|---|---|---|---|---|---|
| v2_reconstruction | 22.68 | 16.15 | 35.1 | 65.63 | 91.2 |
| v2_reconstruction_oas400 | 26.54 | 18.65 | 42.52 | 81.3 | 100.0 |
| v3 | 18.13 | 9.1 | 31.36 | 65.0 | 87.4 |
| v4 | 14.78 | 8.35 | 21.7 | 43.63 | 83.5 |
| v4-vix-only | 16.64 | 8.35 | 28.6 | 61.59 | 86.6 |
| v4-p1-only | 16.28 | 9.1 | 25.44 | 45.63 | 84.0 |
| v4-vix-b | 15.24 | 8.55 | 22.62 | 44.33 | 83.6 |
| v4-p1-capped | 14.73 | 8.35 | 21.7 | 43.63 | 80.1 |
## Saturation census (sessions pegged at 100)
| variant | P1 | P2 | P3 | V1 |
|---|---|---|---|---|
| v2_reconstruction | 46 | 0 | 39 | 14 |
| v2_reconstruction_oas400 | 46 | 0 | 39 | 14 |
| v3 | 46 | 0 | 0 | 14 |
| v4 | 0 | 0 | 0 | 0 |
| v4-vix-only | 46 | 0 | 0 | 0 |
| v4-p1-only | 0 | 0 | 0 | 14 |
| v4-vix-b | 0 | 0 | 0 | 0 |
| v4-p1-capped | 0 | 0 | 0 | 0 |
## Reproduction gates — v2_reconstruction
| figure | published | measured | |
|---|---|---|---|
| v2_state_avg | 22.6 | 22.68 | ok |
| v2_state_p80 | 35.1 | 35.1 | ok |
| v2_state_max | 91.2 | 91.2 | ok |
| v2_p3_pegged | 39 | 39 | ok |
| w1_live_sessions | 108 | 108 | ok |
## Reproduction gates — v3
| figure | published | measured | |
|---|---|---|---|
| v3_state_max | 87.4 | 87.4 | ok |
## v4 band-share grid (watch 20 / elevated 50)
| breaking | stable | watch | elevated | breaking |
|---|---|---|---|---|
| 60 | 78.9 | 13.0 | 2.9 | 5.1 |
| 65 | 78.9 | 13.0 | 4.7 | 3.4 |
| 70 | 78.9 | 13.0 | 6.9 | 1.2 |
## Scenarios (pillar arithmetic, explicit sensor scores)
| scenario | price | breadth | C1 | V1 | State |
|---|---|---|---|---|---|
| S1 ordinary tape | 7.5 | 0.0 | 0.0 | 4.0 | **3.6** |
| S2 10% correction, calm credit | 31.25 | 62.5 | 0.0 | 34.4 | **33.28** |
| S3a 2022-style, calm credit, no death cross | 90.83 | 100.0 | 0.0 | 60.0 | **70.33** |
| S3b 2022-style, calm credit, death cross | 100.0 | 100.0 | 0.0 | 60.0 | **74.0** |
| S4 credit event on top | 100.0 | 100.0 | 75.0 | 86.67 | **93.0** |
| S5 March 2020, everything pegged | 100.0 | 100.0 | 100.0 | 100.0 | **100.0** |
Recommendation: `{'state_bands_candidate': [20.0, 50.0, 65.0], 'provisional': False, 'note': 'confirm against band_grid + scenarios before shipping'}`
scripts/backfill_earnings_events.py
-515
View File
@@ -1,515 +0,0 @@
"""Bulk-only historical earnings backfill for a local SQLite snapshot.
The job uses FMP's date-range earnings-calendar endpoint. One request covers all
symbols in a date window; per-symbol endpoints are intentionally not available
in this task runner. Successful windows are committed independently so a later
run resumes after a daily quota boundary without repeating completed windows.
Example:
python scripts/backfill_earnings_events.py --snapshot backtest_snapshots/prod.sqlite \
--from-date 2012-01-01 --window-days 30 --limit 250
"""
from __future__ import annotations
import argparse
import asyncio
import json
import math
import sys
from datetime import date, datetime, timedelta, timezone
from pathlib import Path
from typing import Any
import httpx
from sqlalchemy import create_engine, text
ROOT = Path(__file__).resolve().parents[1]
if str(ROOT) not in sys.path:
sys.path.insert(0, str(ROOT))
from app.ssl_bootstrap import bootstrap_ssl # noqa: E402
bootstrap_ssl()
FMP_STABLE = "https://financialmodelingprep.com/stable"
EVENTS_DDL = """
CREATE TABLE IF NOT EXISTS earnings_events (
id INTEGER PRIMARY KEY,
symbol TEXT NOT NULL,
announce_date TEXT NOT NULL,
announce_time TEXT,
eps_estimate REAL,
eps_actual REAL,
revenue_estimate REAL,
revenue_actual REAL,
source TEXT NOT NULL,
fetched_at TEXT NOT NULL,
UNIQUE(symbol, announce_date)
)
"""
META_DDL = """
CREATE TABLE IF NOT EXISTS earnings_backfill_meta (
symbol TEXT PRIMARY KEY,
status TEXT NOT NULL,
n_events INTEGER NOT NULL DEFAULT 0,
updated_at TEXT NOT NULL,
note TEXT
)
"""
WINDOW_DDL = """
CREATE TABLE IF NOT EXISTS earnings_backfill_windows (
from_date TEXT NOT NULL,
to_date TEXT NOT NULL,
status TEXT NOT NULL,
requests INTEGER NOT NULL DEFAULT 0,
rows_raw INTEGER NOT NULL DEFAULT 0,
rows_universe INTEGER NOT NULL DEFAULT 0,
duplicate_rows INTEGER NOT NULL DEFAULT 0,
restated_rows INTEGER NOT NULL DEFAULT 0,
updated_at TEXT NOT NULL,
note TEXT,
PRIMARY KEY(from_date, to_date)
)
"""
def _parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--snapshot", default="backtest_snapshots/prod.sqlite")
parser.add_argument("--from-date", default="2012-01-01")
parser.add_argument("--to-date", default=None)
parser.add_argument("--window-days", type=int, default=30)
parser.add_argument("--limit", type=int, default=250)
parser.add_argument("--sleep", type=float, default=0.35)
parser.add_argument(
"--refetch-windows",
action="store_true",
help="Re-fetch date windows already logged as done.",
)
return parser.parse_args()
def _ensure_tables(engine) -> None:
with engine.begin() as conn:
conn.execute(text(EVENTS_DDL))
conn.execute(text(META_DDL))
conn.execute(text(WINDOW_DDL))
def _number(value: Any) -> float | None:
if value is None or value == "":
return None
try:
result = float(value)
except (TypeError, ValueError):
return None
return result if math.isfinite(result) else None
def _normalise_session(value: Any) -> str | None:
if value is None:
return None
cleaned = str(value).strip().lower().replace("_", " ").replace("-", " ")
aliases = {
"bmo": "bmo",
"before market open": "bmo",
"before open": "bmo",
"amc": "amc",
"after market close": "amc",
"after close": "amc",
"during market hours": "during",
"dmh": "during",
}
return aliases.get(cleaned, cleaned or None)
def _parse_bulk_item(item: dict) -> dict | None:
symbol = str(item.get("symbol") or "").strip().upper().replace(".", "-")
raw_date = item.get("date") or item.get("earningsDate")
if not symbol or not raw_date:
return None
return {
"symbol": symbol,
"announce_date": str(raw_date)[:10],
"announce_time": _normalise_session(
item.get("time") or item.get("announceTime")
),
"eps_estimate": _number(
item.get("epsEstimated")
if item.get("epsEstimated") is not None
else item.get("estimatedEarning")
),
"eps_actual": _number(
item.get("epsActual")
if item.get("epsActual") is not None
else item.get("eps")
),
"revenue_estimate": _number(item.get("revenueEstimated")),
"revenue_actual": _number(item.get("revenueActual")),
}
def _windows(start: date, end: date, window_days: int) -> list[tuple[date, date]]:
if window_days < 1:
raise ValueError("window_days must be positive")
result: list[tuple[date, date]] = []
cursor = start
while cursor <= end:
window_end = min(end, cursor + timedelta(days=window_days - 1))
result.append((cursor, window_end))
cursor = window_end + timedelta(days=1)
return result
def _dedupe_bulk_rows(rows: list[dict]) -> tuple[list[dict], int, int]:
"""Prefer the most complete duplicate; use the later row as the tie-break."""
fields = (
"announce_time",
"eps_estimate",
"eps_actual",
"revenue_estimate",
"revenue_actual",
)
chosen: dict[tuple[str, str], dict] = {}
duplicate_extras = 0
restated = 0
for row in rows:
key = (str(row["symbol"]), str(row["announce_date"]))
previous = chosen.get(key)
if previous is None:
chosen[key] = row
continue
duplicate_extras += 1
if any(
previous.get(field) is not None
and row.get(field) is not None
and previous.get(field) != row.get(field)
for field in fields
):
restated += 1
previous_score = sum(previous.get(field) is not None for field in fields)
new_score = sum(row.get(field) is not None for field in fields)
if new_score >= previous_score:
chosen[key] = row
return list(chosen.values()), duplicate_extras, restated
def _upsert_events(conn, rows: list[dict]) -> int:
if not rows:
return 0
fetched_at = datetime.now(timezone.utc).isoformat()
statement = text(
"""
INSERT INTO earnings_events (
symbol, announce_date, announce_time, eps_estimate, eps_actual,
revenue_estimate, revenue_actual, source, fetched_at
) VALUES (
:symbol, :announce_date, :announce_time, :eps_estimate, :eps_actual,
:revenue_estimate, :revenue_actual, 'fmp_earnings_calendar', :fetched_at
)
ON CONFLICT(symbol, announce_date) DO UPDATE SET
announce_time=COALESCE(excluded.announce_time, earnings_events.announce_time),
eps_estimate=COALESCE(excluded.eps_estimate, earnings_events.eps_estimate),
eps_actual=COALESCE(excluded.eps_actual, earnings_events.eps_actual),
revenue_estimate=COALESCE(excluded.revenue_estimate, earnings_events.revenue_estimate),
revenue_actual=COALESCE(excluded.revenue_actual, earnings_events.revenue_actual),
source=excluded.source,
fetched_at=excluded.fetched_at
"""
)
conn.execute(statement, [{**row, "fetched_at": fetched_at} for row in rows])
return len(rows)
async def _fetch_bulk_window(
client: httpx.AsyncClient, api_key: str, start: date, end: date
) -> tuple[list[dict], int, str | None]:
response = await client.get(
f"{FMP_STABLE}/earnings-calendar",
params={"from": start.isoformat(), "to": end.isoformat(), "apikey": api_key},
)
if response.status_code in (402, 403):
return [], response.status_code, "bulk_endpoint_unavailable"
if response.status_code == 429:
