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>
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>
New mode (risk.daily_budget_reset=True) where cash_available and sizing
equity reset to initial_equity at the start of each day, regardless of
how many open positions or realized P&L exist. Unlike fixed_capital_sizing
(단리, sizing only), this also treats buying power as if no positions are
held — useful for evaluating signal quality independent of capital constraints.
- domain.py: daily_budget_reset field on RiskConfig
- run.py: _daily_budget_reset flag; _sizing_equity / _sleeve_equity_est /
_build_portfolio_state all honor the new flag
- backtest_sim.py: daily_budget_reset param threaded through
- direct_runner.py: --daily-budget-reset CLI flag
- routers/backtest.py: BacktestRequest field + cmd arg
- client.ts: BacktestParams / BacktestTask types updated
- Backtest.tsx: checkbox in form + DBR badge in task list
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Bug #2 (paper trader): lookback entries sized using historical entry_price_est
but filled at current market price, causing cash overdraft. Fix: override
entry_price_est with get_latest_bars() close before entering _process_entries.
Bug #3 (paper trader + backtester): paper trader was missing the per-candidate
MHD expiration check that the backtester already had. Also adds
lookback_min_remaining_days (default 3) to reject candidates with too little
holding time remaining — prevents entering a position the day before forced exit.
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>
FilledTrade now carries event_type and score from the Candidate.
These fields are written to trade_blotter.parquet and displayed in
paper backtest trade logs.
Previously score showed as 0.00 for all trades because the field
wasn't propagated from Candidate → FilledTrade → Parquet.
Score=0.00 is valid for trades from engines with score_threshold_override=0.0
(e.g. guidance_unknown_orderly) where engine gates, not score, determine entry.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Track experiment cycles with SQS scoring (0-100), JSONL journal, and
auto-generated leaderboard to prevent duplicate experiments and enable
data-driven strategy decisions.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Remove 5 non-alpha features (earnings surprise, risk penalty, parse confidence,
direction clarity, LM sentiment) from composite score to eliminate double-counting
with hard gates and noise sources. Redistribute weights to 5 alpha features.
Add default-deny for unknown event types, no-follow-through early exit (D+1),
kill switch log-only mode, macro regime size scaler. Remove SUE gate (Gate 8).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Flip scoring weights so event/document quality is primary signal (55%)
and market confirmation is secondary (35%). Add research mode with
kill-switch cooldown/reset, veto gates for bad events, reduced portfolio
risk, and 4 diagnostic analysis scripts.
Phase A: Research mode kill-switch reset, risk reduction (0.5%/trade,
max 4 positions), bullish-only direction for all event types.
Phase B: 2 new sub-scorers (parse_confidence, direction_clarity),
4 veto gates (oneoff risk, parse confidence, unknown/bearish direction).
Phase C: signal_quality, event_type_decomposition, kill_switch_impact,
concurrent_position analysis scripts.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
I Luk Kim