Layout regrouped by relationship, not size: the setup-in-focus card
and the radar sit side by side (they are one decision surface), the
four account ribbons move directly above the open positions they
describe, and a new performance chart closes the page.
- Radar rows are selectable: clicking one swaps the focus card to that
setup - including below-gate rows, whose card shows a muted
"rank N / below gate" badge and the disqualify reason in the footer,
with a "back to top pick" reset. The row currently in focus is
highlighted; ticker links still deep-link without selecting.
- Performance chart (the mockup's missing piece): new
GET /paper-trades/equity-curve computes, per benchmark trading day
since the first paper trade, the book's cumulative P&L (realized +
mark-to-market from stored OHLCV) vs the same cost basis riding SPY
over each trade's window (benchmark_prices). Pure curve math in
paper_trade_service with unit tests; hidden until there are 2+
points of data. Frontend renders both lines with crosshair readout,
zero baseline, and direct end labels.
Backend unit suite: 501 passed.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
A setup's primary target could carry a ~3% probability: the picker
chose the most likely target among those with R:R >= 1.5, and after a
run-up that pool can contain only far "lottery" levels (the near,
likely levels fail the R:R floor). The lottery target's inflated R:R
then became the setup's headline and passed the activation gate's
min_rr floor - the gate's probability check only requires a value to
exist.
Fix, no new tuning knobs: the primary must clear BOTH floors
(R:R >= 1.5 AND probability >= 20%). When nothing does, fall back to
the most likely target overall, so the headline carries an honest low
R:R and the gate rejects the setup on real numbers instead of being
gamed by an unreachable target.
Deliberately NOT pure EV-maximization (p*RR): the probability model
adds strength/alignment bonuses as flat percentage points, so EV
arithmetic would scale those bonuses by (RR+1) and systematically
favor far targets on aligned setups - the same lottery bias through
the back door.
Shared by production (enhance_trade_setup) and the backtest simulator,
so backtest comparisons stay apples-to-apples. Unit tests pin the
degenerate case; full unit suite green (497 passed).
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
The portfolio monitor's Production row now replays the live qualification
flag and the Admin exit policy (mode/ATR multiplier/hold days) instead of a
frozen research-variant gate, so Admin tuning is reflected in the next run.
Single-source the 80/20 strategy_rank weights in momentum_service and pin
every dual-defined constant with a parity test. Behavior-preserving today:
the production sim reproduces the README baseline exactly.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
A local backtest (offline prod snapshot, 506 tickers) evaluated blue-sky
projected targets under the PRODUCTION exit (3x ATR trailing + 30d max hold,
paper_trade_service DEFAULT_EXIT_MODE="atr_trailing"). Blue-sky setups are
dilutive: the qualified book scored 328% return / Sharpe 1.84 / DD -21.0%
WITHOUT them vs 300% / 1.58 / -18.7% WITH them. They rank high on momentum by
construction, so they grab slots from S/R setups that catch bigger runs under
a trailing-stop exit (only ~2pp worse drawdown doesn't justify the lost return
and Sharpe).
Reverts the scanner/TargetGenerator measured-move projection, the stricter
projected activation gate, the frontend qualification mirror, the `projected`
type field, and the projected tests -- all backend files are now byte-identical
to the pre-blue-sky commit.
Keeps the played-out "No current setup" UX (RecommendationPanel): when price
has run past the target (played out) or through the stop (invalidated), the
panel shows a plain no-setup state instead of a stale actionable card. This is
frontend-only (reads last close + existing setup fields) and is what actually
fixes the reported stale-below-price bug -- no backend change or rescan needed.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Fixes stale below-price setups showing as current recommendations. Three
distinct causes share the symptom (get_trade_setups returns the latest stored
setup per direction and never expires it):
- Genuine blue-sky (no overhead S/R): scanner + TargetGenerator now project a
measured-move target (entry +/- 3*ATR, ~2:1 R:R), flagged projected with a
low sr_strength probability haircut. Overhead check keys on level tag OR price
so it never projects through a straddling resistance cluster.
