myTestFreqAI/config_examples/live_XGBoostRegressorMultiTarget.json.bak
2026-02-15 00:10:52 +08:00

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{
"stake_amount": 24,
"dry_run": false,
"exchange": {
"name": "okx",
"key": "cbda9fde-b9e3-4a2d-94f9-e5c3705dfb5c",
"secret": "CD3B7DB459CBBD540E0926E5C48150E1",
"password": "c^-d:g;P2S9?Q#^",
"enable_ws": false,
"ccxt_config": {
"enableRateLimit": true,
"rateLimit": 500,
"options": {
"defaultType": "spot"
}
},
"ccxt_async_config": {
"enableRateLimit": true,
"rateLimit": 3000,
"timeout": 20000
},
"pair_whitelist": [
"ADA/USDT",
"AVAX/USDT",
"BNB/USDT",
"BTC/USDT",
"DOGE/USDT",
"DOT/USDT",
"ETH/USDT",
"LINK/USDT",
"LTC/USDT",
"PEPE/USDT",
"SOL/USDT",
"SUI/USDT",
"TON/USDT",
"TRB/USDT",
"TRUMP/USDT",
"TRX/USDT",
"UNI/USDT",
"WLD/USDT",
"XAUT/USDT",
"XLM/USDT",
"XRP/USDT"
],
"pair_blacklist": [
"OKB/USDT"
]
},
"pairlists": [
{
"method": "StaticPairList"
}
],
"order_types": {
"entry": "limit",
"exit": "limit",
"stoploss": "limit",
"stoploss_on_exchange": false
},
"order_time_in_force": {
"entry": "gtc",
"exit": "gtc"
},
"entry_pricing": {
"price_side": "other",
"use_order_book": false,
"price_last_balance": 0
},
"exit_pricing": {
"price_side": "other",
"use_order_book": false
},
"freqai": {
"enabled": true,
"data_kitchen": {
"fillna": "ffill"
},
"freqaimodel": "XGBoostRegressorMultiTarget",
"purge_old_models": 2,
"identifier": "live-freqai",
"train_period_days": 18,
"backtest_period_days": 2,
"live_retrain_hours": 2,
"outlier_detection": {
"method": "IsolationForest",
"contamination": 0.15 // 适度提高异常值检测阈值,过滤更多噪声
},
"feature_selection": {
"method": "recursive_elimination",
},
"feature_parameters": {
"include_timeframes": [
"3m",
"15m",
"1h",
"4h"
],
"include_corr_pairlist": [
"XAUT/USDT",
"BTC/USDT"
],
"outlier_protection_percentage": 0.1,
"label_period_candles": 100,
"include_shifted_candles": 2, // 减少移位蜡烛数,降低特征冗余
"DI_threshold": 0.5,
"weight_factor": 0.9,
"principal_component_analysis": true, // 开启PCA降维减少特征维度
"use_SVM_to_remove_outliers": true,
"indicator_periods_candles": [
10,
20,
50 // 减少指标周期数,避免特征过多
],
"plot_feature_importances": 10
},
"data_split_parameters": {
"test_size": 0.2, // 增大测试集比例,更严格验证模型泛化能力
"shuffle": false
},
"model_training_parameters": {
"n_estimators": 200, // 减少树的数量,降低模型复杂度
"learning_rate": 0.05, // 适度提高学习率,加快收敛且避免过拟合
"max_depth": 5, // 降低树深度,核心缓解过拟合
"min_child_weight": 3, // 提高子节点最小权重,避免拟合噪声
"subsample": 0.7, // 降低样本采样率,增强正则化
"colsample_bytree": 0.7, // 降低特征采样率,避免过拟合
"verbosity": 0,
"reg_alpha": 0.1, // 新增L1正则化限制权重
"reg_lambda": 1.0 // 新增L2正则化核心缓解过拟合
}
},
"fee": 0.0008,
"api_server": {
"enabled": true,
"listen_ip_address": "0.0.0.0",
"listen_port": 8080
}
}