Add peer_sympathy_entry_timing_policy ("next_open"|"reaction_close") and
peer_sympathy_leader_filing_time_buckets to StrategyEngineConfig. The
reaction_close variant enters peers at peer's T 16:00 ET close on the
SAME trading day as the leader's print, addressing the v1 hypothesis
failure where T+1 gap had already absorbed the news overnight.
Lookahead defenses tightened for the new branch: cutoff is T 16:00 ET
(_bar_close_timestamp(decision_date)) instead of T+1 09:30 ET; bucket
allow-list excludes AMC filings (which under PEAD's reaction_date=T+1
convention pass the timestamp check but defeat same-session sympathy).
LeaderPrint now carries filing_time_bucket from the runner.
Runner: split _schedule_peer_sympathy_candidates into two phases.
reaction_close fires BEFORE _select_candidates_for_date(date) and emits
into _scheduled_add_ons[date]; next_open keeps the existing tail-of-loop
position emitting into _scheduled_delayed_entries[next_date].
v2 backtest (1052 trading days, midlarge-liquid-long-v1 snapshot):
trades 256→120, return -52.9%→-2.4%, MDD 61.6%→24.5%, SQS 19.6→30.2.
Sample sympathy plays: GOOGL on META +7.7%, AVGO on COHR +6.3%,
SLB on HAL +5.5%, GE on HWM +5.1%. Profit factor 0.977 (one tweak
from breakeven). Verdict: VIABLE BUT WEAK — salvage hypothesis
empirically validated, near breakeven, not promoted yet.
35/35 peer_sympathy unit tests pass (29 pre-existing + 6 new for
reaction_close path).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Falsifies the "EarningsRunup as PEAD sleeve adjunct" hypothesis on
v7.356 base. Backtest results:
v7.356 baseline: +2356% / MDD 23.55% / Sharpe 2.69
ER standalone: +254% / MDD 23.97% / Sharpe 1.10
v7.356 + ER comp: +2746% / MDD 25.21% / Sharpe 2.63
Headline composite gain (+390pp) is portfolio-luck distributed across
other sleeves (bullish_raised +$19.8k, parking +$11.2k, idle_form4
+$8.2k). EarningsRunup engine itself contributed -$7.6k in composite.
Symbol orthogonality CONFIRMED (0 ticker overlap between ER/PEAD
within ±14 days). Capital orthogonality FALSIFIED — composite missed
30 of 121 standalone ER entries (25%) due to insufficient_cash;
v7.356 base is already capital-saturated (47% idle baseline → 41%
with ER, indicating cash pressure).
Sharpe and profit_factor BOTH worse in composite, MDD slightly higher.
Verdict: do not integrate at engine_risk_budget=0.20. Engine kept
in-tree as falsification evidence + scaffolding for alternate
integration paths (lower budget, v7.364 base, or fully separate
capital bucket).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Adds three new synthetic-Candidate emitter engines parallel to the
existing leader_follower scheduler hook, plus look-ahead defenses
(LookaheadViolationError + per-engine assertions). Each engine is
covered by a standalone PoC config (no PEAD/parking/idle alpha) for
isolation backtests against the midlarge or broad snapshot.
Engines:
EarningsRunup (libs/backtest/earnings_runup.py)
- Trigger: days_to_earnings ∈ [3,7] AND attention_zscore_20d ≥ 1.5
AND dollar_volume_20d_zscore ≥ 1.0 (all evaluated at T-1 close)
- Entry: T+1 next_open. Exit: -4% / +8% / max_holding_days =
days_to_earnings - buffer (forced flat by close before announcement)
- PIT calendar: PointInTimeEarningsCalendar adapter for backtest;
oracle_surprise_prefetch fallback when parquet calendar absent
- PoC verdict (configs/experiments/earnings_runup_poc_v1.json):
119 trades over 1051 days, +37.27% total return, 44.46% MDD,
SQS 45.2 (profitability=55.5, risk=23.5, robustness=50.1).