- Projected targets clear a stricter activation bar (long-only, momentum >= 90,
confidence >= min+10), independent of the general momentum gate. Mirrored in
frontend qualification.ts.
- Played-out UX (fixes the reported TTWO case, which is R:R-starved under a
resistance cluster, not blue-sky): when price is at/past target or through the
stop, RecommendationPanel shows a "No current setup" state and softens the
stale ticker-level header/reasoning, instead of a stale actionable card.
No migration: the projected flag rides in existing targets_json. 504 backend
unit tests pass; frontend typechecks.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Both _atr_trailing_close (scheduled) and _atr_trailing_level (dashboard
read path) recomputed ATR from scratch on every post-entry bar via
compute_atr(rows[:idx+1]) — O(n*k) per trade. Replace with a single O(n)
Wilder pass, _atr_series_from_rows, that stores round(running, 4) at each
index. compute_atr keeps its running ATR unrounded through the recurrence
and rounds only at return, so this reproduces its per-prefix value exactly
(no behavior change; live-vs-backtest atr_trail3 parity still byte-identical).
Remove the now-unused _atr_from_rows and its compute_atr import. Add a
per-index parity test against compute_atr; existing ATR tests now mock
_atr_series_from_rows (same effect as the old fixed-ATR mock).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Stored TradeSetup rows are point-in-time snapshots from the RR scan, so
the ticker page could show stale confidence/reasoning/composite (e.g.
sentiment=neutral in the setup card while the sentiment panel showed
bullish). Overlay current score/sentiment context onto the API payload
for GET /trades and GET /trades/{symbol}, gate and format Telegram
qualified-setup alerts on the same live values, and apply the
min_confidence/recommended_action filters after the overlay so they
judge what the caller actually sees. Stored setups stay frozen for
outcome analysis and backtests.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Backtest report now includes research-only hold-to-horizon portfolio variants comparing raw vs residual 12-1 momentum, cutoff 80 vs 90, max 10 vs 15 positions, and SPY-200 risk scaling. A dynamic research recommendation panel flags residual momentum, cutoff 90, or regime scaling only when transparent promotion rules pass.
Adds signal_context_snapshots with migration 016 and captures one point-in-time context row per newly generated TradeSetup: setup fields, composite/dimensions, latest sentiment, latest fundamentals, and strategy_version=momentum_12_1_rr_time_v1. This is forward-only; no historical sentiment/fundamental backfill is attempted.
No live gate, paper-trade exit, or production ranking behavior changes.
Verification: 458 backend tests pass, ruff check app/ clean, frontend npm run build clean.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Adds a research-only 12-1 residual momentum signal to the cross-sectional signal-evaluation harness. The signal estimates benchmark beta over the 12-1 formation window and ranks cumulative stock return minus beta-adjusted benchmark return; it only appears when benchmark closes are available.
No production qualification behavior changes. The Backtest signal table labels the new row as 12-1 residual momentum. Tests cover benchmark-gated emission and beta removal while keeping stock-specific drift.
Verification: 453 backend tests pass, ruff check app/ clean, frontend npm run build clean.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Production strategy change based on the July 2026 backtest: paper trades now default to a 30-trading-day hold with the initial stop (classic momentum hold-and-rerank), while target and trailing exits remain available in Admin. The exit policy API/UI now carries hold_days and close_reason can be 'time'.
The activation confidence floor default is now 0/off because the gate ablation showed it added no per-trade edge while filtering out usable setups. Migration 015 clears stored activation_min_confidence and paper_exit_mode so the new defaults take effect; this intentionally resets Track Record comparability from this deploy.
Verification: 451 backend tests pass, ruff check app/ clean, frontend npm run build clean.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
The robustness warning was computed on the target-model distribution
while the same panel recommends the hold exit — internally inconsistent.
