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feat: 适应度门槛95+swing_point策略+多项改进
- 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
This commit is contained in:
silencesdg
+17
-10
@@ -1,10 +1,17 @@
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/GEMINI.md
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/logs
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/myenv
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__pycache__/
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*.json
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*.csv
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.claude/
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.claude/**
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.claude/settings.local.json
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.claude/
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1|/GEMINI.md
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2|/logs
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3|/myenv
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4|__pycache__/
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5|*.json
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6|*.csv
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7|.claude/
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8|.claude/**
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9|.claude/settings.local.json
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10|.claude/
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config_backups/
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optimization_report_*.txt
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optimization_results_*.json
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.ea_pid
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.ea.lock
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.hermes/
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realtime_trades_*
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@@ -1,4 +1,10 @@
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# MT5 代理服务配置
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import importlib as _importlib, sys as _sys
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def reload():
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"""★ 热重载配置 — 运行时调用，无需重启 EA"""
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_importlib.reload(_sys.modules[__name__])
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SERVER_HOST = "0.0.0.0"
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SERVER_PORT = 5555
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@@ -29,7 +35,7 @@ USE_DATE_RANGE = False # 设置为False可强制使用数据量模式
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# 兼容性配置 (如果日期配置不可用，则使用数据量)
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BACKTEST_COUNT = 30000 # 回测数据量
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OPTIMIZER_COUNT = 50000 # 优化器数据量
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OPTIMIZER_COUNT = 6000 # 优化器数据量（约4天M1，更快响应行情）
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RISK_CONFIG_CONST = {
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'enable_time_based_exit': False # 关闭超时平仓，让止盈/止损/跟踪止损接管
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@@ -62,8 +68,8 @@ BACKTEST_CONFIG = {
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REALTIME_CONFIG = {
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"update_interval": 5, # 更新间隔（秒）
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"daily_reset_time": "00:00", # 每日重置时间
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"max_long_positions": 1, # 同方向只持一单，避免重复开仓
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"max_short_positions": 1, # 同方向只持一单
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"max_long_positions": 10, # 同方向最多10单
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"max_short_positions": 10, # 同方向最多10单
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"min_trade_interval": 0, # 最小交易间隔（分钟），0表示无限制
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"enable_auto_trading": True, # 是否启用自动交易
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"dry_run": False, # 是否为模拟运行（不实际下单）
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@@ -104,8 +110,8 @@ DATA_CONFIG = {
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# 遗传算法优化器配置
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GENETIC_OPTIMIZER_CONFIG = {
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# 算法参数
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"population_size": 50, # 种群大小
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"generations": 10, # 进化代数
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"population_size": 20, # 种群大小（精简化，6000bar快速收敛）
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"generations": 6, # 进化代数
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"crossover_probability": 0.7, # 交叉概率
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"mutation_probability": 0.3, # 变异概率
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@@ -135,57 +141,68 @@ GENETIC_OPTIMIZER_CONFIG = {
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# 信号阈值配置（优化器结果 2026-05-10，5万根M1数据）
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SIGNAL_THRESHOLDS = {
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"buy_threshold": 1.0315663601353515,
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"sell_threshold": -2.312867704316165
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"buy_threshold": 2.087211936631902,
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"sell_threshold": -0.6594468309213586
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}
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# 风险管理参数（优化器结果 2026-05-10，5万根M1数据）
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# 风险管理参数（手动设定，优化器不宜优化 — 保证金% 基础值，杠杆自适应缩放）
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RISK_CONFIG = {
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# ★ 以下百分比均为保证金%（开仓成本%），非金价涨跌%
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# 杠杆统一 2000x：0.01手保证金≈$2.33
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# -50%保证金 = -$1.17 ≈ 1.2点（很紧，但2000x下正常）
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"risk_leverage": 0, # 0=自动读MT5杠杆(2000x)
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"stop_loss_pct": -0.50, # -50% 保证金
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"profit_retracement_pct": 0.35, # 35% 保证金（拖尾回撤容忍）
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"min_profit_for_trailing": 0.70, # +70% 保证金后激活拖尾
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"take_profit_pct": 1.00, # +100% 保证金
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# ★ 以下百分比均为保证金%（100x基准），实盘按 (MT5杠杆/100) 缩放
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# 2000x下：-0.70 → -14×杠杆 → -1400%保证金 = -$32 ≈ 32点
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"risk_leverage": 100, # 100x基准计算保证金%（不改实际杠杆）
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"stop_loss_pct": -0.10, # -10% 保证金 ←手动设定，不进优化器
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"profit_retracement_pct": 0.03, # ★ 3% 回撤容忍（相对！3%×峰值利润） ←手动设定
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"retracement_mode": "relative", # ★ 回撤模式从绝对值改为相对（利润的3%）
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"min_profit_for_trailing": 0.10, # +10% 后激活拖尾 ←手动设定
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"take_profit_pct": 0.15, # +15% 止盈 ←手动设定
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"max_daily_loss": -0.3,
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"max_holding_minutes": 133,
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"min_profit_for_time_exit": 0.010,
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"max_holding_minutes": 0, # 0=不启用时间平仓
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"min_profit_for_time_exit": 0.05,
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"cooldown_bars": 30,
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# ★ 同向加仓门槛递增：第N单需要的信号 = 基础阈值 × α^(N-1)
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"entry_escalation_alpha": 1.2, # 递增系数（1.2=每单信号强20%）
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# ★ 波动率自适应拖尾：激活点数 = 基础点数 × (当前ATR / 长周期ATR)
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"vol_adaptive_trailing": True, # 启动波动率自适应拖尾
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"trailing_atr_period": 14, # ATR短周期
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"trailing_atr_baseline": 100, # ATR长周期（基准）
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# ★ 硬止损倍率：MT5 服务器端 SL/TP = 软止损 × 倍率（兜底，仅 EA 挂掉时触发）
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"hard_sl_multiplier": 1.5, # 硬 SL = 软 SL × 1.5（-1.5%→-2.25%账户）
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"hard_tp_multiplier": 1.3, # 硬 TP = 软 TP × 1.3（+3.0%→+3.9%账户）
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"hard_sl_multiplier": 1.5, # 硬 SL = 软 SL × 1.5
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"hard_tp_multiplier": 1.3, # 硬 TP = 软 TP × 1.3
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# ★ 回测适应度门槛：低于此值不开新单（适应度≈回测总盈亏$）
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"min_backtest_fitness": 95, # 适应度<95 → 不开仓（低于95说明市场难做）
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}
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# ★ 最近优化适应度（daily_optimize.py 写入，EA 热加载读取）
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LAST_OPTIMIZATION_FITNESS = 58.63
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# 市场状态分析参数
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MARKET_STATE_CONFIG = {
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"trend_period": 83,
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"retracement_tolerance": 0.15567164907853615,
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"volume_period": 29,
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"volume_ma_period": 23
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"trend_period": 60,
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"retracement_tolerance": 0.3857733104065197,
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"volume_period": 42,
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"volume_ma_period": 30
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}
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# 策略参数配置（优化器结果 2026-05-10，5万根M1数据）
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STRATEGY_CONFIG = {
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"ma_cross": {
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"short_window": 13,
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"long_window": 17
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"short_window": 7,
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"long_window": 18
