Harwinder Sekhon
6bf58fd9d4
Walk-forward "second opinion" stake multiplier (meta-labelling on 65 decision-time features, 3yr Oanda books @ $0.65) FAILED the pre-registered rule on M5 (clean null) and M15 (lift inside the label-shuffle null, explained by BUY-vs-SELL). Nothing that changes numbers ships; the 12-month M5 book re-run is byte-identical to the pinned one. Behaviour-neutral plumbing: - position_sizing.build_features (65 features; multiplier path returns 1.0, no artifact) - SignalReport.penalty_tags / penalty_total / momentum_fading (record-keeping) - TradeSetup.stake_multiplier = None (reserved) - gpu_backtest --export-features (default export stays the pinned 63 columns; extras forced to the tail even when the first trade lacks an indicator) Research tools (backtest/research): book_report, burst_map, burst_precursors, open_trade_bursts, calendar_impact, burst_indicator_map, sizing_model, watch_rule_eval. Findings recorded in backtest/research/sizing_campaign_2026-09.md: M5 stretch measures are positive in both halves; every open-trade protection against violent bars loses on both TFs; high-impact USD releases cause 20-31% of violent bars but trades held through them do better; violent bars come when price is already at an extreme, not from calm. Module 29 tests (13). Suite: 435 passed. Claude-Session: https://claude.ai/code/session_01RWV2yLt3YXHHGVYHSQU2EE
70 lines
2.7 KiB
Plaintext
70 lines
2.7 KiB
Plaintext
Note down entery and exit Why we took trade?
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What were each indicator signal on that specific time when we took trade
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How many indicators agreed at trade time
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Always backtest using engine.py and signal_aggregator.py
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Dont take trades on weekends and Fridays
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Keep TP small 1.3 Reward ratio
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At the end of test make table of results and print all values you observed.
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Trade context
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Time of day and trading session (Asian/London/NY/overlap)
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Day of week (track Mon/Tue/Wed/Thu performance separately)
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Market regime at entry: trending, ranging, or choppy (use ATR or ADX)
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Volatility level (ATR value or ATR percentile vs recent average)
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Pair/instrument traded
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News proximity (high-impact news within 30 min before/after entry)
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Trade execution details
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Spread at entry in pips
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Slippage on entry and exit (planned vs actual fill)
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Time in trade (minutes/hours from entry to exit)
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Maximum Adverse Excursion (MAE): how far against you it went
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Maximum Favorable Excursion (MFE): how far in profit before pullback
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Risk metrics
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Max consecutive losses (streak)
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Max consecutive wins (streak)
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Expectancy: (Win% x Avg Win) minus (Loss% x Avg Loss)
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Expectancy per hour in trade
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Recovery factor: Total R divided by Max DD
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Indicator/signal analysis
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Each indicator's standalone win rate when it agreed with the trade
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Confluence level performance (2 vs 3 vs 4+ indicators agreeing)
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Which indicator disagreed on losing trades (find the "canary")
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Decision quality
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Rate each setup A/B/C at entry, before knowing outcome
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Verify A-grade setups actually win more often
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Track system trades vs discretionary deviations separately
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Process notes
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Screenshot or chart snapshot at entry and exit
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Emotional state (calm, FOMO, revenge, tired)
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What would have happened if you held longer or exited sooner
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Aggregate analysis at end of backtest
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Equity curve, not just total R
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Performance breakdown by session, day, volatility regime
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Win rate over rolling 20-trade windows (check for edge decay)
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Critical extra
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"Do not trade" log: setups flagged but skipped, and what happened after
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--- 2026-09-06: decision-time feature export ---
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- `--export-features` appends stake_multiplier, r_sized and the 65 `f_*` decision-time
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features (position_sizing.FEATURE_ORDER) AFTER the 63 pinned columns; default export is
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byte-identical to the pinned books. Use backtest/research/book_report.py for weekday /
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hour x weekday / session / streak / rolling-WR / feature-quintile (both halves) tables,
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backtest/research/burst_map.py for the burst-by-hour map, and
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backtest/research/sizing_model.py for the walk-forward + shuffle-control harness.
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- Any sizing report must show the FLAT book and the SIZED book side by side and the
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label-shuffle null; the 2026-09-06 campaign verdict (nothing ships) is the reference.
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