_robustness_stats (median, profit factor, ex-top-5% expectancy) is now
shared by _bucket_stats and _time_exit_bucket, the time-exit table shows
Median Net R and Ex-Top-5% per hold length, and _build_recommendation
reads the trimmed expectancy from the recommended exit's bucket (falling
back to the target model when no hold is recommended).
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Robustness (answers 'is the edge just outliers?'):
- _bucket_stats gains median_net_r, profit_factor, and net_avg_r_ex_top5
(expectancy with the top 5% of winners removed); shown as stat tiles.
- Portfolio sim gains per-calendar-year returns, shown in the sim table.
Dynamic recommendation ('What this backtest recommends' panel):
- _build_recommendation derives advice from the report's own numbers on
every run — exit policy (target vs best hold, with sim CAGRs), which
gate floors earn their keep (ablation Hold column), best momentum
cutoff, book-vs-SPY verdict, and an outlier-dependence warning when
the trimmed expectancy goes non-positive.
Retired (conclusions reached, tables removed from report + UI):
- Take-profit sweep (no interior optimum — fixed TP is the wrong tool
for momentum), trailing sweep (converged to the hold-to-horizon exit),
probability calibration (model is display-only by decision).
- _tp_primitives slimmed to _risk_and_stop_day; trailing machinery gone.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Per-trade additions to the report:
- Gap-through-stop fills: stops now fill at the worse of the stop or the
bar's open across every exit model (target, TP, trailing, time), so a
loss can exceed -1R; targets never fill better than their level.
- best_r / worst_r, avg holding days, and net R per day of capital
deployed on the summary buckets and the time-exit sweep.
Portfolio simulation (the stats a per-setup replay cannot give):
- One capital-constrained book over the qualified setups: 10k start, max
10 concurrent positions (one per ticker, best momentum first), 1%
fixed-fractional risk with a 20% no-leverage notional cap, entries at
the detection close, 0.1%/side costs, daily mark-to-market.
- Two exit policies compared: S/R target race vs hold-to-horizon.
- Equity-curve stats: final equity, total return, CAGR, max drawdown,
annualized daily Sharpe, win rate, avg P&L, best/worst trade, avg
hold, entries skipped on a full book, and SPY price return over the
same window (benchmark history refreshed to cover the replay span).
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
The ablation judged floors under the target/stop model, but the exit
sweeps point at replacing that exit with a fixed hold — under which the
R:R floor's rationale (bigger payoff at the target) may not apply. Each
ablation row now also carries hold_avg_r / hold_net_avg_r / hold_total_r
(30d hold, initial stop only), so the Phase 3 gate decision can be read
under the exit policy that would actually be used.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
_window_setups computed them but _replay_ticker dropped them, so the
ablation's NEUTRAL/tightener checks saw None for every candidate and the
'without confidence floor' / 'without R:R floor' rows collapsed to 0
setups (impossible — removing a floor can only add setups). Regression
test now goes through the real _replay_ticker path.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Phase 1 of the strategy-measurement plan — report-only, no production
trading behavior changes:
- Cost haircut: every bucket/sweep now reports net_avg_r/net_total_r
alongside gross (COST_PER_SIDE=0.1% of notional, converted to R via
each setup's stop distance); params carry cost_per_side_pct.
- Gate ablation table: re-qualifies candidates at the current momentum
cutoff with one floor removed per row (confidence / R:R / NEUTRAL /
momentum-only) to show which floors earn their keep.
- Time-based exit sweep: hold 5/10/21/30 days with the initial ATR stop,
exit at the day-N close — the classic momentum implementation, to
disambiguate the wide-trailing result.
- TP sweep extended to +40/+50%, trailing to 25/30% so the optima are
interior instead of starred at the sweep edge.
- BacktestPanel: Net Avg R columns everywhere, gate-ablation and
time-exit tables, stars now mark best net avg R; stale cached reports
still render (all new fields optional/guarded).