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},
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"rsi": {
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"period": 30,
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"overbought": 68,
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"oversold": 32
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"period": 19,
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"overbought": 75,
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"oversold": 31
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},
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"bollinger": {
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"period": 20,
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"std_dev": 2.4663422649047333
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"std_dev": 2.2757986281351896
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},
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"macd": {
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"fast_ema": 14,
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"slow_ema": 24,
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"signal_period": 9
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"fast_ema": 19,
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"slow_ema": 23,
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"signal_period": 13
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},
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"mean_reversion": {
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"period": 29,
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@@ -193,105 +210,108 @@ STRATEGY_CONFIG = {
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},
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"momentum_breakout": {
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"period": 15,
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"momentum_period": 13
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"momentum_period": 19
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},
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"kdj": {
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"period": 21
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},
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"turtle": {
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"period": 42
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"swing_point": {
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"left_bars": 4,
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"right_bars": 3,
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"tolerance_pct": 0.0006056955205083707,
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"num_swings": 2
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},
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"daily_breakout": {
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"bars_count": 2008
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"bars_count": 1392
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},
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"wave_theory": {
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"ema_short": 3,
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"ema_short": 8,
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"ema_medium": 11,
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"ema_long": 46,
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"wave_period": 11,
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"range_period": 35,
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"adx_period": 20,
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"ema_long": 39,
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"wave_period": 19,
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"range_period": 15,
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"adx_period": 24,
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"momentum_period": 10,
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"range_threshold": 0.01,
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"adx_threshold": 21
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"range_threshold": 0.0023249568571626195,
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"adx_threshold": 28
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}
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}
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# 市场趋势判断权重配置
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TREND_INDICATOR_WEIGHTS = {
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"price_breakout": 0.1504199442999585,
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"volume_confirmation": 0.12782205952949638,
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"price_breakout": 0.2506453627492913,
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"volume_confirmation": 0.2851951176719032,
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"momentum oscillator": 0.3677,
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"moving_average": 0.4092273363154768
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"moving_average": 0.353451489159348
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}
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# 趋势判断阈值
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TREND_THRESHOLDS = {
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"strong_trend": 0.7940886082643032,
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"weak_trend": 0.4565953163045441,
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"volume_spike": 2.7329673335105396,
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"oversold": 32,
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"overbought": 68
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"strong_trend": 0.4040165929726079,
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"weak_trend": 0.2963326526031991,
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"volume_spike": 1.5234871986051206,
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"oversold": 31,
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"overbought": 75
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}
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# 动态权重配置（优化器结果 2026-05-10，5万根M1数据）
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DEFAULT_WEIGHTS = {
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"ma_cross": 0.32048324544087786,
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"rsi": 1.906009222972592,
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"bollinger": 0.9232444456662969,
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"mean_reversion": 1.1649306516234597,
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"momentum_breakout": 0.8769099086178171,
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"macd": 0.748930985323352,
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"kdj": 1.4647470623067596,
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"turtle": 1.637931136008917,
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"daily_breakout": 1.478598649389487,
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"wave_theory": 0.818283180489803
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"ma_cross": 0.4771175228437661,
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"rsi": 0.478479356771334,
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"bollinger": 1.3391582231825303,
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"mean_reversion": 1.9958459833789346,
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"momentum_breakout": 0.7567594572894596,
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"macd": 1.170771336653618,
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"kdj": 0.013296663814979182,
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"swing_point": 0.5194535227966377,
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"daily_breakout": 0.6506588091889913,
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"wave_theory": 1.6765424080384428
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}
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# 市场状态策略权重配置
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MARKET_STATE_WEIGHTS = {
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"uptrend": {
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"ma_cross": 0.32048324544087786,
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"momentum_breakout": 0.8769099086178171,
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"turtle": 1.637931136008917,
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"macd": 0.748930985323352,
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"daily_breakout": 1.478598649389487,
|
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"rsi": 1.906009222972592,
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"bollinger": 0.9232444456662969,
|
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"kdj": 1.4647470623067596,
|
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"mean_reversion": 1.1649306516234597,
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"wave_theory": 0.818283180489803
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"ma_cross": 0.4771175228437661,
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"momentum_breakout": 0.7567594572894596,
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"swing_point": 0.5194535227966377,
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"macd": 1.170771336653618,
|
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"daily_breakout": 0.6506588091889913,
|
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"rsi": 0.478479356771334,
|
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"bollinger": 1.3391582231825303,
|
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"kdj": 0.013296663814979182,
|
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"mean_reversion": 1.9958459833789346,
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"wave_theory": 1.6765424080384428
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},
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"downtrend": {
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"ma_cross": 0.32048324544087786,
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"momentum_breakout": 0.8769099086178171,
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"turtle": 1.637931136008917,
|
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"macd": 0.748930985323352,
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"daily_breakout": 1.478598649389487,
|
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"rsi": 1.906009222972592,
|
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"bollinger": 0.9232444456662969,
|
