Harwinder Sekhon
042b079861
Gate 6h (big-candle entry gate): blocks all XAU entries (core, XAU_PB, XAU_CP) when the primary TF's last 24h contains a bar with range >= K x prior-bar ATR. Ships default-OFF: validation rejected every threshold (K 3-6 on M5/M15 12mo) — removed trades were net-POSITIVE at every cell; M5 quiet-window trades are the only losing bucket. Config knobs big_candle_atr_multiple/lookback_hours, --big-candle backtest flag, Module 17 tests. M5 relaxation SHIPPED (validated 2026-07-30, Oanda bars, $0.30 spread): m5_min_abs_score 30->25, m5_min_confirmation_score 10->5, hour-21 UTC unblocked M5-only. Beats control in ALL 6 calendar years of the 5yr replay (+230.9R/2319 PF 1.26 DD 21.3 vs +168.0R/2048 PF 1.21 DD 27.0), 12mo +107.4R PF 1.36 vs +84.6R PF 1.33, OOS +26.1R vs +17.2R, $0.70 spread +86.9R vs +66.4R. M15: every relaxation retested negative — config unchanged. Declined-but-open: M5 hour-22 block, M15 hour-16 unblock (both fail 5yr/OOS). Evidence in the factory docstring. Tooling: generic --set PATH=JSON research override in gpu_backtest (typo-proof, logged); diagnose_filters filter_tags refreshed for gates 6d-6i with a staleness-guard test (Module 18); research scripts hour_analysis/burst_profile/book_diff. Suite: 268 tests. Claude-Session: https://claude.ai/code/session_014jqHpmNYmwYMowF6bPZyeD
43 lines
1.6 KiB
Python
43 lines
1.6 KiB
Python
"""Burst-candle frequency by UTC hour — the empirical "hedge fund hours".
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Usage:
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python backtest/research/burst_profile.py PARQUET_GLOB
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Per UTC bar-close hour of the given bars: % of bars whose range is
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>= K x the PRIOR bar's Wilder ATR(14), for K in {3,4,5}; mean bar
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range; and mean signed drift over the 6 bars following a >=4x burst
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(does price continue or snap back?). Analysis tooling only.
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"""
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import glob
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import sys
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import pandas as pd
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def burst_table(parquet_glob: str, bar_minutes: int = 5):
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files = sorted(glob.glob(parquet_glob))
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if not files:
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raise SystemExit(f"no parquet matches {parquet_glob}")
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df = pd.concat([pd.read_parquet(f) for f in files]).sort_index()
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df = df[~df.index.duplicated(keep="last")]
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h, l, c = df["High"], df["Low"], df["Close"]
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tr = pd.concat([h - l, (h - c.shift()).abs(), (l - c.shift()).abs()],
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axis=1).max(axis=1)
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atr = tr.ewm(alpha=1 / 14, min_periods=14, adjust=False).mean()
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ratio = (h - l) / atr.shift(1)
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close_hour = (df.index + pd.Timedelta(minutes=bar_minutes)).hour
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fwd6 = c.shift(-6) - c # signed move over the 6 bars after this one
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out = pd.DataFrame({
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"bars": ratio.groupby(close_hour).size(),
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**{f"pct_ge_{k}x": (ratio >= k).groupby(close_hour).mean() * 100
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for k in (3, 4, 5)},
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"mean_range": (h - l).groupby(close_hour).mean(),
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"post4x_drift6": fwd6[ratio >= 4].groupby(close_hour[ratio >= 4]).mean(),
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})
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return out.round(3)
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if __name__ == "__main__":
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print(burst_table(sys.argv[1],
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int(sys.argv[2]) if len(sys.argv) > 2 else 5).to_string())
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