return [], response.status_code, "daily_limit_reached"
response.raise_for_status()
payload = response.json()
if not isinstance(payload, list):
return [], response.status_code, f"unexpected_payload:{type(payload).__name__}"
rows = []
for item in payload:
if isinstance(item, dict):
parsed = _parse_bulk_item(item)
if parsed:
rows.append(parsed)
return rows, response.status_code, None
def _write_window_status(
engine,
*,
start: date,
end: date,
status: str,
raw_n: int = 0,
universe_n: int = 0,
duplicate_n: int = 0,
restated_n: int = 0,
note: str | None = None,
) -> None:
with engine.begin() as conn:
conn.execute(
text(
"""
INSERT INTO earnings_backfill_windows(
from_date, to_date, status, requests, rows_raw, rows_universe,
duplicate_rows, restated_rows, updated_at, note
) VALUES (:a, :b, :status, 1, :raw, :uni, :dup, :rest, :now, :note)
ON CONFLICT(from_date, to_date) DO UPDATE SET
status=excluded.status,
requests=earnings_backfill_windows.requests + 1,
rows_raw=excluded.rows_raw,
rows_universe=excluded.rows_universe,
duplicate_rows=excluded.duplicate_rows,
restated_rows=excluded.restated_rows,
updated_at=excluded.updated_at,
note=excluded.note
"""
),
{
"a": start.isoformat(),
"b": end.isoformat(),
"status": status,
"raw": raw_n,
"uni": universe_n,
"dup": duplicate_n,
"rest": restated_n,
"now": datetime.now(timezone.utc).isoformat(),
"note": note,
},
)
async def _main() -> None:
args = _parse_args()
snapshot = Path(args.snapshot)
if not snapshot.exists():
raise SystemExit(f"Snapshot not found: {snapshot}")
from app.config import settings
if not settings.fmp_api_key:
raise SystemExit("FMP_API_KEY required")
start = date.fromisoformat(args.from_date)
end = date.fromisoformat(args.to_date) if args.to_date else date.today()
if start > end:
raise SystemExit("--from-date must not be after --to-date")
engine = create_engine(f"sqlite:///{snapshot.resolve().as_posix()}", future=True)
_ensure_tables(engine)
all_windows = _windows(start, end, int(args.window_days))
with engine.connect() as conn:
symbols = [
str(row[0]).upper().replace(".", "-")
for row in conn.execute(text("SELECT symbol FROM tickers ORDER BY symbol"))
]
completed = {
(str(row[0]), str(row[1]))
for row in conn.execute(
text(
"SELECT from_date, to_date FROM earnings_backfill_windows "
"WHERE status='done'"
)
)
}
pending = [
window
for window in all_windows
if args.refetch_windows
or (window[0].isoformat(), window[1].isoformat()) not in completed
]
universe = set(symbols)
print(f"Snapshot: {snapshot}")
print(f"Universe: {len(symbols)} symbols")
print(f"Window: {start} -> {end}")
print(
f"Bulk windows: {len(all_windows)} total; "
f"{len(all_windows) - len(pending)} done; {len(pending)} pending"
)
print("Provider: FMP bulk earnings-calendar only")
requests_this_run = 0
rows_upserted = 0
duplicate_rows = 0
restated_rows = 0
stop_note: str | None = None
async with httpx.AsyncClient(timeout=60.0) as client:
for index, (window_start, window_end) in enumerate(pending, 1):
if requests_this_run >= int(args.limit):
stop_note = "request_budget_exhausted"
break
try:
raw_rows, status_code, error = await _fetch_bulk_window(
client, settings.fmp_api_key, window_start, window_end
)
except Exception as exc:
raw_rows, status_code = [], 0
error = f"request_error:{type(exc).__name__}:{exc}"
requests_this_run += 1
if error:
_write_window_status(
engine,
start=window_start,
end=window_end,
status="error",
note=f"http={status_code} {error}"[:300],
)
stop_note = error
print(
f"STOP {window_start}..{window_end}: {error} "
f"(http={status_code}, request={requests_this_run})"
)
break
in_universe = [row for row in raw_rows if row["symbol"] in universe]
deduped, duplicate_n, restated_n = _dedupe_bulk_rows(in_universe)
with engine.begin() as conn:
rows_upserted += _upsert_events(conn, deduped)
_write_window_status(
engine,
start=window_start,
end=window_end,
status="done",
raw_n=len(raw_rows),
universe_n=len(deduped),
duplicate_n=duplicate_n,
restated_n=restated_n,
note="bulk",
)
duplicate_rows += duplicate_n
restated_rows += restated_n
if index == 1 or index % 10 == 0 or index == len(pending):
print(
f"progress windows={index}/{len(pending)} "
f"requests={requests_this_run}/{args.limit} "
f"last={window_start}..{window_end} rows={len(deduped)}"
)
if args.sleep > 0:
await asyncio.sleep(float(args.sleep))
with engine.begin() as conn:
windows_done = int(
conn.execute(
text(
"SELECT COUNT(*) FROM earnings_backfill_windows "
"WHERE status='done' AND from_date >= :a AND to_date <= :b"
),
{"a": start.isoformat(), "b": end.isoformat()},
).scalar_one()
)
complete = windows_done >= len(all_windows)
if complete:
now = datetime.now(timezone.utc).isoformat()
for symbol in symbols:
count = int(
conn.execute(
text(
"SELECT COUNT(*) FROM earnings_events "
"WHERE symbol=:symbol AND announce_date BETWEEN :a AND :b"
),
{"symbol": symbol, "a": start.isoformat(), "b": end.isoformat()},
).scalar_one()
)
conn.execute(
text(
"""
INSERT INTO earnings_backfill_meta(symbol, status, n_events, updated_at, note)
VALUES (:symbol, 'done', :count, :now, 'bulk_complete')
ON CONFLICT(symbol) DO UPDATE SET
status='done', n_events=excluded.n_events,
updated_at=excluded.updated_at, note=excluded.note
"""
),
{"symbol": symbol, "count": count, "now": now},
)
params = {"a": start.isoformat(), "b": end.isoformat()}
total_events = int(
conn.execute(
text(
"SELECT COUNT(*) FROM earnings_events "
"WHERE symbol IN (SELECT symbol FROM tickers) "
"AND announce_date BETWEEN :a AND :b"
),
params,
).scalar_one()
)
paired_events = int(
conn.execute(
text(
"SELECT COUNT(*) FROM earnings_events "
"WHERE symbol IN (SELECT symbol FROM tickers) "
"AND announce_date BETWEEN :a AND :b "
"AND eps_actual IS NOT NULL AND eps_estimate IS NOT NULL"
),
params,
).scalar_one()
)
date_range = conn.execute(
text(
"SELECT MIN(announce_date), MAX(announce_date) FROM earnings_events "
"WHERE symbol IN (SELECT symbol FROM tickers) "
"AND announce_date BETWEEN :a AND :b"
),
params,
).fetchone()
done_symbols = int(
conn.execute(
text("SELECT COUNT(*) FROM earnings_backfill_meta WHERE status='done'")
).scalar_one()
)
totals = conn.execute(
text(
"SELECT COALESCE(SUM(requests),0), COALESCE(SUM(duplicate_rows),0), "
"COALESCE(SUM(restated_rows),0) FROM earnings_backfill_windows "
"WHERE from_date >= :a AND to_date <= :b"
),
params,
).fetchone()
summary = {
"mode": "fmp_bulk_date_range_only",
"window": {"from": start.isoformat(), "to": end.isoformat()},
"window_days": int(args.window_days),
"bulk_windows_total": len(all_windows),
"bulk_windows_done": windows_done,
"bulk_requests_this_run": requests_this_run,
"bulk_requests_logged_total": int(totals[0]),
"rows_upserted_this_run": rows_upserted,
"duplicate_rows_this_run": duplicate_rows,
"restated_rows_this_run": restated_rows,
"duplicate_rows_logged_total": int(totals[1]),
"restated_rows_logged_total": int(totals[2]),
"dedupe_policy": (
"UNIQUE(symbol, announce_date); prefer more non-null fields, then "
"the provider's later occurrence; non-null bulk fields replace prior "
"values while null bulk fields retain existing values"
),
"events_in_window": total_events,
"events_with_actual_and_estimate": paired_events,
"symbols_done": done_symbols,
"symbols_universe": len(symbols),
"announce_date_range": {"min": date_range[0], "max": date_range[1]},
"request_budget": int(args.limit),
"stop_note": stop_note,
"complete": complete,
}
output = Path("reports/earnings-backfill-status.json")
output.parent.mkdir(parents=True, exist_ok=True)
output.write_text(json.dumps(summary, indent=2) + "\n", encoding="utf-8")
print(json.dumps(summary, indent=2))
print(f"Wrote {output}")
if __name__ == "__main__":
asyncio.run(_main())
scripts/extend_snapshot_universe.py
+2 -11
View File
@@ -128,11 +128,10 @@ async def _resolve_pool() -> tuple[list[str], dict[str, str]]:
"""Return sorted unique symbols and source labels.
Offline-safe: does **not** use production Postgres or SystemSetting cache
(those require a schema). Public sources first, then FMP, then seeds.
(those require a schema). Public sources first, then seeds.
"""
from app.services.ticker_universe_service import (
_SEED_UNIVERSES,
_fetch_universe_symbols_from_fmp,
_fetch_universe_symbols_from_public,
_normalise_symbols,
)
@@ -150,19 +149,11 @@ async def _resolve_pool() -> tuple[list[str], dict[str, str]]:
cleaned = _normalise_symbols(public_symbols)
if cleaned:
src = public_source or "public"
else:
if public_failures:
elif public_failures:
print(
f" WARNING: public fetch {universe}: "
f"{'; '.join(public_failures[:3])}"
)
try:
fmp_symbols = await _fetch_universe_symbols_from_fmp(universe)
cleaned = _normalise_symbols(fmp_symbols)
if cleaned:
src = "fmp"
except Exception as exc:
print(f" WARNING: FMP fetch {universe}: {exc}")
if not cleaned:
cleaned = _normalise_symbols(_SEED_UNIVERSES.get(universe, []))
scripts/portfolio_capacity_research.py
-764
View File
@@ -1,764 +0,0 @@
'''Pure helpers for the focused daily portfolio-capacity research matrix.'''