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"kdj": 1.4647470623067596,
|
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"mean_reversion": 1.1649306516234597,
|
||||
"wave_theory": 0.818283180489803
|
||||
"ma_cross": 0.4771175228437661,
|
||||
"momentum_breakout": 0.7567594572894596,
|
||||
"swing_point": 0.5194535227966377,
|
||||
"macd": 1.170771336653618,
|
||||
"daily_breakout": 0.6506588091889913,
|
||||
"rsi": 0.478479356771334,
|
||||
"bollinger": 1.3391582231825303,
|
||||
"kdj": 0.013296663814979182,
|
||||
"mean_reversion": 1.9958459833789346,
|
||||
"wave_theory": 1.6765424080384428
|
||||
},
|
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"ranging": {
|
||||
"rsi": 1.906009222972592,
|
||||
"bollinger": 0.9232444456662969,
|
||||
"mean_reversion": 1.1649306516234597,
|
||||
"kdj": 1.4647470623067596,
|
||||
"wave_theory": 0.818283180489803,
|
||||
"ma_cross": 0.32048324544087786,
|
||||
"macd": 0.748930985323352,
|
||||
"turtle": 1.637931136008917,
|
||||
"momentum_breakout": 0.8769099086178171,
|
||||
"daily_breakout": 1.478598649389487
|
||||
"rsi": 0.478479356771334,
|
||||
"bollinger": 1.3391582231825303,
|
||||
"mean_reversion": 1.9958459833789346,
|
||||
"kdj": 0.013296663814979182,
|
||||
"wave_theory": 1.6765424080384428,
|
||||
"ma_cross": 0.4771175228437661,
|
||||
"macd": 1.170771336653618,
|
||||
"swing_point": 0.5194535227966377,
|
||||
"momentum_breakout": 0.7567594572894596,
|
||||
"daily_breakout": 0.6506588091889913
|
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},
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"none": DEFAULT_WEIGHTS
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}
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# 市场趋势置信度阈值配置
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CONFIDENCE_THRESHOLDS = {
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"high_confidence": 0.6813641209473742,
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"medium_confidence": 0.6336441706563001
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"high_confidence": 0.5050416445534804,
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"medium_confidence": 0.6102467437388168
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}
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+10
-3
@@ -54,15 +54,22 @@ class TrailingStopRule(BaseExitRule):
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def check(self, ctx: ExitContext) -> tuple[str, str]:
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min_profit = self.config.get("min_profit_for_trailing", 0.01)
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retracement_pct = self.config.get("profit_retracement_pct", 0.10)
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retracement_mode = self.config.get("retracement_mode", "absolute")
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if ctx.peak_profit_pct <= min_profit:
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return "none", ""
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# ★ 回撤从峰值绝对值扣除（账户%，非相对%）：峰值+2.0%回撤1.0%→止损在+1.0%
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stop_level = ctx.peak_profit_pct - retracement_pct
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if retracement_mode == "relative":
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# ★ 相对回撤：止损 = 峰值 × (1-回撤%)
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# 例: 峰值+50% 回撤30% → 止损+35%（利润从50%回落到35%时平仓）
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stop_level = ctx.peak_profit_pct * (1 - retracement_pct)
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else:
|
||||
# ★ 绝对值扣除（旧模式）：峰值+2.0%回撤1.0%→止损在+1.0%
|
||||
stop_level = ctx.peak_profit_pct - retracement_pct
|
||||
if ctx.current_profit_pct <= stop_level:
|
||||
mode_tag = "相对" if retracement_mode == "relative" else "绝对"
|
||||
return "close", (
|
||||
f"追踪止损触发 "
|
||||
f"追踪止损触发({mode_tag}回撤) "
|
||||
f"(峰值 {ctx.peak_profit_pct:.2%} 回落至 {ctx.current_profit_pct:.2%})"
|
||||
)
|
||||
return "none", ""
|
||||
|
||||
@@ -1,6 +1,7 @@
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
from logger import logger
|
||||
import config
|
||||
from config import (
|
||||
MARKET_STATE_CONFIG, SYMBOL, DEFAULT_WEIGHTS,
|
||||
TREND_INDICATOR_WEIGHTS, TREND_THRESHOLDS,
|
||||
@@ -288,7 +289,7 @@ class MarketStateAnalyzer:
|
||||
base_weights = dict(individual_weights)
|
||||
else:
|
||||
# 正常模式：从配置获取市场状态对应权重
|
||||
base_weights = dict(MARKET_STATE_WEIGHTS.get(market_state, DEFAULT_WEIGHTS))
|
||||
base_weights = dict(config.MARKET_STATE_WEIGHTS.get(market_state, config.DEFAULT_WEIGHTS))
|
||||
|
||||
high_conf = self.confidence_thresholds.get("high_confidence", 0.7)
|
||||
medium_conf = self.confidence_thresholds.get("medium_confidence", 0.4)
|
||||
@@ -297,9 +298,9 @@ class MarketStateAnalyzer:
|
||||
return {k: v * confidence for k, v in base_weights.items()}
|
||||
elif confidence > medium_conf:
|
||||
return {
|
||||
k: (v * confidence + DEFAULT_WEIGHTS.get(k, 1.0) * (1 - confidence))
|
||||
k: (v * confidence + config.DEFAULT_WEIGHTS.get(k, 1.0) * (1 - confidence))
|
||||
for k, v in base_weights.items()
|
||||
}
|
||||
else:
|
||||
# 低置信度：individual_weights 优先（优化器模式），否则回退到 DEFAULT_WEIGHTS
|
||||
return dict(individual_weights) if individual_weights is not None else dict(DEFAULT_WEIGHTS)
|
||||
return dict(individual_weights) if individual_weights is not None else dict(config.DEFAULT_WEIGHTS)
|
||||
|
||||
+73
-8
@@ -3,10 +3,8 @@ import numpy as np
|
||||
import json
|
||||
import os
|
||||
from logger import logger
|
||||
from config import (
|
||||
RISK_CONFIG, SYMBOL, INITIAL_CAPITAL, CAPITAL_ALLOCATION,
|
||||
RISK_CONFIG_CONST, SIMULATION_CONFIG
|
||||
)
|
||||
import config
|
||||
from config import RISK_CONFIG, SYMBOL, INITIAL_CAPITAL, CAPITAL_ALLOCATION, RISK_CONFIG_CONST, SIMULATION_CONFIG
|
||||
from core.risk.exit_rules import ExitRuleEngine, ExitContext
|
||||
|
||||
|
||||
@@ -61,6 +59,7 @@ class PositionManager:
|
||||
self.min_profit_for_trailing = risk.get("min_profit_for_trailing", 0.01) * self._lev_ratio
|
||||
self.take_profit_pct = risk.get("take_profit_pct", 0.20) * self._lev_ratio
|
||||
self.min_profit_for_time_exit = risk.get("min_profit_for_time_exit", 0.001) * self._lev_ratio
|
||||
self.retracement_mode = risk.get("retracement_mode", "absolute") # relative/absolute
|
||||
|
||||
# 资金管理
|
||||
self.initial_capital = INITIAL_CAPITAL
|
||||
@@ -89,13 +88,24 @@ class PositionManager:
|
||||
"max_holding_minutes": self.max_holding_minutes,
|
||||
"min_profit_for_time_exit": self.min_profit_for_time_exit,
|
||||
"max_daily_loss": self.max_daily_loss,
|
||||
"retracement_mode": self.retracement_mode,
|
||||
}
|
||||
self.exit_engine = ExitRuleEngine(exit_config)
|
||||
self._exit_config = exit_config # 保存引用，供波动率自适应更新
|
||||
|
||||
# ★ 波动率自适应拖尾 — 保存基准值
|
||||
self._base_min_profit_for_trailing = self.min_profit_for_trailing
|
||||
self._base_profit_retracement_pct = self.profit_retracement_pct
|
||||
self._vol_adaptive = risk.get("vol_adaptive_trailing", False)
|
||||
self._trailing_atr_period = risk.get("trailing_atr_period", 14)
|
||||
self._trailing_atr_baseline = risk.get("trailing_atr_baseline", 100)
|
||||
self._last_atr_update = None # 节流：最多1分钟更新一次
|
||||
if self._vol_adaptive:
|
||||
logger.info(f"📐 波动率自适应拖尾已启用 (ATR{self._trailing_atr_period}/ATR{self._trailing_atr_baseline})")
|
||||
|
||||
# 峰值数据持久化（优化器中禁用文件I/O避免多进程竞争）
|
||||
self.peak_data_file = "position_peaks.json"
|
||||
if self._persist_peaks:
|
||||
self._load_peak_data()
|
||||
self._saved_peaks = self._load_peak_data() if self._persist_peaks else {}
|
||||
|
||||
# 对冲管理器
|
||||
from config import HEDGE_CONFIG
|
||||
@@ -106,6 +116,54 @@ class PositionManager:
|
||||
self._pending_long = 0
|
||||
self._pending_short = 0
|
||||
|
||||
# ── ATR 计算与波动率自适应 ──
|
||||
|
||||
def _compute_atr(self, period: int) -> float | None:
|
||||
"""计算指定周期的 ATR（Average True Range）"""
|
||||
try:
|
||||
import numpy as np
|
||||
rates = self.data_provider.get_historical_data(self.symbol, 1, period + 1)
|
||||
if rates is None or len(rates) < period + 1:
|
||||
return None
|
||||
highs = np.array([r[2] for r in rates[-period-1:]]) # high
|
||||
lows = np.array([r[3] for r in rates[-period-1:]]) # low
|
||||
closes = np.array([r[4] for r in rates[-period-1:]]) # close
|
||||
tr = np.maximum(
|
||||
highs[1:] - lows[1:],
|
||||
np.maximum(
|
||||
np.abs(highs[1:] - closes[:-1]),
|
||||
np.abs(lows[1:] - closes[:-1])
|
||||
)
|
||||
)
|
||||
return float(np.mean(tr))
|
||||
except Exception as e:
|
||||
logger.debug(f"ATR 计算失败: {e}")
|
||||
return None
|
||||
|
||||
def _update_volatility_trailing(self):
|
||||
"""波动率自适应：按 ATR 比率调整拖尾激活和回撤参数"""
|
||||
if not self._vol_adaptive:
|
||||
return
|
||||
# 节流：最多每分钟更新一次
|
||||
from datetime import datetime
|
||||
now = datetime.now()
|
||||
if self._last_atr_update is not None:
|
||||
if (now - self._last_atr_update).total_seconds() < 60:
|
||||
return
|
||||
short_atr = self._compute_atr(self._trailing_atr_period)
|
||||
long_atr = self._compute_atr(self._trailing_atr_baseline)
|
||||
if short_atr is None or long_atr is None or long_atr <= 0:
|
||||
return
|
||||
vol_ratio = short_atr / long_atr
|
||||
# 限制极端值：0.5 ~ 2.0
|
||||
vol_ratio = max(0.5, min(2.0, vol_ratio))
|
||||
# 更新
|
||||
self.min_profit_for_trailing = self._base_min_profit_for_trailing * vol_ratio
|
||||
self.profit_retracement_pct = self._base_profit_retracement_pct * vol_ratio
|
||||
self._exit_config["min_profit_for_trailing"] = self.min_profit_for_trailing
|
||||
self._exit_config["profit_retracement_pct"] = self.profit_retracement_pct
|
||||
self._last_atr_update = now
|
||||
|
||||
# ── 仓位计算 ──
|
||||
|
||||
def _calculate_position_size(self, capital_to_allocate, current_price):
|
||||
@@ -277,6 +335,9 @@ class PositionManager:
|
||||
if not self.positions:
|
||||
return
|
||||
|
||||
# ★ 波动率自适应拖尾 — 每分钟更新一次
|
||||
self._update_volatility_trailing()
|
||||
|
||||
current_time = current_price.get('time', pd.Timestamp.now())
|
||||
|
||||
positions_to_remove = []
|
||||
@@ -326,7 +387,7 @@ class PositionManager:
|
||||
self.cleanup_peak_data()
|
||||
|
||||
# ── 对冲评估 ──
|
||||
if self.positions and self.hedge_manager:
|
||||
if self.positions and hasattr(self, 'hedge_manager') and self.hedge_manager:
|
||||
hedge_actions = self.hedge_manager.evaluate(weighted_signal, current_price)
|
||||
for action, target, reason in hedge_actions:
|
||||
if action == "hedge":
|
||||
@@ -441,6 +502,8 @@ class PositionManager:
|
||||
if live_positions is None:
|
||||
return
|
||||
saved_peaks = self._load_peak_data()
|
||||
# 合并构造器中加载的峰值（优先已持久化）
|
||||
saved_peaks = {**saved_peaks, **getattr(self, '_saved_peaks', {})}
|
||||
existing_peaks = {pos['ticket']: pos.get('peak_profit_pct', 0.0) for pos in self.positions}
|
||||
merged_peaks = {**existing_peaks, **saved_peaks}
|
||||
self.positions.clear()
|
||||
@@ -496,7 +559,9 @@ class PositionManager:
|
||||
try:
|
||||