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
get_score computed base_score / sentiment_score / sentiment_adjustment /
max_sentiment_adjustment, but the router's _map_composite_breakdown built the
response model from only the five original keys and silently dropped the rest — so
the API always returned null for them. That's why the ticker page showed neither the
"Composite = Base + Sentiment" caption nor the ± marker on the sentiment row despite
the frontend and scoring service both supporting it. Pass the fields through, with a
guard test so they can't be dropped again.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Going from no sentiment to a bullish read used to be able to *lower* the composite:
sentiment was blended into the weighted average as an absolute level, so a bullish
75 diluted a ticker already scoring 78. That's backwards for a directional signal.
Now the non-sentiment dimensions form a re-normalized weighted-average base, and
sentiment is applied as a signed adjustment around neutral (50):
composite = clamp(base + MAX_ADJ * (sentiment - 50) / 50)
MAX_ADJ = sentiment weight * 100 (default weight 0.10 → ±10)
Neutral leaves the base unchanged, bullish adds and bearish subtracts (scaled by
confidence, since a 50%-confidence call maps to 50 → no effect), and no sentiment
never penalises. Default sentiment weight 0.15 → 0.10; the weight now means "max ±
points." Composite breakdown exposes base_score/sentiment_score/sentiment_adjustment,
and the ScoreCard shows "Base 78 · sentiment +5.0" plus the per-dimension adjustment.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Applies the backtest-validated trailing stop to live paper trading, and surfaces
it transparently.
Exit (A):
- New paper-trade exit policy (paper_exit_mode=trailing, paper_trailing_pct=12),
tunable in Admin → Paper-Trade Exit. resolve_open_trades runs a trailing stop
(initial stop as floor, ratchets up from the peak; target ignored — the
validated rule) and records close_reason (trailing|stop|target|manual; +migration
013).
- list_trades enriches open trades with the live trailing-stop level + distance %.
Open Trades panel shows the active tactic and a Trail Stop column.
Alerts (B):
- Daily digest now lists open trades with unrealized gain, trailing stop, and how
far away it is.
- New "trade closed" alert: one summary per auto-close (trailing/target/stop, not
manual) — direction, reason, days held, P&L abs+%/R — covering wins AND
stop-loss losses. Deduped by trade id; toggle in Admin alerts.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Third exit model alongside target-vs-stop and the fixed take-profit. The TP sweep
showed the edge lives in the fat tail (avg R keeps rising as you let winners run),
but a fixed wide target is win-rate-brutal and gives everything back on a reversal.
A trailing stop harvests the tail while protecting gains.
Per setup the replay computes the realized R for several trail widths (3/5/7/10/
15/20%) in a single conservative pass — stop ratchets up via max(initial_stop,
peak*(1-trail)), exit on the pullback or at the horizon close, R vs the initial
risk. Aggregated into a trailing sweep (win rate = share closed in profit, avg R,
total R) over the qualified set and shown as a new table in the Backtest panel.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The target-vs-stop model counts a near-miss of a far S/R target as a full loss
and ignores the partial gains you actually bank — so it measures a different
strategy than "scalp the early pop, take +8%". Add a realistic take-profit exit
model next to it (original untouched).
Per setup the replay now also records risk%, whether the stop was hit, the
favourable excursion reachable before the stop (MFE), and the horizon-close move.
From those a fixed-take-profit sweep (4/6/8/10/12/15%) is scored in R: bank +X%
if reached before the stop, else -1R, else the horizon close. Hit rate = how
often +X% was banked (the MFE CDF), so you can pick the EV-optimal TP without
top-ticking fantasy. Shown as a new table in the Backtest panel; the IC,
calibration and momentum sweep are unchanged.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
A NEUTRAL ("No Clear Setup") recommendation means the engine found no clear
directional trade, yet such setups could still qualify and even be crowned the
top pick purely on momentum rank (e.g. an extended momentum leader with a far,
5%-probability target). A NEUTRAL signal isn't actionable, so it shouldn't
qualify.
