Display-only Da/Gurun/Warachka information discreteness on the ticker
indicator panel. Shared compute with the backtest harness; not wired into
gate or rank.
Document Phase A (max-hold/vol/corr closed; next-open as decision baseline).
Add stale_close and next_open gap-cap fill modes plus a small matrix to test
whether near-close scheduling recovers overnight momentum drift.
Ship shared Sharpe SE/PSR diagnostics, next-open fill and equity-curve vol targeting in the portfolio simulator, re-derived fip_id, and a checkpointed offline matrix runner for Mac-side validation sweeps.
Ship greenfield min_rr=2.0 and conf=0, read-only Structural S/R, indicator
cache invalidation, and UI/gate language that treats GTL as screening not exit.
Align strategy_rank missing-vol fallback live vs backtest, single-source
PRIMARY_TARGET_MIN_RR, expand prod parity tests, and drop dead FE clients.
- Remove unused _gate_eligible_levels filtering logic and its tests (research-only)
- Add prominent RESEARCH/DIAGNOSTIC markers and docs to clear-air/ATR fallback helpers
- Document production vs research BACKTEST_* environment variables in backtest_service
- Minor cleanups: update legacy report text, improve outdated function docstring
min_rr = 2.0 was hand-set in Admin (2026-06-24) and never swept — the gate
ablation only tested the floor on-vs-off, never its level. It was the last
un-swept knob in the live gate.
Swept against portfolio Sharpe under the real exit, with a parity self-check
(reproduces_production_gate: the row at the live floor must rebuild production's
exact 1,089-setup qualified set — it does).
min_rr qualified in-sample Sh/CAGR OOS Sh/CAGR (entries >= 2024-07)
0.0 6636 1.98 / 58.5% 2.02 / 66.2%
1.2 3897 1.34 / 33.9% 1.12 / 28.8%
1.5 3127 1.20 / 29.6% 1.12 / 28.8%
1.75 1974 1.64 / 44.5% 1.15 / 27.4%
2.0 (live) 1089 2.04 / 50.4% 2.78 / 73.3%
2.25 577 1.64 / 31.8% 1.71 / 31.9%
2.5 286 1.67 / 29.0% 0.68 / 8.7%
KEEP 2.0. It is the optimum in both windows, and a peak that reproduces in data
it was never fitted to is real evidence. But treat it as fragile: unlike the ATR
trail (a plateau), this is a spike with a trough beside it — +/-0.25 costs ~0.4
Sharpe in-sample and ~1.6 out-of-sample — and the curve is bimodal (floor-off is
good, 1.2-1.75 is bad, 2.0 is good). The hand-set value landed on the peak by
luck, not by tuning. Do not nudge it.
Worth knowing: turning the floor OFF entirely is the second-best row in both
windows, with substantially higher CAGR (58.5% / 66.2%) and more trades. If CAGR
ever outranks Sharpe here, "no R:R floor" is a live option — and it would sever
the gate's last dependency on the weak S/R detector.
Also fixes a metric artifact in the holdout harness. The train book's equity curve
ran to the end of the data while its entries stopped at the split, so it sat in
flat cash for two years and deflated its own CAGR/Sharpe (reported 0.95 / 14.6%;
actually 1.31 / 29.6%). _simulate_portfolio now truncates the calendar to
hold_days after the last entry when end_date is set — it only triggers on the
holdout train window, so no other number moves. The clear-air OOS verdict is
unaffected: it rests on the test row, whose entries and curve both start at the
split and were always clean. Both holdout reports regenerated.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Investigated whether our support/resistance detection follows best practice
and whether we actually use it that way. Three findings, all backed by runs
against the prod snapshot and written up in docs/research/sr-levels-and-exits.md:
- The S/R target must NOT become an exit. Honoring it as a take-profit on top
of the 3x ATR trail drops Sharpe 2.04 -> 1.47 and halves CAGR. Win rate rises
(37.5% -> 40.0%), which is the tell: it truncates the right tail where
momentum's edge lives.
- The clear-air fallback (synthesize a 3xATR target where no resistance exists,
so 52-week-high breakouts stop being vetoed) looked strictly better in-sample
(Sharpe 2.04 -> 2.07, CAGR 50.4% -> 62.3%, DD 21.4% -> 20.1%) but FAILED a
real out-of-sample holdout: on entries after 2024-07-01 it is worse on Sharpe
(2.78 -> 2.45) and Calmar, better only on raw CAGR. Not shipped.
- The detector itself is weak vs best practice (POC/VAH/VAL computed then
discarded, HVN = any above-mean bin, 1.48x volume double-counting, "touch"
counts pass-throughs, no round numbers), but its only causal path to P&L is
the entry gate. Fix it for the displayed levels, not for returns.
Method note: nested lookback windows are NOT out-of-sample. The in-sample result
was clean, large, and consistent across five windows, and still did not survive
a proper entry-date split.
All research paths are off by default and the default report is unchanged:
BACKTEST_RESEARCH_EXITS=1 take-profit exit rows
BACKTEST_ATR_TARGET_FALLBACK=k synthetic k*ATR target when S/R offers none
BACKTEST_FALLBACK_CLEAR_AIR_ONLY=1 restrict that to genuinely clear air
BACKTEST_HOLDOUT_SPLIT=YYYY-MM-DD train/test split by entry date
Also fixes two reproducibility holes found while reconciling our local baseline
against the live report:
- create_backtest_snapshot.py now copies paper_% settings. The production
monitor row replays the runtime exit policy via get_exit_policy(); without
those keys a snapshot silently falls back to code defaults, so a live-tuned
exit would never be reflected.
- Migration 020 drops activation_min_expected_value and
activation_min_target_probability. Both are orphans of the June EV-gate
redesign, read by no code path, but prod carries min_target_probability = 50.0
which implies a probability floor that is not enforced (the real floor is the
20% constant in qualification.py).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Three follow-ups to the gate probability floor (8f41143):
- Signals table shows the starred primary target (shared primaryTarget
helper) instead of an independently computed max-probability best,
so Overview, Signals and ticker details agree by construction.
- Targets pinned at the 3% probability clamp floor collapse to the
nearest one (enhance_trade_setup + backtest candidates in parity):
floor-pinned levels are indistinguishable to the model, so farther
ones were duplicate 3% rows inviting lottery headlines.
- get_trade_setups only returns setups re-emitted within
LIVE_SETUP_MAX_AGE_DAYS (3): an older latest row means the daily
scan no longer confirms the setup, and such rows otherwise surface
forever on Overview/Signals/ticker/alerts. History endpoints keep
full history.
Backtest on the Jul-3 snapshot is metric-identical to the gate-floor
run on all qualified stats (1089 qualified, Sharpe 2.02, CAGR +49.6%,
DD -15.8%): the prune only removes noise the gate already rejected.
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>
Fixes ruff E741 in the lookbacks comprehension of _portfolio_monitor,
which failed the CI lint step and blocked the deploy.
Co-Authored-By: Claude Opus 4.8 <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>
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>
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>
Avg R was still rising at the previous top level (+15%), so the optimum was off
the table. Extend TP_LEVELS to 20/25/30% to reveal where letting winners run
stops paying (it plateaus toward "just hold to the horizon close").
Also clarify in the panel that the take-profit model deliberately does NOT use
the setup's S/R target — it's a standalone fixed-% exit; exiting at the target is
the target-vs-stop model above. The two are complementary ends, not in conflict.
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>
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.
dennisthiessen
