227 lines
8.4 KiB
Python
227 lines
8.4 KiB
Python
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"""
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Optuna 训练入口 - 在 2020-2022 数据上搜索最优参数
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"""
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import json
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import sys
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import os
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sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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import optuna
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from optuna.samplers import TPESampler
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import numpy as np
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from strategy.data_loader import load_klines
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from strategy.indicators import compute_all_indicators
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from strategy.strategy_signal import (
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generate_indicator_signals, compute_composite_score,
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apply_htf_filter, WEIGHT_KEYS,
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)
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from strategy.backtest_engine import BacktestEngine
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# ============================================================
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# 全局加载数据 (只加载一次)
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# ============================================================
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print("正在加载 2020-2022 训练数据...")
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DF_5M = load_klines('5m', '2020-01-01', '2023-01-01')
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DF_1H = load_klines('1h', '2020-01-01', '2023-01-01')
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print(f" 5m: {len(DF_5M)} 条, 1h: {len(DF_1H)} 条")
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print("数据加载完成。\n")
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def build_params(trial: optuna.Trial) -> dict:
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"""从 Optuna trial 构建完整参数字典"""
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p = {}
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# --- 指标参数 ---
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p['bb_period'] = trial.suggest_int('bb_period', 10, 50)
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p['bb_std'] = trial.suggest_float('bb_std', 1.0, 3.5, step=0.1)
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p['kc_period'] = trial.suggest_int('kc_period', 10, 50)
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p['kc_mult'] = trial.suggest_float('kc_mult', 0.5, 3.0, step=0.1)
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p['dc_period'] = trial.suggest_int('dc_period', 10, 50)
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p['ema_fast'] = trial.suggest_int('ema_fast', 3, 20)
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p['ema_slow'] = trial.suggest_int('ema_slow', 15, 60)
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p['macd_fast'] = trial.suggest_int('macd_fast', 6, 20)
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p['macd_slow'] = trial.suggest_int('macd_slow', 18, 40)
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p['macd_signal'] = trial.suggest_int('macd_signal', 5, 15)
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p['adx_period'] = trial.suggest_int('adx_period', 7, 30)
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p['st_period'] = trial.suggest_int('st_period', 5, 20)
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p['st_mult'] = trial.suggest_float('st_mult', 1.0, 5.0, step=0.1)
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p['rsi_period'] = trial.suggest_int('rsi_period', 7, 28)
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p['stoch_k'] = trial.suggest_int('stoch_k', 5, 21)
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p['stoch_d'] = trial.suggest_int('stoch_d', 2, 7)
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p['stoch_smooth'] = trial.suggest_int('stoch_smooth', 2, 7)
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p['cci_period'] = trial.suggest_int('cci_period', 10, 40)
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p['wr_period'] = trial.suggest_int('wr_period', 7, 28)
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p['wma_period'] = trial.suggest_int('wma_period', 10, 50)
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# --- 信号阈值参数 ---
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p['bb_oversold'] = trial.suggest_float('bb_oversold', -0.3, 0.3, step=0.05)
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p['bb_overbought'] = trial.suggest_float('bb_overbought', 0.7, 1.3, step=0.05)
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p['kc_oversold'] = trial.suggest_float('kc_oversold', -0.3, 0.3, step=0.05)
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p['kc_overbought'] = trial.suggest_float('kc_overbought', 0.7, 1.3, step=0.05)
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p['dc_oversold'] = trial.suggest_float('dc_oversold', 0.0, 0.3, step=0.05)
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p['dc_overbought'] = trial.suggest_float('dc_overbought', 0.7, 1.0, step=0.05)
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p['adx_threshold'] = trial.suggest_float('adx_threshold', 15, 35, step=1)
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p['rsi_overbought'] = trial.suggest_float('rsi_overbought', 60, 85, step=1)
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p['rsi_oversold'] = trial.suggest_float('rsi_oversold', 15, 40, step=1)
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p['stoch_overbought'] = trial.suggest_float('stoch_overbought', 70, 90, step=1)
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p['stoch_oversold'] = trial.suggest_float('stoch_oversold', 10, 30, step=1)
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p['cci_overbought'] = trial.suggest_float('cci_overbought', 80, 200, step=5)
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p['cci_oversold'] = trial.suggest_float('cci_oversold', -200, -80, step=5)
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p['wr_overbought'] = trial.suggest_float('wr_overbought', -30, -10, step=1)
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p['wr_oversold'] = trial.suggest_float('wr_oversold', -90, -70, step=1)
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# --- 权重 ---
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for wk in WEIGHT_KEYS:
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p[wk] = trial.suggest_float(wk, 0.0, 1.0, step=0.05)
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# --- 回测参数 ---
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p['open_threshold'] = trial.suggest_float('open_threshold', 0.1, 0.6, step=0.02)
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p['max_positions'] = trial.suggest_int('max_positions', 1, 3)
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p['take_profit_pct'] = trial.suggest_float('take_profit_pct', 0.003, 0.025, step=0.001)
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# 止损约束: N单同时止损 + 手续费 <= 50U
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# N * 1250 * sl_pct + N * 1.25 <= 50
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# sl_pct <= (50 - N*1.25) / (N*1250)
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n = p['max_positions']
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max_sl = (50.0 - n * 1.25) / (n * 1250.0)
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max_sl = round(max(max_sl, 0.002), 3) # 至少 0.2%
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p['stop_loss_pct'] = trial.suggest_float('stop_loss_pct', 0.002, max_sl, step=0.001)
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return p
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def objective(trial: optuna.Trial) -> float:
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params = build_params(trial)
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# 确保 ema_slow > ema_fast, macd_slow > macd_fast
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if params['ema_slow'] <= params['ema_fast']:
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return -1e6
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if params['macd_slow'] <= params['macd_fast']:
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return -1e6
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try:
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# 计算指标
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df_5m = compute_all_indicators(DF_5M, params)
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df_1h = compute_all_indicators(DF_1H, params)
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# 生成信号
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df_5m = generate_indicator_signals(df_5m, params)
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df_1h = generate_indicator_signals(df_1h, params)
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# 综合得分
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score = compute_composite_score(df_5m, params)
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# 高时间框架过滤
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score = apply_htf_filter(score, df_1h, params)
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# 回测
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engine = BacktestEngine(
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initial_capital=1000.0,
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margin_per_trade=25.0,
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leverage=50,
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fee_rate=0.0005,
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rebate_ratio=0.70,
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max_daily_drawdown=50.0,
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min_hold_bars=1,
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stop_loss_pct=params['stop_loss_pct'],
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take_profit_pct=params['take_profit_pct'],
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max_positions=params['max_positions'],
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)
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result = engine.run(df_5m, score, open_threshold=params['open_threshold'])
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num_trades = result['num_trades']
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if num_trades < 50:
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return -1e6 # 交易次数太少，不可靠
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total_pnl = result['total_pnl']
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max_dd = result['max_daily_dd'] # 负数 (引擎已保证 >= -50)
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avg_daily = result['avg_daily_pnl']
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# 引擎内部已经有每日 50U 回撤熔断，这里不再硬约束
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# 目标: 最大化总收益
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score_val = total_pnl
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# 奖励日均收益高的方案
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if avg_daily >= 50:
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score_val *= 1.3
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elif avg_daily >= 30:
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score_val *= 1.15
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trial.set_user_attr('total_pnl', total_pnl)
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trial.set_user_attr('num_trades', num_trades)
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trial.set_user_attr('win_rate', result['win_rate'])
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trial.set_user_attr('max_daily_dd', max_dd)
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trial.set_user_attr('avg_daily_pnl', avg_daily)
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trial.set_user_attr('profit_factor', result['profit_factor'])
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return score_val
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except Exception as e:
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print(f"Trial {trial.number} 异常: {e}")
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return -1e6
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def main():
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study = optuna.create_study(
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direction='maximize',
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sampler=TPESampler(seed=42, n_startup_trials=30),
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study_name='eth_strategy_v1',
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)
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# 设置日志级别
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optuna.logging.set_verbosity(optuna.logging.WARNING)
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n_trials = 1000
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print(f"开始 Optuna 优化, 共 {n_trials} 次试验 (多单并发版)...")
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print("=" * 60)
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def callback(study, trial):
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if trial.number % 10 == 0:
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best = study.best_trial
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print(f"[Trial {trial.number:>4d}] "
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f"当前值={trial.value:.2f} | "
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f"最佳值={best.value:.2f} | "
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f"PnL={best.user_attrs.get('total_pnl', 0):.1f}U | "
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f"胜率={best.user_attrs.get('win_rate', 0):.1%} | "
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f"日均={best.user_attrs.get('avg_daily_pnl', 0):.1f}U | "
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f"最大日回撤={best.user_attrs.get('max_daily_dd', 0):.1f}U")
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study.optimize(objective, n_trials=n_trials, callbacks=[callback], show_progress_bar=True)
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# 输出最佳结果
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best = study.best_trial
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print("\n" + "=" * 60)
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print("训练完成！最佳参数:")
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print("=" * 60)
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print(f" 目标值: {best.value:.4f}")
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print(f" 总收益: {best.user_attrs.get('total_pnl', 0):.2f}U")
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print(f" 交易次数: {best.user_attrs.get('num_trades', 0)}")
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print(f" 胜率: {best.user_attrs.get('win_rate', 0):.2%}")
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print(f" 日均收益: {best.user_attrs.get('avg_daily_pnl', 0):.2f}U")
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print(f" 最大日回撤: {best.user_attrs.get('max_daily_dd', 0):.2f}U")
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print(f" 盈亏比: {best.user_attrs.get('profit_factor', 0):.2f}")
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# 保存最佳参数
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output_path = os.path.join(os.path.dirname(__file__), 'best_params_2020_2022.json')
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with open(output_path, 'w') as f:
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json.dump(best.params, f, indent=2, ensure_ascii=False)
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print(f"\n最佳参数已保存到: {output_path}")
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if __name__ == '__main__':
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main()
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