New `exclude_neutral` activation flag (default on): setup_qualifies drops setups
whose recommended_action is NEUTRAL. It lives in the shared gate, so it flows
through the dashboard's qualified/top-pick selection, the track record's
qualified stats, and the backtest (which computes recommended_action and gates on
meets_core). Toggleable in Admin → Settings → Activation; the frontend mirror and
activationSummary ("directional") match.
Re-run the backtest after enabling to confirm it holds/improves expectancy.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Fetching a symbol the provider doesn't cover (e.g. RHM/Rheinmetall — Alpaca
serves US listings only) returned 0 bars but reported "complete · Successfully
ingested 0 records", which the UI showed as green success.
fetch_and_ingest now returns a distinct `no_data` status when the provider
returns nothing AND the ticker has no history (vs. "already up to date" when bars
exist). The fetch endpoint maps it to a `warning` source status, and the fetch
toast renders it as ⚠ with the provider message instead of success.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The outcome stats were dominated by quick stop-outs: near stops resolve as losses
within days while far targets take weeks, so a young sample (mostly pending,
0 expired) skewed sharply negative (e.g. 13.8% hit / -0.46R vs the backtest's
35.8% / +0.18R) — a maturation artifact, not a real result.
get_performance_stats now counts only setups whose full ~30-day window has
elapsed (_MATURITY_DAYS), so winners had as long as losers (unbiased, and
comparable to the backtest). A new `maturing` count reports the younger setups
held back. The Track Record UI relabels "Evaluated" -> "Matured", shows the
maturing count, and explains the window in the empty state + methodology note.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Three usability fixes:
1. Global ticker search in the sidebar (TickerSearch) — typeahead over the
tracked universe that opens a ticker's detail page without adding it to the
watchlist. Also wired into the mobile nav.
2. Watchlist table shows the ticker's 12-1 momentum percentile (the top-pick
selector) instead of the noisy full S/R-level list. Enriched from the setup
already loaded in watchlist_service._enrich_entry — no extra query.
3. Alpha vs the S&P 500 on paper trades (open + closed). New benchmark_prices
table + benchmark_service store SPY daily closes (a standalone series, not a
Ticker, so it never enters the scanner / momentum ranking / rankings) via a
new daily-pipeline step. paper_trade_service computes per-trade
benchmark_return / alpha_pct / alpha_usd over each holding period; the open-
trades table, dashboard, and closed-trades panel surface per-trade and total
alpha. The list read path never makes a provider call.
Deploy: alembic upgrade head, then run the benchmark/daily job once to populate
SPY closes (alpha shows "—" until then).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Tiered, uncapped sentiment scope so the names that matter are never shown
without sentiment.
- Priority (always fully refreshed): top-pick feeders — momentum leaders with a
tradeable long setup over the R:R floor (the tickers that are, or could become
with positive sentiment, the dashboard top pick) — plus the curated watchlist
and open paper trades.
- Filler: top-N by composite, a discovery net, fetched after the priority set so
a mid-run rate limit lands the important names first.
- Removed the per-run cap (sentiment_max_per_run): the relevant set is naturally
bounded (watchlist <= 20, composite <= top_composite), so a full refresh stays
inside the free tier. extra="ignore" keeps a stale env var from breaking startup.
- Refresh window 72h -> 120h (5 days): sentiment shifts slowly, score window is 7d.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Fires once when the regime monitor shifts quadrant (regime index x early
warning), so you don't have to watch the tab. Two guards against spam:
- Hysteresis: each axis only flips once the value crosses its divider by a
margin, so a point parked on a boundary keeps its quadrant instead of
flip-flopping day to day.
- Cooldown: a genuine change stays quiet for a few days after the last alert.