from __future__ import annotations
import hashlib
import math
import random
import statistics
from collections import defaultdict
from datetime import date, timedelta
from typing import Any, Iterable
ARMS: tuple[dict[str, Any], ...] = (
{
'id': 'cap10_incumbent',
'label': 'Cap 10, arrival-order incumbents',
'max_positions': 10,
'min_initial_risk_fraction': None,
'weekly_top_n_rebalance': False,
},
{
'id': 'cash_unbounded',
'label': 'Cash-constrained, no count cap',
'max_positions': None,
'min_initial_risk_fraction': 0.005,
'weekly_top_n_rebalance': False,
},
{
'id': 'cap10_weekly_top10',
'label': 'Cap 10, weekly current-rank top 10',
'max_positions': 10,
'min_initial_risk_fraction': None,
'weekly_top_n_rebalance': True,
},
{
'id': 'cap15_incumbent',
'label': 'Cap 15, arrival-order incumbents',
'max_positions': 15,
'min_initial_risk_fraction': None,
'weekly_top_n_rebalance': False,
},
)
ARM_BY_ID = {arm['id']: arm for arm in ARMS}
RISK_FLOOR_ARMS: tuple[dict[str, Any], ...] = (
ARMS[0],
{
'id': 'cap10_min_risk_005',
'label': 'Cap 10, 0.5% minimum effective initial risk',
'max_positions': 10,
'min_initial_risk_fraction': 0.005,
'weekly_top_n_rebalance': False,
},
)
COSTS_PER_SIDE_PCT = (0.1, 0.2)
ANCHOR_YEARS = tuple(range(2019, 2026))
SCORING_SESSIONS = 504
MEASUREMENT_SESSIONS = 252
RESIDUAL_BENCHMARK_SESSIONS = 252
WARM_SEED_MIN_OFFSET = 63
WARM_SEED_MAX_OFFSET = 126
BOOTSTRAP_REPLICATES = 10_000
BOOTSTRAP_SEED = 20260805
PRIMARY_METRICS = (
'ev_net_r',
'calmar',
'profit_factor',
'gain_to_pain',
'sortino',
)
PAIRED_METRICS = (
*PRIMARY_METRICS,
'cagr_pct',
'max_drawdown_pct',
'total_return_pct',
'sharpe',
)
def _end_exclusive(
sessions: list[date], start_index: int, count: int
) -> date:
end_index = start_index + count
if end_index < len(sessions):
return sessions[end_index]
return sessions[-1] + timedelta(days=1)
def build_cohort_manifest(session_dates: Iterable[date]) -> dict[str, Any]:
sessions = sorted(set(session_dates))
minimum = RESIDUAL_BENCHMARK_SESSIONS + SCORING_SESSIONS
if len(sessions) <= minimum + MEASUREMENT_SESSIONS:
raise ValueError('Snapshot is too short for the frozen cohort design')
index_of = {session: index for index, session in enumerate(sessions)}
first_eligible_index = RESIDUAL_BENCHMARK_SESSIONS - 1 + SCORING_SESSIONS
last_eligible_index = len(sessions) - MEASUREMENT_SESSIONS
first_by_month: dict[tuple[int, int], date] = {}
for session in sessions:
first_by_month.setdefault((session.year, session.month), session)
empty: list[dict[str, Any]] = []
for (year, month), session in sorted(first_by_month.items()):
index = index_of[session]
if year not in ANCHOR_YEARS:
continue
if index < first_eligible_index or index > last_eligible_index:
continue
empty.append({
'protocol': 'empty_book',
'path_id': f'empty-{year:04d}-{month:02d}',
'cluster': year,
'simulation_start': session.isoformat(),
'measurement_start': session.isoformat(),
'hard_end_exclusive': _end_exclusive(
sessions, index, MEASUREMENT_SESSIONS
).isoformat(),
})
first_by_year: dict[int, date] = {}
for session in sessions:
first_by_year.setdefault(session.year, session)
warm: list[dict[str, Any]] = []
warm_seed_counts: dict[str, int] = {}
for year in ANCHOR_YEARS:
anchor = first_by_year.get(year)
if anchor is None:
continue
anchor_index = index_of[anchor]
if (
anchor_index < WARM_SEED_MAX_OFFSET
or anchor_index > last_eligible_index
):
continue
seed_window = sessions[
anchor_index - WARM_SEED_MAX_OFFSET:
anchor_index - WARM_SEED_MIN_OFFSET + 1
]
first_by_iso_week: dict[tuple[int, int], date] = {}
for session in seed_window:
iso = session.isocalendar()
first_by_iso_week.setdefault((iso.year, iso.week), session)
seeds = sorted(first_by_iso_week.values())
warm_seed_counts[str(year)] = len(seeds)
for seed_index, seed in enumerate(seeds, 1):
warm.append({
'protocol': 'warm_book',
'path_id': f'warm-{year}-seed-{seed_index:02d}',
'cluster': year,
'simulation_start': seed.isoformat(),
'measurement_start': anchor.isoformat(),
'hard_end_exclusive': _end_exclusive(
sessions, anchor_index, MEASUREMENT_SESSIONS
).isoformat(),
'seed_offset_sessions': anchor_index - index_of[seed],
})
return {
'snapshot_first_session': sessions[0].isoformat(),
'snapshot_last_session': sessions[-1].isoformat(),
'session_count': len(sessions),
'expected_clusters': list(ANCHOR_YEARS),
'empty_book': empty,
'warm_book': warm,
'empty_cluster_counts': dict(
sorted(
(
str(year),
sum(1 for row in empty if row['cluster'] == year),
)
for year in {row['cluster'] for row in empty}
)
),
'warm_seed_counts': warm_seed_counts,
'empty_cluster_count': len({row['cluster'] for row in empty}),
'warm_cluster_count': len({row['cluster'] for row in warm}),
}
def validate_cohort_manifest(manifest: dict[str, Any]) -> list[str]:
errors: list[str] = []
expected = set(ANCHOR_YEARS)
empty_clusters = {row['cluster'] for row in manifest['empty_book']}
warm_clusters = {row['cluster'] for row in manifest['warm_book']}
if empty_clusters != expected:
errors.append(
f'empty-book clusters {sorted(empty_clusters)} != {sorted(expected)}'
)
if warm_clusters != expected:
errors.append(
f'warm-book clusters {sorted(warm_clusters)} != {sorted(expected)}'
)
for year in ANCHOR_YEARS:
seed_count = int(manifest['warm_seed_counts'].get(str(year), 0))
if seed_count < 12:
errors.append(f'warm anchor {year} has only {seed_count} seeds')
return errors
def build_cells(
manifest: dict[str, Any],
*,
arms: tuple[dict[str, Any], ...] = ARMS,
protocols: tuple[str, ...] = ('empty_book', 'warm_book'),
costs: tuple[float, ...] = COSTS_PER_SIDE_PCT,
) -> list[dict[str, Any]]:
paths = [
path
for protocol in protocols
for path in manifest[protocol]
]
cells: list[dict[str, Any]] = []
for cost in costs:
for path in paths:
for arm in arms:
cell_id = (
f'{arm["id"]}|{path["protocol"]}|{path["path_id"]}'
f'|cost={cost:.1f}'
)
cells.append({
**path,
'cell_id': cell_id,
'arm_id': arm['id'],
'cost_per_side_pct': cost,
})
return cells
def percentile(values: Iterable[float], probability: float) -> float | None:
ordered = sorted(float(value) for value in values if value is not None)
if not ordered:
return None
if len(ordered) == 1:
return ordered[0]
location = (len(ordered) - 1) * probability
lower = math.floor(location)
upper = math.ceil(location)
if lower == upper:
return ordered[lower]
weight = location - lower
return ordered[lower] * (1.0 - weight) + ordered[upper] * weight
def iqr(values: Iterable[float]) -> float | None:
clean: list[float] = []
for value in values:
if value is None:
continue
parsed = float(value)
if math.isfinite(parsed):
clean.append(parsed)
q25 = percentile(clean, 0.25)
q75 = percentile(clean, 0.75)
if q25 is None or q75 is None:
return None
return q75 - q25
def median(values: Iterable[float | None]) -> float | None:
clean = [float(value) for value in values if value is not None]
return statistics.median(clean) if clean else None
def _safe_ratio(numerator: float | None, denominator: float | None) -> float | None:
if numerator is None or denominator is None:
return None
if abs(denominator) <= 1e-12:
return 1.0 if abs(numerator) <= 1e-12 else None
return numerator / denominator
def _stable_seed(*parts: object) -> int:
digest = hashlib.sha256('|'.join(map(str, parts)).encode('utf-8')).digest()
return BOOTSTRAP_SEED + int.from_bytes(digest[:4], 'big')
def bootstrap_median_interval(
values: Iterable[float | None],
*,
seed_parts: tuple[object, ...],
replicates: int = BOOTSTRAP_REPLICATES,
) -> dict[str, float | int | None]:
clean = [float(value) for value in values if value is not None]
if not clean:
return {'n': 0, 'point': None, 'p05': None, 'p95': None}
rng = random.Random(_stable_seed(*seed_parts))
draws = [
statistics.median(rng.choices(clean, k=len(clean)))
for _ in range(replicates)
]
return {
'n': len(clean),
'replicates': replicates,
'point': statistics.median(clean),
'p05': percentile(draws, 0.05),
'p95': percentile(draws, 0.95),
}
def _monthly_returns(
equity_curve: list[dict[str, Any]], base_equity: float
) -> list[float]:
month_ends: dict[tuple[int, int], float] = {}
for point in equity_curve:
point_date = date.fromisoformat(str(point['date']))
month_ends[(point_date.year, point_date.month)] = float(point['equity'])
previous = float(base_equity)
returns: list[float] = []
for month in sorted(month_ends):
equity = month_ends[month]
if previous > 0:
returns.append(equity / previous - 1.0)
previous = equity
return returns
def _time_underwater(equities: list[float]) -> tuple[int, float]:
peak = float('-inf')
current = 0
longest = 0
underwater = 0
for equity in equities:
peak = max(peak, equity)
if peak > 0 and equity < peak - 1e-9:
current += 1
underwater += 1
longest = max(longest, current)
else:
current = 0
percentage = underwater / len(equities) * 100.0 if equities else 0.0
return longest, percentage
def summarize_simulation(sim: dict[str, Any]) -> dict[str, Any]:
trades = list(sim.get('trade_details') or [])
equity_curve = list(sim.get('equity_curve') or [])
net_rs = [float(trade['net_r']) for trade in trades]
positive_rs = [value for value in net_rs if value > 0]
negative_rs = [value for value in net_rs if value < 0]
ev_net_r = statistics.fmean(net_rs) if net_rs else None
profit_factor = (
sum(positive_rs) / abs(sum(negative_rs))
if negative_rs
else None
)
base_equity = float(
sim.get('measurement_start_equity') or sim.get('starting_capital') or 0.0
)
curve_equities = [float(point['equity']) for point in equity_curve]
daily_equities = [base_equity, *curve_equities]
daily_returns = [
current / previous - 1.0
for previous, current in zip(daily_equities, daily_equities[1:])
if previous > 0
]
downside_deviation = (
math.sqrt(
statistics.fmean(min(value, 0.0) ** 2 for value in daily_returns)
)
if daily_returns
else None
)
sortino = (
statistics.fmean(daily_returns) / downside_deviation * math.sqrt(252.0)
if downside_deviation is not None and downside_deviation > 0
else None
)
monthly_returns = _monthly_returns(equity_curve, base_equity)
negative_monthly = sum(value for value in monthly_returns if value < 0)
gain_to_pain = (
sum(monthly_returns) / abs(negative_monthly)
if negative_monthly < 0
else None
)
longest_underwater, underwater_pct = _time_underwater(daily_equities)
transaction_cost = sum(
float(trade.get('transaction_cost') or 0.0) for trade in trades
)
traded_notional = sum(
float(trade.get('shares') or 0.0)
* (float(trade.get('entry') or 0.0) + float(trade.get('fill') or 0.0))
for trade in trades
)
turnover_multiple = (
traded_notional / base_equity if base_equity > 0 else None
)
ordered_rs = sorted(net_rs, reverse=True)
ev_without_best: dict[str, float | None] = {}
for count in (1, 5, 10):
remaining = ordered_rs[count:]
ev_without_best[str(count)] = (
statistics.fmean(remaining) if remaining else None
)
events = list(sim.get('weekly_rebalance_events') or [])
entrant_sizes = [int(event['fresh_entrant_pool']) for event in events]
eligible_sizes = [
int(event['rank_eligible_entrant_pool']) for event in events
]
replacements = [int(event['replacements']) for event in events]
capacity_skips = int(
sim.get('measurement_skipped_book_full', sim.get('skipped_book_full', 0))
)
opened = int(sim.get('opened_positions', sim.get('trades', 0)))
capacity_opportunities = opened + capacity_skips
result = {
'start_date': sim.get('start_date'),
'end_date': sim.get('end_date'),
'simulation_start_date': sim.get('simulation_start_date'),
'measurement_start_equity': base_equity,
'measurement_start_positions': sim.get('measurement_start_positions', 0),
'trades': len(trades),
'ev_net_r': ev_net_r,
'profit_factor': profit_factor,
'gain_to_pain': gain_to_pain,
'sortino': sortino,
'ev_without_best': ev_without_best,
'total_return_pct': sim.get('total_return_pct'),
'cagr_pct': sim.get('cagr_pct'),
'max_drawdown_pct': sim.get('max_drawdown_pct'),
'calmar': sim.get('calmar'),
'sharpe': sim.get('sharpe'),
'win_rate': sim.get('win_rate'),
'avg_hold_days': sim.get('avg_hold_days'),
'longest_underwater_sessions': longest_underwater,
'underwater_pct': underwater_pct,
'transaction_cost': transaction_cost,
