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)
73 lines
3.1 KiB
Python
73 lines
3.1 KiB
Python
"""Check indicator values + signal conditions at 2025-01-02 01:50 vs 15:45.
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MT5 first trade fired 2025.01.02 01:55 LONG @ 2624.05
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→ signal bar 01:50 close=2623.84, fill at 01:55 open=2623.84
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Python first trade fired 2025-01-02 15:50 LONG @ 2642.57
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→ signal bar 15:45 close=2642.54, fill at 15:50 open=2642.54
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Both engines should fire same signals on same bars. Why do they differ?
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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 numpy as np
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import pandas as pd
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from shared.indicators.base import atr, ema, rsi
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from shared.data.loaders import load_bars
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from strategies.gold_scalper_pro.search_space import FROZEN_BASELINE
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import optuna
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# Load finalist #1 params
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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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from shared.optimizer.selector import select_diverse_topn
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from strategies.gold_scalper_pro.search_space import SEARCH_SPACE
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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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print(f"InpFastEmaPeriod={params['InpFastEmaPeriod']}, "
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f"InpSlowEmaPeriod={params['InpSlowEmaPeriod']}, "
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f"InpRsiPeriod={params['InpRsiPeriod']}, "
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f"InpAtrPeriod={params['InpAtrPeriod']}")
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print(f"InpRsiBuyLevel={params['InpRsiBuyLevel']}, "
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f"InpPullbackAtrMult={params['InpPullbackAtrMult']}")
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m5 = load_bars(PROJECT / "data" / "XAUUSD_M5_2024-06-26_2026-06-26.parquet")
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window = m5[(m5["timestamp"] >= "2024-12-01 00:00:00") & (m5["timestamp"] < "2025-01-03 00:00:00")].reset_index(drop=True)
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close = window["close"].to_numpy(dtype=float)
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high = window["high"].to_numpy(dtype=float)
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low = window["low"].to_numpy(dtype=float)
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fast = ema(close, int(params["InpFastEmaPeriod"]))
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slow = ema(close, int(params["InpSlowEmaPeriod"]))
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rsi_arr = rsi(close, int(params["InpRsiPeriod"]))
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atr_arr = atr(high, low, close, int(params["InpAtrPeriod"]))
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# Find rows on 2025-01-02 around 01:50 and 15:45
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window["fast"] = fast
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window["slow"] = slow
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window["rsi"] = rsi_arr
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window["atr"] = atr_arr
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window["trend_up"] = (fast > slow) & (close > slow)
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window["near_fast"] = np.abs(close - fast) <= (params["InpPullbackAtrMult"] * atr_arr)
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window["rsi_prev"] = np.roll(rsi_arr, 1)
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window["buy_cross"] = (window["rsi_prev"] < params["InpRsiBuyLevel"]) & (rsi_arr >= params["InpRsiBuyLevel"])
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window["buy_signal"] = window["trend_up"] & window["near_fast"] & window["buy_cross"]
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print("\n=== Around 2025-01-02 01:45-02:00 ===")
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sub = window[(window["timestamp"] >= "2025-01-02 01:45:00") & (window["timestamp"] <= "2025-01-02 02:00:00")]
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print(sub[["timestamp", "open", "high", "low", "close", "fast", "slow",
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"rsi", "atr", "trend_up", "near_fast", "buy_cross", "buy_signal"]].to_string())
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print("\n=== Around 2025-01-02 15:40-15:55 ===")
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sub = window[(window["timestamp"] >= "2025-01-02 15:40:00") & (window["timestamp"] <= "2025-01-02 15:55:00")]
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print(sub[["timestamp", "open", "high", "low", "close", "fast", "slow",
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"rsi", "atr", "trend_up", "near_fast", "buy_cross", "buy_signal"]].to_string())
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