phase 7-8 完成 + warmup 修复 + 产物结构化重组

主要内容： - Phase 8 PROMOTE: finalist #1 (trial #324) registry 条目，自动生成 - Optuna objective warmup bug 修复 (shared/optimizer/objective.py) - studies/ 目录按用途重组为 optuna/ + finalists/ + features/ 三层 - reports/ 加入 Optuna 中文 dashboard (5 主图 + 18 slice + 15 contour) - 新增 PROJECT_GUIDE.md 项目说明文档 - 新增 build_registry_entry.py / build_optuna_dashboard.py / build_feature_datasets.py - .gitignore: 允许提交 studies/*.db (Optuna DB) 和 reports/*.html (MT5 + dashboard)
2026-06-27 00:28:07 +08:00
parent 0dcbfe0781
commit 63a829cc46
45 changed files with 7240 additions and 9 deletions
@@ -0,0 +1,88 @@
+"""Diagnostic: print the ATR value Python computes at the first signal bar
+(2025-01-02 01:50) and derive what MT5's ATR must have been (from the observed
+0.16 lots). If they differ, the gap is in the bar data, not in the ATR math.
+
+Also dump the OHLC of the M5 bars around 2025-01-02 01:50 so we can compare
+against what MT5 sees.
+"""
+from __future__ import annotations
+import sys
+from pathlib import Path
+PROJECT = Path(__file__).resolve().parent.parent
+sys.path.insert(0, str(PROJECT))
+
+import optuna
+import pandas as pd
+
+from shared.data.loaders import load_bars
+from shared.indicators.base import atr
+from shared.optimizer.selector import select_diverse_topn
+from strategies.gold_scalper_pro.instruments import XAUUSD_REAL
+from strategies.gold_scalper_pro.search_space import FROZEN_BASELINE, SEARCH_SPACE
+
+
+def main() -> int:
+    db = PROJECT / "studies" / "optuna" / "gold_scalper_pro_is2025.db"
+    study = optuna.load_study(
+        study_name="gold_scalper_pro_is2025",
+        storage=f"sqlite:///{db}",
+    )
+    finalists = select_diverse_topn(study, n=3, ranges=SEARCH_SPACE)
+    f1 = finalists[0]
+    merged = {**FROZEN_BASELINE, **f1.params}
+    atr_period = int(merged["InpAtrPeriod"])
+    sl_mult = float(merged["InpAtrSLMult"])
+    risk_pct = float(merged["InpRiskPercent"])
+    init_deposit = 1000.0
+
+    print(f"finalist #1  atr_period={atr_period}  sl_mult={sl_mult}  risk={risk_pct}%")
+
+    full_m5 = load_bars(PROJECT / "data" / "XAUUSD_M5_2024-06-26_2026-06-26.parquet")
+    close = full_m5["close"].to_numpy(dtype=float)
+    high = full_m5["high"].to_numpy(dtype=float)
+    low = full_m5["low"].to_numpy(dtype=float)
+    atr_arr = atr(high, low, close, atr_period)
+
+    # Find the 2025-01-02 01:50 bar (signal bar for first trade).
+    target = pd.Timestamp("2025-01-02 01:50:00")
+    ts = pd.to_datetime(full_m5["timestamp"].to_numpy())
+    idx = int(ts.searchsorted(target, side="left"))
+    print(f"\n  first-signal bar @ {ts[idx]}  (idx {idx})")
+    print(f"    OHLC = O={full_m5['open'].iloc[idx]:.2f}  H={full_m5['high'].iloc[idx]:.2f}  "
+          f"L={full_m5['low'].iloc[idx]:.2f}  C={full_m5['close'].iloc[idx]:.2f}  "
+          f"spread={full_m5['spread'].iloc[idx]}")
+    print(f"    ATR({atr_period}) at this bar = {atr_arr[idx]:.6f}")
+    print(f"    sl_dist = {sl_mult} × ATR = {sl_mult * atr_arr[idx]:.6f}")
+    print(f"    loss_per_lot = sl_dist / tick_size × tick_value = "
+          f"{sl_mult * atr_arr[idx] / XAUUSD_REAL.tick_size * XAUUSD_REAL.tick_value:.4f}")
+    risk_money = init_deposit * risk_pct / 100.0
+    sl_dist = sl_mult * atr_arr[idx]
+    loss_per_lot = sl_dist / XAUUSD_REAL.tick_size * XAUUSD_REAL.tick_value
+    lots = risk_money / loss_per_lot
+    print(f"    risk_money = ${risk_money:.4f}")
+    print(f"    Python computed lots = {lots:.6f}  → rounded to {XAUUSD_REAL.round_volume(lots):.4f}")
+
+    # What would MT5's ATR have to be to produce 0.16 lots?
+    mt5_lots = 0.16
+    mt5_loss_per_lot = risk_money / mt5_lots
+    mt5_sl_dist = mt5_loss_per_lot * XAUUSD_REAL.tick_size / XAUUSD_REAL.tick_value
+    mt5_atr = mt5_sl_dist / sl_mult
+    print(f"\n  MT5 first trade: {mt5_lots} lots  →  sl_dist={mt5_sl_dist:.6f}  →  ATR={mt5_atr:.6f}")
+    print(f"  ratio Python/MT5 ATR = {atr_arr[idx] / mt5_atr:.4f}  ({(atr_arr[idx]/mt5_atr - 1)*100:+.1f}%)")
+
+    # Dump 30 bars around the signal to inspect the recent volatility.
+    print(f"\n  last {atr_period + 5} bars before signal (for ATR warmup):")
+    print(f"  {'ts':<22} {'open':>9} {'high':>9} {'low':>9} {'close':>9} {'TR':>9}")
+    for j in range(max(0, idx - atr_period - 5), idx + 1):
+        tr = max(
+            high[j] - low[j],
+            abs(high[j] - close[j-1]) if j > 0 else high[j] - low[j],
+            abs(low[j] - close[j-1]) if j > 0 else high[j] - low[j],
+        )
+        print(f"  {str(ts[j]):<22} {full_m5['open'].iloc[j]:>9.2f} {full_m5['high'].iloc[j]:>9.2f} "
+              f"{full_m5['low'].iloc[j]:>9.2f} {full_m5['close'].iloc[j]:>9.2f} {tr:>9.4f}")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())