if os.path.exists(self.peak_data_file):
|
||||
with open(self.peak_data_file, 'r', encoding='utf-8') as f:
|
||||
return json.load(f)
|
||||
raw = json.load(f)
|
||||
# ★ JSON 键是字符串，MT5 ticket 是整数 → 统一转 int
|
||||
return {int(k): v for k, v in raw.items()}
|
||||
except Exception as e:
|
||||
logger.error(f"加载峰值数据失败: {e}")
|
||||
return {}
|
||||
|
||||
+132
-282
@@ -1,21 +1,21 @@
|
||||
"""遗传算法优化器（重写版）
|
||||
"""贝叶斯优化器（Optuna TPE）
|
||||
高效替代 DEAP 遗传算法，50次试验 ≈ 2分钟收敛
|
||||
|
||||
关键改进：
|
||||
1. 权重基因通过 individual_weights → MarketStateAnalyzer.get_strategy_weights() 真正生效
|
||||
2. 使用 StrategyRegistry 单例消，除重复的策略列表
|
||||
3. 使用 MultiTimeframeDataStore 支持正确的多周期市场状态分析
|
||||
4. evaluate_fitness 逐 bar 调用 get_market_state(bar_index) 获取每根bar的动态权重
|
||||
1. TPE sampler 建模适应度曲面，采样效率高 3-5 倍
|
||||
2. 权重基因通过 individual_weights → MarketStateAnalyzer.get_strategy_weights() 真正生效
|
||||
3. evaluate_fitness 逐 bar 调用 get_market_state(bar_index) 获取每根bar的动态权重
|
||||
"""
|
||||
|
||||
import random
|
||||
import numpy as np
|
||||
from deap import base, creator, tools, algorithms
|
||||
import multiprocessing
|
||||
import pandas as pd
|
||||
import sys
|
||||
import os
|
||||
import logging
|
||||
from datetime import datetime
|
||||
from tqdm import tqdm
|
||||
|
||||
import optuna
|
||||
|
||||
SEED = 42
|
||||
random.seed(SEED)
|
||||
@@ -31,7 +31,7 @@ from core.signal.combiner import SignalCombiner
|
||||
from config import (
|
||||
SYMBOL, TIMEFRAME, OPTIMIZER_COUNT, OPTIMIZER_START_DATE, OPTIMIZER_END_DATE,
|
||||
USE_DATE_RANGE, INITIAL_CAPITAL, SIGNAL_THRESHOLDS, DEFAULT_WEIGHTS,
|
||||
RISK_CONFIG, GENETIC_OPTIMIZER_CONFIG,
|
||||
RISK_CONFIG,
|
||||
MARKET_STATE_CONFIG, TREND_INDICATOR_WEIGHTS, TREND_THRESHOLDS, CONFIDENCE_THRESHOLDS,
|
||||
DATA_PROVIDER_MODE, REMOTE_SERVER_HOST, REMOTE_SERVER_PORT
|
||||
)
|
||||
@@ -45,8 +45,6 @@ import strategies # noqa: F401
|
||||
|
||||
# ── 全局缓存 ──
|
||||
_multi_tf: MultiTimeframeDataStore | None = None
|
||||
_cached_signals: pd.DataFrame | None = None
|
||||
_registry: StrategyRegistry | None = None
|
||||
|
||||
# MT5 结构化数组 dtype（用于远程 API JSON → numpy 转换）
|
||||
_MT5_RATES_DTYPE = np.dtype([
|
||||
@@ -61,15 +59,7 @@ _MT5_RATES_DTYPE = np.dtype([
|
||||
])
|
||||
|
||||
|
||||
def init_worker():
|
||||
"""多进程worker初始化：抑制日志噪音"""
|
||||
import logging
|
||||
logging.getLogger().setLevel(logging.WARNING)
|
||||
for name in ["StrategyLogger", "PositionManager", "RiskController", "DataProvider"]:
|
||||
logging.getLogger(name).setLevel(logging.WARNING)
|
||||
|
||||
|
||||
# ── 参数定义（与旧版一致，保持兼容）──
|
||||
# ── 参数定义 ──
|
||||
PARAMETER_DEFINITIONS = [
|
||||
# MACrossStrategy
|
||||
{'name': 'ma_cross_short_window', 'type': 'int', 'min': 3, 'max': 15, 'strategy': 'MACrossStrategy'},
|
||||
@@ -93,8 +83,11 @@ PARAMETER_DEFINITIONS = [
|
||||
{'name': 'momentum_breakout_momentum_period', 'type': 'int', 'min': 5, 'max': 30, 'strategy': 'MomentumBreakoutStrategy'},
|
||||
# KDJStrategy
|
||||
{'name': 'kdj_period', 'type': 'int', 'min': 5, 'max': 21, 'strategy': 'KDJStrategy'},
|
||||
# TurtleStrategy
|
||||
{'name': 'turtle_period', 'type': 'int', 'min': 10, 'max': 50, 'strategy': 'TurtleStrategy'},
|
||||
# SwingPointRetestStrategy — 前高前低回踩
|
||||
{'name': 'swing_point_left_bars', 'type': 'int', 'min': 2, 'max': 5, 'strategy': 'SwingPointRetestStrategy'},
|
||||
{'name': 'swing_point_right_bars', 'type': 'int', 'min': 2, 'max': 5, 'strategy': 'SwingPointRetestStrategy'},
|
||||
{'name': 'swing_point_tolerance_pct', 'type': 'float', 'min': 0.0003, 'max': 0.002, 'strategy': 'SwingPointRetestStrategy'},
|
||||
{'name': 'swing_point_num_swings', 'type': 'int', 'min': 1, 'max': 3, 'strategy': 'SwingPointRetestStrategy'},
|
||||
# WaveTheoryStrategy
|
||||
{'name': 'wave_ema_short', 'type': 'int', 'min': 3, 'max': 10, 'strategy': 'WaveTheoryStrategy'},
|
||||
{'name': 'wave_ema_medium', 'type': 'int', 'min': 8, 'max': 20, 'strategy': 'WaveTheoryStrategy'},
|
||||
@@ -110,14 +103,7 @@ PARAMETER_DEFINITIONS = [
|
||||
# Signal Thresholds
|
||||
{'name': 'buy_threshold', 'type': 'float', 'min': 0.5, 'max': 3.0, 'strategy': 'signal'},
|
||||
{'name': 'sell_threshold', 'type': 'float', 'min': -3.0, 'max': -0.5, 'strategy': 'signal'},
|
||||
# Risk Management（范围适配保证金%基准）
|
||||
{'name': 'stop_loss_pct', 'type': 'float', 'min': -1.00, 'max': -0.10, 'strategy': 'risk'},
|
||||
{'name': 'profit_retracement_pct', 'type': 'float', 'min': 0.10, 'max': 0.80, 'strategy': 'risk'},
|
||||
{'name': 'min_profit_for_trailing', 'type': 'float', 'min': 0.30, 'max': 2.00, 'strategy': 'risk'},
|
||||
{'name': 'take_profit_pct', 'type': 'float', 'min': 0.50, 'max': 3.00, 'strategy': 'risk'},
|
||||
{'name': 'max_holding_minutes', 'type': 'int', 'min': 30, 'max': 180, 'strategy': 'risk'},
|
||||
{'name': 'min_profit_for_time_exit', 'type': 'float', 'min': 0.02, 'max': 0.20, 'strategy': 'risk'},
|
||||
# ★ Strategy Weights — 这些基因现在真正影响适应度
|
||||
# ★ Strategy Weights
|
||||
{'name': 'weight_MACrossStrategy', 'type': 'float', 'min': 0.0, 'max': 2.0, 'strategy': 'weight'},
|
||||
{'name': 'weight_RSIStrategy', 'type': 'float', 'min': 0.0, 'max': 2.0, 'strategy': 'weight'},
|
||||
{'name': 'weight_BollingerStrategy', 'type': 'float', 'min': 0.0, 'max': 2.0, 'strategy': 'weight'},
|
||||
@@ -125,7 +111,7 @@ PARAMETER_DEFINITIONS = [
|
||||
{'name': 'weight_MomentumBreakoutStrategy', 'type': 'float', 'min': 0.0, 'max': 2.0, 'strategy': 'weight'},
|
||||
{'name': 'weight_MACDStrategy', 'type': 'float', 'min': 0.0, 'max': 2.0, 'strategy': 'weight'},
|
||||
{'name': 'weight_KDJStrategy', 'type': 'float', 'min': 0.0, 'max': 2.0, 'strategy': 'weight'},
|
||||
{'name': 'weight_TurtleStrategy', 'type': 'float', 'min': 0.0, 'max': 2.0, 'strategy': 'weight'},
|
||||
{'name': 'weight_SwingPointRetestStrategy', 'type': 'float', 'min': 0.0, 'max': 2.0, 'strategy': 'weight'},
|
||||
{'name': 'weight_DailyBreakoutStrategy', 'type': 'float', 'min': 0.0, 'max': 2.0, 'strategy': 'weight'},
|
||||
{'name': 'weight_WaveTheoryStrategy', 'type': 'float', 'min': 0.0, 'max': 2.0, 'strategy': 'weight'},
|
||||
# Market State Analysis parameters
|
||||
@@ -149,7 +135,6 @@ PARAMETER_DEFINITIONS = [
|
||||
{'name': 'confidence_medium', 'type': 'float', 'min': 0.3, 'max': 0.7, 'strategy': 'confidence'},
|
||||
]
|
||||
|
||||
# class_name → config_key 映射（从 StrategyRegistry 获取）
|
||||
_class_name_to_config_key = {
|
||||
"MACrossStrategy": "ma_cross",
|
||||
"RSIStrategy": "rsi",
|
||||
@@ -158,27 +143,27 @@ _class_name_to_config_key = {
|
||||
"MomentumBreakoutStrategy": "momentum_breakout",
|
||||
"MACDStrategy": "macd",
|
||||
"KDJStrategy": "kdj",
|
||||
"TurtleStrategy": "turtle",
|
||||
"SwingPointRetestStrategy": "swing_point",
|
||||
"DailyBreakoutStrategy": "daily_breakout",
|
||||
"WaveTheoryStrategy": "wave_theory",
|
||||
}
|
||||
|
||||
|
||||
def _parse_individual(individual):
|
||||
"""解析个体基因为命名参数字典，并裁剪到定义范围"""
|
||||
def _trial_to_params(trial: optuna.Trial) -> dict:
|
||||
"""从 Optuna trial 提取参数 → 兼容旧 _parse_individual 格式"""
|
||||
parsed = {}
|
||||
for idx, param_def in enumerate(PARAMETER_DEFINITIONS):
|
||||
val = individual[idx]
|
||||
for param_def in PARAMETER_DEFINITIONS:
|
||||
name = param_def['name']
|
||||
if param_def['type'] == 'int':
|
||||
val = int(round(val))
|
||||
# 裁剪到 [min, max] 防止变异越界
|
||||
val = max(param_def['min'], min(param_def['max'], val))
|
||||
parsed[param_def['name']] = val
|
||||
val = trial.suggest_int(name, param_def['min'], param_def['max'])
|
||||
else:
|
||||
val = trial.suggest_float(name, param_def['min'], param_def['max'])
|
||||
parsed[name] = val
|
||||
return parsed
|
||||
|
||||
|
||||
def _extract_strategy_params(parsed: dict) -> dict:
|
||||
"""从解析后的基因提取策略参数字典"""
|
||||
"""从解析后的参数提取策略参数字典"""
|
||||
strategy_params = {}
|
||||
for param_def in PARAMETER_DEFINITIONS:
|
||||
strategy_name = param_def.get('strategy')
|
||||
@@ -188,57 +173,37 @@ def _extract_strategy_params(parsed: dict) -> dict:
|
||||
if strategy_name not in strategy_params:
|
||||
strategy_params[strategy_name] = {}
|
||||
|
||||
# 参数名映射（保持与旧版兼容）
|
||||
param_name = param_def['name']
|
||||
if param_name == 'ma_cross_short_window':
|
||||
param_name = 'short_window'
|
||||
elif param_name == 'ma_cross_long_window':
|
||||
param_name = 'long_window'
|
||||
elif param_name == 'rsi_period':
|
||||
param_name = 'period'
|
||||
elif param_name == 'rsi_overbought':
|
||||
param_name = 'overbought'
|
||||
elif param_name == 'rsi_oversold':
|
||||
param_name = 'oversold'
|
||||
elif param_name == 'bollinger_period':
|
||||
param_name = 'period'
|
||||
elif param_name == 'bollinger_std_dev':
|
||||
param_name = 'std_dev'
|
||||
elif param_name == 'macd_fast_ema':
|
||||
param_name = 'fast_ema'
|
||||
elif param_name == 'macd_slow_ema':
|
||||
param_name = 'slow_ema'
|
||||
elif param_name == 'macd_signal_period':
|
||||
param_name = 'signal_period'
|
||||
elif param_name == 'mean_reversion_period':
|
||||
param_name = 'period'
|
||||
elif param_name == 'mean_reversion_std_dev':
|
||||
param_name = 'std_dev'
|
||||
elif param_name == 'momentum_breakout_period':
|
||||
param_name = 'period'
|
||||
elif param_name == 'momentum_breakout_momentum_period':
|
||||
param_name = 'momentum_period'
|
||||
elif param_name == 'kdj_period':
|
||||
param_name = 'period'
|
||||
elif param_name == 'turtle_period':
|
||||
param_name = 'period'
|
||||
if param_name == 'ma_cross_short_window': param_name = 'short_window'
|
||||
elif param_name == 'ma_cross_long_window': param_name = 'long_window'
|
||||
elif param_name == 'rsi_period': param_name = 'period'
|
||||
elif param_name == 'rsi_overbought': param_name = 'overbought'
|
||||
elif param_name == 'rsi_oversold': param_name = 'oversold'
|
||||
elif param_name == 'bollinger_period': param_name = 'period'
|
||||
elif param_name == 'bollinger_std_dev': param_name = 'std_dev'
|
||||
elif param_name == 'macd_fast_ema': param_name = 'fast_ema'
|
||||
elif param_name == 'macd_slow_ema': param_name = 'slow_ema'
|
||||
elif param_name == 'macd_signal_period': param_name = 'signal_period'
|
||||
elif param_name == 'mean_reversion_period': param_name = 'period'
|
||||
elif param_name == 'mean_reversion_std_dev': param_name = 'std_dev'
|
||||
elif param_name == 'momentum_breakout_period': param_name = 'period'
|
||||
elif param_name == 'momentum_breakout_momentum_period': param_name = 'momentum_period'
|
||||
elif param_name == 'kdj_period': param_name = 'period'
|
||||
elif param_name == 'swing_point_left_bars': param_name = 'left_bars'
|
||||
elif param_name == 'swing_point_right_bars': param_name = 'right_bars'
|
||||
elif param_name == 'swing_point_tolerance_pct': param_name = 'tolerance_pct'
|
||||
elif param_name == 'swing_point_num_swings': param_name = 'num_swings'
|
||||
elif param_name.startswith("wave_"):
|
||||
param_name = param_name[5:]
|
||||
if param_name == "period":
|
||||
param_name = "wave_period"
|
||||
elif param_name == 'daily_breakout_bars_count':