'turnover_multiple': turnover_multiple,
'skipped_book_full': capacity_skips,
'opened_positions': opened,
'capacity_opportunities': capacity_opportunities,
'blocked_fraction': (
capacity_skips / capacity_opportunities
if capacity_opportunities
else 0.0
),
'skipped_min_initial_risk': int(
sim.get('measurement_skipped_min_initial_risk', 0)
),
'avg_positions': sim.get('avg_positions'),
'peak_positions': sim.get('peak_positions'),
'sessions_at_capacity': sim.get('sessions_at_capacity'),
'sessions_measured': sim.get('sessions_measured'),
'avg_cash_pct': sim.get('avg_cash_pct'),
'avg_gross_exposure_pct': sim.get('avg_gross_exposure_pct'),
'exit_reasons': sim.get('exit_reasons'),
}
if events:
result['weekly_rebalance'] = {
'events': len(events),
'zero_entrant_fraction': (
sum(1 for value in entrant_sizes if value == 0) / len(events)
),
'entrant_pool_mean': statistics.fmean(entrant_sizes),
'entrant_pool_median': statistics.median(entrant_sizes),
'entrant_pool_p90': percentile(entrant_sizes, 0.9),
'eligible_pool_mean': statistics.fmean(eligible_sizes),
'replacements': sum(replacements),
'weekly_rank_rejected_entries': int(
sim.get('weekly_rank_rejected_entries', 0)
),
'reentries_within_5_sessions': int(
sim.get('rebalance_reentries_within_5_sessions', 0)
),
'reentries_within_10_sessions': int(
sim.get('rebalance_reentries_within_10_sessions', 0)
),
'reentries_within_20_sessions': int(
sim.get('rebalance_reentries_within_20_sessions', 0)
),
}
return result
def _cluster_rows(
cells: list[dict[str, Any]],
*,
arm_id: str,
protocol: str,
cost: float,
) -> list[dict[str, Any]]:
treatment = {
row['path_id']: row
for row in cells
if row['arm_id'] == arm_id
and row['protocol'] == protocol
and float(row['cost_per_side_pct']) == cost
}
control = {
row['path_id']: row
for row in cells
if row['arm_id'] == 'cap10_incumbent'
and row['protocol'] == protocol
and float(row['cost_per_side_pct']) == cost
}
shared_paths = sorted(set(treatment) & set(control))
by_cluster: dict[int, list[tuple[dict, dict]]] = defaultdict(list)
for path_id in shared_paths:
row = treatment[path_id]
by_cluster[int(row['cluster'])].append((row, control[path_id]))
summaries: list[dict[str, Any]] = []
for cluster, pairs in sorted(by_cluster.items()):
metrics: dict[str, Any] = {}
for metric in PAIRED_METRICS:
arm_values = [
pair[0]['metrics'].get(metric)
for pair in pairs
if pair[0]['metrics'].get(metric) is not None
and math.isfinite(float(pair[0]['metrics'][metric]))
]
control_values = [
pair[1]['metrics'].get(metric)
for pair in pairs
if pair[1]['metrics'].get(metric) is not None
and math.isfinite(float(pair[1]['metrics'][metric]))
]
deltas = [
float(arm['metrics'][metric])
- float(base['metrics'][metric])
for arm, base in pairs
if arm['metrics'].get(metric) is not None
and base['metrics'].get(metric) is not None
and math.isfinite(float(arm['metrics'][metric]))
and math.isfinite(float(base['metrics'][metric]))
]
arm_median = median(arm_values)
control_median = median(control_values)
metrics[metric] = {
'arm_median': arm_median,
'control_median': control_median,
'paired_delta_median': median(deltas),
'arm_control_ratio': _safe_ratio(
arm_median, control_median
),
'paired_paths': len(deltas),
}
summaries.append({
'cluster': cluster,
'paths': len(pairs),
'metrics': metrics,
})
return summaries
def aggregate_results(
cells: list[dict[str, Any]],
*,
arms: tuple[dict[str, Any], ...] = ARMS,
protocols: tuple[str, ...] = ('empty_book', 'warm_book'),
costs: tuple[float, ...] = COSTS_PER_SIDE_PCT,
include_warm_dispersion: bool = True,
) -> dict[str, Any]:
paired: list[dict[str, Any]] = []
path_distributions: list[dict[str, Any]] = []
for cost in costs:
for protocol in protocols:
control_by_path = {
row['path_id']: row
for row in cells
if row['arm_id'] == 'cap10_incumbent'
and row['protocol'] == protocol
and float(row['cost_per_side_pct']) == float(cost)
}
for arm in arms:
arm_id = str(arm['id'])
clusters = _cluster_rows(
cells,
arm_id=arm_id,
protocol=protocol,
cost=float(cost),
)
headline: dict[str, Any] = {}
for metric in PAIRED_METRICS:
deltas = [
cluster['metrics'][metric]['paired_delta_median']
for cluster in clusters
]
arm_levels = [
cluster['metrics'][metric]['arm_median']
for cluster in clusters
]
control_levels = [
cluster['metrics'][metric]['control_median']
for cluster in clusters
]
arm_level = median(arm_levels)
control_level = median(control_levels)
metric_summary: dict[str, Any] = {
'paired_delta_median': median(deltas),
'arm_median': arm_level,
'control_median': control_level,
'arm_control_ratio': _safe_ratio(
arm_level, control_level
),
}
if metric in ('ev_net_r', 'calmar'):
metric_summary['bootstrap_90'] = (
bootstrap_median_interval(
deltas,
seed_parts=(
arm_id,
protocol,
cost,
metric,
'paired-delta',
),
)
)
headline[metric] = metric_summary
paired.append({
'arm_id': arm_id,
'protocol': protocol,
'cost_per_side_pct': cost,
'clusters': clusters,
'headline': headline,
})
treatment_by_path = {
row['path_id']: row
for row in cells
if row['arm_id'] == arm_id
and row['protocol'] == protocol
and float(row['cost_per_side_pct']) == float(cost)
}
shared_paths = sorted(
set(treatment_by_path) & set(control_by_path)
)
path_metrics: dict[str, Any] = {}
for metric in PAIRED_METRICS:
deltas = [
float(treatment_by_path[path_id]['metrics'][metric])
- float(control_by_path[path_id]['metrics'][metric])
for path_id in shared_paths
if treatment_by_path[path_id]['metrics'].get(metric)
is not None
and control_by_path[path_id]['metrics'].get(metric)
is not None
and math.isfinite(
float(treatment_by_path[path_id]['metrics'][metric])
)
and math.isfinite(
float(control_by_path[path_id]['metrics'][metric])
)
]
path_metrics[metric] = {
'paired_paths': len(deltas),
'paired_delta_mean': (
statistics.fmean(deltas) if deltas else None
),
'paired_delta_median': median(deltas),
'paired_delta_p25': percentile(deltas, 0.25),
'paired_delta_p75': percentile(deltas, 0.75),
'positive_fraction': (
sum(delta > 0.0 for delta in deltas) / len(deltas)
if deltas
else None
),
'identical_fraction': (
sum(abs(delta) <= 1e-12 for delta in deltas)
/ len(deltas)
if deltas
else None
),
}
path_distributions.append({
'arm_id': arm_id,
'protocol': protocol,
'cost_per_side_pct': cost,
'metrics': path_metrics,
})
warm_rows = [
row for row in cells if row['protocol'] == 'warm_book'
]
warm_dispersion: list[dict[str, Any]] = []
for cost in costs:
for arm in arms:
arm_id = str(arm['id'])
anchor_rows: list[dict[str, Any]] = []
for cluster in ANCHOR_YEARS:
arm_paths = [
row
for row in warm_rows
if row['arm_id'] == arm_id
and int(row['cluster']) == cluster
and float(row['cost_per_side_pct']) == float(cost)
]
control_by_path = {
row['path_id']: row
for row in warm_rows
if row['arm_id'] == 'cap10_incumbent'
and int(row['cluster']) == cluster
and float(row['cost_per_side_pct']) == float(cost)
}
metric_rows: dict[str, Any] = {}
for metric in ('ev_net_r', 'calmar'):
arm_spread = iqr(
row['metrics'].get(metric) for row in arm_paths
)
control_spread = iqr(
control_by_path[row['path_id']]['metrics'].get(metric)
for row in arm_paths
if row['path_id'] in control_by_path
)
metric_rows[metric] = {
'arm_iqr': arm_spread,
'control_iqr': control_spread,
'iqr_ratio': _safe_ratio(
arm_spread, control_spread
),
}
anchor_rows.append({
'cluster': cluster,
'seeds': len(arm_paths),
'metrics': metric_rows,
})
headline: dict[str, Any] = {}
for metric in ('ev_net_r', 'calmar'):
ratios = [
row['metrics'][metric]['iqr_ratio']
for row in anchor_rows
]
headline[metric] = {
'median_iqr_ratio': median(ratios),
'bootstrap_90': bootstrap_median_interval(
ratios,
seed_parts=(
arm_id,
cost,
metric,
'warm-iqr-ratio',
),
),
}
warm_dispersion.append({
'arm_id': arm_id,
'cost_per_side_pct': cost,
'anchors': anchor_rows,
'headline': headline,
})
if not include_warm_dispersion:
warm_dispersion = []
return {
'paired_per_year': paired,
'paired_path_distributions': path_distributions,
'warm_seed_dispersion': warm_dispersion,
'bootstrap': {
'replicates': BOOTSTRAP_REPLICATES,
'seed': BOOTSTRAP_SEED,
'interval': 'central 90% percentile, context only',
'resampling_unit': 'seven annual paired summaries',
},
}
scripts/reparse_fundamentals.py
+4 -4
View File
@@ -15,10 +15,10 @@ Cost: a reparse cannot be served from the database -- the facts a fixed parser n
accepts were never stored -- so it refetches Company Facts for every tracked issuer
under the SEC fair-access throttle. Expect a long run and a lot of network.
Scope note: this rewrites ``fundamental_snapshots`` only. As of the A5 gate those
rows feed the fundamentals API/UI and the parity report; scoring still reads the
legacy ``fundamental_data`` table, so a reparse does not move composite scores or
backtests until the cutover happens.
Scope note: this rewrites ``fundamental_snapshots`` only. Those rows now feed both
the fundamentals API/UI *and* — through the nightly ``fundamental_data`` refresh —
the fundamental dimension of the composite score, so a reparse does move scores
and backtests. Run it deliberately.
Examples
--------
scripts/run_daily_reentry_matrix.py
-1
View File
@@ -31,7 +31,6 @@ if str(ROOT) not in sys.path:
from scripts.research_rankings import ( # noqa: E402
_live_universe_rank_map,
_period_percentiles,
)
POLICY_NAMES = (
scripts/run_execution_recovery_matrix.py
-1
View File
@@ -57,7 +57,6 @@ if str(ROOT) not in sys.path:
from scripts.research_rankings import ( # noqa: E402
_live_universe_rank_map,
_period_percentiles,
)
# Must match Phase A cache when reusing research-cands.pkl
scripts/run_fip_breadth_diagnostics.py
+6 -7
View File
@@ -162,13 +162,13 @@ def _load_job(conn, symbol: str, spy: dict) -> tuple | None:
if len(rows) < 90:
return None
ords, opens, highs, lows, closes, vols = [], [], [], [], [], []
for d, o, h, l, c, v in rows:
for d, o, h, lo, c, v in rows:
if isinstance(d, str):
d = date.fromisoformat(d[:10])
ords.append(d.toordinal())
opens.append(float(o))
highs.append(float(h))
lows.append(float(l))
lows.append(float(lo))
closes.append(float(c))
vols.append(float(v or 0))
return (symbol, ords, opens, highs, lows, closes, vols, spy)
@@ -275,7 +275,8 @@ def main() -> None:
vol_weeks = collected.get("vol_6m") or {}
momr_weeks = collected.get("mom_12_1_resid") or {}
# Index mom/vol by (week, symbol) for joins
# Index mom by (week, symbol) for joins. vol/momr are consumed as week maps
# directly further down, so they need no index.
def _index(weeks_map: dict) -> dict[tuple, dict]:
out: dict[tuple, dict] = {}
for wk, recs in weeks_map.items():
@@ -290,8 +291,6 @@ def main() -> None:
return out
mom_ix = _index(mom_weeks)
vol_ix = _index(vol_weeks)
momr_ix = _index(momr_weeks)
# Per-week membership + extended checks via shared rich filter
same_week: dict[tuple, list[tuple[float, float]]] = defaultdict(list)
@@ -606,8 +605,8 @@ def _update_md(path: Path, results: dict, artifact: Path) -> None:
"",
"### Authoritative unconditional fip (liquid top-N, post-mask)",
"",
f"| metric | value |",
f"|---|---|",
"| metric | value |",
"|---|---|",
f"| mean_ic | {h.get('mean_ic')} |",
f"| ic_t_stat | {h.get('ic_t_stat')} |",
f"| weeks | {h.get('weeks')} |",
scripts/run_fip_breadth_research.py
+2 -2
View File
@@ -162,8 +162,8 @@ def _write_md(path: Path, payload: dict) -> None:
row = br.get("row") or br
if row:
lines.extend([
f"| metric | value |",
f"|---|---|",
"| metric | value |",
"|---|---|",
f"| mean_ic | {row.get('mean_ic')} |",
f"| ic_t_stat | {row.get('ic_t_stat')} |",
f"| ic_positive_pct | {row.get('ic_positive_pct')} |",
scripts/run_portfolio_construction_matrix.py
-1373
View File
File diff suppressed because it is too large Load Diff
scripts/run_prod_book_universe_matrix.py
-1
View File
@@ -26,7 +26,6 @@ import os
import pickle
import sys
import time
from collections import defaultdict
from concurrent.futures import ProcessPoolExecutor
from datetime import date, datetime
from pathlib import Path
scripts/run_regime_monitor_calibration.py
+877
View File
@@ -0,0 +1,877 @@
"""Offline replay of the AI/Tech Risk Monitor, for calibrating a methodology cut.
