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)
114 lines
4.5 KiB
Python
114 lines
4.5 KiB
Python
"""Diagnose the IS sizing mismatch: Python avg net/trade = $0.38 vs MT5 $2.97.
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If gross P/L scales proportionally to MT5 (factor ~1/7.8) and trade count
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matches, it's pure sizing. If PF also shifts, the BE/trailing logic differs.
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"""
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from __future__ import annotations
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import json
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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.core.metrics import compute_metrics
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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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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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IS_START = pd.Timestamp("2025-01-01 00:00:00")
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IS_END = pd.Timestamp("2026-01-01 00:00:00")
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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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t = finalists[0]
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merged = {**FROZEN_BASELINE, **t.params}
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print(f"finalist #1 (trial #{t.number})")
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print(f" InpRiskPercent = {merged['InpRiskPercent']}")
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print(f" InpAtrSLMult = {merged['InpAtrSLMult']}")
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print(f" InpAtrTPMult = {merged['InpAtrTPMult']}")
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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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is_bars = m5[(m5["timestamp"] >= IS_START) & (m5["timestamp"] < IS_END)].reset_index(drop=True)
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is_m1 = m1[(m1["timestamp"] >= IS_START) & (m1["timestamp"] < IS_END)].reset_index(drop=True)
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print(f" IS bars: {len(is_bars):,} IS M1: {len(is_m1):,}")
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pack = build_signals(merged, is_bars, XAUUSD_REAL)
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engine = ScalperEngine()
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result = engine.run(
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is_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=is_m1,
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**engine_kwargs_from_params(merged),
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)
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m = compute_metrics(result, periods_per_year=252 * 24 * 12)
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gross_profit = sum(t.pnl for t in result.trades if t.pnl > 0)
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gross_loss = sum(t.pnl for t in result.trades if t.pnl < 0)
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print(f"\nPython IS:")
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print(f" trades = {m.total_trades}")
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print(f" gross profit = {gross_profit:.2f}")
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print(f" gross loss = {gross_loss:.2f}")
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print(f" net = {gross_profit + gross_loss:.2f}")
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print(f" PF = {gross_profit / -gross_loss:.4f}" if gross_loss < 0 else " PF = inf")
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print(f" avg net/trade = {(gross_profit + gross_loss) / m.total_trades:.4f}")
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# Sample first 5 trades — check lot sizes & prices.
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print(f"\nFirst 5 trades:")
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print(f" {'time':<21} {'dir':<5} {'entry':>10} {'exit':>10} {'lots':>8} {'pnl':>9} {'reason'}")
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for t in result.trades[:5]:
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d = "LONG" if t.direction.name == "LONG" else "SHRT"
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print(f" {str(t.entry_time):<21} {d:<5} {t.entry_price:>10.2f} "
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f"{t.exit_price:>10.2f} {t.lots:>8.4f} {t.pnl:>9.4f} {t.exit_reason}")
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# Distribution of lots.
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import numpy as np
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lots_arr = np.array([t.lots for t in result.trades])
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print(f"\n lots: min={lots_arr.min():.4f} max={lots_arr.max():.4f} "
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f"mean={lots_arr.mean():.4f} median={np.median(lots_arr):.4f}")
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print(f" lots unique count: {len(np.unique(lots_arr))}")
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print(f" lots histogram (top 5):")
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vals, counts = np.unique(lots_arr, return_counts=True)
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for v, c in sorted(zip(vals, counts), key=lambda x: -x[1])[:5]:
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print(f" {v:.4f} ×{c}")
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# MT5 comparison.
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print(f"\nMT5 IS (from report):")
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print(f" gross profit = 23416.75")
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print(f" gross loss = -16429.41")
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print(f" net = 6987.34")
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print(f" PF = 1.43")
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print(f" trades = 2348")
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print(f" avg net/trade = {6987.34/2348:.4f}")
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# Scaling check: if Python lots were 7.8x larger, would P/L match?
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py_gross = gross_profit
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mt5_gross = 23416.75
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print(f"\n scaling factor (MT5 gross profit / Python gross profit): "
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f"{mt5_gross/py_gross:.2f}x")
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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