265 lines
12 KiB
Diff
265 lines
12 KiB
Diff
diff --git "a/4. **\346\270\205\347\220\206\347\274\223\345\255\230**\357\274\232" "b/4. **\346\270\205\347\220\206\347\274\223\345\255\230**\357\274\232"
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new file mode 100644
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index 0000000..3bb7671
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--- /dev/null
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+++ "b/4. **\346\270\205\347\220\206\347\274\223\345\255\230**\357\274\232"
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@@ -0,0 +1,5 @@
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+
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+<<<<<<< HEAD
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+=======
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+ rm -rf /freqtrade/user_data/models/test62/
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+>>>>>>> Snippet
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diff --git "a/5. **\351\207\215\346\226\260\350\256\255\347\273\203**\357\274\232" "b/5. **\351\207\215\346\226\260\350\256\255\347\273\203**\357\274\232"
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new file mode 100644
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index 0000000..8a18d3d
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--- /dev/null
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+++ "b/5. **\351\207\215\346\226\260\350\256\255\347\273\203**\357\274\232"
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@@ -0,0 +1,5 @@
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+
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+<<<<<<< HEAD
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+=======
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+ freqtrade trade --config config_examples/config_freqai.okx.json --strategy FreqaiExampleStrategy
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+>>>>>>> Snippet
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diff --git a/config_examples/config_freqai.okx.json b/config_examples/config_freqai.okx.json
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index 1816983..4535010 100644
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--- a/config_examples/config_freqai.okx.json
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+++ b/config_examples/config_freqai.okx.json
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@@ -67,45 +67,31 @@
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"freqaimodel": "CatboostClassifier",
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"purge_old_models": 2,
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"train_period_days": 15,
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- "identifier": "test62",
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- "train_period_days": 30,
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- "backtest_period_days": 10,
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+ "train_period_days": 180,
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+ "backtest_period_days": 60,
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"live_retrain_hours": 0,
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"feature_selection": {
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"method": "recursive_elimination"
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},
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"feature_parameters": {
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- "include_timeframes": [
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- "3m",
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- "15m",
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- "1h"
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- ],
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- "include_corr_pairlist": [
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- "BTC/USDT",
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- "SOL/USDT"
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- ],
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- "label_period_candles": 20,
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- "include_shifted_candles": 2,
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- "DI_threshold": 0.9,
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+ "include_timeframes": ["15m"],
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+ "include_corr_pairlist": ["BTC/USDT"],
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+ "label_period_candles": 10,
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+ "include_shifted_candles": 1,
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+ "DI_threshold": 0.7,
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"weight_factor": 0.9,
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"principal_component_analysis": false,
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"use_SVM_to_remove_outliers": false,
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- "indicator_periods_candles": [
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- 10,
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- 20,
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- 50
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- ],
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- "plot_feature_importances": 0
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+ "indicator_periods_candles": [14],
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},
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"data_split_parameters": {
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"test_size": 0.2
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},
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- "model_training_parameters": {
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- "n_estimators": 100,
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- "learning_rate": 0.05,
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- "max_depth": 5,
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- "num_leaves": 31
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- }
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+ "model_training_parameters": {
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+ "n_estimators": 100,
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+ "learning_rate": 0.05,
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+ "max_depth": 5
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+ }
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},
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"api_server": {
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"enabled": true,
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diff --git a/docker-compose.yml b/docker-compose.yml
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index defe81e..aeb31e6 100644
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--- a/docker-compose.yml
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+++ b/docker-compose.yml
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@@ -64,7 +64,7 @@ services:
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command: >
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backtesting
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--logfile /freqtrade/user_data/logs/freqtrade.log
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- --freqaimodel LightGBMRegressor
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+ --freqaimodel XGBoostRegressor