VIABLE BUT NEEDS WORK — signal exists; standalone risk profile
too aggressive for v7.356 baseline (8.8% MDD on v7.364). Path
forward: per_trade_risk reduction, VIX gate, position cap, or
integrate as PEAD sleeve adjunct (not as standalone replacement).
PeerSympathy (libs/backtest/peer_sympathy.py)
- Trigger: leader passes PEAD filter (earnings_release / guidance_update
/ material_contract) AND leader reaction_close ≥ +5% AND peer 60d
correlation ≥ 0.55 over [T-65, T-5]. Top-2 peers by correlation
from leader_follower_extra_peer_symbols_by_sector + sector ETF
holdings.
- Entry: T+1 next_open on peer. Exit: -3.5% / +6% / max_holding=3 /
peer-earnings blackout
- PoC verdict (configs/experiments/peer_sympathy_poc_v1.json):
256 trades over 1051 days, -52.92% total return, 54.47% MDD,
SQS 19.6 (profitability=0.0, risk=5.4, robustness=100.0).
DEAD. The leader's catalyst is already absorbed by T+1 next_open
— peers gap up overnight before entry. robustness=100 confirms
the negative result is not noise. Salvage paths (not implemented):
reaction_close entry, raised-guidance-only restriction.
- Note: initial run_id was 0 trades due to a select_candidates
filter mismatch (engine.event_types=['peer_sympathy'] dropping
real event_type='earnings_release' rows). The runner adapter
was patched to bypass strategy_engine filtering for leader
selection; the manual peer_sympathy_leader_event_types filter
does the gating.
VolBreakout52w (libs/backtest/vol_breakout_52w.py)
- Trigger: close_T-1 > max(high[T-252:T-2]) AND volume_T-1 ≥
2 × median_volume_20d_T-2 AND ATR_14_T-1/close ∈ [0.015, 0.06].
Entry T next_open, exit -3% / +5% / max_holding=2 / MOC.
- Honest, look-ahead-safe descendant of the retired topgainer v1-v54
family. Five layers of strict-before assertions guard the bar
provider, candidate construction, trigger evaluation, and feature
timestamps. A leaky-provider proof-by-contradiction test
demonstrates the categorical catch.
- PoC verdict (configs/experiments/vol_breakout_52w_poc_v1.json,
broad-liquid universe): 1,332 trades, -87.28% total return,
88.74% MDD, SQS 24.4 (profitability=0.0, robustness=100.0).
DEAD AND HONEST. This is the most important finding of the three
PoCs: the topgainer v1-v54 lineage's headline returns (+267%
Sharpe 13.73 in best variants) were 100% lookahead bug. With
the bug removed, the 52w-high + volume + ATR signal has no real
alpha — the lookahead-corrected -4.3% from prior memory is
confirmed and amplified to -87% on a fuller universe and longer
horizon. Future "revive topgainer" proposals can cite this run
(bt_return_max_long_v1_broad-liquid_20260509042903892342_3bb473d9)
as definitive falsification.
- Pre-open gap guard inactive (no premarket data in broad snapshot).
skip_if_no_gap_data=true; the +4% gap-fade guard would not move
the result given the magnitude.
Shared infrastructure additions:
- libs/backtest/domain.py: LookaheadViolationError class +
StrategyEngineConfig fields (11 EarningsRunup + 11 PeerSympathy
+ 13 VolBreakout52w = 35 new fields)
- apps/backtester/run.py: _BacktestAttentionZscoreAdapter,
_RunnerPeerResolver, _schedule_earnings_runup_candidates,
_schedule_peer_sympathy_candidates,
_schedule_vol_breakout_52w_candidates wired into the daily
scheduler block. PeerSympathy adapter bypasses strategy_engine
filtering on leader selection (manual filter handles gating).
Tests: 21 (EarningsRunup) + 27 (PeerSympathy) + 38 (VolBreakout52w)
= 86 new unit tests, all passing. Broader unit suite: 1392 passed,
2 pre-existing failures unrelated.