Reproduces the State/Warning series session by session from the same inputs the
live job uses -- Alpaca for prices, FRED for VIX and HY OAS -- with no database,
so a sensor change can be measured against real history before it ships.
v3 was calibrated this way ad-hoc and the harness was never committed, which is
why its published numbers cannot be re-derived today. This is that harness.
**It never reimplements an unchanged live sensor.** ``_compute_index``,
``_score_pillars``, breadth, divergence, P2, P4 and the Warning sensors are
imported and called. Only *candidate* formulas (proposed for v4) and *retired*
ones (v2, no longer in the codebase) are defined here and patched onto the
service for the duration of a variant. Once a candidate ships, delete it here and
import the shipped function instead, or the two will drift.
The script refuses to emit a band recommendation unless every hard gate passes.
That is deliberate: it must be structurally impossible to read a calibration
result out of a run whose pipeline did not validate.
Research branch only. Example:
.\\.venv\\Scripts\\python.exe scripts\\run_regime_monitor_calibration.py ^
--end 2026-07-24 --sessions 408 --methodology v3,v4 ^
--cache-dir .calib-cache
"""
from __future__ import annotations
import argparse
import asyncio
import contextlib
import json
import math
import statistics
import subprocess
import sys
from collections.abc import Iterator
from copy import deepcopy
from datetime import date, datetime, timedelta
from pathlib import Path
from typing import Any, Callable
ROOT = Path(__file__).resolve().parents[1]
if str(ROOT) not in sys.path:
sys.path.insert(0, str(ROOT))
from app.config import settings # noqa: E402
from app.providers.alpaca import AlpacaOHLCVProvider # noqa: E402
from app.services import breadth_service # noqa: E402
from app.services import regime_monitor_service as rms # noqa: E402
# Published v3/v2 figures from docs/research/regime-monitor-v3.md. The v3 pair is
# DERIVED there (22.6 - 0.4; 91.2 - 3.8), not measured, so its tolerance is loose
# on purpose -- anything tighter would be false precision.
PUBLISHED = {
"window_end": "2026-07-24",
# The session count alone is tautological -- the harness slices the tail of
# leader_series, so it can only ever equal what was asked for. The start date
# is what actually validates the calendar.
"window_first": "2024-12-05",
"sessions": 408,
"w1_live_sessions": 108,
"v2_state_avg": 22.6,
"v2_state_p80": 35.1,
"v2_state_max": 91.2,
"v2_p3_pegged": 39,
# No v3 average is published: the doc's "-0.4" is measured against
# v3-with-the-percentile-leg, not against v2, so only the max is checkable.
"v3_state_max": 87.4,
}
# Every symbol comes from one source on one split-adjustment basis. Mixing a
# sqlite snapshot for the basket with Alpaca for the leaders would splice two
# adjustment bases mid-200-DMA for any symbol that split in between.
LEADER, CONFIRM, MARKET = "SMH", "QQQ", "SPY"
# ---------------------------------------------------------------------------
# Candidate formulas (proposed for v4) -- patched in, never shipped from here
# ---------------------------------------------------------------------------
P5_VIX_ANCHORS_A = ((15.0, 0.0), (20.0, 20.0), (25.0, 38.0), (30.0, 55.0), (40.0, 80.0), (55.0, 100.0))
P5_VIX_ANCHORS_B = ((15.0, 0.0), (20.0, 25.0), (25.0, 45.0), (30.0, 65.0), (40.0, 85.0), (55.0, 100.0))
P1_TREND_BREAK_ANCHORS = ((0.0, 20.0), (3.0, 35.0), (8.0, 55.0), (15.0, 75.0), (25.0, 100.0))
def _candidate_under_200(closes: list[float], anchors=P1_TREND_BREAK_ANCHORS) -> float | None:
"""Graduated trend break: 0 above the 200-DMA, else scaled by depth below it.
The live version returns a bare 0/100, which pins the price pillar's max()
at 100 through any real selloff and stops P3's ladder resolving. The step at
the crossing (0 -> 20) is kept deliberately: the break itself is a genuine
binary event and deserves a floor; only the depth past it is graduated.
"""
sma200 = rms._sma(closes, 200)
if sma200 is None or sma200 <= 0:
return None
pct_below = (sma200 - closes[-1]) / sma200 * 100.0
if pct_below <= 0:
return 0.0
return rms._clamp(rms._interpolate(pct_below, anchors))
def _candidate_p1(anchors=P1_TREND_BREAK_ANCHORS) -> Callable:
def p1_trend_break(smh, qqq, leader_weight: float = 2.0):
return rms._blend(
_candidate_under_200(smh, anchors), _candidate_under_200(qqq, anchors), leader_weight
)
return p1_trend_break
def _candidate_p5(anchors) -> Callable:
def p5_volatility(vix: float | None) -> float | None:
if vix is None:
return None
return rms._clamp(rms._interpolate(vix, anchors))
return p5_volatility
def _capped(fn: Callable, cap: float) -> Callable:
"""P1_SCORE_CAP fallback: cap the sensor score after the blend, before max()."""
def wrapped(smh, qqq, leader_weight: float = 2.0):
value = fn(smh, qqq, leader_weight)
return None if value is None else min(value, cap)
return wrapped
# ---------------------------------------------------------------------------
# Retired formulas -- reconstructed, no longer in the codebase
# ---------------------------------------------------------------------------
def _v3_under_200(closes: list[float]) -> float | None:
"""v3's binary trend break, retired when v4 graduated it."""
sma200 = rms._sma(closes, 200)
if sma200 is None:
return None
return 100.0 if closes[-1] < sma200 else 0.0
def _v3_p1_trend_break(smh, qqq, leader_weight: float = 2.0) -> float | None:
return rms._blend(_v3_under_200(smh), _v3_under_200(qqq), leader_weight)
def _v3_p5_volatility(vix: float | None) -> float | None:
"""v3's linear VIX ramp, retired when v4 anchored it. Saturated at 30."""
if vix is None:
return None
return rms._clamp((vix - 15.0) / 15.0 * 100.0)
def _v2_drawdown(closes: list[float]) -> float | None:
if len(closes) < 30:
return None
peak = max(closes[-252:])
if peak <= 0:
return None
return rms._clamp((peak - closes[-1]) / peak * 100.0 * 5.0)
def _v2_p3_drawdown(smh, qqq, leader_weight: float = 2.0) -> float | None:
"""v2 took max() across the legs, so the more volatile leader always won."""
vals = [v for v in (_v2_drawdown(smh), _v2_drawdown(qqq)) if v is not None]
return max(vals) if vals else None
def _v2_divergence_series(breadth, benchmark_closes, lookback: int = 20):
"""v2's hard price gate: the sensor ZEROED during any decline.
v3 replaced this with a taper, which is why v2 shows W1 nonzero on only 108
of 408 sessions while v3 shows it nonzero far more often. Reconstructing it
is the only way to check that published figure.
"""
bench = {d: c for d, c in benchmark_closes}
common = sorted(d for d in bench if d in breadth)
out: dict[date, float] = {}
for i in range(lookback, len(common)):
d, d0 = common[i], common[i - lookback]
if bench[d0] <= 0:
continue
price_ret = (bench[d] / bench[d0] - 1.0) * 100.0
deterioration = max(0.0, -(breadth[d] - breadth[d0]))
score = deterioration * 5.0 if price_ret >= 0 else 0.0
out[d] = max(0.0, min(100.0, round(score, 2)))
return out
def _v2_f2_credit_spreads(oas_values: list[float]) -> float | None:
"""70% named anchors + 30% upper-tail percentile over whatever window it got."""
if not oas_values:
return None
latest = oas_values[-1]
absolute = rms._oas_absolute_score(latest)
if len(oas_values) < 30:
return round(absolute, 2)
less = sum(1 for v in oas_values if v < latest)
equal = sum(1 for v in oas_values if v == latest)
percentile = (less + 0.5 * equal) / len(oas_values) * 100.0
relative = rms._clamp((percentile - 50.0) / 45.0 * 100.0)
return round(absolute * 0.7 + relative * 0.3, 2)
# ---------------------------------------------------------------------------
# Variants
# ---------------------------------------------------------------------------
VARIANTS: dict[str, dict[str, Callable]] = {
# Retired since the v4 cutover -- "nothing patched" is now v4, so v3 has to
# be reconstructed like v2 to stay comparable.
"v3": {
"p1_trend_break": _v3_p1_trend_break,
"p5_volatility": _v3_p5_volatility,
},
# SHIPPED as of v4 -- nothing patched, so this variant exercises live code.
# Keeping a private copy here would let the harness and the service drift.
"v4": {},
"v4-vix-b": {
"p1_trend_break": _candidate_p1(),
"p5_volatility": _candidate_p5(P5_VIX_ANCHORS_B),
},
"v4-p1-capped": {
"p1_trend_break": _capped(_candidate_p1(), 50.0),
"p5_volatility": _candidate_p5(P5_VIX_ANCHORS_A),
},
"v4-vix-only": {"p1_trend_break": _v3_p1_trend_break},
"v4-p1-only": {"p5_volatility": _v3_p5_volatility},
# (4) v2 as production actually fetched it: a 400-calendar-day OAS source,
# which left the oldest rows with no credit at all. Truncating the SERIES is
# the only faithful simulation -- patching the per-session window is not,
# because the data was simply absent.
"v2_reconstruction_oas400": {
"p1_trend_break": _v3_p1_trend_break,
"p5_volatility": _v3_p5_volatility,
"p3_drawdown": _v2_p3_drawdown,
"f2_credit_spreads": _v2_f2_credit_spreads,
"HY_OAS_WINDOW_DAYS": 3653,
},
# v2 State sensors + the v2 divergence gate that feeds W1. The v2 *Warning
# composition* (F1/F3 fundamentals, 20 of 100 points) is NOT reconstructed,
# so only State statistics and the W1 census are comparable to the published
# v2 figures -- not the Warning score.
"v2_reconstruction": {
# v2 shared v3's binary trend break and linear VIX ramp verbatim, so both
# are retired now and must be restored here too -- otherwise a "v2" replay
# silently picks up v4's graded sensors.
"p1_trend_break": _v3_p1_trend_break,
"p5_volatility": _v3_p5_volatility,
"p3_drawdown": _v2_p3_drawdown,
"f2_credit_spreads": _v2_f2_credit_spreads,
# v2 sliced HY_OAS_REFERENCE_YEARS = 10.0 per session. The percentile leg
# ranks the current spread against that window, so replaying it against
# a 700-day slice gives systematically different mid-distribution scores.
"HY_OAS_WINDOW_DAYS": 3653,
},
}
# Variants needing the retired divergence formula rather than the live one.
V2_DIVERGENCE_VARIANTS = {"v2_reconstruction", "v2_reconstruction_oas400"}
# Variants whose OAS *source series* is truncated before replay, in calendar days.
OAS_SOURCE_TRUNCATION = {"v2_reconstruction_oas400": 400}
# A v4 recommendation is meaningless without both of these: the row-wise
# state_v4 <= state_v3 invariant needs them, and it is a hard gate.
# v2_reconstruction is required too: it carries every published figure the
# reproduction rests on (avg/p80/max/P3-pegged/W1-live). Without it a run could
# emit a confident recommendation having checked nothing against v2 at all,
# while the methodology doc claims v2 and v3 are reproduced first.
REQUIRED_VARIANTS = ("v2_reconstruction", "v3", "v4")
@contextlib.contextmanager
def patched(overrides: dict[str, Callable]) -> Iterator[None]:
"""Swap functions on the service module, then restore exactly."""
