152 lines
4.2 KiB
Python
152 lines
4.2 KiB
Python
"""
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全局配置 — 交易参数、指标参数、模型参数
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"""
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from pathlib import Path
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# ============ 路径 ============
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PROJECT_ROOT = Path(__file__).parent.parent
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DB_PATH = PROJECT_ROOT / 'models' / 'database.db'
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# ============ 交易参数 ============
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TRADE_CONFIG = {
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'symbol': 'ETHUSDT',
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'commission': 0.0006, # 名义手续费 0.06%
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'commission_rebate': 0.90, # 90% 返佣（次日 8 点结算），实付 = commission * (1 - rebate)
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'slippage': 0.0001, # 滑点 0.01%
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'initial_capital': 10000, # 本金 USDT
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'leverage': 100, # 杠杆倍数
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'position_notional_usd': 500, # 每笔名义 500U（开 100 倍），目标月均收益约 500U
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'max_drawdown_limit': 300, # 最大回撤硬约束：权益从峰值回落超过 300U 则不再开新仓
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# 兼容旧逻辑（若用比例算仓则用此项）
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'position_size': 0.95,
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}
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# ============ K线周期 ============
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KLINE_PERIODS = {
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1: '1m',
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3: '3m',
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5: '5m',
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15: '15m',
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30: '30m',
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60: '1h',
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}
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# 主周期（用于生成信号）
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PRIMARY_PERIOD = 15 # 15分钟
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# 辅助周期（用于多周期融合特征）；加入 1m 可捕捉 15m 内短时冲高回落，减少“涨到一半又跌止损”
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AUX_PERIODS = [1, 5, 60]
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# ============ 指标参数 ============
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INDICATOR_PARAMS = {
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# 趋势类
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'sma_windows': [5, 10, 20, 50, 200],
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'ema_windows': [12, 26],
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'macd_fast': 12,
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'macd_slow': 26,
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'macd_signal': 9,
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'adx_window': 14,
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'ichimoku_conversion': 9,
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'ichimoku_base': 26,
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'ichimoku_span_b': 52,
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'trix_window': 15,
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'aroon_window': 25,
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'cci_window': 20,
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'dpo_window': 20,
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'kst_roc1': 10,
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'kst_roc2': 15,
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'kst_roc3': 20,
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'kst_roc4': 30,
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'vortex_window': 14,
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# 动量类
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'rsi_window': 14,
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'stoch_window': 14,
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'stoch_smooth': 3,
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'williams_window': 14,
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'roc_window': 12,
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'mfi_window': 14,
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'tsi_slow': 25,
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'tsi_fast': 13,
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'uo_short': 7,
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'uo_medium': 14,
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'uo_long': 28,
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'ao_short': 5,
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'ao_long': 34,
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'kama_window': 10,
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'ppo_slow': 26,
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'ppo_fast': 12,
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'stoch_rsi_window': 14,
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'stoch_rsi_smooth': 3,
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# 波动率类
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'bb_window': 20,
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'bb_std': 2,
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'atr_window': 14,
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'kc_window': 20,
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'dc_window': 20,
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# 成交量类（部分指标需要volume，K线数据可能无volume则跳过）
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'obv_enabled': True,
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'cmf_window': 20,
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'emv_window': 14,
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'fi_window': 13,
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}
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# ============ 特征工程参数 ============
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FEATURE_CONFIG = {
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'label_forward_periods': 10, # 未来N根K线用于生成标签
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'label_threshold': 0.002, # 涨跌阈值（0.2%以内算震荡）
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'lookback_lags': [1, 3, 5], # 滞后特征的lag值
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'normalize': True, # 是否标准化
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}
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# ============ 模型参数 ============
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MODEL_CONFIG = {
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'walk_forward_train_size': 20000, # Walk-Forward 训练窗口大小
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'walk_forward_test_size': 2000, # Walk-Forward 测试窗口大小
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'walk_forward_step': 2000, # 滚动步长
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'warm_up_months': 12, # 指定回测区间时向前加载的月数，使区间首月即有预测
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'lightgbm': {
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'n_estimators': 300,
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'max_depth': 4,
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'learning_rate': 0.03,
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'num_leaves': 15,
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'min_child_samples': 50,
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'subsample': 0.7,
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'colsample_bytree': 0.6,
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'reg_alpha': 1.0,
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'reg_lambda': 1.0,
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'objective': 'multiclass',
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'num_class': 3,
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'verbose': -1,
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},
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'xgboost': {
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'n_estimators': 300,
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'max_depth': 4,
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'learning_rate': 0.03,
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'subsample': 0.7,
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'colsample_bytree': 0.6,
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'reg_alpha': 1.0,
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'reg_lambda': 1.0,
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'objective': 'multi:softprob',
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'num_class': 3,
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'verbosity': 0,
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},
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}
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# ============ 统计筛选参数 ============
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STAT_CONFIG = {
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'top_n_features': 15, # 筛选Top N个指标
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'correlation_threshold': 0.9, # 去除高相关特征的阈值
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'grid_search_cv': 3, # 网格搜索交叉验证折数
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}
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# ============ 信号标签映射 ============
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SIGNAL_MAP = {
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0: '观望',
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1: '做多',
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2: '做空',
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}
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