Seeds the baseline silently on first run; reuses the existing Telegram dispatch
+ AlertLog. New per-trigger toggle in Admin → Alerts (on by default).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The early-warning score showed n/a because it required an exact date match
between the live benchmark (Alpaca, may have today's bar) and the stored
universe breadth (DB, often a day behind), which blanked the newest snapshot —
the one the UI displays.
- Look up the divergence as-of the snapshot date (newest value within a 7-day
lag) instead of requiring an exact match.
- Backfill early_warning + combined onto recent existing snapshots (the index
history predates this signal) so the 7/30-day trends populate on the first run
rather than only filling in over the coming weeks.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The event study showed the breadth-divergence signal genuinely leads (warned
before 7/11 drawdowns, ~6 weeks median, where the coincident baseline almost
never did). Surface it live to observe before deciding how to embed it — kept
separate from the index, not folded into its weights.
- regime_monitor daily job now computes breadth-divergence live and attaches a
separate early_warning score plus a combined blend (weighted mean, default
0.6/0.4, configurable via combined_weights) to each snapshot, including the
backfill so the 7/30-day trends populate immediately. Stored in breakdown_json
— no schema change. Best-effort: a breadth failure can't break the index.
- get_regime_monitor returns the index, early_warning, and combined scores each
with 7/30-day deltas.
- Regime tab shows three gauges (generalized ScoreGauge): coincident index,
early warning, and a compact combined blend. Stale snapshots render "—".
Note: the daily regime job now also does a universe-wide breadth scan.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The first run gave only 2 events (N=2 is anecdote, not evidence) and an unfairly
weak coincident baseline, so the +42d lead couldn't be trusted. This makes the
measurement meaningful:
- More, cleaner events: default drawdown threshold 15%→10%, and dedup switched
from "recover to the high" to a rising-edge + cooldown (40d), so distinct
drawdowns each register instead of merging.
- Fair comparison: each indicator now warns at its OWN 80th percentile instead of
a shared absolute 60, removing the artifact that muted the coincident baseline.
- Per-event breakdown (date · depth · breadth lead · coincident lead) so a median
over a tiny sample can't hide an apples-to-oranges comparison — you see whether
both warned on the same drawdown.
- Surface precision/recall (best row) + base rate per indicator — the honest edge
read, not just lead time.
Re-run the Event Study job to regenerate the cached report in the new shape.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Adds a leading-by-construction candidate and the harness to measure whether it
actually leads regime breaks, before any of it earns weight in the live index.
- breadth_service: % of the stored universe above its own 200-DMA + a divergence
score (benchmark price up while breadth falls, nudged by low breadth). Genuinely
leading because it keys on divergence, not level. Not wired into the live score.
- event_study_service: detect drawdown events on the benchmark, then measure each
indicator's median lead time (event-centered) and precision/recall vs. the base
rate (signal-centered). Compares breadth-divergence against the deterministic
coincident price composite (reuses the regime price sub-scores). Price/breadth
only — reproducible, no LLM/FRED.
- Manual "Event Study" job (Admin → Jobs), GET /regime/event-study, and an
inline early-warning panel on the Regime tab with an honest small-sample caveat.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
A new /regime tab scoring how far the AI/Tech bull regime has deteriorated
toward a re-rating as a single 0-100 index with per-signal breakdown and a
7/30-day trend. Intentionally decoupled: nothing reads its output to gate or
score trades — the daily-pipeline membership is scheduling only.
- regime_monitor_service: price sub-scores (P1-P6 via Alpaca, like
market_regime), VIX + HY credit spreads via a small FRED helper, weighted
aggregation over available signals (missing source -> n/a, dropped from the
denominator), one snapshot row/day, and a ~90-day history backfill by
replaying the already-fetched series as-of each past day.
- F1/F3 fundamentals proposed by the configured grounded LLM (reuses
sentiment_provider_service config resolution), with a manual override + lock.