original = {name: getattr(rms, name) for name in overrides}
try:
for name, fn in overrides.items():
setattr(rms, name, fn)
yield
finally:
for name, fn in original.items():
setattr(rms, name, fn)
# ---------------------------------------------------------------------------
# Inputs
# ---------------------------------------------------------------------------
def _basket(config: dict) -> list[str]:
return list(config["breadth_basket"])
def _all_symbols(config: dict) -> list[str]:
return list(dict.fromkeys(_basket(config) + [LEADER, CONFIRM, MARKET]))
async def _load_prices(
symbols: list[str], start: date, end: date, cache_dir: Path | None, quiet: bool
) -> dict[str, list[tuple[date, float]]]:
cache = None
if cache_dir:
cache_dir.mkdir(parents=True, exist_ok=True)
cache = cache_dir / f"prices-{start}-{end}.json"
if cache.exists():
raw = json.loads(cache.read_text(encoding="utf-8"))
if set(raw) >= set(symbols):
if not quiet:
print(f"prices: cache hit ({len(raw)} symbols)", flush=True)
return {
s: [(date.fromisoformat(d), float(c)) for d, c in raw[s]] for s in symbols
}
provider = AlpacaOHLCVProvider(settings.alpaca_api_key, settings.alpaca_api_secret)
out: dict[str, list[tuple[date, float]]] = {}
for index, symbol in enumerate(symbols, 1):
bars = await provider.fetch_ohlcv(symbol, start, end)
out[symbol] = sorted((b.date, float(b.close)) for b in bars)
if not quiet:
print(f" [{index}/{len(symbols)}] {symbol}: {len(out[symbol])} bars", flush=True)
if cache:
cache.write_text(
json.dumps({s: [[d.isoformat(), c] for d, c in v] for s, v in out.items()}),
encoding="utf-8",
)
return out
async def _load_fred(series_id: str, start: date, end: date, cache_dir: Path | None):
cache = cache_dir / f"{series_id}-{start}-{end}.json" if cache_dir else None
if cache and cache.exists():
raw = json.loads(cache.read_text(encoding="utf-8"))
return [(date.fromisoformat(d), float(v)) for d, v in raw]
series = await rms._fetch_fred_series(series_id, start, end)
if cache and series:
cache.write_text(
json.dumps([[d.isoformat(), v] for d, v in series]), encoding="utf-8"
)
return series
# ---------------------------------------------------------------------------
# Replay
# ---------------------------------------------------------------------------
def _replay(
variant: str,
prices: dict[str, list[tuple[date, float]]],
vix, oas, config: dict, sessions: list[date],
breadth_series, divergence_by_variant: dict[str, Any], breadth_counts,
) -> list[dict]:
# Divergence is computed outside _compute_index, so the retired v2 gate has
# to be selected here rather than patched onto the module.
divergence_series = divergence_by_variant[
"v2" if variant in V2_DIVERGENCE_VARIANTS else "live"
]
truncate_days = OAS_SOURCE_TRUNCATION.get(variant)
if truncate_days is not None and oas:
cutoff = max(d for d, _ in oas) - timedelta(days=truncate_days)
oas = [(d, v) for d, v in oas if d >= cutoff]
rows: list[dict] = []
with patched(VARIANTS[variant]):
for as_of in sessions:
rows.append(
rms._compute_index(
prices, vix, oas, {}, deepcopy(config), as_of,
breadth_series, divergence_series, breadth_counts,
)
)
return rows
def _percentile(values: list[float], pct: float) -> float | None:
if not values:
return None
ordered = sorted(values)
k = (len(ordered) - 1) * pct / 100.0
lo, hi = math.floor(k), math.ceil(k)
if lo == hi:
return ordered[int(k)]
return ordered[lo] + (ordered[hi] - ordered[lo]) * (k - lo)
def _sensor_score(row: dict, pillar_id: str, sensor_id: str) -> float | None:
"""Search both axes: W1/W2/W3 live under ``warning``, P*/B1/C1/V1 under ``state``."""
for axis in ("state", "warning"):
for pillar in row[axis]["pillars"]:
if pillar["id"] == pillar_id:
for sensor in pillar["sensors"]:
if sensor["id"] == sensor_id:
return sensor["score"]
return None
def _band_shares(scores: list[float], bands: tuple[float, float, float]) -> dict[str, float]:
if not scores:
return {}
counts = {"stable": 0, "watch": 0, "elevated": 0, "breaking": 0}
for score in scores:
counts[rms.band_for(score, bands)] += 1 # bands passed explicitly -- see module docstring
return {k: round(v / len(scores) * 100.0, 1) for k, v in counts.items()}
def _stats(rows: list[dict], label: str) -> dict:
states = [r["state"]["score"] for r in rows if r["state"]["score"] is not None]
warnings = [r["warning"]["score"] for r in rows if r["warning"]["score"] is not None]
def pegged(pillar: str, sensor: str) -> int:
return sum(1 for r in rows if (_sensor_score(r, pillar, sensor) or 0) >= 100.0)
argmax_sole, argmax_tied, ties = {"P1": 0, "P2": 0, "P3": 0}, {"P1": 0, "P2": 0, "P3": 0}, 0
# The P1_SCORE_CAP rule is "sole argmax on >80% of sessions with State >= 40",
# so the all-session count does not evaluate it. Track the conditional
# population separately rather than deciding off the wrong denominator.
stressed_sole, stressed_total = {"P1": 0, "P2": 0, "P3": 0}, 0
for row in rows:
legs = {s: _sensor_score(row, "price", s) for s in ("P1", "P2", "P3")}
live = {k: v for k, v in legs.items() if v is not None}
if not live:
continue
top = max(live.values())
winners = [k for k, v in live.items() if v == top]
if len(winners) > 1:
ties += 1
for w in winners:
argmax_tied[w] += 1
if len(winners) == 1:
argmax_sole[winners[0]] += 1
if (row["state"]["score"] or 0) >= 40.0:
stressed_total += 1
if len(winners) == 1:
stressed_sole[winners[0]] += 1
return {
"label": label,
"sessions": len(rows),
"state": {
"avg": round(statistics.fmean(states), 2) if states else None,
"median": round(statistics.median(states), 2) if states else None,
"p80": round(_percentile(states, 80), 2) if states else None,
"p90": round(_percentile(states, 90), 2) if states else None,
"max": round(max(states), 2) if states else None,
"scored": len(states),
},
"warning_avg": round(statistics.fmean(warnings), 2) if warnings else None,
"saturation_census": {
"p3_pegged": pegged("price", "P3"),
"p2_pegged": pegged("price", "P2"),
"v1_pegged": pegged("volatility", "V1"),
"p1_pegged": pegged("price", "P1"),
},
"price_argmax_sole": argmax_sole,
"price_argmax_tie_inclusive": argmax_tied,
"price_argmax_ties": ties,
"price_argmax_when_state_ge_40": {
"sessions": stressed_total,
"sole": stressed_sole,
"p1_sole_share_pct": round(stressed_sole["P1"] / stressed_total * 100.0, 1)
if stressed_total else None,
"cap_rule": "P1_SCORE_CAP warranted if p1_sole_share_pct > 80",
},
"w1_nonzero_sessions": sum(
1 for r in rows if (_sensor_score(r, "breadth_divergence", "W1") or 0) > 0
),
"band_shares_current": _band_shares(states, rms.STATE_BANDS),
}
# ---------------------------------------------------------------------------
# Gates
# ---------------------------------------------------------------------------
def _completeness_gates(
prices: dict, symbols: list[str], sessions: list[date], breadth_counts: dict, basket_size: int
) -> list[dict]:
"""Without these, every gate below can pass on partial data.
_breadth_with_counts publishes on min_tickers=20, so 20 of 30 basket names
still yields 100% State coverage and a plausible W1 count.
"""
first, last = sessions[0], sessions[-1]
missing = [s for s in symbols if not prices.get(s)]
thin = [
s for s in symbols
if len([d for d, _ in prices.get(s, []) if d < first]) < 252
]
truncated = [s for s in symbols if not prices.get(s) or prices[s][-1][0] < last]
short_basket = sorted(
d.isoformat() for d in sessions if breadth_counts.get(d, 0) != basket_size
)
return [
{"gate": "symbols_fetched", "expected": len(symbols),
"measured": len(symbols) - len(missing), "passed": not missing, "detail": missing},
{"gate": "per_symbol_warmup_252_bars", "expected": "all",
"measured": len(symbols) - len(thin), "passed": not thin, "detail": thin},
{"gate": "per_symbol_reaches_last_session", "expected": last.isoformat(),
"measured": len(symbols) - len(truncated), "passed": not truncated, "detail": truncated},
{"gate": "breadth_counts_full_basket", "expected": basket_size,
"measured": f"{len(sessions) - len(short_basket)}/{len(sessions)} sessions",
"passed": not short_basket, "detail": short_basket[:20]},
]
def _pipeline_gates(rows: list[dict], sessions: list[date], expected_first: str) -> list[dict]:
coverage_bad = [
r["date"] for r in rows if (r["state"]["coverage"] or 0) < 100.0
]
w1_available = sum(
1 for r in rows if _sensor_score(r, "breadth_divergence", "W1") is not None
)
stale = [r["date"] for r in rows if r["data_quality"]["stale_inputs"]]
gates = [
{"gate": "sessions_scored", "expected": PUBLISHED["sessions"],
"measured": len(rows), "passed": len(rows) == PUBLISHED["sessions"]},
{"gate": "last_scored_date", "expected": PUBLISHED["window_end"],
"measured": rows[-1]["date"], "passed": rows[-1]["date"] == PUBLISHED["window_end"]},
# Availability, not the published "W1 live 108" -- that figure counts
# NONZERO sessions under v2's hard price gate and is checked there.
{"gate": "w1_available_every_session", "expected": len(rows),
"measured": w1_available, "passed": w1_available == len(rows)},
{"gate": "state_coverage_100_every_row", "expected": 0,
"measured": len(coverage_bad), "passed": not coverage_bad, "detail": coverage_bad[:20]},
{"gate": "no_stale_inputs", "expected": 0,
"measured": len(stale), "passed": not stale, "detail": stale[:20]},
]
# Unconditional: sessions_scored is tautological when the harness slices the
# tail of leader_series, so the start date is the only real calendar check.
# An optional gate is not a gate.
gates.append({
"gate": "first_scored_date", "expected": expected_first,
"measured": rows[0]["date"], "passed": rows[0]["date"] == expected_first,
})
return gates
def _invariant_gate(v3_rows: list[dict], v4_rows: list[dict]) -> dict:
"""state_v4 <= state_v3 on every aligned row.
Provable, not heuristic: graduated P1 never exceeds binary P1, anchored VIX
never exceeds (vix-15)/15*100, max() is monotone in its arguments, and no
other State sensor or weight changes. A violation means the harness is
mis-wired, not that the calibration is interesting.
"""
violations = []
for a, b in zip(v3_rows, v4_rows):
assert a["date"] == b["date"], "row misalignment"
s3, s4 = a["state"]["score"], b["state"]["score"]
if s3 is not None and s4 is not None and s4 > s3 + 1e-9:
violations.append({"date": a["date"], "v3": s3, "v4": s4})
return {
"gate": "state_v4_le_v3_every_row", "expected": 0,
"measured": len(violations), "passed": not violations, "detail": violations[:20],
}
def _soft_gates(stats: dict, variant: str) -> list[dict]:
if variant == "v3":
# The doc states no v3 average: its "-0.4" is measured against
# v3-with-the-percentile-leg, not against v2. Only the max is checkable.
pairs = [("v3_state_max", stats["state"]["max"], 0.5)]
else:
pairs = [("v2_state_avg", stats["state"]["avg"], 0.3),
("v2_state_p80", stats["state"]["p80"], 0.5),
("v2_state_max", stats["state"]["max"], 0.5),
("v2_p3_pegged", stats["saturation_census"]["p3_pegged"], 0),
("w1_live_sessions", stats["w1_nonzero_sessions"], 0)]
out = []
for key, measured, tol in pairs:
expected = PUBLISHED[key]
ok = measured is not None and abs(measured - expected) <= tol
out.append({"gate": key, "expected": expected, "measured": measured,
"tolerance": tol, "passed": ok})
return out
# ---------------------------------------------------------------------------
# Scenarios -- pillar arithmetic, stated as explicit sensor scores
# ---------------------------------------------------------------------------
def _scenarios(vix_anchors, p1_anchors) -> list[dict]:
"""The meaning anchors for the band choice, machine-checked rather than prose.