|
||||
param_name = 'bars_count'
|
||||
if param_name == "period": param_name = "wave_period"
|
||||
elif param_name == 'daily_breakout_bars_count': param_name = 'bars_count'
|
||||
|
||||
strategy_params[strategy_name][param_name] = parsed[param_def['name']]
|
||||
return strategy_params
|
||||
|
||||
|
||||
def _extract_weight_genes(parsed: dict) -> dict:
|
||||
"""★ 提取权重基因 → {config_key: weight}
|
||||
|
||||
这些权重会通过 individual_weights 参数传递给
|
||||
MarketStateAnalyzer.get_strategy_weights()，使权重基因真正生效。
|
||||
"""
|
||||
"""提取权重基因 → {config_key: weight}"""
|
||||
weight_genes = {}
|
||||
for param_def in PARAMETER_DEFINITIONS:
|
||||
if param_def.get('strategy') != 'weight':
|
||||
@@ -258,7 +223,6 @@ def _precompute_h1_states(multi_tf: MultiTimeframeDataStore, analyzer: MarketSta
|
||||
m1_index = multi_tf.main_df.index
|
||||
h1_index = h1_df.index
|
||||
|
||||
# 计算每个H1 bar的状态
|
||||
h1_states = []
|
||||
for i in range(len(h1_df)):
|
||||
lookback = analyzer.trend_period + 10
|
||||
@@ -267,11 +231,9 @@ def _precompute_h1_states(multi_tf: MultiTimeframeDataStore, analyzer: MarketSta
|
||||
state, conf = analyzer._calculate_state_from_df(df_slice)
|
||||
h1_states.append((state, conf))
|
||||
|
||||
# 映射到M1时间轴
|
||||
m1_states = []
|
||||
h1_pos = 0
|
||||
first_h1_time = h1_index[0]
|
||||
|
||||
for m1_time in m1_index:
|
||||
if m1_time < first_h1_time:
|
||||
m1_states.append(("none", 0.0))
|
||||
@@ -282,44 +244,34 @@ def _precompute_h1_states(multi_tf: MultiTimeframeDataStore, analyzer: MarketSta
|
||||
m1_states.append(h1_states[h1_pos])
|
||||
else:
|
||||
m1_states.append(("none", 0.0))
|
||||
|
||||
return m1_states
|
||||
|
||||
|
||||
def evaluate_fitness(individual, multi_tf: MultiTimeframeDataStore,
|
||||
_signals_df_unused=None) -> tuple:
|
||||
"""★ 重写的适应度函数 — 所有权重基因、策略参数和风控参数真正生效
|
||||
def evaluate_fitness(trial: optuna.Trial, multi_tf: MultiTimeframeDataStore) -> float:
|
||||
"""★ Optuna 适应度函数 — 所有权重/策略/趋势参数真正生效"""
|
||||
parsed = _trial_to_params(trial)
|
||||
|
||||
三个关键基因全部生效：
|
||||
1. 策略参数 → 重新实例化策略并 run_backtest → 影响信号序列
|
||||
2. 权重基因 → per-bar get_strategy_weights(individual_weights=...) → 影响信号组合
|
||||
3. 风控参数 → PositionManager 在回测中真正使用
|
||||
"""
|
||||
parsed = _parse_individual(individual)
|
||||
|
||||
# 1. 提取策略参数
|
||||
# 1. 策略参数
|
||||
strategy_params = _extract_strategy_params(parsed)
|
||||
|
||||
# 2. ★ 提取权重基因（核心修复）
|
||||
# 2. 权重基因
|
||||
weight_genes = _extract_weight_genes(parsed)
|
||||
|
||||
# 3. 提取风控参数
|
||||
# 3. 风控参数（从 config 读，不进优化器）
|
||||
risk_params = {
|
||||
'stop_loss_pct': parsed.get('stop_loss_pct', RISK_CONFIG.get('stop_loss_pct', -0.01)),
|
||||
'profit_retracement_pct': parsed.get('profit_retracement_pct', RISK_CONFIG.get('profit_retracement_pct', 0.10)),
|
||||
'min_profit_for_trailing': parsed.get('min_profit_for_trailing', RISK_CONFIG.get('min_profit_for_trailing', 0.01)),
|
||||
'take_profit_pct': parsed.get('take_profit_pct', RISK_CONFIG.get('take_profit_pct', 0.20)),
|
||||
'max_holding_minutes': parsed.get('max_holding_minutes', RISK_CONFIG.get('max_holding_minutes', 60)),
|
||||
'min_profit_for_time_exit': parsed.get('min_profit_for_time_exit', RISK_CONFIG.get('min_profit_for_time_exit', 0.005)),
|
||||
'max_daily_loss': parsed.get('max_daily_loss', RISK_CONFIG.get('max_daily_loss', -0.30)),
|
||||
'stop_loss_pct': RISK_CONFIG.get('stop_loss_pct', -0.50),
|
||||
'profit_retracement_pct': RISK_CONFIG.get('profit_retracement_pct', 0.35),
|
||||
'min_profit_for_trailing': RISK_CONFIG.get('min_profit_for_trailing', 0.70),
|
||||
'take_profit_pct': RISK_CONFIG.get('take_profit_pct', 1.00),
|
||||
'max_holding_minutes': RISK_CONFIG.get('max_holding_minutes', 0),
|
||||
'min_profit_for_time_exit': RISK_CONFIG.get('min_profit_for_time_exit', 0.005),
|
||||
'max_daily_loss': RISK_CONFIG.get('max_daily_loss', -0.30),
|
||||
}
|
||||
|
||||
# 4. 提取市场状态和趋势参数
|
||||
# 4. 市场状态和趋势参数
|
||||
market_params = {
|
||||
'trend_period': parsed.get('market_trend_period', MARKET_STATE_CONFIG.get('trend_period', 50)),
|
||||
'retracement_tolerance': parsed.get('market_retracement_tolerance', MARKET_STATE_CONFIG.get('retracement_tolerance', 0.30)),
|
||||
'volume_period': parsed.get('market_volume_period', MARKET_STATE_CONFIG.get('volume_period', 20)),
|
||||
'volume_ma_period': parsed.get('market_volume_ma_period', MARKET_STATE_CONFIG.get('volume_ma_period', 10)),
|
||||
'trend_period': parsed.get('market_trend_period', 50),
|
||||
'retracement_tolerance': parsed.get('market_retracement_tolerance', 0.30),
|
||||
'volume_period': parsed.get('market_volume_period', 20),
|
||||
'volume_ma_period': parsed.get('market_volume_ma_period', 10),
|
||||
'hourly_data_count': 100,
|
||||
}
|
||||
trend_weights = {
|
||||
@@ -340,7 +292,7 @@ def evaluate_fitness(individual, multi_tf: MultiTimeframeDataStore,
|
||||
'medium_confidence': parsed.get('confidence_medium', 0.4),
|
||||
}
|
||||
|
||||
# 5. 构建 MarketStateAnalyzer 并预计算H1状态
|
||||
# 5. MarketStateAnalyzer
|
||||
analyzer = MarketStateAnalyzer(
|
||||
timeframe=PERIOD_H1,
|
||||
market_state_params=market_params,
|
||||
@@ -350,8 +302,7 @@ def evaluate_fitness(individual, multi_tf: MultiTimeframeDataStore,
|
||||
)
|
||||
analyzer._precomputed_states = _precompute_h1_states(multi_tf, analyzer)
|
||||
|
||||
# 5.5. ★ 使用个体的策略参数重新实例化策略并重新计算信号
|
||||
# 将 class_name → {params} 映射为 config_key → {params}
|
||||
# 5.5 用个体策略参数重新计算信号
|
||||
strategy_params_by_key = {
|
||||
_class_name_to_config_key.get(cn, cn.lower()): params
|
||||
for cn, params in strategy_params.items()
|
||||
@@ -375,29 +326,19 @@ def evaluate_fitness(individual, multi_tf: MultiTimeframeDataStore,
|
||||
sell_th = parsed.get('sell_threshold', -1.5)
|
||||
total_bars = multi_tf.length
|
||||
|
||||
# 7. ★ 逐 bar 回测（权重基因真正影响信号组合）
|
||||
# 7. 逐 bar 回测
|
||||
for bar_index in range(total_bars):
|
||||
current_price = data_provider.get_current_price(SYMBOL)
|
||||
if not current_price:
|
||||
data_provider.tick()
|
||||
continue
|
||||
|
||||
# 获取当前 bar 的市场状态
|
||||
state, conf = analyzer.get_market_state(bar_index)
|
||||
weights = analyzer.get_strategy_weights(state, conf, individual_weights=weight_genes)
|
||||
|
||||
# ★ 权重基因在这里生效
|
||||
weights = analyzer.get_strategy_weights(
|
||||
state, conf, individual_weights=weight_genes
|
||||
)
|
||||
|
||||
# 组合当前 bar 的信号（使用个体参数重新计算的信号）
|
||||
bar_signals = {
|
||||
col: signals_df[col].iloc[bar_index]
|
||||
for col in signals_df.columns
|
||||
}
|
||||
bar_signals = {col: signals_df[col].iloc[bar_index] for col in signals_df.columns}
|
||||
final_signal = SignalCombiner.combine_at_bar(bar_signals, weights, buy_th, sell_th)
|
||||
|
||||
# 执行交易
|
||||
if final_signal == 1:
|
||||
pm.open_position("buy", current_price, 1.0, dry_run=True)
|
||||
elif final_signal == -1:
|
||||
@@ -407,20 +348,16 @@ def evaluate_fitness(individual, multi_tf: MultiTimeframeDataStore,
|
||||
data_provider.tick()
|
||||
|
||||
# 8. 适应度 = 总盈亏
|
||||
total_pnl = pm.total_equity - INITIAL_CAPITAL
|
||||
return (total_pnl,)
|
||||
return pm.total_equity - INITIAL_CAPITAL
|
||||
|
||||
|
||||
def _json_to_mt5_rates(rates_data):
|
||||
"""将远程API JSON rates 转换为 MT5 兼容的 numpy 结构化数组"""
|
||||
"""远程API JSON → MT5 numpy 结构化数组"""
|
||||
if not rates_data:
|
||||
return None
|
||||
records = []
|
||||
for r in rates_data:
|
||||
records.append((
|
||||
r['time'], r['open'], r['high'], r['low'], r['close'],
|
||||
r.get('tick_volume', 0), r.get('spread', 0), r.get('real_volume', 0),
|
||||
))
|
||||
records = [(r['time'], r['open'], r['high'], r['low'], r['close'],
|
||||
r.get('tick_volume', 0), r.get('spread', 0), r.get('real_volume', 0))
|
||||
for r in rates_data]
|
||||
return np.array(records, dtype=_MT5_RATES_DTYPE)
|
||||
|
||||
|
||||
@@ -430,152 +367,96 @@ def load_historical_data():
|
||||
provider = RemoteDataProvider(host=REMOTE_SERVER_HOST, port=REMOTE_SERVER_PORT)
|
||||
if not provider.initialize():
|
||||
raise RuntimeError("远程MT5 API初始化失败，请检查 Windows MT5 是否运行")
|
||||
|
||||
rates_json = provider.get_historical_data(SYMBOL, TIMEFRAME, OPTIMIZER_COUNT)
|
||||
provider.shutdown()
|
||||
|
||||
if not rates_json:
|
||||
raise RuntimeError("远程获取历史数据失败")
|
||||
|
||||
rates = _json_to_mt5_rates(rates_json)
|
||||
else:
|
||||
initialize()
|
||||
rates = (
|
||||
get_rates(SYMBOL, TIMEFRAME, OPTIMIZER_COUNT, OPTIMIZER_START_DATE, OPTIMIZER_END_DATE)
|
||||
if USE_DATE_RANGE else
|
||||
get_rates(SYMBOL, TIMEFRAME, OPTIMIZER_COUNT)
|
||||
)
|
||||
rates = (get_rates(SYMBOL, TIMEFRAME, OPTIMIZER_COUNT, OPTIMIZER_START_DATE, OPTIMIZER_END_DATE)
|
||||
if USE_DATE_RANGE else get_rates(SYMBOL, TIMEFRAME, OPTIMIZER_COUNT))
|
||||
shutdown()
|
||||
|
||||
if rates is None or len(rates) == 0:
|
||||
raise RuntimeError("获取历史数据失败")
|
||||
|
||||
if len(rates) > OPTIMIZER_COUNT:
|
||||
rates = rates[-OPTIMIZER_COUNT:]
|
||||
|
||||
store = MultiTimeframeDataStore()
|
||||
store.load_m1_data(rates)
|
||||
|
||||
# 预生成H1数据
|
||||
store.ensure_timeframe(PERIOD_H1)
|
||||
|
||||
return store
|
||||
|
||||
|
||||
def precompute_all_signals(multi_tf: MultiTimeframeDataStore,
|
||||
strategy_params: dict = None) -> pd.DataFrame:
|
||||
"""使用默认参数预计算所有策略的回测信号"""
|
||||
registry = StrategyRegistry()
|
||||
strategies = registry.instantiate_all(SYMBOL, TIMEFRAME, strategy_params)
|
||||
signals_df = pd.DataFrame(index=multi_tf.main_df.index)
|
||||
def save_optimization_results(best_params: dict, best_fitness: float, study: optuna.Study):
|
||||
"""保存优化结果（兼容旧格式）"""
|
||||
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
|
||||
import json
|
||||
|
||||
for config_key, strategy in strategies.items():
|
||||
try:
|
||||
sig = strategy.run_backtest(multi_tf.main_df)
|
||||
signals_df[config_key] = sig if sig is not None else pd.Series(0, index=signals_df.index)
|
||||
except Exception:
|
||||
signals_df[config_key] = pd.Series(0, index=signals_df.index)
|
||||
results = {
|
||||
'best_fitness': best_fitness,
|
||||
'best_params': best_params,
|
||||
'n_trials': len(study.trials),
|
||||
'optimizer': 'optuna_tpe',
|
||||
}
|
||||
json_path = f"optimization_results_{timestamp}.json"
|
||||
with open(json_path, 'w', encoding='utf-8') as f:
|
||||
json.dump(results, f, ensure_ascii=False, indent=2, default=str)
|
||||
logger.info(f"结果已保存: {json_path}")
|
||||
|
||||
return signals_df
|
||||
txt_path = f"optimization_report_{timestamp}.txt"
|
||||
with open(txt_path, 'w', encoding='utf-8') as f:
|
||||
f.write(f"优化完成时间: {datetime.now()}\n")
|
||||
f.write(f"最佳适应度: {best_fitness:.2f}\n")
|
||||
f.write(f"试验次数: {len(study.trials)}\n\n")
|
||||
f.write("最佳参数:\n")
|
||||
for key, value in best_params.items():
|
||||
f.write(f" {key}: {value}\n")
|
||||
logger.info(f"报告已保存: {txt_path}")
|
||||
|
||||
|
||||
def run_optimizer():
|
||||
"""运行遗传算法优化（所有基因真正生效）"""
|
||||
"""★ Optuna TPE 贝叶斯优化（替代 DEAP 遗传算法）"""
|
||||
try:
|
||||
# 1. 加载数据（一次性）
|
||||
# 1. 加载数据
|
||||
logger.info("加载历史数据...")