- regime_snapshots table (migration 011); endpoints on the existing market
router; admin-editable weights/threshold; standalone /regime page.
Data needs: prices via Alpaca, VIX/credit via FRED (optional key — signals show
n/a without it). No LLM needed for history.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Qualified setups could carry no sentiment because the sentiment job scoped
its relevant-set to watchlist + open trades + top-N composite score, while
the activation gate qualifies on 12-1 momentum percentile — a different axis.
A top-momentum ticker outside the composite top-N never got sentiment, so the
R:R scan enhanced it as neutral.
Add the gate's momentum leaders (percentile >= activation min_momentum_percentile)
to the sentiment relevant-set so scope tracks the gate. Best-effort: a momentum
or config failure falls back to the base set rather than aborting collection.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Behavior-preserving cleanup (345 tests pass, ruff clean):
- scheduler: replace 62 inline logger.x(json.dumps({...})) calls with a
_log_event helper, and collapse 11 identical _job_runtime dicts into an
_idle_runtime() factory over _JOB_NAMES.
- settings: add app/services/settings_store.py (get_setting/get_value/get_map/
upsert_setting) and route ~13 hand-rolled SystemSetting queries + two
identical _settings_map helpers through it.
- scoring.get_rankings: collapse the per-ticker N+1 (3-4 queries + a commit each)
into 2 bulk reads + a single conditional commit; drop the redundant re-fetch.
Lazy recompute-on-read is preserved. Adds first tests for get_rankings.
Net ~ -245 lines across the touched modules.
min_target_probability is gone: it filtered on the probability model the
calibration has repeatedly shown to be weak and overconfident, it was redundant
with the momentum gate, and as an off-by-default knob it just invited bad tuning.
Removed from the backend gate, activation config/schema, the frontend mirror
(qualifiesSetup / activationSummary), and ActivationSettings. The probability
model stays where it does real work (primary-target selection + display).
Charts: with multi-year history the all-bars default was unreadable. Added
time-range presets (1M / 3M / 6M / YTD / 1Y / 3Y / 5Y / All), defaulting to 1Y;
clicking a preset always re-applies (snaps back after a manual zoom). Y-axis
autoscale and wheel-zoom / drag-pan were already there.
339 backend tests pass; frontend build clean.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Part 1 — long-only. The momentum edge is long top-momentum; the gate was
qualifying shorts on high-momentum names (fighting the trend), which showed as
the -0.13R Short(qual.) drag. While the gate is active, shorts no longer qualify
(backend qualification, backtest _momentum_qualifies, and the frontend mirror).
Part 2 — production wiring. Live setups now carry a real momentum rank, so the
dashboard, the Track Record's qualified stats, and outcome evaluation all gate on
the same value instead of deferring to floors:
- new momentum_service.compute_momentum_percentiles: 12-1 momentum per ticker,
ranked across the universe into a {symbol: percentile} map.
- the daily R:R scan ranks the universe up front and stores each setup's
percentile (new trade_setups.momentum_percentile column, migration 010).
- enhance_trade_setup mutates the same row, so the percentile is preserved;
_trade_setup_to_dict + TradeSetupResponse expose it to the API.
Until a fresh scan runs, pre-existing setups have a null percentile and the gate
falls back to floors for them (longs) / excludes them (shorts) — they fill in on
the next scan. 341 backend tests pass; frontend build clean.
Needs the alembic upgrade (migration 010) on deploy.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The replay was CPU-bound and single-core: the earlier asyncio.to_thread offload
kept the API responsive but, because of the GIL, ran on one core. Per-ticker
replay is independent, so fan it out across worker processes (which sidestep the
GIL) for real multi-core speedup.
- New `settings.backtest_workers` (default 4), capped to cpu_count-1 so a core
stays free for the web server.
- Uses a `forkserver` context (workers forked from a clean single-threaded
server — avoids the fork-with-threads deadlock); falls back to `fork`. On
spawn-only platforms (Windows) and for 1-ticker runs it uses the thread path,
so dev/tests are unaffected.
