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