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--config /freqtrade/config_examples/config_freqai.okx.json
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--config /freqtrade/templates/FreqaiExampleStrategy.json
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--strategy-path /freqtrade/templates
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diff --git a/freqtrade/templates/FreqaiExampleStrategy.py b/freqtrade/templates/FreqaiExampleStrategy.py
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index 688e644..e27e17b 100644
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--- a/freqtrade/templates/FreqaiExampleStrategy.py
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+++ b/freqtrade/templates/FreqaiExampleStrategy.py
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@@ -30,12 +30,12 @@ class FreqaiExampleStrategy(IStrategy):
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# FreqAI 配置
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freqai_info = {
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- "model": "XGBoostRegressor", # 改用XGBoost
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+ "model": "CatboostClassifier", # 与config保持一致
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"feature_parameters": {
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- "include_timeframes": ["5m", "15m", "1h"],
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- "include_corr_pairlist": [],
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- "label_period_candles": 12,
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- "include_shifted_candles": 3,
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+ "include_timeframes": ["3m", "15m", "1h"], # 与config一致
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+ "include_corr_pairlist": ["BTC/USDT", "SOL/USDT"], # 添加相关交易对
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+ "label_period_candles": 20, # 与config一致
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+ "include_shifted_candles": 2, # 与config一致
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},
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"data_split_parameters": {
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"test_size": 0.2,
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@@ -72,54 +72,26 @@ class FreqaiExampleStrategy(IStrategy):
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}
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def feature_engineering_expand_all(self, dataframe: DataFrame, period: int, metadata: dict, **kwargs) -> DataFrame:
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- # 添加更多技术指标
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+ # 保留关键的技术指标
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dataframe["%-rsi"] = ta.RSI(dataframe, timeperiod=14)
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- dataframe["%-mfi"] = ta.MFI(dataframe, timeperiod=14)
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- dataframe["%-sma"] = ta.SMA(dataframe, timeperiod=20)
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- dataframe["%-ema"] = ta.EMA(dataframe, timeperiod=20)
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- dataframe["%-adx"] = ta.ADX(dataframe, timeperiod=14)
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- dataframe["%-atr"] = ta.ATR(dataframe, timeperiod=14)
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- dataframe["%-obv"] = ta.OBV(dataframe)
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- dataframe["%-cci"] = ta.CCI(dataframe, timeperiod=20)
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- dataframe["%-stoch"] = ta.STOCH(dataframe)['slowk']
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- dataframe["%-macd"] = ta.MACD(dataframe)['macd']
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- dataframe["%-macdsignal"] = ta.MACD(dataframe)['macdsignal']
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- dataframe["%-macdhist"] = ta.MACD(dataframe)['macdhist']
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- dataframe["%-willr"] = ta.WILLR(dataframe, timeperiod=14)
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- dataframe["%-ultosc"] = ta.ULTOSC(dataframe)
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- dataframe["%-trix"] = ta.TRIX(dataframe, timeperiod=14)
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- dataframe["%-ad"] = ta.ADOSC(dataframe)
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- dataframe["%-mom"] = ta.MOM(dataframe, timeperiod=10)
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- dataframe["%-roc"] = ta.ROC(dataframe, timeperiod=10)
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-
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- # 添加布林带相关特征
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+ macd = ta.MACD(dataframe, fastperiod=12, slowperiod=26, signalperiod=9)
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+ dataframe["%-macd"] = macd["macd"]
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+ dataframe["%-macdsignal"] = macd["macdsignal"]
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+
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+ # 保留布林带相关特征
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bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
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dataframe["bb_lowerband"] = bollinger["lower"]
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dataframe["bb_middleband"] = bollinger["mid"]
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dataframe["bb_upperband"] = bollinger["upper"]
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dataframe["bb_width"] = (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"]
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dataframe["bb_pct"] = (dataframe["close"] - dataframe["bb_lowerband"]) / (dataframe["bb_upperband"] - dataframe["bb_lowerband"])
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-
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- # 添加成交量相关特征
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+
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+ # 保留成交量相关特征
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dataframe["volume_ma"] = dataframe["volume"].rolling(window=20).mean()
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dataframe["volume_roc"] = dataframe["volume"].pct_change(periods=10)
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- dataframe["volume_obv"] = ta.OBV(dataframe)
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-
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- # 添加价格相关特征
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- dataframe["close_ma"] = dataframe["close"].rolling(window=20).mean()
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+
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+ # 保留价格变化率
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dataframe["close_roc"] = dataframe["close"].pct_change(periods=10)
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- dataframe["close_log_ret"] = np.log(dataframe["close"]).diff()
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- dataframe["close_zscore"] = (dataframe["close"] - dataframe["close"].rolling(window=20).mean()) / dataframe["close"].rolling(window=20).std()
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-
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- # 添加时间相关特征
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- dataframe["hour"] = dataframe["date"].dt.hour