Stated as explicit sensor scores because drawdown + VIX + OAS does not
determine State: the price pillar is max(P1, P2, P3) and P2 is set by the
50/200-DMA gap, which no drawdown figure implies.
"""
def state(p1, p2, p3, breadth, c1, v1):
price = max(p1, p2, p3)
return round((price * 40 + breadth * 25 + c1 * 20 + v1 * 15) / 100, 2)
def p1_at(pct_below):
return round(rms._interpolate(pct_below, p1_anchors), 2) if pct_below > 0 else 0.0
def p3_at(dd):
return round(rms._interpolate(dd, rms.P3_DRAWDOWN_ANCHORS), 2)
def v1_at(vix):
return round(rms._interpolate(vix, vix_anchors), 2)
rows = [
("S1 ordinary tape", 0.0, 0.0, p3_at(3), rms.breadth_level_score(65), 0.0, v1_at(16)),
("S2 10% correction, calm credit", p1_at(2), 0.0, p3_at(10), rms.breadth_level_score(35), 0.0, v1_at(24)),
("S3a 2022-style, calm credit, no death cross", p1_at(20), 0.0, p3_at(35), rms.breadth_level_score(8), 0.0, v1_at(32)),
("S3b 2022-style, calm credit, death cross", p1_at(20), 100.0, p3_at(35), rms.breadth_level_score(8), 0.0, v1_at(32)),
("S4 credit event on top", p1_at(25), 100.0, p3_at(40), rms.breadth_level_score(5), rms.f2_credit_spreads([6.0]), v1_at(45)),
("S5 March 2020, everything pegged", 100.0, 100.0, 100.0, 100.0, 100.0, 100.0),
]
return [
{"scenario": name, "P1": p1, "P2": p2, "P3": p3, "price": max(p1, p2, p3),
"breadth": br, "C1": c1, "V1": v1, "state": state(p1, p2, p3, br, c1, v1)}
for name, p1, p2, p3, br, c1, v1 in rows
]
def _markdown(report: dict) -> str:
"""Scannable sibling to the JSON. Never written over a curated docs/ file."""
lines = ["# Regime Monitor v4 calibration", ""]
src = report["source"]
dirty = " **(dirty working tree)**" if src.get("git_dirty") else ""
lines.append(f"Generated {report['generated_at']} at `{src['git_rev']}`{dirty}, "
f"{report['provenance']['scored_range'][0]} → {report['provenance']['scored_range'][1]}.")
lines += ["", "Source hashes (sha256, first 16):", ""]
for rel, digest in src["source_sha256"].items():
lines.append(f"- `{rel}` — `{digest}`")
lines += ["", "## Hard gates", "", "| gate | expected | measured | |", "|---|---|---|---|"]
for g in report["hard_gates"]:
lines.append(f"| {g['gate']} | {g['expected']} | {g['measured']} | {'ok' if g['passed'] else '**FAIL**'} |")
lines += ["", "## Distributions", "", "| variant | avg | median | p80 | p90 | max |", "|---|---|---|---|---|---|"]
for name, v in report["variants"].items():
st = v["state"]
lines.append(f"| {name} | {st['avg']} | {st['median']} | {st['p80']} | {st['p90']} | {st['max']} |")
lines += ["", "## Saturation census (sessions pegged at 100)", "",
"| variant | P1 | P2 | P3 | V1 |", "|---|---|---|---|---|"]
for name, v in report["variants"].items():
c = v["saturation_census"]
lines.append(f"| {name} | {c['p1_pegged']} | {c['p2_pegged']} | {c['p3_pegged']} | {c['v1_pegged']} |")
for name, v in report["variants"].items():
if v.get("soft_gates"):
lines += ["", f"## Reproduction gates — {name}", "",
"| figure | published | measured | |", "|---|---|---|---|"]
for g in v["soft_gates"]:
lines.append(f"| {g['gate']} | {g['expected']} | {g['measured']} | {'ok' if g['passed'] else '**miss**'} |")
if "v4" in report["variants"]:
lines += ["", "## v4 band-share grid (watch 20 / elevated 50)", "",
"| breaking | stable | watch | elevated | breaking |", "|---|---|---|---|---|"]
for row in report["variants"]["v4"]["band_grid"]:
w, e, b = row["bands"]
if w == 20.0 and e == 50.0:
sh = row["shares"]
lines.append(f"| {b:.0f} | {sh['stable']} | {sh['watch']} | {sh['elevated']} | {sh['breaking']} |")
lines += ["", "## Scenarios (pillar arithmetic, explicit sensor scores)", "",
"| scenario | price | breadth | C1 | V1 | State |", "|---|---|---|---|---|---|"]
for sc in report["scenarios"]["vix_a"]:
lines.append(f"| {sc['scenario']} | {sc['price']} | {sc['breadth']} | {sc['C1']} | {sc['V1']} | **{sc['state']}** |")
lines += ["", f"Recommendation: `{report['v4_recommendation']}`", ""]
return "\n".join(lines)
def _band_grid(states: list[float]) -> list[dict]:
grid = []
for watch in (15.0, 20.0, 25.0):
for elevated in (40.0, 50.0):
for breaking in (60.0, 65.0, 70.0):
grid.append({
"bands": [watch, elevated, breaking],
"shares": _band_shares(states, (watch, elevated, breaking)),
})
return grid
# ---------------------------------------------------------------------------
# Main
# ---------------------------------------------------------------------------
def _parse_args() -> argparse.Namespace:
p = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
p.add_argument("--end", default=PUBLISHED["window_end"])
p.add_argument("--sessions", type=int, default=PUBLISHED["sessions"])
p.add_argument("--history-days", type=int, default=1200, help="matches production _fetch_prices")
p.add_argument("--oas-window-days", type=int, default=rms.HY_OAS_WINDOW_DAYS,
help="per-session slice for the canonical run")
p.add_argument("--oas-fetch-days", type=int, default=int(365.25 * 13),
help="fetch range; matches v2's request (ICE truncates to ~3y)")
p.add_argument("--methodology", default=",".join(REQUIRED_VARIANTS))
p.add_argument("--expected-first-session", default=None,
help="assert the first replayed date. Defaults to the published "
"window's start; REQUIRED when --end/--sessions are overridden, "
"since the count alone is tautological.")
p.add_argument("--cache-dir", default=None)
p.add_argument("--out", default=None)
p.add_argument("--quiet", action="store_true")
return p.parse_args()
def _git_rev() -> str:
try:
return subprocess.run(["git", "rev-parse", "--short", "HEAD"], cwd=ROOT,
capture_output=True, text=True, check=True).stdout.strip()
except Exception:
return "unknown"
def _source_state() -> dict:
"""Identify the code that produced this run, not just the commit HEAD names.
A run from a dirty tree is not reproducible by checking out git_rev -- which
is exactly how the first v4 artifact was generated, with HEAD still on the
harness commit while the v4 sensors lived only in the working tree. The
hashes make that visible instead of implied.
"""
import hashlib
tracked = [
"app/services/regime_monitor_service.py",
"app/services/breadth_service.py",
"scripts/run_regime_monitor_calibration.py",
]
digests = {}
for rel in tracked:
path = ROOT / rel
digests[rel] = hashlib.sha256(path.read_bytes()).hexdigest()[:16] if path.exists() else None
try:
dirty = bool(subprocess.run(["git", "status", "--porcelain"], cwd=ROOT,
capture_output=True, text=True, check=True).stdout.strip())
except Exception:
dirty = None
return {"git_rev": _git_rev(), "git_dirty": dirty, "source_sha256": digests}
async def _main() -> int:
args = _parse_args()
end = date.fromisoformat(args.end)
cache_dir = Path(args.cache_dir) if args.cache_dir else None
config = deepcopy(rms.DEFAULT_CONFIG)
symbols = _all_symbols(config)
variants = [v.strip() for v in args.methodology.split(",") if v.strip()]
expected_first = args.expected_first_session
if not expected_first:
if args.end != PUBLISHED["window_end"] or args.sessions != PUBLISHED["sessions"]:
print("--expected-first-session is required when --end or --sessions "
"differ from the published window.", file=sys.stderr)
return 2
expected_first = PUBLISHED["window_first"]
unknown = [v for v in variants if v not in VARIANTS]
if unknown:
print(f"unknown variant(s): {unknown}; known: {sorted(VARIANTS)}", file=sys.stderr)
return 2
missing = [v for v in REQUIRED_VARIANTS if v not in variants]
if missing:
print(f"--methodology must include {list(REQUIRED_VARIANTS)}; missing {missing}. "
"v2_reconstruction carries the published reproduction figures, and "
"v3+v4 are needed for the state_v4 <= state_v3 invariant gate.",
file=sys.stderr)
return 2
if not args.quiet:
print(f"fetching {len(symbols)} symbols from Alpaca...", flush=True)
prices = await _load_prices(symbols, end - timedelta(days=args.history_days), end, cache_dir, args.quiet)
vix = await _load_fred("VIXCLS", end - timedelta(days=args.history_days), end, cache_dir)
oas = await _load_fred("BAMLH0A0HYM2", end - timedelta(days=args.oas_fetch_days), end, cache_dir)
leader = prices.get(LEADER, [])
if not leader:
print("no leader (SMH) price data — cannot replay", file=sys.stderr)
return 2
sessions = [d for d, _ in leader if d <= end][-args.sessions:]
breadth, breadth_counts = breadth_service._breadth_with_counts(
{s: prices[s] for s in _basket(config) if prices.get(s)}, window=200, min_tickers=20
)
# Required glue: _item_asof breaks on the first date > as_of, so unsorted
# input silently returns a wrong value rather than erroring.
breadth_series = rms._mapping_series(breadth)
divergence_by_variant = {
"live": rms._mapping_series(breadth_service.compute_divergence_series(breadth, leader)),
"v2": rms._mapping_series(_v2_divergence_series(breadth, leader)),
}
if args.oas_window_days != rms.HY_OAS_WINDOW_DAYS:
rms.HY_OAS_WINDOW_DAYS = args.oas_window_days
results: dict[str, Any] = {}
rows_by_variant: dict[str, list[dict]] = {}
for variant in variants:
if not args.quiet:
print(f"replaying {variant} over {len(sessions)} sessions...", flush=True)
rows = _replay(variant, prices, vix, oas, config, sessions,
breadth_series, divergence_by_variant, breadth_counts)
rows_by_variant[variant] = rows
results[variant] = _stats(rows, variant)
results[variant]["soft_gates"] = _soft_gates(results[variant], variant) \
if variant in ("v3", "v2_reconstruction") else []
states = [r["state"]["score"] for r in rows if r["state"]["score"] is not None]
if variant.startswith("v4"):
results[variant]["band_grid"] = _band_grid(states)
canonical = rows_by_variant.get("v3") or next(iter(rows_by_variant.values()))
hard_gates = _completeness_gates(prices, symbols, sessions, breadth_counts, len(_basket(config)))
hard_gates += _pipeline_gates(canonical, sessions, expected_first)
if "v3" in rows_by_variant and "v4" in rows_by_variant:
hard_gates.append(_invariant_gate(rows_by_variant["v3"], rows_by_variant["v4"]))
blocked_by = [g["gate"] for g in hard_gates if not g["passed"]]
provisional = any(
not g["passed"] for v in results.values() for g in v.get("soft_gates", [])
)
