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228
ensemble_meta.py
228
ensemble_meta.py
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"""
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集成学习 + Meta 模型 + 动态风控模块
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"""
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import numpy as np
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import pandas as pd
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import lightgbm as lgb
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import datetime
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ENSEMBLE_CONFIGS = [
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{'num_leaves': 31, 'max_depth': 6, 'learning_rate': 0.05,
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'subsample': 0.8, 'colsample_bytree': 0.8,
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'reg_alpha': 0.1, 'reg_lambda': 0.1, 'seed': 42},
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{'num_leaves': 63, 'max_depth': 8, 'learning_rate': 0.03,
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'subsample': 0.7, 'colsample_bytree': 0.7,
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'reg_alpha': 0.05, 'reg_lambda': 0.05, 'seed': 123},
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{'num_leaves': 15, 'max_depth': 5, 'learning_rate': 0.08,
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'subsample': 0.9, 'colsample_bytree': 0.9,
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'reg_alpha': 0.5, 'reg_lambda': 0.5, 'seed': 456},
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{'num_leaves': 31, 'max_depth': 7, 'learning_rate': 0.04,
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'subsample': 0.75, 'colsample_bytree': 0.6,
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'reg_alpha': 1.0, 'reg_lambda': 1.0, 'seed': 789},
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{'num_leaves': 47, 'max_depth': 6, 'learning_rate': 0.06,
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'subsample': 0.85, 'colsample_bytree': 0.85,
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'reg_alpha': 0.2, 'reg_lambda': 0.3, 'seed': 2024},
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]
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META_EXTRA = ['bb_pct', 'rsi', 'zscore', 'atr_pct', 'vol_20', 'stoch_k', 'cci']
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def _train_one(X, y, cfg, rounds=250):
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params = {'objective': 'multiclass', 'num_class': 3, 'metric': 'multi_logloss',
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'min_child_samples': 50, 'verbose': -1, 'n_jobs': -1, **cfg}
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ds = lgb.Dataset(X, label=y)
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return lgb.train(params, ds, num_boost_round=rounds)
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def train_ensemble_with_meta(
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df_train, df_test, feature_cols,
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forward_bars, label_threshold, lgb_rounds=250, n_models=5,
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):
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"""集成学习+Meta模型: 5个多样化LightGBM基模型投票 + Meta二次过滤"""
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train_df = df_train.dropna(subset=feature_cols + ['label'])
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if len(train_df) < 2000:
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return None, None, None, None, None
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test_df = df_test.dropna(subset=feature_cols)
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if len(test_df) < 100:
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return None, None, None, None, None
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X_full = train_df[feature_cols].values
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y_full = (train_df['label'].values + 1).astype(int)
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X_test = test_df[feature_cols].values
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cfgs = ENSEMBLE_CONFIGS[:n_models]
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# Step1: 训练N个多样化基模型，在测试集上集成预测
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models = []
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test_probas = []
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for cfg in cfgs:
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m = _train_one(X_full, y_full, cfg, lgb_rounds)
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models.append(m)
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test_probas.append(m.predict(X_test))
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avg_proba = np.mean(test_probas, axis=0)
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std_proba = np.std(test_probas, axis=0)
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# Step2: Meta模型 - 训练集内时间序列划分
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split_idx = int(len(train_df) * 0.7)
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base_part = train_df.iloc[:split_idx]
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meta_part = train_df.iloc[split_idx:]
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X_base = base_part[feature_cols].values
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y_base = (base_part['label'].values + 1).astype(int)
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X_meta_f = meta_part[feature_cols].values
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meta_probas = []
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for cfg in cfgs:
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tmp = _train_one(X_base, y_base, cfg, lgb_rounds)
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meta_probas.append(tmp.predict(X_meta_f))
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m_avg = np.mean(meta_probas, axis=0)
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m_std = np.std(meta_probas, axis=0)
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me_cols = [c for c in META_EXTRA if c in feature_cols]
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X_me = meta_part[me_cols].values if me_cols else np.zeros((len(meta_part), 1))
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X_mi = np.hstack([m_avg, m_std, X_me])
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# Meta标签: 模型预测方向与真实方向一致=1(好信号), 否则=0
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pred_dir = np.argmax(m_avg, axis=1)
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true_dir = (meta_part['label'].values + 1).astype(int)
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ml = (pred_dir == true_dir).astype(int)
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has_sig = pred_dir != 1
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meta_model = None
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if has_sig.sum() > 500:
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mp = {'objective': 'binary', 'metric': 'binary_logloss',
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'num_leaves': 15, 'max_depth': 4, 'learning_rate': 0.05,
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'min_child_samples': 100, 'subsample': 0.8, 'colsample_bytree': 0.8,
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'reg_alpha': 1.0, 'reg_lambda': 1.0, 'verbose': -1, 'n_jobs': -1, 'seed': 42}
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dm = lgb.Dataset(X_mi[has_sig], label=ml[has_sig])
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meta_model = lgb.train(mp, dm, num_boost_round=150)
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# Step3: 测试集Meta置信度
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X_te = test_df[me_cols].values if me_cols else np.zeros((len(test_df), 1))
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X_tm = np.hstack([avg_proba, std_proba, X_te])
