229 lines
8.6 KiB
Python
229 lines
8.6 KiB
Python
|
"""
|
|||
|
|
集成学习 + Meta 模型 + 动态风控模块
|
|||
|
|
"""
|
|||
|
|
import numpy as np
|
|||
|
|
import pandas as pd
|
|||
|
|
import lightgbm as lgb
|
|||
|
|
import datetime
|
|||
|
|
|
|||
|
|
ENSEMBLE_CONFIGS = [
|
|||
|
|
{'num_leaves': 31, 'max_depth': 6, 'learning_rate': 0.05,
|
|||
|
|
'subsample': 0.8, 'colsample_bytree': 0.8,
|
|||
|
|
'reg_alpha': 0.1, 'reg_lambda': 0.1, 'seed': 42},
|
|||
|
|
{'num_leaves': 63, 'max_depth': 8, 'learning_rate': 0.03,
|
|||
|
|
'subsample': 0.7, 'colsample_bytree': 0.7,
|
|||
|
|
'reg_alpha': 0.05, 'reg_lambda': 0.05, 'seed': 123},
|
|||
|
|
{'num_leaves': 15, 'max_depth': 5, 'learning_rate': 0.08,
|
|||
|
|
'subsample': 0.9, 'colsample_bytree': 0.9,
|
|||
|
|
'reg_alpha': 0.5, 'reg_lambda': 0.5, 'seed': 456},
|
|||
|
|
{'num_leaves': 31, 'max_depth': 7, 'learning_rate': 0.04,
|
|||
|
|
'subsample': 0.75, 'colsample_bytree': 0.6,
|
|||
|
|
'reg_alpha': 1.0, 'reg_lambda': 1.0, 'seed': 789},
|
|||
|
|
{'num_leaves': 47, 'max_depth': 6, 'learning_rate': 0.06,
|
|||
|
|
'subsample': 0.85, 'colsample_bytree': 0.85,
|
|||
|
|
'reg_alpha': 0.2, 'reg_lambda': 0.3, 'seed': 2024},
|
|||
|
|
]
|
|||
|
|
|
|||
|
|
META_EXTRA = ['bb_pct', 'rsi', 'zscore', 'atr_pct', 'vol_20', 'stoch_k', 'cci']
|
|||
|
|
|
|||
|
|
|
|||
|
|
def _train_one(X, y, cfg, rounds=250):
|
|||
|
|
params = {'objective': 'multiclass', 'num_class': 3, 'metric': 'multi_logloss',
|
|||
|
|
'min_child_samples': 50, 'verbose': -1, 'n_jobs': -1, **cfg}
|
|||
|
|
ds = lgb.Dataset(X, label=y)
|
|||
|
|
return lgb.train(params, ds, num_boost_round=rounds)
|
|||
|
|
|
|||
|
|
|
|||
|
|
|
|||
|
|
def train_ensemble_with_meta(
|
|||
|
|
df_train, df_test, feature_cols,
|
|||
|
|
forward_bars, label_threshold, lgb_rounds=250, n_models=5,
|
|||
|
|
):
|
|||
|
|
"""集成学习+Meta模型: 5个多样化LightGBM基模型投票 + Meta二次过滤"""
|
|||
|
|
train_df = df_train.dropna(subset=feature_cols + ['label'])
|
|||
|
|
if len(train_df) < 2000:
|
|||
|
|
return None, None, None, None, None
|
|||
|
|
test_df = df_test.dropna(subset=feature_cols)
|
|||
|
|
if len(test_df) < 100:
|
|||
|
|
return None, None, None, None, None
|
|||
|
|
X_full = train_df[feature_cols].values
|
|||
|
|
y_full = (train_df['label'].values + 1).astype(int)
|
|||
|
|
X_test = test_df[feature_cols].values
|
|||
|
|
cfgs = ENSEMBLE_CONFIGS[:n_models]
|
|||
|
|
# Step1: 训练N个多样化基模型，在测试集上集成预测
|
|||
|
|
models = []
|
|||
|
|
test_probas = []
|
|||
|
|
for cfg in cfgs:
|
|||
|
|
m = _train_one(X_full, y_full, cfg, lgb_rounds)
|
|||
|
|
models.append(m)
|
|||
|
|
test_probas.append(m.predict(X_test))
|
|||
|
|
avg_proba = np.mean(test_probas, axis=0)
|
|||
|
|
std_proba = np.std(test_probas, axis=0)
|
|||
|
|
# Step2: Meta模型 - 训练集内时间序列划分
|
|||
|
|
split_idx = int(len(train_df) * 0.7)
|
|||
|
|
base_part = train_df.iloc[:split_idx]
|
|||
|
|
meta_part = train_df.iloc[split_idx:]
|
|||
|
|
X_base = base_part[feature_cols].values
|
|||
|
|
