gavindiaz
63a829cc46
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)
83 lines
3.4 KiB
Python
83 lines
3.4 KiB
Python
"""Trace engine execution on 2025-01-02 to find why first trade fires at 15:50
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instead of 01:55 (signal trigger bar 01:50 has buy_signal=True).
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Patches ScalperEngine._entry_allowed to log every call, plus dumps the
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position state across the day.
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"""
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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 pandas as pd
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from shared.core.engine import SizingInputs
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from shared.data.loaders import load_bars
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from strategies.gold_scalper_pro.instruments import XAUUSD_REAL
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from strategies.gold_scalper_pro.scalper_engine import (
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ScalperEngine,
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ScalperConfig,
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engine_kwargs_from_params,
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)
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from strategies.gold_scalper_pro.search_space import FROZEN_BASELINE, SEARCH_SPACE
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from strategies.gold_scalper_pro.signals import build_signals
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import optuna
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from shared.optimizer.selector import select_diverse_topn
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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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params = {**FROZEN_BASELINE, **finalists[0].params}
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m5 = load_bars(PROJECT / "data" / "XAUUSD_M5_2024-06-26_2026-06-26.parquet")
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m1 = load_bars(PROJECT / "data" / "XAUUSD_M1_2024-06-26_2026-06-26.parquet")
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# Use a window starting 2024-12-01 so indicators warm up by 2025-01-01.
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START = pd.Timestamp("2024-12-01 00:00:00")
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END = pd.Timestamp("2025-01-03 00:00:00")
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bars = m5[(m5["timestamp"] >= START) & (m5["timestamp"] < END)].reset_index(drop=True)
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m1_bars = m1[(m1["timestamp"] >= START) & (m1["timestamp"] < END)].reset_index(drop=True)
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pack = build_signals(params, bars, XAUUSD_REAL)
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# Find all signal bars on 2025-01-02
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import numpy as np
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sig_idx = np.where(pack.signals_long | pack.signals_short)[0]
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print(f"signal bars on 2024-12-01..2025-01-02: {len(sig_idx)}")
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for i in sig_idx[-10:]:
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t = bars["timestamp"].iloc[i]
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sig_dir = "LONG" if pack.signals_long[i] else "SHORT"
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sl = pack.sl_prices[i] if not np.isnan(pack.sl_prices[i]) else float("nan")
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tp = pack.tp_prices[i] if not np.isnan(pack.tp_prices[i]) else float("nan")
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print(f" bar {i} ts={t} sig={sig_dir} close={bars['close'].iloc[i]:.2f} "
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f"sl_price={sl:.2f} tp_price={tp:.2f}")
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# Monkey-patch _entry_allowed to log all calls on 2025-01-02
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orig = ScalperEngine._entry_allowed
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def traced(self, cfg, t, trades_today, last_trade_ts, i, sl, sh):
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res = orig(self, cfg, t, trades_today, last_trade_ts, i, sl, sh)
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if pd.Timestamp("2025-01-02 00:00:00") <= t <= pd.Timestamp("2025-01-02 23:59:59"):
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if sl[i] or sh[i]:
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print(f" _entry_allowed(bar={i}, ts={t}, long={sl[i]}, short={sh[i]}, "
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f"trades_today={trades_today}, last={last_trade_ts}) → {res}")
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return res
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ScalperEngine._entry_allowed = traced
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print("\n--- Running engine on 2024-12-01..2025-01-02 window ---")
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engine = ScalperEngine()
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result = engine.run(
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bars, pack.signals_long, pack.signals_short,
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pack.sl_prices, pack.tp_prices,
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XAUUSD_REAL, SizingInputs(), 1000.0,
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m1_bars=m1_bars,
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**engine_kwargs_from_params(params),
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)
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print(f"\ntrades: {len(result.trades)}")
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for tr in result.trades[:5]:
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d = "LONG" if tr.direction.name == "LONG" else "SHRT"
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print(f" {tr.entry_time} {d} entry={tr.entry_price:.2f} lots={tr.lots:.4f} "
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f"pnl={tr.pnl:.4f} reason={tr.exit_reason}")
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