Net engine state: EarningsRunup is the only viable new engine class.
PeerSympathy and VolBreakout52w are kept in-tree as falsification
evidence, not as production engines.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
- registry.json: change _ftb_fix_v2 from manual_only to auto_full_rebuild so
backtest auto-refreshes when snapshot doesn't cover the requested period
- run.py: return [] (not all_trading_days fallback) when parking cap pushes
requested_end before requested_start, preventing silent wrong-date-range runs
- run.py: allow 1-trading-day lag tolerance in parking cap so a single lagging
symbol (e.g. QQQM shortly after close) doesn't cap the whole simulation
- backtest_sim.py: log incremental_update_failed_falling_back warning so
silent fallback to full rebuild is visible in direct-mode logs
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- v7.356 config: swap dataset_snapshot_id from manual_only ftb_fix_v2 to
auto_full_rebuild base canonical so paper trader can refresh snapshot
(root cause of processed_events=0 for 30 days)
- Multi-session order isolation (1.A.2/1.A.3): tag client_order_id with
pt-{session_id[:8]}-{uuid} prefix on all entry orders; _cancel_stale_orders
filters by own session prefix so one session no longer ghost-cancels another's
orders on shared Alpaca account
- Pipeline halt on failure (1.B.1): _run_pipeline returns bool and stops on
first subprocess failure instead of silently progressing with stale data
- Daemon restart window skip (2.2): run_open/run_close only marked completed
if processed_phases DB confirms prior execution — no more trading-less days
after mid-day restart
- event_parser: periodic batch commits every 500 docs (hypothesis fix for
3h hangs; unverified — may just be slow serial Oracle calls)
- Tests updated for _verify_order_fill tuple return + new cross-session
isolation test; all 23 paper_trader unit tests green
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
V23 400d TRUE (correct pipeline, 2026-04-21): +146.09%, DD -13.66%, Sharpe 2.33.
Prior result (+120.87%, DD -23.97%) was from buggy AH-close pipeline; data fix
improved DD by 10.31pp. V23 now strictly dominates Safe v9 on ALL 400d metrics
(+45pp return, +3.5pp DD, +0.34 Sharpe). Updated Safe v9 status to
validated_200d_only and clarified Korean framing to remove the misleading
"최종 검증 전략" without qualification.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- orb_simulator.py: min_abs_gap_pct filter, premarket dollar vol filter,
rolling_loss circuit breaker, drawdown_governor, streak_sizing,
trailing_tighten_at_r, allow_doji/red_to_green breakout, abs_gap scoring
for gainers_leader, ORBSimulationState, run_orb_simulation_with_state API
- metrics.py: loss_containment_score and related metrics
- features.py: enrich_daily_bars with gap_zscore, ATR ratio, range compression
- domain.py: extended ORBStrategyParams with new fields
- cache.py: DailyBarCache with merged parquet storage and coverage metadata
- simulator.py: base simulator updates for new entry/exit mechanics
- configs/intraday: updated orb_gainers_v23.yaml with canonical params
- Added BLD to midlarge symbol snapshot
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- screener: switch from non-existent single-ticker endpoint to multi-ticker
/alpaca/intraday batch calls (grouped by date, chunk ≤ 75); fixes 0-trades
- cache: bump version 2→3 to invalidate stale IEX Parquet files
- oracle_client: add get_multi_intraday_bars_today() for IEX real-time feed
- paper_trader: use /alpaca/intraday/today for live sessions, /alpaca/intraday
for historical (SIP)
- intraday.py: define _BUILTIN_STRATEGIES={} to fix /api/orb/strategies import
- delete orb_p1–p10_winner + variant configs; add strategies/orb_default.yaml
(Phase 10 params) as the single registered web strategy
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Add run_pre_screen() at 9:20 ET: fetch daily bars + enrichment + quality filter
before market open, narrowing universe for faster orb_detect intraday fetch
- run_orb_detection() uses cached pre-screen data when available; falls back to