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if meta_model is not None:
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mc_arr = meta_model.predict(X_tm)
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else:
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mc_arr = 1.0 - std_proba.mean(axis=1)
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pl = pd.Series(0.0, index=df_test.index)
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ps = pd.Series(0.0, index=df_test.index)
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mc = pd.Series(0.5, index=df_test.index)
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ps.loc[test_df.index] = avg_proba[:, 0]
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pl.loc[test_df.index] = avg_proba[:, 2]
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mc.loc[test_df.index] = mc_arr
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return pl, ps, mc, models, meta_model
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def backtest_with_risk_control(
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df, proba_long, proba_short,
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notional=10000.0,
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prob_threshold=0.45,
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min_hold_seconds=180,
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max_hold_seconds=1800,
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sl_pct=0.004,
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tp_pct=0.006,
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meta_conf=None,
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meta_threshold=0.55,
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daily_loss_limit=80.0,
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dd_circuit_breaker=400.0,
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cooldown_bars=30,
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max_consec_losses=5,
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):
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"""带动态风控的回测: 日亏损限制+回撤熔断+连损冷却+Meta过滤"""
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pos = 0
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open_price = 0.0
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open_time = None
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trades = []
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# 风控状态
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daily_pnl = 0.0
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current_day = None
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cum_pnl = 0.0
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peak_pnl = 0.0
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consec_losses = 0
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cooldown_until = 0
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net_fee = notional * 0.0006 * 2 * 0.10 # 净手续费
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for i in range(len(df)):
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dt = df.index[i]
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price = df['close'].iloc[i]
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pl = proba_long.iloc[i]
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ps = proba_short.iloc[i]
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mc = meta_conf.iloc[i] if meta_conf is not None else 1.0
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day = dt.date()
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# 日切重置
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if current_day != day:
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current_day = day
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daily_pnl = 0.0
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# 持仓中: 检查平仓条件
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if pos != 0 and open_time is not None:
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pnl_pct = (price - open_price) / open_price if pos == 1 else (open_price - price) / open_price
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hold_sec = (dt - open_time).total_seconds()
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# 硬止损
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if -pnl_pct >= sl_pct * 1.5:
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pnl_usdt = notional * pnl_pct
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trades.append((pos, open_price, price, pnl_usdt, hold_sec, '硬止损', open_time, dt))
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cum_pnl += pnl_usdt - net_fee
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daily_pnl += pnl_usdt - net_fee
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if pnl_usdt < 0:
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consec_losses += 1
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else:
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consec_losses = 0
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if cum_pnl > peak_pnl:
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peak_pnl = cum_pnl
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pos = 0
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continue
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if hold_sec >= min_hold_seconds:
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closed = False
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reason = ''
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if -pnl_pct >= sl_pct:
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reason = '止损'
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closed = True
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elif pnl_pct >= tp_pct:
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reason = '止盈'
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closed = True
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elif hold_sec >= max_hold_seconds:
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reason = '超时'
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closed = True
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elif pos == 1 and ps > prob_threshold + 0.05:
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reason = 'AI反转'
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closed = True
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elif pos == -1 and pl > prob_threshold + 0.05:
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reason = 'AI反转'
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closed = True
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if closed:
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pnl_usdt = notional * pnl_pct
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trades.append((pos, open_price, price, pnl_usdt, hold_sec, reason, open_time, dt))
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cum_pnl += pnl_usdt - net_fee
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daily_pnl += pnl_usdt - net_fee
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if pnl_usdt < 0:
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consec_losses += 1
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else:
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consec_losses = 0
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if cum_pnl > peak_pnl:
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peak_pnl = cum_pnl
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if consec_losses >= max_consec_losses:
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cooldown_until = i + cooldown_bars
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pos = 0
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# 开仓: 检查风控条件
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if pos == 0:
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# 风控检查: 冷却期、日亏损限制、回撤熔断
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if i < cooldown_until:
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continue
