mirror of
https://github.com/silencesdg/mt5_python_ea_suite.git
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silencesdg
e1691c3c41
- 新增适应度门槛: min_backtest_fitness=95, 适应度<95暂停开仓 - 新增 SwingPointRetest 策略替代 Turtle - 新增 monday_reset.py 周重置脚本 - exit_rules: 拖尾止损相对回撤模式 - market_state: 趋势检测优化 - position: 一票制并发锁+合约规格缓存 - optimize: Optuna 替代 DEAP 遗传算法 - realtime_trader: 适应度门槛+同向递增 - weights: 动态权重管理 - cron_optimize: PYTHONPATH 修复 - .gitignore: 排除生成文件
297 lines
10 KiB
Python
Executable File
297 lines
10 KiB
Python
Executable File
#!/usr/bin/env python3
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"""每日自动优化 — 遗传算法跑参数 → 写入config → EA热加载生效（无需重启）
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通过 cron 调用: python3 scripts/daily_optimize.py
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"""
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import sys, os, json, re, time, signal, shutil
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from datetime import datetime
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from pathlib import Path
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PROJECT_DIR = Path(__file__).resolve().parent.parent
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os.chdir(str(PROJECT_DIR))
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sys.path.insert(0, str(PROJECT_DIR))
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from logger import setup_logger
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setup_logger("INFO")
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from logger import logger
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CONFIG_PATH = PROJECT_DIR / "config.py"
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BACKUP_DIR = PROJECT_DIR / "config_backups"
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RESTART_SIGNAL = PROJECT_DIR / ".restart_signal"
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def run_optimizer():
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"""运行遗传算法优化，返回 (best_params dict, fitness)"""
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from execution.optimize import run_optimizer as _run
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logger.info("🧬 开始遗传算法优化...")
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best_params, fitness = _run()
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logger.info(f"✅ 优化完成 适应度={fitness:.2f}")
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return best_params, fitness
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def backup_config():
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"""备份当前 config.py"""
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BACKUP_DIR.mkdir(exist_ok=True)
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ts = datetime.now().strftime("%Y%m%d_%H%M%S")
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dst = BACKUP_DIR / f"config_{ts}.py"
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shutil.copy(CONFIG_PATH, dst)
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logger.info(f"📦 已备份配置: {dst}")
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def update_config(best_params: dict, fitness: float = 0.0):
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"""将优化结果写回 config.py"""
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content = CONFIG_PATH.read_text(encoding="utf-8")
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# ═══ RISK_CONFIG — 手动设定，不进优化器 ═══
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# （optimizer.py PARAM_SPACE 已移除风控基因，此 map 清空）
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risk_map = {
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}
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for opt_key, cfg_key in risk_map.items():
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if opt_key in best_params:
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val = best_params[opt_key]
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content = re.sub(
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rf'("{cfg_key}":\s*)[\d.\-e]+',
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rf'\g<1>{val}',
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content
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)
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# ═══ SIGNAL_THRESHOLDS ═══
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if "buy_threshold" in best_params:
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content = re.sub(
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r'("buy_threshold":\s*)[\d.\-e]+',
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rf'\g<1>{best_params["buy_threshold"]}',
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content
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)
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if "sell_threshold" in best_params:
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content = re.sub(
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r'("sell_threshold":\s*)[\d.\-e]+',
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rf'\g<1>{best_params["sell_threshold"]}',
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content
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)
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# ═══ MARKET_STATE_CONFIG ═══
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market_map = {
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"market_trend_period": "trend_period",
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"market_retracement_tolerance": "retracement_tolerance",
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"market_volume_period": "volume_period",
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"market_volume_ma_period": "volume_ma_period",
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}
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for opt_key, cfg_key in market_map.items():
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if opt_key in best_params:
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val = int(best_params[opt_key]) if "period" in opt_key else best_params[opt_key]
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content = re.sub(
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rf'("{cfg_key}":\s*)[\d.\-e]+',
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rf'\g<1>{val}',
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content
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)
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# ═══ STRATEGY_CONFIG (strategy params) ═══
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strategy_param_map = {
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# MACrossStrategy
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"ma_cross_short_window": ("ma_cross", "short_window"),
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"ma_cross_long_window": ("ma_cross", "long_window"),
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# RSIStrategy
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"rsi_period": ("rsi", "period"),
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"rsi_overbought": ("rsi", "overbought"),
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"rsi_oversold": ("rsi", "oversold"),
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# BollingerStrategy