full pipeline if pre_screen missed (late start, failure)
- Add _last_trading_day() helper to skip weekends/holidays for bars_end,
preventing Alpaca 502 on Mondays (today-1 = Sunday was causing failures)
- Fix Oracle client chunk_size 300→75: Alpaca rejects 100+ ticker URL requests
- Add pre_screen event to build_schedule() at 9:20 ET and dispatch in _run_trading()
- run_session_now() runs pre_screen before orb_detect for efficiency
- Add ORB daemon, engine, models, state, screener, and intraday strategy configs
- Add intraday library (libs/intraday/) and web routes for ORB/intraday trading
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Delete v7.360-v7.363 experiment configs (rotation/momentum tests)
- Remove _schedule_momentum_breakout_candidates() from backtester run.py
- Remove MomentumBreakoutConfig from domain.py
- Delete momentum_calendar.py, momentum_screener.py, build_momentum_calendar.py
- Delete data/momentum_calendar/ parquet data
Valid period performance was -31.36% vs +152.4% baseline — sleeve is not viable
without walk-forward validation. Abandoning for now.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
When a backtest starts mid-stream (via --start), events that fired
before the start date but are still within their max_holding_days
window can now be entered on the first simulation day.
- Add `lookback_entry_enabled: bool = False` to ExecutionConfig
- On first sim day, _collect_lookback_candidates() gathers pre-start
events, runs them through the same select_candidates() pipeline,
and injects them before normal candidates
- Entry fills at the first day's open price; gap-cap check is skipped
since the event is multi-days old
- days_held is initialized to the elapsed trading days so TIME exits
fire at the correct time relative to the original event date
- Store slice is extended backward by max_mhd calendar buffer so
pre-start rows survive slice_by_date_range when feature is enabled
- Enabled in return_max_long_v7.119 for testing
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Backtester (run.py):
- cash_available = (self._cash + parking_value) * multiplier caused trades to be
approved even when self._cash ≈ 0 (all money in SGOV/QQQ). Trades executed
by deducting from self._cash → negative cash (phantom money).
- Fix: after simulate_entry, if self._cash < actual trade cost and parking exists,
call _liquidate_parking_for_cash(shortfall) before deducting from cash.
- Verified: 2022-2026 backtest with qqqm_low_dd shows 0 cash_negative events.
Live engine (engine.py):
- Add _parking_liquidate_for_event(): frees parking cash to fund event entries.
SGOV (virtual) reduces entry_value in DB; QQQM/QQQ sells real shares via broker.
- Both entry loops (engines mode + flat/reaction_close mode) now attempt parking
liquidation when plan.skip_reason == "insufficient_cash" before giving up.
Also includes prior session work (accumulated since last commit):
- 6 novel parking gate signals: VRP, Market Temperature, Hurst exponent, Rolling
Kurtosis, Return Autocorrelation, SPY-QQQ Correlation (composite risk score v2)
- QQQM parking symbol support (lower expense ratio vs QQQ)
- Snapshot auto-refresh + bar extension cache (pickle) to avoid 10-min re-fetches
- Bar extension clamps to last market-closed date (ET 4PM check)
- fithia2 refresh command; --no-refresh flag for paper backtest
- Paper backtest macro extension beyond last event date (parking-only periods)
- parking_state DB schema: 7 new columns (peak_price, gate_in_sgov,
committed_target, pending_target, pending_days, sgov_entry_value, sold_today)
- Live engine: target confirmation (2-day), top-up drawdown gate, trailing stop,
SGOV interest accrual, full 6-signal gate evaluation
- New PARKING_PRESETS: qqqm_low_dd, composite_v2, vv_24_vrp8, vt_24_t13, etc.
- Web GUI / CLI result parity fix (Oracle URL via get_settings().stock_oracle_url)
- Force-close uses last_exec_date (has bar data); parking liquidates at last_date
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Boosting inline/guidance engine per_trade_risk from 0.015-0.020 to 0.040.