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if daily_pnl <= -daily_loss_limit:
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continue
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dd_now = peak_pnl - cum_pnl
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if dd_now >= dd_circuit_breaker:
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continue
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# Meta过滤: 置信度不够则跳过
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if mc < meta_threshold:
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continue
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if pl > prob_threshold and pl > ps:
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pos = 1
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open_price = price
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open_time = dt
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elif ps > prob_threshold and ps > pl:
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pos = -1
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open_price = price
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open_time = dt
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if pos != 0:
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price = df['close'].iloc[-1]
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dt = df.index[-1]
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pnl_pct = (price - open_price) / open_price if pos == 1 else (open_price - price) / open_price
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hold_sec = (dt - open_time).total_seconds()
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trades.append((pos, open_price, price, notional * pnl_pct, hold_sec, '结束', open_time, dt))
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return trades
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@@ -1,47 +0,0 @@
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[
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"bb_pct",
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"bb_width",
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"keltner_pct",
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"keltner_width",
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"donchian_pct",
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"donchian_width",
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"stoch_k",
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"stoch_d",
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"cci",
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"willr",
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"rsi",
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"atr_band_pct",
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"atr_band_width",
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"zscore",
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"zscore_abs",
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"lr_pct",
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"lr_width",
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"price_vs_median",
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"median_band_pct",
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"pct_rank",
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"chandelier_pct",
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"chandelier_width",
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"std_band_pct",
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"std_band_width",
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"elder_bull",
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"elder_bear",
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"elder_dist",
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"ema_fast_slow",
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"price_vs_ema120",
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"ema8_slope",
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"macd",
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"macd_signal",
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"macd_hist",
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"atr_pct",
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"ret_1",
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"ret_3",
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"ret_5",
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"ret_10",
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"ret_20",
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"vol_5",
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"vol_20",
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"body_pct",
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"price_position_20",
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"hour_sin",
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"hour_cos"
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]
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{
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"bb_period": 20,
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"bb_std": 2.5,
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"keltner_period": 20,
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"keltner_atr_mult": 2.5,
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"donchian_period": 14,
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"stoch_k_period": 14,
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"stoch_d_period": 3,
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"cci_period": 14,
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"willr_period": 14,
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"rsi_period": 14,
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"atr_band_period": 20,
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"atr_band_mult": 2.0,
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"zscore_period": 20,
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"lr_period": 14,
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"lr_std_mult": 2.0,
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"median_band_period": 14,
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"pct_rank_period": 20,
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"chandelier_period": 14,
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"chandelier_mult": 2.5,
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"std_band_period": 20,
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"std_band_mult": 2.0,
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"elder_period": 13,
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"forward_bars": 15,
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"label_threshold": 0.002,
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"prob_threshold": 0.52,
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"sl_pct": 0.002,
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"tp_pct": 0.005,
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"min_hold_seconds": 180,
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"max_hold_seconds": 1800,
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"margin_per_trade": 100.0,
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"leverage": 100,
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"notional": 10000.0,
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"rebate_rate": 0.9,
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"train_period": "2020-01-01 ~ 2021-01-01",
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"test_period": "2021-01-01 ~ 2022-01-01",
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"kline_period": "1h",
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"initial_balance": 10000.0,
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"max_dd_target": 300.0
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}
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1049
训练AI策略_ETH合约.py
1049
训练AI策略_ETH合约.py
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