|
||||
multi_tf = load_historical_data()
|
||||
logger.info(f"数据加载完成: {multi_tf.length} 条M1数据")
|
||||
|
||||
# 2. DEAP 设置（策略信号在 evaluate_fitness 内部逐个体重新计算）
|
||||
creator.create("FitnessMax", base.Fitness, weights=(1.0,))
|
||||
creator.create("Individual", list, fitness=creator.FitnessMax)
|
||||
# 2. Optuna study
|
||||
sampler = optuna.samplers.TPESampler(seed=SEED, n_startup_trials=10)
|
||||
study = optuna.create_study(direction="maximize", sampler=sampler)
|
||||
|
||||
toolbox = base.Toolbox()
|
||||
|
||||
for i, param_def in enumerate(PARAMETER_DEFINITIONS):
|
||||
if param_def['type'] == 'int':
|
||||
toolbox.register(f"attr_param_{i}", random.randint, param_def['min'], param_def['max'])
|
||||
else:
|
||||
toolbox.register(f"attr_param_{i}", random.uniform, param_def['min'], param_def['max'])
|
||||
|
||||
toolbox.register("individual", tools.initIterate, creator.Individual,
|
||||
lambda: [toolbox.__getattribute__(f"attr_param_{j}")()
|
||||
for j in range(len(PARAMETER_DEFINITIONS))])
|
||||
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
|
||||
|
||||
toolbox.register("evaluate", evaluate_fitness,
|
||||
multi_tf=multi_tf)
|
||||
toolbox.register("mate", tools.cxTwoPoint)
|
||||
toolbox.register("mutate", tools.mutGaussian,
|
||||
mu=GENETIC_OPTIMIZER_CONFIG["mutation_mu"],
|
||||
sigma=GENETIC_OPTIMIZER_CONFIG["mutation_sigma"],
|
||||
indpb=GENETIC_OPTIMIZER_CONFIG["mutation_indpb"])
|
||||
toolbox.register("select", tools.selTournament,
|
||||
tournsize=GENETIC_OPTIMIZER_CONFIG["tournament_size"])
|
||||
|
||||
# 多进程
|
||||
if GENETIC_OPTIMIZER_CONFIG["enable_multiprocessing"]:
|
||||
pool = multiprocessing.Pool(
|
||||
processes=GENETIC_OPTIMIZER_CONFIG["processes"],
|
||||
initializer=init_worker
|
||||
# 3. 优化（4进程并行，静默回测日志避免 I/O 争用）
|
||||
optuna.logging.set_verbosity(optuna.logging.WARNING)
|
||||
import logging as _logging
|
||||
_old_level = logger.level
|
||||
_old_handler_levels = [h.level for h in logger.handlers]
|
||||
logger.setLevel(_logging.WARNING)
|
||||
for h in logger.handlers:
|
||||
h.setLevel(_logging.WARNING)
|
||||
logger.info(f"开始贝叶斯优化: 50 次试验, TPE sampler (回测日志已静默)")
|
||||
try:
|
||||
study.optimize(
|
||||
lambda trial: evaluate_fitness(trial, multi_tf),
|
||||
n_trials=50,
|
||||
n_jobs=4,
|
||||
show_progress_bar=False,
|
||||
)
|
||||
toolbox.register("map", pool.map)
|
||||
else:
|
||||
toolbox.register("map", map)
|
||||
finally:
|
||||
logger.setLevel(_old_level)
|
||||
for h, lvl in zip(logger.handlers, _old_handler_levels):
|
||||
h.setLevel(lvl)
|
||||
|
||||
# 4. 统计
|
||||
stats = tools.Statistics(lambda ind: ind.fitness.values)
|
||||
stats.register("avg", np.mean)
|
||||
stats.register("std", np.std)
|
||||
stats.register("min", np.min)
|
||||
stats.register("max", np.max)
|
||||
# 4. 结果
|
||||
best_fitness = study.best_value
|
||||
best_params = study.best_params
|
||||
logger.info(f"优化完成: 最佳适应度={best_fitness:.2f}, 试验次数={len(study.trials)}")
|
||||
|
||||
# 5. 运行
|
||||
population = toolbox.population(n=GENETIC_OPTIMIZER_CONFIG["population_size"])
|
||||
ngen = GENETIC_OPTIMIZER_CONFIG["generations"]
|
||||
cxpb = GENETIC_OPTIMIZER_CONFIG["crossover_probability"]
|
||||
mutpb = GENETIC_OPTIMIZER_CONFIG["mutation_probability"]
|
||||
save_optimization_results(best_params, best_fitness, study)
|
||||
|
||||
logger.info(f"开始进化: 种群 {len(population)}, {ngen} 代")
|
||||
generation_info = []
|
||||
|
||||
for gen in range(ngen):
|
||||
offspring = toolbox.select(population, len(population))
|
||||
offspring = algorithms.varAnd(offspring, toolbox, cxpb, mutpb)
|
||||
fits = toolbox.map(toolbox.evaluate, offspring)
|
||||
|
||||
for fit, ind in zip(fits, offspring):
|
||||
ind.fitness.values = fit
|
||||
|
||||
population[:] = offspring
|
||||
|
||||
best = tools.selBest(population, k=1)[0]
|
||||
best_fit = best.fitness.values[0]
|
||||
avg_fit = np.mean([ind.fitness.values[0] for ind in population])
|
||||
|
||||
if GENETIC_OPTIMIZER_CONFIG.get("save_generation_info", True):
|
||||
generation_info.append({
|
||||
'generation': gen + 1,
|
||||
'best_fitness': best_fit,
|
||||
'avg_fitness': avg_fit,
|
||||
})
|
||||
|
||||
if GENETIC_OPTIMIZER_CONFIG.get("verbose", True):
|
||||
logger.info(f"代数 {gen + 1}/{ngen}: 最佳={best_fit:.2f}, 平均={avg_fit:.2f}")
|
||||
|
||||
# 6. 结果
|
||||
if GENETIC_OPTIMIZER_CONFIG["enable_multiprocessing"]:
|
||||
pool.close()
|
||||
pool.join()
|
||||
|
||||
best_individual = tools.selBest(population, k=1)[0]
|
||||
best_fitness = best_individual.fitness.values[0]
|
||||
|
||||
logger.info(f"优化完成: 最佳适应度={best_fitness:.2f}")
|
||||
save_optimization_results(best_individual, best_fitness, generation_info)
|
||||
|
||||
return _parse_individual(best_individual), best_fitness
|
||||
return best_params, best_fitness
|
||||
|
||||
except Exception as e:
|
||||
import traceback
|
||||
@@ -583,36 +464,5 @@ def run_optimizer():
|
||||
raise
|
||||
|
||||
|
||||
def save_optimization_results(best_individual, best_fitness, generation_info):
|
||||
"""保存优化结果"""
|
||||
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
|
||||
parsed = _parse_individual(best_individual)
|
||||
|
||||
# JSON
|
||||
import json
|
||||
results = {
|
||||
'best_fitness': best_fitness,
|
||||
'best_params': parsed,
|
||||
'generation_info': generation_info,
|
||||
}
|
||||
json_path = f"optimization_results_{timestamp}.json"
|
||||
with open(json_path, 'w', encoding='utf-8') as f:
|
||||
json.dump(results, f, ensure_ascii=False, indent=2, default=str)
|
||||
logger.info(f"结果已保存: {json_path}")
|
||||
|
||||
# TXT报告
|
||||
txt_path = f"optimization_report_{timestamp}.txt"
|
||||
with open(txt_path, 'w', encoding='utf-8') as f:
|
||||
f.write(f"优化完成时间: {datetime.now()}\n")
|
||||
f.write(f"最佳适应度: {best_fitness:.2f}\n\n")
|
||||
f.write("最佳参数:\n")
|
||||
for key, value in parsed.items():
|
||||
f.write(f" {key}: {value}\n")
|
||||
f.write("\n代数统计:\n")
|
||||
for gi in generation_info:
|
||||
f.write(f" 代数 {gi['generation']}: 最佳={gi['best_fitness']:.2f}, 平均={gi['avg_fitness']:.2f}\n")
|
||||
logger.info(f"报告已保存: {txt_path}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
run_optimizer()
|
||||
|
||||
@@ -3,12 +3,12 @@ import signal
|
||||
import sys
|
||||
from datetime import datetime
|
||||
from logger import logger
|
||||
from config import SYMBOL, TIMEFRAME, REALTIME_CONFIG, SIGNAL_THRESHOLDS, RISK_CONFIG, RISK_CONFIG_CONST
|
||||
import config
|
||||
from core.risk import RiskController
|
||||
from execution.weights import DynamicWeightManager
|
||||
|
||||
class RealtimeTrader:
|
||||
"""实时交易器 (已重构为依赖注入)"""
|
||||
"""实时交易器 (已重构为依赖注入) — ★ 每周期热加载配置"""
|
||||
|
||||
def __init__(self, data_provider, update_interval=60):
|
||||
self.data_provider = data_provider
|
||||
@@ -29,27 +29,28 @@ class RealtimeTrader:
|
||||
signal.signal(signal.SIGINT, self._signal_handler)
|
||||
signal.signal(signal.SIGTERM, self._signal_handler)
|
||||
|
||||
# ★ 杠杆自适应：打印有效值
|
||||
# ★ 打印风控参数（保证金%基准）
|
||||
try:
|
||||
acct = self.data_provider.get_account_info()
|
||||
lev = acct.leverage if hasattr(acct, 'leverage') else acct.get('leverage', 2000)
|
||||
except Exception:
|
||||
lev = 2000
|
||||
ratio = lev / 100.0
|
||||
rl = config.RISK_CONFIG.get('risk_leverage', 100)
|
||||
|
||||
# ★ 启动参数一览
|
||||
logger.info("=" * 50)
|
||||
logger.info(f"品种: {SYMBOL} | 周期: M{TIMEFRAME} | 间隔: {self.update_interval}s | 杠杆: {lev}x")
|
||||
logger.info(f"风控: 止损={RISK_CONFIG['stop_loss_pct']*ratio:.1%} | "
|
||||
f"止盈={RISK_CONFIG['take_profit_pct']*ratio:.1%} | "
|
||||
f"拖尾激活={RISK_CONFIG['min_profit_for_trailing']*ratio:.1%} | "
|
||||
f"拖尾回撤={RISK_CONFIG['profit_retracement_pct']*ratio:.1%}")
|
||||
logger.info(f"信号: 买入阈值={SIGNAL_THRESHOLDS.get('buy_threshold',1.5)} | "
|
||||
f"卖出阈值={SIGNAL_THRESHOLDS.get('sell_threshold',-1.5)}")
|
||||
logger.info(f"仓位: 最多多={REALTIME_CONFIG['max_long_positions']} 最多空={REALTIME_CONFIG['max_short_positions']} | "
|
||||
f"超时平仓={'开' if RISK_CONFIG_CONST.get('enable_time_based_exit',True) else '关'}")
|
||||
logger.info(f"对冲: 信号对冲={'开' if REALTIME_CONFIG.get('hedge_enabled',False) else '关'} | "
|
||||
f"锁仓={'开' if REALTIME_CONFIG.get('lock_enabled',False) else '关'}")
|
||||
logger.info(f"品种: {config.SYMBOL} | 周期: M{config.TIMEFRAME} | 间隔: {self.update_interval}s | 杠杆: {lev}x")
|
||||
logger.info(f"风控: 止损={config.RISK_CONFIG['stop_loss_pct']:.0%} | "
|
||||
f"止盈={config.RISK_CONFIG['take_profit_pct']:.0%} | "
|
||||
f"拖尾激活={config.RISK_CONFIG['min_profit_for_trailing']:.0%} | "
|
||||
f"拖尾回撤={config.RISK_CONFIG['profit_retracement_pct']:.0%}"
|
||||
f" (基准={rl}x)")
|
||||
logger.info(f"信号: 买入阈值={config.SIGNAL_THRESHOLDS.get('buy_threshold',1.5)} | "
|
||||
f"卖出阈值={config.SIGNAL_THRESHOLDS.get('sell_threshold',-1.5)}")
|
||||
logger.info(f"仓位: 最多多={config.REALTIME_CONFIG['max_long_positions']} 最多空={config.REALTIME_CONFIG['max_short_positions']} | "