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- dataframe["day_of_week"] = dataframe["date"].dt.dayofweek
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- dataframe["is_weekend"] = dataframe["day_of_week"].isin([5, 6]).astype(int)
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-
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- # 添加波动率相关特征
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- dataframe["volatility"] = dataframe["close"].pct_change().rolling(window=20).std()
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- dataframe["volatility_ma"] = dataframe["volatility"].rolling(window=20).mean()
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- dataframe["volatility_roc"] = dataframe["volatility"].pct_change(periods=10)
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# 改进数据清理
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for col in dataframe.columns:
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@@ -170,16 +142,23 @@ class FreqaiExampleStrategy(IStrategy):
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try:
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label_period = self.freqai_info["feature_parameters"]["label_period_candles"]
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+
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+ # 生成更复杂的目标变量 up_or_down
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+ dataframe["up_or_down"] = np.where(
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+ dataframe["close"].shift(-label_period) > dataframe["close"], 1, 0
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+ )
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+ # 确保目标变量是二维数组
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+ if dataframe["up_or_down"].ndim == 1:
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+ dataframe["up_or_down"] = dataframe["up_or_down"].values.reshape(-1, 1)
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+
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# 生成 %-volatility 特征
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dataframe["%-volatility"] = dataframe["close"].pct_change().rolling(20).std()
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- # 单一回归目标
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- # 移除对未来的数据依赖
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# 确保 &-buy_rsi 列的值计算正确
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dataframe["&-buy_rsi"] = ta.RSI(dataframe, timeperiod=14)
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# 数据清理
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- for col in ["&-buy_rsi"]:
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+ for col in ["&-buy_rsi", "up_or_down", "%-volatility"]:
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# 使用直接操作避免链式赋值
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dataframe[col] = dataframe[col].replace([np.inf, -np.inf], np.nan)
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dataframe[col] = dataframe[col].ffill() # 替代 fillna(method='ffill')
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@@ -187,19 +166,13 @@ class FreqaiExampleStrategy(IStrategy):
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if dataframe[col].isna().any():
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logger.warning(f"目标列 {col} 仍包含 NaN，填充为默认值")
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- # 数据清理
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- for col in ["&-buy_rsi", "%-volatility"]:
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- # 使用直接操作避免链式赋值
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- dataframe[col] = dataframe[col].replace([np.inf, -np.inf], 0)
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- dataframe[col] = dataframe[col].ffill() # 替代 fillna(method='ffill')
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- dataframe[col] = dataframe[col].fillna(0)
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- if dataframe[col].isna().any():
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- logger.warning(f"目标列 {col} 仍包含 NaN，检查数据生成逻辑")
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except Exception as e:
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logger.error(f"创建 FreqAI 目标失败：{str(e)}")
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raise
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- logger.info(f"目标列预览：\n{dataframe[['&-buy_rsi']].head().to_string()}")
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+ # Log the shape of the target variable for debugging
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+ logger.info(f"目标列形状：{dataframe['up_or_down'].shape}")
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+ logger.info(f"目标列预览：\n{dataframe[['up_or_down', '&-buy_rsi']].head().to_string()}")
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return dataframe
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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@@ -237,13 +210,13 @@ class FreqaiExampleStrategy(IStrategy):
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dataframe[col] = dataframe[col].fillna(-0.1 if col == "&-stoploss" else 0)
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# 简化动态参数生成逻辑
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-# 简化 buy_rsi 和 sell_rsi 的生成逻辑
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+ # 放松 buy_rsi 和 sell_rsi 的生成逻辑
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# 计算 buy_rsi_pred 并清理 NaN 值
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- dataframe["buy_rsi_pred"] = dataframe["&-buy_rsi"].rolling(window=10).mean().clip(20, 40)
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+ dataframe["buy_rsi_pred"] = dataframe["&-buy_rsi"].rolling(window=10).mean().clip(30, 50)
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dataframe["buy_rsi_pred"] = dataframe["buy_rsi_pred"].fillna(dataframe["buy_rsi_pred"].mean())
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# 计算 sell_rsi_pred 并清理 NaN 值
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- dataframe["sell_rsi_pred"] = dataframe["buy_rsi_pred"] + 20
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+ dataframe["sell_rsi_pred"] = dataframe["buy_rsi_pred"] + 40
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dataframe["sell_rsi_pred"] = dataframe["sell_rsi_pred"].fillna(dataframe["sell_rsi_pred"].mean())
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# 计算 stoploss_pred 并清理 NaN 值
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@@ -308,8 +281,9 @@ class FreqaiExampleStrategy(IStrategy):
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# 改进买入信号条件
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enter_long_conditions = [
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(df["rsi"] < df["buy_rsi_pred"]), # RSI 低于买入阈值
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- (df["volume"] > df["volume"].rolling(window=10).mean()), # 成交量高于近期均值
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- (df["close"] > df["bb_middleband"]) # 价格高于布林带中轨
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+ (df["volume"] > df["volume"].rolling(window=10).mean() * 1.2), # 成交量高于近期均值20%
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+ (df["close"] > df["bb_middleband"]), # 价格高于布林带中轨
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+ (df["do_predict"] == 1) # 确保模型预测为买入
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]
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if enter_long_conditions:
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df.loc[
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