report = {
"generated_at": datetime.now().isoformat(timespec="seconds"),
"source": _source_state(),
"params": vars(args),
"provenance": {
"symbols": {s: {"bars": len(prices.get(s, [])),
"first": prices[s][0][0].isoformat() if prices.get(s) else None,
"last": prices[s][-1][0].isoformat() if prices.get(s) else None}
for s in symbols},
"vix": {"points": len(vix or []),
"first": vix[0][0].isoformat() if vix else None,
"last": vix[-1][0].isoformat() if vix else None},
"oas": {"points": len(oas or []),
"first": oas[0][0].isoformat() if oas else None,
"last": oas[-1][0].isoformat() if oas else None},
"scored_range": [sessions[0].isoformat(), sessions[-1].isoformat()],
},
"hard_gates": hard_gates,
"blocked_by": blocked_by,
"provisional": provisional,
"variants": results,
"scenarios": {
"note": "pillar arithmetic on explicit sensor scores; State weights unchanged",
"vix_a": _scenarios(P5_VIX_ANCHORS_A, P1_TREND_BREAK_ANCHORS),
},
"diagnostics": {
"vix_top10": sorted(((d.isoformat(), v) for d, v in (vix or [])),
key=lambda x: -x[1])[:10],
"candidate_anchors": {
"P5_VIX_ANCHORS_A": P5_VIX_ANCHORS_A,
"P5_VIX_ANCHORS_B": P5_VIX_ANCHORS_B,
"P1_TREND_BREAK_ANCHORS": P1_TREND_BREAK_ANCHORS,
},
},
# Structurally impossible to read a recommendation out of a run whose
# pipeline did not validate.
"v4_recommendation": None if blocked_by else {
"state_bands_candidate": [20.0, 50.0, 65.0],
"provisional": provisional,
"note": "confirm against band_grid + scenarios before shipping",
},
}
stamp = datetime.now().strftime("%Y%m%d-%H%M%S")
out = Path(args.out) if args.out else ROOT / "reports" / f"regime-monitor-v4-calibration-{stamp}.json"
out.parent.mkdir(parents=True, exist_ok=True)
tmp = out.with_suffix(out.suffix + ".tmp")
tmp.write_text(json.dumps(report, indent=2, default=str), encoding="utf-8")
tmp.replace(out)
md = out.with_suffix(".md")
md_tmp = md.with_suffix(".md.tmp")
md_tmp.write_text(_markdown(report), encoding="utf-8")
md_tmp.replace(md)
if not args.quiet:
print(f"wrote {out}", flush=True)
for gate in hard_gates:
mark = "ok " if gate["passed"] else "FAIL"
print(f" [{mark}] {gate['gate']}: expected {gate['expected']}, got {gate['measured']}")
if blocked_by:
print(f"HARD GATES FAILED: {blocked_by} — no v4 recommendation emitted", file=sys.stderr)
return 1
return 0
if __name__ == "__main__":
raise SystemExit(asyncio.run(_main()))
scripts/run_research_matrix.py
-1
View File
@@ -70,7 +70,6 @@ if str(ROOT) not in sys.path:
from scripts.research_rankings import ( # noqa: E402
_live_universe_rank_map,
_period_percentiles,
)
CACHE_VERSION = "research-matrix-v1-daily-prod"
scripts/run_tier1_macbook.sh
+1 -17
View File
@@ -3,7 +3,6 @@
#
# Kept after Tier-1 cleanup:
# --ssl-check diagnose corporate CA / proxy
# --earnings-only resume FMP earnings backfill + 2a/2b (parked)
# --prod-book-matrix re-run 505 vs liquid universe × horizon book matrix
#
# Prerequisites: git checkout research branch, .env, deep research.sqlite for
@@ -21,8 +20,6 @@ cd "$ROOT"
RESEARCH_SNAP="${RESEARCH_SNAP:-backtest_snapshots/research.sqlite}"
PROD_SNAP="${PROD_SNAP:-backtest_snapshots/prod.sqlite}"
WORKERS="${WORKERS:-8}"
FMP_LIMIT="${FMP_LIMIT:-250}"
FMP_SLEEP="${FMP_SLEEP:-0.35}"
PYTHON="${PYTHON:-python3}"
USE_CORP_PROXY="${USE_CORP_PROXY:-0}"
PHASE=""
@@ -35,7 +32,6 @@ usage() {
while [[ $# -gt 0 ]]; do
case "$1" in
--ssl-check) PHASE=ssl; shift ;;
--earnings-only) PHASE=earnings; shift ;;
--prod-book-matrix) PHASE=prod_book; shift ;;
--corp-proxy) USE_CORP_PROXY=1; shift ;;
--workers) WORKERS="$2"; shift 2 ;;
@@ -46,7 +42,7 @@ while [[ $# -gt 0 ]]; do
done
if [[ -z "$PHASE" ]]; then
echo "Pick a phase: --ssl-check | --earnings-only | --prod-book-matrix" >&2
echo "Pick a phase: --ssl-check | --prod-book-matrix" >&2
usage 1
fi
@@ -104,7 +100,6 @@ print(json.dumps(ssl_status(), indent=2))
print("bootstrap ->", bootstrap_ssl())
for url in (
"https://data.alpaca.markets/v2/stocks/SPY/bars?timeframe=1Day&limit=1",
"https://financialmodelingprep.com/stable/profile?symbol=AAPL",
):
try:
req = urllib.request.Request(url, headers={"User-Agent": "ssl-check"})
@@ -118,17 +113,6 @@ PY
setup_ssl
case "$PHASE" in
ssl) ssl_check ;;
earnings)
need_file "$PROD_SNAP"
need_file "$RESEARCH_SNAP"
log "Earnings Task 2 bulk backfill + registered 2a/2b closeout"
"$PYTHON" scripts/backfill_earnings_events.py \
--snapshot "$PROD_SNAP" --from-date 2016-01-04 --window-days 30 \
--limit "$FMP_LIMIT" --sleep "$FMP_SLEEP"
"$PYTHON" scripts/run_earnings_research.py \
--snapshot "$RESEARCH_SNAP" --universe-snapshot "$PROD_SNAP" \
--earnings-snapshot "$PROD_SNAP" --workers "$WORKERS" --allow-spawn
;;
prod_book)
need_file "$RESEARCH_SNAP"
log "Production book universe × horizon matrix"
tests/unit/test_activation_settings.py
-17
View File
@@ -8,9 +8,7 @@ from sqlalchemy.ext.asyncio import AsyncSession
from app.exceptions import ValidationError
from app.services.admin_service import (
get_activation_config,
get_fundamentals_cutover_config,
update_activation_config,
update_fundamentals_cutover_config,
)
@@ -78,18 +76,3 @@ class TestActivationConfig:
async def test_rejects_out_of_range_confidence(self, session: AsyncSession):
with pytest.raises(ValidationError):
await update_activation_config(session, {"min_confidence": 120.0})
class TestFundamentalsCutoverConfig:
async def test_defaults_off_when_unset(self, session: AsyncSession):
assert await get_fundamentals_cutover_config(session) == {"enabled": False}
async def test_round_trips_explicit_switch(self, session: AsyncSession):
assert await update_fundamentals_cutover_config(session, True) == {
"enabled": True
}
assert await get_fundamentals_cutover_config(session) == {"enabled": True}
assert await update_fundamentals_cutover_config(session, False) == {
"enabled": False
}
tests/unit/test_admin_jobs.py
+110
View File
@@ -0,0 +1,110 @@
"""Admin → Jobs listing: categories, ordering, and next-run coherence.
The panel used to render 19 jobs as one alphabetical list in which a pipeline
step, a cron job and a manual job were indistinguishable, and a triggered job
could advertise a next run ten years out.
"""
from datetime import datetime, timedelta, timezone
import pytest
from app import job_catalog
from app.scheduler import configure_scheduler, scheduler
from app.services.admin_service import _visible_next_run, list_jobs
@pytest.fixture(autouse=True)
def _configured_scheduler():
scheduler.remove_all_jobs()
configure_scheduler()
yield
scheduler.remove_all_jobs()
def _by_name(jobs: list[dict]) -> dict[str, dict]:
return {job["name"]: job for job in jobs}
class TestVisibleNextRun:
def test_parked_backstop_is_not_a_schedule(self):
"""Paused jobs carry a 520-week interval; triggering one re-arms it."""
backstop = datetime.now(timezone.utc) + timedelta(weeks=520)
assert _visible_next_run(backstop) is None
def test_a_real_upcoming_run_passes_through(self):
soon = datetime.now(timezone.utc) + timedelta(hours=6)
assert _visible_next_run(soon) == soon
def test_none_stays_none(self):
assert _visible_next_run(None) is None
class TestListJobs:
async def test_hidden_jobs_are_not_listed_but_stay_valid(self, db_session):
jobs = _by_name(await list_jobs(db_session))
assert "data_backfill" not in jobs
# Still triggerable through the API, and still registered.
assert "data_backfill" in job_catalog.VALID_JOB_NAMES
assert scheduler.get_job("data_backfill") is not None
async def test_every_visible_job_has_a_category(self, db_session):
jobs = await list_jobs(db_session)
assert {j["name"] for j in jobs} == set(
job_catalog.VALID_JOB_NAMES - job_catalog.HIDDEN_JOBS
)
assert all(j["category"] in job_catalog.CATEGORY_ORDER for j in jobs)
async def test_jobs_arrive_grouped_by_category(self, db_session):
"""The frontend renders sections in payload order, so ordering is the
API's job — not something each client re-derives."""
categories = [j["category"] for j in await list_jobs(db_session)]
ranks = [job_catalog.CATEGORY_ORDER.index(c) for c in categories]
assert ranks == sorted(ranks)
async def test_pipeline_steps_defer_their_schedule_to_the_parent(self, db_session):
jobs = _by_name(await list_jobs(db_session))
step = jobs["rr_scanner"]
assert step["category"] == job_catalog.CATEGORY_STEP
assert step["next_run_at"] is None
assert step["next_run_source"] == "via_pipeline"
assert step["pipelines"] == ["near_close_pipeline"]
async def test_step_reports_the_soonest_enabled_parent(self, db_session):
due = datetime.now(timezone.utc) + timedelta(hours=3)
scheduler.modify_job("daily_pipeline", next_run_time=due)
collector = _by_name(await list_jobs(db_session))["data_collector"]
assert collector["via_next_run_job"] == "daily_pipeline"
assert collector["via_next_run_at"] == due.isoformat()
# Runs in all four pipelines — the reason steps are not nested under one.
assert set(collector["pipelines"]) == set(job_catalog.PIPELINE_JOBS)
async def test_manual_jobs_say_so_instead_of_showing_a_date(self, db_session):
study = _by_name(await list_jobs(db_session))["event_study"]
assert study["category"] == job_catalog.CATEGORY_MANUAL
assert study["next_run_source"] == "manual_only"
assert study["next_run_at"] is None
async def test_a_triggered_manual_job_still_shows_no_next_run(self, db_session):
"""Regression: triggering re-armed the 520-week backstop, which the panel
rendered as a real 'next run in ~87600h'."""
scheduler.modify_job("event_study", next_run_time=datetime.now(timezone.utc))
scheduler.modify_job("event_study", next_run_time=None)
study = _by_name(await list_jobs(db_session))["event_study"]
assert study["next_run_at"] is None
async def test_pipelines_report_their_own_schedule_and_steps(self, db_session):
pipeline = _by_name(await list_jobs(db_session))["daily_pipeline"]
assert pipeline["category"] == job_catalog.CATEGORY_PIPELINE
assert pipeline["next_run_source"] == "own_schedule"
assert pipeline["steps"] == [
step for step, _ in job_catalog.PIPELINE_STEPS["daily_pipeline"]
]
async def test_standalone_jobs_keep_their_own_schedule(self, db_session):
backtest = _by_name(await list_jobs(db_session))["backtest"]
assert backtest["category"] == job_catalog.CATEGORY_SCHEDULED
assert backtest["next_run_source"] == "own_schedule"
assert backtest["pipelines"] == []

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