|
||||
f"超时平仓={'开' if config.RISK_CONFIG_CONST.get('enable_time_based_exit',True) else '关'}")
|
||||
logger.info(f"对冲: 信号对冲={'开' if config.REALTIME_CONFIG.get('hedge_enabled',False) else '关'} | "
|
||||
f"锁仓={'开' if config.REALTIME_CONFIG.get('lock_enabled',False) else '关'}")
|
||||
logger.info("=" * 50)
|
||||
return True
|
||||
|
||||
@@ -60,9 +61,13 @@ class RealtimeTrader:
|
||||
def _run_cycle(self):
|
||||
try:
|
||||
self._cycle_count += 1
|
||||
|
||||
# ★ 热加载配置：cron 优化器改完 config.py 后自动生效
|
||||
config.reload()
|
||||
|
||||
self.risk_controller.sync_state()
|
||||
|
||||
current_price = self.data_provider.get_current_price(SYMBOL)
|
||||
current_price = self.data_provider.get_current_price(config.SYMBOL)
|
||||
if not current_price:
|
||||
return
|
||||
|
||||
@@ -75,8 +80,8 @@ class RealtimeTrader:
|
||||
weights.append(weight)
|
||||
|
||||
weighted_signal_sum = sum(s * w for s, w in zip(signals, weights))
|
||||
buy_threshold = SIGNAL_THRESHOLDS.get('buy_threshold', 1.5)
|
||||
sell_threshold = SIGNAL_THRESHOLDS.get('sell_threshold', -1.5)
|
||||
buy_threshold = config.SIGNAL_THRESHOLDS.get('buy_threshold', 1.5)
|
||||
sell_threshold = config.SIGNAL_THRESHOLDS.get('sell_threshold', -1.5)
|
||||
|
||||
direction = None
|
||||
if weighted_signal_sum > buy_threshold:
|
||||
@@ -84,6 +89,33 @@ class RealtimeTrader:
|
||||
elif weighted_signal_sum < sell_threshold:
|
||||
direction = "sell"
|
||||
|
||||
# ★ 同向门槛递增：已有N单同向时，第N+1单需要更强信号
|
||||
if direction:
|
||||
# ★ 适应度门槛：回测亏钱就不开新单
|
||||
last_fitness = getattr(config, 'LAST_OPTIMIZATION_FITNESS', 0)
|
||||
min_fitness = config.RISK_CONFIG.get('min_backtest_fitness', 250)
|
||||
if last_fitness < min_fitness:
|
||||
if self._cycle_count % 30 == 0:
|
||||
logger.warning(f"⚠️ 回测适应度{last_fitness:.0f}<{min_fitness}，暂停开仓（监控持仓中）")
|
||||
direction = None
|
||||
|
||||
if direction:
|
||||
alpha = config.RISK_CONFIG.get('entry_escalation_alpha', 1.2)
|
||||
pm = self.risk_controller.position_manager
|
||||
n_long = sum(1 for p in pm.positions if p['position_type'] == 'long')
|
||||
n_short = sum(1 for p in pm.positions if p['position_type'] == 'short')
|
||||
|
||||
if direction == 'buy':
|
||||
adjusted = buy_threshold * (alpha ** n_long)
|
||||
if weighted_signal_sum <= adjusted:
|
||||
logger.debug(f"BUY信号{weighted_signal_sum:.2f}<调整阈值{adjusted:.2f}(已有{n_long}多单), 忽略")
|
||||
direction = None
|
||||
elif direction == 'sell':
|
||||
adjusted = sell_threshold * (alpha ** n_short)
|
||||
if weighted_signal_sum >= adjusted:
|
||||
logger.debug(f"SELL信号{weighted_signal_sum:.2f}>调整阈值{adjusted:.2f}(已有{n_short}空单), 忽略")
|
||||
direction = None
|
||||
|
||||
# 只在信号触发时打印决策依据
|
||||
if direction:
|
||||
logger.info(f"⚡ 信号触发 | 加权={weighted_signal_sum:.2f} | "
|
||||
@@ -124,6 +156,7 @@ class RealtimeTrader:
|
||||
self.running = False
|
||||
try:
|
||||
if self.risk_controller:
|
||||
self.risk_controller.position_manager.cleanup_peak_data()
|
||||
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
|
||||
self.risk_controller.save_trade_history(f"realtime_trades_{timestamp}")
|
||||
summary = self.risk_controller.position_manager.get_trade_summary()
|
||||
|
||||
@@ -10,7 +10,7 @@ import strategies # noqa: F401 — 触发策略注册
|
||||
from logger import logger
|
||||
from core.risk.market_state import MarketStateAnalyzer
|
||||
from core.signal.registry import StrategyRegistry
|
||||
from config import SYMBOL, TIMEFRAME
|
||||
import config
|
||||
|
||||
|
||||
class DynamicWeightManager:
|
||||
@@ -27,7 +27,7 @@ class DynamicWeightManager:
|
||||
|
||||
def _ensure_strategies(self):
|
||||
if not self._strategies_initialized:
|
||||
self.registry.instantiate_all(SYMBOL, TIMEFRAME,
|
||||
self.registry.instantiate_all(config.SYMBOL, config.TIMEFRAME,
|
||||
data_provider=self.data_provider)
|
||||
self._strategies_initialized = True
|
||||
|
||||
@@ -46,7 +46,8 @@ class DynamicWeightManager:
|
||||
return result
|
||||
|
||||
def get_current_weights(self) -> dict:
|
||||
"""获取当前实时权重"""
|
||||
"""获取当前实时权重 — ★ 热加载配置"""
|
||||
config.reload()
|
||||
market_state, confidence = self.analyzer.get_market_state()
|
||||
return self.analyzer.get_strategy_weights(market_state, confidence)
|
||||
|
||||
|
||||
@@ -4,6 +4,7 @@
|
||||
|
||||
import sys
|
||||
import os
|
||||
import fcntl
|
||||
from datetime import datetime
|
||||
|
||||
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
|
||||
@@ -16,8 +17,18 @@ from config import (
|
||||
)
|
||||
from logger import setup_logger
|
||||
|
||||
LOCK_FILE = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), '.ea.lock')
|
||||
|
||||
|
||||
def main():
|
||||
# ★ 进程锁：防止多实例同时运行
|
||||
lock_fd = os.open(LOCK_FILE, os.O_CREAT | os.O_RDWR, 0o644)
|
||||
try:
|
||||
fcntl.flock(lock_fd, fcntl.LOCK_EX | fcntl.LOCK_NB)
|
||||
except BlockingIOError:
|
||||
print("❌ 已有 EA 实例在运行，退出")
|
||||
os.close(lock_fd)
|
||||
return
|
||||
log_level = REALTIME_CONFIG.get('logging_level', 'INFO')
|
||||
setup_logger(log_level)
|
||||
|
||||
|
||||
@@ -1,5 +1,6 @@
|
||||
#!/bin/bash
|
||||
# cron 环境修复：设置正确的 PYTHONPATH 后运行优化器
|
||||
export PYTHONPATH="/home/songkl/.hermes/profiles/bot1/home/.local/lib/python3.13/site-packages:$PYTHONPATH"
|
||||
# 每日自动优化 + 重启 EA
|
||||
# 设置 PYTHONPATH 确保 cron 环境下能找到 user-site packages（如 deap, moocore 等）
|
||||
export PYTHONPATH="$HOME/.local/lib/python$(python3 -c 'import sys; print(f"{sys.version_info.major}.{sys.version_info.minor}")')/site-packages:$PYTHONPATH"
|
||||
cd /home/songkl/mt5_python_ea_suite
|
||||
exec /usr/bin/python3 scripts/daily_optimize.py "$@"
|
||||
exec python3 scripts/daily_optimize.py "$@"
|
||||
|
||||
+39
-23
@@ -1,5 +1,5 @@
|
||||
#!/usr/bin/env python3
|
||||
"""每日自动优化 — 遗传算法跑参数 → 写入config → 重启实盘EA
|
||||
"""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
|
||||
"""
|
||||
@@ -22,12 +22,12 @@ RESTART_SIGNAL = PROJECT_DIR / ".restart_signal"
|
||||
|
||||
|
||||
def run_optimizer():
|
||||
"""运行遗传算法优化，返回 best_params dict"""
|
||||
"""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
|
||||
return best_params, fitness
|
||||
|
||||
|
||||
def backup_config():
|
||||
@@ -39,18 +39,13 @@ def backup_config():
|
||||
logger.info(f"📦 已备份配置: {dst}")
|
||||
|
||||
|
||||
def update_config(best_params: dict):
|
||||
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 ═══
|
||||
# ═══ 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 = {
|
||||
"stop_loss_pct": "stop_loss_pct",
|
||||
"profit_retracement_pct": "profit_retracement_pct",
|
||||
"min_profit_for_trailing": "min_profit_for_trailing",
|
||||
"take_profit_pct": "take_profit_pct",
|
||||
"max_holding_minutes": "max_holding_minutes",
|
||||
"min_profit_for_time_exit": "min_profit_for_time_exit",
|
||||
}
|
||||
for opt_key, cfg_key in risk_map.items():
|
||||
if opt_key in best_params:
|
||||
@@ -115,8 +110,11 @@ def update_config(best_params: dict):
|
||||
"momentum_breakout_momentum_period": ("momentum_breakout", "momentum_period"),
|
||||
# KDJStrategy
|
||||
"kdj_period": ("kdj", "period"),
|
||||
# TurtleStrategy
|
||||
"turtle_period": ("turtle", "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
|
||||
@@ -151,7 +149,7 @@ def update_config(best_params: dict):
|
||||
"weight_MomentumBreakoutStrategy": "momentum_breakout",
|
||||
"weight_MACDStrategy": "macd",
|
||||
"weight_KDJStrategy": "kdj",
|
||||
"weight_TurtleStrategy": "turtle",
|
||||
"weight_SwingPointRetestStrategy": "swing_point",
|
||||
"weight_DailyBreakoutStrategy": "daily_breakout",
|
||||
"weight_WaveTheoryStrategy": "wave_theory",
|
||||
}
|
||||
@@ -213,6 +211,13 @@ def update_config(best_params: dict):
|
||||
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")
|
||||
|
||||
@@ -222,11 +227,24 @@ def restart_ea():
|
||||
import subprocess
|
||||
logger.info("🔄 重启 EA...")
|
||||
|
||||
# 杀旧进程
|
||||
# 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)
|
||||
time.sleep(1)
|
||||
# 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_*"):
|
||||
@@ -259,20 +277,18 @@ def main():
|
||||
|
||||
# 2. 运行优化
|
||||
try:
|
||||
best_params = run_optimizer()
|
||||
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
|
||||
update_config(best_params)
|
||||
# 3. 写入 config.py（含适应度）
|
||||
update_config(best_params, fitness)
|
||||
|
||||
# 4. 触发重启
|
||||
restart_ea()
|
||||
|
||||
logger.info("✅ 每日优化流程完成")
|
||||
# 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
|
||||
|
||||
|
||||
|
||||
@@ -0,0 +1,61 @@
|
||||
#!/usr/bin/env python3
|
||||
"""开市后全平持仓 + 清峰值 + 重启 EA"""
|
||||
import requests, json, time, sys, os, subprocess
|
||||
|
||||
API = "http://192.168.1.5:5555/api"
|
||||
SYMBOL = "XAUUSDz"
|
||||
PROJECT_DIR = "/home/songkl/mt5_python_ea_suite"
|
||||
|
||||
def log(msg):
|
||||
print(f"[{time.strftime('%H:%M:%S')}] {msg}")
|
||||
|
||||
# 1. 等市场开市（重试最多30次，间隔10秒）
|
||||
log("等待市场开市...")
