89 lines
4.0 KiB
Python
89 lines
4.0 KiB
Python
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"""Diagnostic: print the ATR value Python computes at the first signal bar
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(2025-01-02 01:50) and derive what MT5's ATR must have been (from the observed
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0.16 lots). If they differ, the gap is in the bar data, not in the ATR math.
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Also dump the OHLC of the M5 bars around 2025-01-02 01:50 so we can compare
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against what MT5 sees.
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"""
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from __future__ import annotations
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import sys
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from pathlib import Path
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PROJECT = Path(__file__).resolve().parent.parent
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sys.path.insert(0, str(PROJECT))
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import optuna
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import pandas as pd
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from shared.data.loaders import load_bars
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from shared.indicators.base import atr
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from shared.optimizer.selector import select_diverse_topn
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from strategies.gold_scalper_pro.instruments import XAUUSD_REAL
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from strategies.gold_scalper_pro.search_space import FROZEN_BASELINE, SEARCH_SPACE
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def main() -> int:
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db = PROJECT / "studies" / "optuna" / "gold_scalper_pro_is2025.db"
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study = optuna.load_study(
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study_name="gold_scalper_pro_is2025",
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storage=f"sqlite:///{db}",
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)
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finalists = select_diverse_topn(study, n=3, ranges=SEARCH_SPACE)
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f1 = finalists[0]
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merged = {**FROZEN_BASELINE, **f1.params}
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atr_period = int(merged["InpAtrPeriod"])
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sl_mult = float(merged["InpAtrSLMult"])
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risk_pct = float(merged["InpRiskPercent"])
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init_deposit = 1000.0
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print(f"finalist #1 atr_period={atr_period} sl_mult={sl_mult} risk={risk_pct}%")
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full_m5 = load_bars(PROJECT / "data" / "XAUUSD_M5_2024-06-26_2026-06-26.parquet")
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close = full_m5["close"].to_numpy(dtype=float)
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high = full_m5["high"].to_numpy(dtype=float)
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low = full_m5["low"].to_numpy(dtype=float)
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atr_arr = atr(high, low, close, atr_period)
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# Find the 2025-01-02 01:50 bar (signal bar for first trade).
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target = pd.Timestamp("2025-01-02 01:50:00")
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ts = pd.to_datetime(full_m5["timestamp"].to_numpy())
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idx = int(ts.searchsorted(target, side="left"))
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print(f"\n first-signal bar @ {ts[idx]} (idx {idx})")
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print(f" OHLC = O={full_m5['open'].iloc[idx]:.2f} H={full_m5['high'].iloc[idx]:.2f} "
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f"L={full_m5['low'].iloc[idx]:.2f} C={full_m5['close'].iloc[idx]:.2f} "
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f"spread={full_m5['spread'].iloc[idx]}")
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print(f" ATR({atr_period}) at this bar = {atr_arr[idx]:.6f}")
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print(f" sl_dist = {sl_mult} × ATR = {sl_mult * atr_arr[idx]:.6f}")
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print(f" loss_per_lot = sl_dist / tick_size × tick_value = "
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f"{sl_mult * atr_arr[idx] / XAUUSD_REAL.tick_size * XAUUSD_REAL.tick_value:.4f}")
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risk_money = init_deposit * risk_pct / 100.0
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sl_dist = sl_mult * atr_arr[idx]
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loss_per_lot = sl_dist / XAUUSD_REAL.tick_size * XAUUSD_REAL.tick_value
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lots = risk_money / loss_per_lot
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print(f" risk_money = ${risk_money:.4f}")
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print(f" Python computed lots = {lots:.6f} → rounded to {XAUUSD_REAL.round_volume(lots):.4f}")
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# What would MT5's ATR have to be to produce 0.16 lots?
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mt5_lots = 0.16
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mt5_loss_per_lot = risk_money / mt5_lots
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mt5_sl_dist = mt5_loss_per_lot * XAUUSD_REAL.tick_size / XAUUSD_REAL.tick_value
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mt5_atr = mt5_sl_dist / sl_mult
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print(f"\n MT5 first trade: {mt5_lots} lots → sl_dist={mt5_sl_dist:.6f} → ATR={mt5_atr:.6f}")
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print(f" ratio Python/MT5 ATR = {atr_arr[idx] / mt5_atr:.4f} ({(atr_arr[idx]/mt5_atr - 1)*100:+.1f}%)")
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# Dump 30 bars around the signal to inspect the recent volatility.
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print(f"\n last {atr_period + 5} bars before signal (for ATR warmup):")
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print(f" {'ts':<22} {'open':>9} {'high':>9} {'low':>9} {'close':>9} {'TR':>9}")
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for j in range(max(0, idx - atr_period - 5), idx + 1):
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tr = max(
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high[j] - low[j],
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abs(high[j] - close[j-1]) if j > 0 else high[j] - low[j],
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abs(low[j] - close[j-1]) if j > 0 else high[j] - low[j],
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)
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print(f" {str(ts[j]):<22} {full_m5['open'].iloc[j]:>9.2f} {full_m5['high'].iloc[j]:>9.2f} "
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f"{full_m5['low'].iloc[j]:>9.2f} {full_m5['close'].iloc[j]:>9.2f} {tr:>9.4f}")
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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