y_base = (base_part['label'].values + 1).astype(int)
|
|||
|
|
X_meta_f = meta_part[feature_cols].values
|
|||
|
|
meta_probas = []
|
|||
|
|
for cfg in cfgs:
|
|||
|
|
tmp = _train_one(X_base, y_base, cfg, lgb_rounds)
|
|||
|
|
meta_probas.append(tmp.predict(X_meta_f))
|
|||
|
|
m_avg = np.mean(meta_probas, axis=0)
|
|||
|
|
m_std = np.std(meta_probas, axis=0)
|
|||
|
|
me_cols = [c for c in META_EXTRA if c in feature_cols]
|
|||
|
|
X_me = meta_part[me_cols].values if me_cols else np.zeros((len(meta_part), 1))
|
|||
|
|
X_mi = np.hstack([m_avg, m_std, X_me])
|
|||
|
|
# Meta标签: 模型预测方向与真实方向一致=1(好信号), 否则=0
|
|||
|
|
pred_dir = np.argmax(m_avg, axis=1)
|
|||
|
|
true_dir = (meta_part['label'].values + 1).astype(int)
|
|||
|
|
ml = (pred_dir == true_dir).astype(int)
|
|||
|
|
has_sig = pred_dir != 1
|
|||
|
|
meta_model = None
|
|||
|
|
if has_sig.sum() > 500:
|
|||
|
|
mp = {'objective': 'binary', 'metric': 'binary_logloss',
|
|||
|
|
'num_leaves': 15, 'max_depth': 4, 'learning_rate': 0.05,
|
|||
|
|
'min_child_samples': 100, 'subsample': 0.8, 'colsample_bytree': 0.8,
|
|||
|
|
'reg_alpha': 1.0, 'reg_lambda': 1.0, 'verbose': -1, 'n_jobs': -1, 'seed': 42}
|
|||
|
|
dm = lgb.Dataset(X_mi[has_sig], label=ml[has_sig])
|
|||
|
|
meta_model = lgb.train(mp, dm, num_boost_round=150)
|
|||
|
|
# Step3: 测试集Meta置信度
|
|||
|
|
X_te = test_df[me_cols].values if me_cols else np.zeros((len(test_df), 1))
|
|||
|
|
X_tm = np.hstack([avg_proba, std_proba, X_te])
|
|||
|
|
if meta_model is not None:
|
|||
|
|
mc_arr = meta_model.predict(X_tm)
|
|||
|
|
else:
|
|||
|
|
mc_arr = 1.0 - std_proba.mean(axis=1)
|
|||
|
|
pl = pd.Series(0.0, index=df_test.index)
|
|||
|
|
ps = pd.Series(0.0, index=df_test.index)
|
|||
|
|
mc = pd.Series(0.5, index=df_test.index)
|
|||
|
|
ps.loc[test_df.index] = avg_proba[:, 0]
|
|||
|
|
pl.loc[test_df.index] = avg_proba[:, 2]
|
|||
|
|
mc.loc[test_df.index] = mc_arr
|
|||
|
|
return pl, ps, mc, models, meta_model
|
|||
|
|
|
|||
|
|
|
|||
|
|
def backtest_with_risk_control(
|
|||
|
|
df, proba_long, proba_short,
|
|||
|
|
notional=10000.0,
|
|||
|
|
prob_threshold=0.45,
|
|||
|
|
min_hold_seconds=180,
|
|||
|
|
max_hold_seconds=1800,
|
|||
|
|
sl_pct=0.004,
|
|||
|
|
tp_pct=0.006,
|
|||
|
|
meta_conf=None,
|
|||
|
|
meta_threshold=0.55,
|
|||
|
|
daily_loss_limit=80.0,
|
|||
|
|
dd_circuit_breaker=400.0,
|
|||
|
|
cooldown_bars=30,
|
|||
|
|
max_consec_losses=5,
|
|||
|
|
):
|
|||
|
|
"""带动态风控的回测: 日亏损限制+回撤熔断+连损冷却+Meta过滤"""
|
|||
|
|
pos = 0
|
|||
|
|
open_price = 0.0
|
|||
|
|
open_time = None
|
|||
|
|
trades = []
|
|||
|
|
# 风控状态
|
|||
|
|
daily_pnl = 0.0
|
|||
|
|
current_day = None
|
|||
|
|
cum_pnl = 0.0
|
|||
|
|
peak_pnl = 0.0
|
|||
|
|
consec_losses = 0
|
|||
|
|
cooldown_until = 0
|
|||
|
|
net_fee = notional * 0.0006 * 2 * 0.10 # 净手续费
|
|||
|
|
|
|||
|
|
for i in range(len(df)):
|
|||
|
|
dt = df.index[i]
|
|||
|
|
price = df['close'].iloc[i]
|
|||
|
|
pl = proba_long.iloc[i]
|
|||
|
|
ps = proba_short.iloc[i]
|
|||
|
|