|
||||
for i in range(30):
|
||||
try:
|
||||
r = requests.get(f"{API}/account", timeout=5)
|
||||
if r.status_code == 200:
|
||||
acct = r.json()
|
||||
if acct.get("trade_allowed"):
|
||||
log("✅ 市场已开市")
|
||||
break
|
||||
except:
|
||||
pass
|
||||
time.sleep(10)
|
||||
else:
|
||||
log("❌ 等待超时，市场未开市")
|
||||
sys.exit(1)
|
||||
|
||||
# 2. 获取所有持仓并平仓
|
||||
log("获取持仓...")
|
||||
r = requests.get(f"{API}/positions/{SYMBOL}")
|
||||
positions = r.json().get("positions", [])
|
||||
log(f"共 {len(positions)} 单")
|
||||
|
||||
total_pnl = 0
|
||||
for p in positions:
|
||||
resp = requests.post(f"{API}/close",
|
||||
json={"ticket": p["ticket"], "symbol": SYMBOL, "volume": p["volume"]})
|
||||
ok = resp.json().get("success", False)
|
||||
total_pnl += p["profit"]
|
||||
log(f"{'✅' if ok else '❌'} {p['ticket']} ${p['profit']:+.2f}")
|
||||
log(f"总盈亏: ${total_pnl:+.2f}")
|
||||
|
||||
# 3. 清峰值数据
|
||||
peak_file = os.path.join(PROJECT_DIR, "position_peaks.json")
|
||||
if os.path.exists(peak_file):
|
||||
os.remove(peak_file)
|
||||
log("✅ 峰值数据已清除")
|
||||
|
||||
# 4. 杀掉旧 EA，启动新 EA
|
||||
subprocess.run(["pkill", "-9", "-f", "python.*run/realtime.py"], capture_output=True)
|
||||
time.sleep(2)
|
||||
|
||||
log_file = os.path.join(PROJECT_DIR, "logs", "strategy.log")
|
||||
with open(log_file, "a") as f:
|
||||
proc = subprocess.Popen(
|
||||
[sys.executable, "run/realtime.py"],
|
||||
cwd=PROJECT_DIR,
|
||||
stdout=f, stderr=subprocess.STDOUT,
|
||||
)
|
||||
log(f"✅ EA 已重启 PID={proc.pid}")
|
||||
@@ -5,7 +5,7 @@ from strategies.rsi import RSIStrategy
|
||||
from strategies.bollinger import BollingerStrategy
|
||||
from strategies.macd import MACDStrategy
|
||||
from strategies.kdj import KDJStrategy
|
||||
from strategies.turtle import TurtleStrategy
|
||||
from strategies.swing_point import SwingPointRetestStrategy
|
||||
from strategies.mean_reversion import MeanReversionStrategy
|
||||
from strategies.momentum_breakout import MomentumBreakoutStrategy
|
||||
from strategies.daily_breakout import DailyBreakoutStrategy
|
||||
@@ -18,7 +18,7 @@ _registry.register('rsi', RSIStrategy)
|
||||
_registry.register('bollinger', BollingerStrategy)
|
||||
_registry.register('macd', MACDStrategy)
|
||||
_registry.register('kdj', KDJStrategy)
|
||||
_registry.register('turtle', TurtleStrategy)
|
||||
_registry.register('swing_point', SwingPointRetestStrategy)
|
||||
_registry.register('mean_reversion', MeanReversionStrategy)
|
||||
_registry.register('momentum_breakout', MomentumBreakoutStrategy)
|
||||
_registry.register('daily_breakout', DailyBreakoutStrategy)
|
||||
|
||||
@@ -0,0 +1,177 @@
|
||||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
SwingPointRetestStrategy — 摆动点回踩策略
|
||||
|
||||
基于前高前低的支撑阻力位：
|
||||
1. 找局部摆动高/低点（比左右各N根K线更高/更低）
|
||||
2. 价格接近前高 → SELL（阻力反弹），接近前低 → BUY（支撑反弹）
|
||||
3. ★ 回踩确认：价格突破后回踩原支撑/阻力位 → 更高胜率的反转信号
|
||||
|
||||
黄金M1参数：左右各3根K线，容差 0.05%~0.1%（约2~5点）
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
from .base_strategy import BaseStrategy
|
||||
from config import STRATEGY_CONFIG
|
||||
|
||||
|
||||
class SwingPointRetestStrategy(BaseStrategy):
|
||||
"""摆动点回踩策略 — 前高前低 + 回踩确认"""
|
||||
|
||||
def __init__(self, data_provider, symbol, timeframe,
|
||||
left_bars=None, right_bars=None,
|
||||
tolerance_pct=None, num_swings=None):
|
||||
super().__init__(data_provider, symbol, timeframe)
|
||||
config = STRATEGY_CONFIG.get('swing_point', {})
|
||||
self.left_bars = left_bars if left_bars is not None else config.get('left_bars', 3)
|
||||
self.right_bars = right_bars if right_bars is not None else config.get('right_bars', 3)
|
||||
self.tolerance_pct = tolerance_pct if tolerance_pct is not None else config.get('tolerance_pct', 0.0008)
|
||||
self.num_swings = num_swings if num_swings is not None else config.get('num_swings', 2)
|
||||
self.lookback = max(self.left_bars + self.right_bars + 10, 50)
|
||||
|
||||
def _find_swing_points(self, df):
|
||||
"""找摆动高低点"""
|
||||
highs = df['high'].values
|
||||
lows = df['low'].values
|
||||
n = len(df)
|
||||
L, R = self.left_bars, self.right_bars
|
||||
|
||||
swing_highs = [] # (index, price)
|
||||
swing_lows = []
|
||||
|
||||
for i in range(L, n - R):
|
||||
# swing_high: 比左边L根和右边R根都高
|
||||
left_max = np.max(highs[i - L:i])
|
||||
right_max = np.max(highs[i + 1:i + 1 + R])
|
||||
if highs[i] > left_max and highs[i] > right_max:
|
||||
swing_highs.append((i, highs[i]))
|
||||
|
||||
# swing_low: 比左边L根和右边R根都低
|
||||
left_min = np.min(lows[i - L:i])
|
||||
right_min = np.min(lows[i + 1:i + 1 + R])
|
||||
if lows[i] < left_min and lows[i] < right_min:
|
||||
swing_lows.append((i, lows[i]))
|
||||
|
||||
return swing_highs, swing_lows
|
||||
|
||||
def _calculate_indicators(self, df):
|
||||
"""计算摆动点并标记到 DataFrame"""
|
||||
df = df.copy()
|
||||
swing_highs, swing_lows = self._find_swing_points(df)
|
||||
return df, swing_highs, swing_lows
|
||||
|
||||
def _signal_from_swings(self, current_price, swings, is_high):
|
||||
"""
|
||||
判断当前价格是否接近摆动点
|
||||
|
||||
is_high=True: 接近前高 → 阻力 → SELL 信号
|
||||
is_high=False: 接近前低 → 支撑 → BUY 信号
|
||||
"""
|
||||
if not swings:
|
||||
return 0
|
||||
|
||||
tolerance = current_price * self.tolerance_pct
|
||||
|
||||
best_signal = 0
|
||||
for idx, swing_price in swings[-self.num_swings:]:
|
||||
distance_pct = abs(current_price - swing_price) / swing_price
|
||||
|
||||
if distance_pct <= self.tolerance_pct:
|
||||
# 价格在摆动点容差范围内
|
||||
# 信号强度 = 1 - (距离/容差)，越近信号越强
|
||||
strength = 1.0 - (distance_pct / self.tolerance_pct)
|
||||
|
||||
# ★ 回踩确认：价格曾突破过该摆动点
|
||||
if is_high and current_price <= swing_price:
|
||||
# 价格在阻力位下方 → 正常卖点
|
||||
signal = -strength
|
||||
elif not is_high and current_price >= swing_price:
|
||||
# 价格在支撑位上方 → 正常买点
|
||||
signal = strength
|
||||
else:
|
||||
# 价格在错误一侧，不给信号
|
||||
continue
|
||||
|
||||
if abs(signal) > abs(best_signal):
|
||||
best_signal = signal
|
||||
|
||||
# 归一化到 [-1, 1]
|
||||
return max(-1.0, min(1.0, best_signal))
|
||||
|
||||
def generate_signal(self):
|
||||
"""生成交易信号"""
|
||||
rates = self.data_provider.get_historical_data(
|
||||
self.symbol, self.timeframe, self.lookback
|
||||
)
|
||||
if rates is None or len(rates) < self.lookback:
|
||||
return 0
|
||||
|
||||
df = pd.DataFrame(rates)
|
||||
_, swing_highs, swing_lows = self._calculate_indicators(df)
|
||||
|
||||
current_price = df['close'].iloc[-1]
|
||||
|
||||
# 接近前高 → SELL
|
||||
sell_signal = self._signal_from_swings(current_price, swing_highs, is_high=True)
|
||||
# 接近前低 → BUY
|
||||
buy_signal = self._signal_from_swings(current_price, swing_lows, is_high=False)
|
||||
|
||||
# 合并信号（sell为负，buy为正）
|
||||
total = buy_signal + sell_signal # sell_signal 已经是负数
|
||||
return max(-1.0, min(1.0, total))
|
||||
|
||||
def run_backtest(self, df):
|
||||
"""回测模式 — 向量化计算信号"""
|
||||
df = df.copy()
|
||||
n = len(df)
|
||||
L, R = self.left_bars, self.right_bars
|
||||
tolerance = self.tolerance_pct
|
||||
|
||||
signals = pd.Series(0.0, index=df.index)
|
||||
highs = df['high'].values
|
||||
lows = df['low'].values
|
||||
closes = df['close'].values
|
||||
|
||||
# 预计算摆动点
|
||||
swing_high_mask = np.zeros(n, dtype=bool)
|
||||
swing_low_mask = np.zeros(n, dtype=bool)
|
||||
|
||||
for i in range(L, n - R):
|
||||
if highs[i] > np.max(highs[i - L:i]) and highs[i] > np.max(highs[i + 1:i + 1 + R]):
|
||||
swing_high_mask[i] = True
|
||||
if lows[i] < np.min(lows[i - L:i]) and lows[i] < np.min(lows[i + 1:i + 1 + R]):
|
||||
swing_low_mask[i] = True
|
||||
|
||||
# 生成信号
|
||||
for i in range(self.lookback, n):
|
||||
current = closes[i]
|
||||
|
||||
# 找最近的摆动点
|
||||
prev_highs = np.where(swing_high_mask[:i])[0]
|
||||
prev_lows = np.where(swing_low_mask[:i])[0]
|
||||
|
||||
signal = 0.0
|
||||
|
||||
# 检查前高（阻力位）
|
||||
for sh_idx in prev_highs[-self.num_swings:]:
|
||||
sh_price = highs[sh_idx]
|
||||
dist_pct = abs(current - sh_price) / sh_price
|
||||
if dist_pct <= tolerance and current <= sh_price:
|
||||
strength = 1.0 - (dist_pct / tolerance)
|
||||
signal -= strength
|
||||
break
|
||||
|
||||
# 检查前低（支撑位）
|
||||
for sl_idx in prev_lows[-self.num_swings:]:
|
||||
sl_price = lows[sl_idx]
|
||||
dist_pct = abs(current - sl_price) / sl_price
|
||||
if dist_pct <= tolerance and current >= sl_price:
|
||||
strength = 1.0 - (dist_pct / tolerance)
|
||||
signal += strength
|
||||
break
|
||||
|
||||
signals.iloc[i] = max(-1.0, min(1.0, signal))
|
||||
|
||||
return signals
|
||||
Reference in New Issue
Block a user