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"bollinger_period": ("bollinger", "period"),
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"bollinger_std_dev": ("bollinger", "std_dev"),
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# MACDStrategy
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"macd_fast_ema": ("macd", "fast_ema"),
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"macd_slow_ema": ("macd", "slow_ema"),
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"macd_signal_period": ("macd", "signal_period"),
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# MeanReversionStrategy
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"mean_reversion_period": ("mean_reversion", "period"),
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"mean_reversion_std_dev": ("mean_reversion", "std_dev"),
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# MomentumBreakoutStrategy
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"momentum_breakout_period": ("momentum_breakout", "period"),
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"momentum_breakout_momentum_period": ("momentum_breakout", "momentum_period"),
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# KDJStrategy
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"kdj_period": ("kdj", "period"),
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# SwingPointRetestStrategy
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"swing_point_left_bars": ("swing_point", "left_bars"),
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"swing_point_right_bars": ("swing_point", "right_bars"),
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"swing_point_tolerance_pct": ("swing_point", "tolerance_pct"),
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"swing_point_num_swings": ("swing_point", "num_swings"),
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# DailyBreakoutStrategy
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"daily_breakout_bars_count": ("daily_breakout", "bars_count"),
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# WaveTheoryStrategy
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"wave_ema_short": ("wave_theory", "ema_short"),
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"wave_ema_medium": ("wave_theory", "ema_medium"),
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"wave_ema_long": ("wave_theory", "ema_long"),
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"wave_period": ("wave_theory", "wave_period"),
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"wave_range_period": ("wave_theory", "range_period"),
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"wave_adx_period": ("wave_theory", "adx_period"),
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"wave_momentum_period": ("wave_theory", "momentum_period"),
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"wave_range_threshold": ("wave_theory", "range_threshold"),
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"wave_adx_threshold": ("wave_theory", "adx_threshold"),
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}
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for opt_key, (section, key) in strategy_param_map.items():
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if opt_key in best_params:
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val = best_params[opt_key]
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if isinstance(val, float) and abs(val - round(val)) < 1e-6:
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val = int(round(val))
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content = re.sub(
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rf'("{key}":\s*)[\d.\-e]+',
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rf'\g<1>{val}',
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content,
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count=1,
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)
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# ═══ DEFAULT_WEIGHTS ═══
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weight_map = {
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"weight_MACrossStrategy": "ma_cross",
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"weight_RSIStrategy": "rsi",
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"weight_BollingerStrategy": "bollinger",
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"weight_MeanReversionStrategy": "mean_reversion",
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"weight_MomentumBreakoutStrategy": "momentum_breakout",
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"weight_MACDStrategy": "macd",
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"weight_KDJStrategy": "kdj",
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"weight_SwingPointRetestStrategy": "swing_point",
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"weight_DailyBreakoutStrategy": "daily_breakout",
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"weight_WaveTheoryStrategy": "wave_theory",
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}
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for opt_key, cfg_key in weight_map.items():
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if opt_key in best_params:
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val = best_params[opt_key]
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content = re.sub(
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rf'("{cfg_key}":\s*)[\d.\-e]+',
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rf'\g<1>{val}',
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content
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)
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# ═══ TREND_INDICATOR_WEIGHTS ═══
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trend_weight_map = {
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"trend_price_breakout_weight": "price_breakout",
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"trend_volume_confirmation_weight": "volume_confirmation",
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"trend_momentum_oscillator_weight": "momentum_oscillator",
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"trend_moving_average_weight": "moving_average",
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}
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for opt_key, cfg_key in trend_weight_map.items():
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if opt_key in best_params:
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val = best_params[opt_key]
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content = re.sub(
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rf'("{cfg_key}":\s*)[\d.\-e]+',
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rf'\g<1>{val}',
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content
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)
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# ═══ TREND_THRESHOLDS ═══
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trend_thresh_map = {
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"trend_strong_threshold": "strong_trend",
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"trend_weak_threshold": "weak_trend",
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"trend_volume_spike": "volume_spike",
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"trend_oversold": "oversold",