- Worker takes primitive column arrays (cheap to pickle), rebuilds bars, and
returns (candidates, plain-dict signal series) — both picklable across the
process boundary. Bars are still fetched in the event loop (ORM-safe).
- Pool creation is guarded: if the pool can't start, the job falls back to the
sequential thread path instead of failing.
334 backend tests pass (parallel path is POSIX/server-only, so it's covered by
construction + the picklability/worker-count tests; the thread fallback is
exercised by the run_backtest smoke test).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The 5-year backtest confirmed the EV gate adds negative value (high threshold =
worst expectancy) and that 12-1 month momentum is the one price signal with a
plausible, right-signed cross-sectional IC (~0.05). So "qualified" now means:
clears the R:R + confidence floors AND the ticker ranks in the top
`min_momentum_percentile` of the universe by 12-1 momentum that week.
- qualification.py: drop expected_value_r / the EV gate; add a momentum-percentile
gate (duck-typed `momentum_percentile`, only enforced when attached + threshold
set, else defers to floors). Mirrored in frontend qualification.ts.
- activation config/schema: min_expected_value -> min_momentum_percentile
(default 80 = top quintile). ActivationSettings, DashboardPage (ranks/【shows】
momentum instead of EV), and the BacktestPanel sweep follow.
- backtest: rank each ISO week's universe by 12-1 momentum, assign a percentile,
and qualify the top slice; the sweep now sweeps the percentile cutoff.
Also offload the backtest's per-ticker compute to a worker thread so the heavy
~5y run no longer blocks the API event loop (the "backend offline" flicker).
Production setups don't carry momentum_percentile yet — wiring the scanner to
attach it (a universe momentum-rank step) is the next step; until then the live
gate defers to floors while the backtest measures the momentum selection. 330
backend tests pass; frontend build clean.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Two changes so the cross-sectional signal results can actually be trusted.
(a) History depth — the binding constraint. Ingestion defaulted to 365 days, so
long-lookback factors (12-month momentum, 52-week high) were only computable on a
handful of weeks at the tail, and every IC reflected a single market regime.
- New `settings.ohlcv_history_days` (default 1825 ≈ 5y); new tickers backfill this
far instead of 1 year.
- New manual "data_backfill" job (Admin → Jobs) re-fetches the full window for
every ticker, ignoring incremental resume — run once to deepen existing
1-year histories. Idempotent (upsert); resumes after rate limits.
(b) Factor-IC honesty. The IC was averaged over weekly rebalances whose 30-day
forward windows overlap, inflating the t-stat ~sqrt(6)x.
- IC now measured on NON-OVERLAPPING windows (weeks thinned to ~HORIZON apart).
- Each signal carries a `reliable` flag (>= 12 independent windows); BacktestPanel
greys out and de-stars thin signals so a lucky 9-week IC of 0.3 can't masquerade
as an edge.
332 backend tests pass; frontend build clean. No migration (config + job + an
added JSON field on the cached backtest report).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The per-setup hit-rate report can't tell whether a signal predicts returns —
only how a target/stop structure built on one performs. This adds a
cross-sectional factor-IC pass: each week the universe is ranked by a price-only
signal and graded by its rank correlation (Spearman IC) and top-minus-bottom-
quintile spread against the forward 30-day return.
Candidate signals (point-in-time from price; sentiment/fundamentals have no
history in the replay): 12-1/6-1/3-1 month momentum, 1-month reversal,
price-vs-200d SMA, proximity to the 52-week high (George/Hwang), and 126-day
realized volatility (low-vol anomaly).
Reuses the existing per-ticker replay loop (no new data, no second DB pass);
results land in the cached backtest_report as `signal_eval` and render as a
"Signal edge" table in BacktestPanel beside the calibration curve.
330 backend tests pass (10 new in test_signal_eval); frontend build clean.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