These engines have 73-100% WR — bigger positions on best signals.
v6new.307: SQS 74.4 (#1), CW 350.2%, Test +46.8%, MaxDD 2.4%, PF 14.74
Full progression: 262.9% → 284.4% → 312.6% → 320.5% → 350.2%
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Extends selector with new scoring model support, adds execution
enhancements, and improves snapshot store loading and split handling.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
OME engine early_failure_no_progress_days=1 (from day 2 in v272) further
improves capital recovery speed. 198 trades, 312.6% CW return.
SQS 72.7 = new #1 on leaderboard (including overlays).
Test return +45.5%, Profit Factor 19.23.
Key: cutting OME losers at day 1 instead of day 2 frees capital faster
while losing only marginal OME winners that needed >1 day to show progress.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Trade analysis revealed OME engines had 54-60% WR with stop-heavy exits.
Adding early_failure (day 2, R=0.0) for OME engines frees capital faster,
enabling 6 more trades (194→200) and boosting CW from 293.2% to 310.8%.
Key insight: cutting low-quality engine losers early improves compounding
more than any scoring/feature/sizing change tested in this session.
v6new.272: SQS 72.4 (#6), CW 310.8%, 200 trades, test +44.9%
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Lowering market cap floors to 2B added 15 trades (194→209) but CW return
dropped from 293.2% to 278.5%. Small-cap PEAD events have lower average
quality — individual outliers like SEDG exist but don't compensate.
v6new.255 (293.2%) confirmed as optimal trade-quality balance.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Bounce engine (buy negative reaction, bet on mean reversion) could not
execute: system architecture ties scoring to single model per backtest,
and selector/store indexes are optimized for positive-reaction PEAD.
Negative-reaction candidates get score=0 from PEAD scoring, blocking
engine selection regardless of engine-level threshold overrides.
Implementing bounce trades requires: dual scoring model support,
selector changes for negative-reaction candidate routing, and
store indexing changes. Deferred to future refactor.
Current best CW return: 293.2% (v6new.255)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
v6new.196 = v29 base + entropy scoring (v13e) + per_trade_risk 0.058
+ doc_quality 0.50. CW return 284.4% (+21.5pp over v122), 194 trades.
Key finding: v29 base with doc_quality 0.50 (not 0.55) is the optimal
quality gate when combined with entropy scoring.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Data analysis revealed OBV Q1 (distribution) has 56.4% WR vs Q5 51.2% —
contrarian signal confirmed. Previous OBV bonus was applied in wrong
direction. Corrected with v15 scoring models.
Best result: v6new.185 (entropy + risk 0.058) CW 274.4% but SQS 72.2,
still below v6new.122 (72.4). WFV/robustness offsets CW gains.
v6new.122 confirmed as optimal under current SQS v4 formula.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
New engine: next_open_long_mean_reversion_high_vix
Targets: react < -7%, close 0.15-0.60, bearish/mixed/unknown direction
Signal: VIX>20 + big drop = 62.9% WR, +3.91% 5d mean (n=167)
VIX 25-30 sweet spot: 75% WR, +5.46% 5d mean
Test split: 3 MR trades, 67% WR, +5.59% total PnL
Paper BT: 51 trades vs 48 (v6new.30), return ~equal
Also fixes _rows_to_table to handle sparse feature columns.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Short Side (Direction 3):
Bearish events: 45-55% WR for short — no actionable edge
Mean reversion after large drops cancels short PEAD
Execution Model (Direction 2):
T+2 delayed entry: loses 54% of alpha (Day 1 = 54% of 5d return)
Wider stops (v6new.34): -0.68pp — smaller positions offset fewer stop-outs
Tighter trailing (v6new.35): -12.48pp — cuts winners too early
reaction_close >> next_open (86% vs 57% WR) but post_market can't use RC
Direction 1 (new data: Form 4, XBRL, Earnings Surprise) requires Oracle API
implementation. Free sources identified: SEC EDGAR, Alpha Vantage, FINRA.
v6new.30 is the confirmed framework optimum.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
I Luk Kim