mc = meta_conf.iloc[i] if meta_conf is not None else 1.0
|
|||
|
|
day = dt.date()
|
|||
|
|
# 日切重置
|
|||
|
|
if current_day != day:
|
|||
|
|
current_day = day
|
|||
|
|
daily_pnl = 0.0
|
|||
|
|
|
|||
|
|
# 持仓中: 检查平仓条件
|
|||
|
|
if pos != 0 and open_time is not None:
|
|||
|
|
pnl_pct = (price - open_price) / open_price if pos == 1 else (open_price - price) / open_price
|
|||
|
|
hold_sec = (dt - open_time).total_seconds()
|
|||
|
|
# 硬止损
|
|||
|
|
if -pnl_pct >= sl_pct * 1.5:
|
|||
|
|
pnl_usdt = notional * pnl_pct
|
|||
|
|
trades.append((pos, open_price, price, pnl_usdt, hold_sec, '硬止损', open_time, dt))
|
|||
|
|
cum_pnl += pnl_usdt - net_fee
|
|||
|
|
daily_pnl += pnl_usdt - net_fee
|
|||
|
|
if pnl_usdt < 0:
|
|||
|
|
consec_losses += 1
|
|||
|
|
else:
|
|||
|
|
consec_losses = 0
|
|||
|
|
if cum_pnl > peak_pnl:
|
|||
|
|
peak_pnl = cum_pnl
|
|||
|
|
pos = 0
|
|||
|
|
continue
|
|||
|
|
if hold_sec >= min_hold_seconds:
|
|||
|
|
closed = False
|
|||
|
|
reason = ''
|
|||
|
|
if -pnl_pct >= sl_pct:
|
|||
|
|
reason = '止损'
|
|||
|
|
closed = True
|
|||
|
|
elif pnl_pct >= tp_pct:
|
|||
|
|
reason = '止盈'
|
|||
|
|
closed = True
|
|||
|
|
elif hold_sec >= max_hold_seconds:
|
|||
|
|
reason = '超时'
|
|||
|
|
closed = True
|
|||
|
|
elif pos == 1 and ps > prob_threshold + 0.05:
|
|||
|
|
reason = 'AI反转'
|
|||
|
|
closed = True
|
|||
|
|
elif pos == -1 and pl > prob_threshold + 0.05:
|
|||
|
|
reason = 'AI反转'
|
|||
|
|
closed = True
|
|||
|
|
if closed:
|
|||
|
|
pnl_usdt = notional * pnl_pct
|
|||
|
|
trades.append((pos, open_price, price, pnl_usdt, hold_sec, reason, open_time, dt))
|
|||
|
|
cum_pnl += pnl_usdt - net_fee
|
|||
|
|
daily_pnl += pnl_usdt - net_fee
|
|||
|
|
if pnl_usdt < 0:
|
|||
|
|
consec_losses += 1
|
|||
|
|
else:
|
|||
|
|
consec_losses = 0
|
|||
|
|
if cum_pnl > peak_pnl:
|
|||
|
|
peak_pnl = cum_pnl
|
|||
|
|
if consec_losses >= max_consec_losses:
|
|||
|
|
cooldown_until = i + cooldown_bars
|
|||
|
|
pos = 0
|
|||
|
|
|
|||
|
|
# 开仓: 检查风控条件
|
|||
|
|
if pos == 0:
|
|||
|
|
# 风控检查: 冷却期、日亏损限制、回撤熔断
|
|||
|
|
if i < cooldown_until:
|
|||
|
|
continue
|
|||
|
|
if daily_pnl <= -daily_loss_limit:
|
|||
|
|
continue
|
|||
|
|
dd_now = peak_pnl - cum_pnl
|
|||
|
|
if dd_now >= dd_circuit_breaker:
|
|||
|
|
continue
|
|||
|
|
# Meta过滤: 置信度不够则跳过
|
|||
|
|
if mc < meta_threshold:
|
|||
|
|
continue
|
|||
|
|
if pl > prob_threshold and pl > ps:
|
|||
|
|
pos = 1
|
|||
|
|
open_price = price
|
|||
|
|
open_time = dt
|
|||
|
|
elif ps > prob_threshold and ps > pl:
|
|||
|
|
pos = -1
|
|||
|
|
open_price = price
|
|||
|
|
open_time = dt
|
|||
|
|
|
|||
|
|
if pos != 0:
|
|||
|
|
price = df['close'].iloc[-1]
|
|||
|
|
dt = df.index[-1]
|
|||
|
|
pnl_pct = (price - open_price) / open_price if pos == 1 else (open_price - price) / open_price
|
|||
|
|
hold_sec = (dt - open_time).total_seconds()
|
|||
|
|
trades.append((pos, open_price, price, notional * pnl_pct, hold_sec, '结束', open_time, dt))
|
|||
|
|
|
|||
|
|
return trades
|