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"trend_overbought": "overbought",
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}
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for opt_key, cfg_key in trend_thresh_map.items():
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if opt_key in best_params:
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val = best_params[opt_key]
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if "sold" in opt_key or "bought" in opt_key:
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val = int(round(val))
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content = re.sub(
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rf'("{cfg_key}":\s*)[\d.\-e]+',
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rf'\g<1>{val}',
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content
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)
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# ═══ CONFIDENCE_THRESHOLDS ═══
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conf_map = {
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"confidence_high": "high_confidence",
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"confidence_medium": "medium_confidence",
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}
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for opt_key, cfg_key in conf_map.items():
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if opt_key in best_params:
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val = best_params[opt_key]
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content = re.sub(
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rf'("{cfg_key}":\s*)[\d.\-e]+',
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rf'\g<1>{val}',
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content
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)
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# ═══ LAST_OPTIMIZATION_FITNESS ═══
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content = re.sub(
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r'LAST_OPTIMIZATION_FITNESS\s*=\s*[\d.\-e]+',
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f'LAST_OPTIMIZATION_FITNESS = {fitness:.2f}',
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content
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)
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CONFIG_PATH.write_text(content, encoding="utf-8")
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logger.info("✏️ 配置已更新")
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def restart_ea():
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"""杀掉旧 EA → 清日志 → 启动新 EA（应用优化后的配置）"""
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import subprocess
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logger.info("🔄 重启 EA...")
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# 杀旧进程，等锁释放再启动新的
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subprocess.run(["pkill", "-f", "python.*run/realtime.py"], capture_output=True)
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import time; time.sleep(2)
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subprocess.run(["pkill", "-9", "-f", "python.*run/realtime.py"], capture_output=True)
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# 确认旧进程已死 + 锁已释放（轮询最多等 5 秒）
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lock_file = PROJECT_DIR / ".ea.lock"
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import fcntl as _fcntl
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for _ in range(10):
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time.sleep(0.5)
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try:
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fd = os.open(str(lock_file), os.O_RDONLY)
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_fcntl.flock(fd, _fcntl.LOCK_EX | _fcntl.LOCK_NB)
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os.close(fd) # 立即释放，只是测试
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break
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except (BlockingIOError, OSError):
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pass
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else:
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logger.warning("⚠️ 旧进程锁未释放，强制启动（旧进程可能僵死）")
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# 清空旧交易记录
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for f in PROJECT_DIR.glob("realtime_trades_*"):
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f.unlink(missing_ok=True)
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# 清日志
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log_file = PROJECT_DIR / "logs" / "strategy.log"
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log_file.write_text("")
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# 启动新 EA（后台）
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log = open(log_file, "a")
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proc = subprocess.Popen(
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[sys.executable, "run/realtime.py"],
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cwd=str(PROJECT_DIR),
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stdout=log, stderr=subprocess.STDOUT,
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)
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pid_file = PROJECT_DIR / ".ea_pid"
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pid_file.write_text(str(proc.pid))
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logger.info(f"✅ EA 已重启 PID={proc.pid}")
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def main():
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logger.info("=" * 60)
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logger.info("📅 每日自动优化启动")
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logger.info(f"时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
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logger.info("=" * 60)
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# 1. 备份当前配置
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backup_config()
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# 2. 运行优化
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try:
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best_params, fitness = run_optimizer()
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except Exception as e:
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logger.error(f"优化失败: {e}")
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import traceback
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traceback.print_exc()
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return 1
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# 3. 写入 config.py（含适应度）
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update_config(best_params, fitness)
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# 4. ★ EA 通过 config.reload() 自动热加载，无需重启
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logger.info("✅ 每日优化流程完成 (EA 将在下个周期自动读取新配置)")
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return 0
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if __name__ == "__main__":
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sys.exit(main())
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