解决了一个ccxt的bug, okx接口返回内容有baseCcy或者quoteCcy为null, 挂载了一个新的文件替换了容器内的版本, 算是临时解决了,以后要考虑如何持续集成的问题
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zhangkun9038@dingtalk.com
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f815ee89ee
commit
05122af764
8325
ccxt/async_support/okx.py
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8325
ccxt/async_support/okx.py
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File diff suppressed because it is too large
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@ -31,14 +31,17 @@
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},
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"ccxt_async_config": {
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"enableRateLimit": true,
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"rateLimit": 1000,
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"rateLimit": 500,
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"timeout": 20000
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},
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"pair_whitelist": [
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"BTC/USDT",
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"SOL/USDT"
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],
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"pair_blacklist": []
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"pair_blacklist": [
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"LAYER/USD",
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"LAYER/USDT"
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]
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},
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"entry_pricing": {
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"price_side": "same",
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@ -67,7 +70,7 @@
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},
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"freqaimodel": "CatboostClassifier",
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"purge_old_models": 2,
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"identifier": "test130",
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"identifier": "test131",
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"train_period_days": 30,
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"backtest_period_days": 10,
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"live_retrain_hours": 0,
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@ -23,6 +23,7 @@ services:
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- "./config_examples:/freqtrade/config_examples"
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- "./freqtrade/templates:/freqtrade/templates"
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- "./freqtrade/exchange/:/freqtrade/exchange"
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- "./ccxt/async_support/okx.py:/home/ftuser/.local/lib/python3.12/site-packages/ccxt/async_support/okx.py"
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# Expose api on port 8080 (localhost only)
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# Please read the https://www.freqtrade.io/en/stable/rest-api/ documentation
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# for more information.
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@ -11,9 +11,9 @@ logger = logging.getLogger(__name__)
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class FreqaiExampleStrategy(IStrategy):
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minimal_roi = {
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"0": 0.076,
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"7": 0.034,
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"13": 0.007,
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"0": 0.02,
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"7": 0.01,
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"13": 0.005,
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"60": 0
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}
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@ -24,29 +24,25 @@ class FreqaiExampleStrategy(IStrategy):
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startup_candle_count: int = 40
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can_short = False
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# Hyperopt 参数
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buy_rsi = IntParameter(low=10, high=50, default=27, space="buy", optimize=True, load=True)
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sell_rsi = IntParameter(low=50, high=90, default=59, space="sell", optimize=True, load=True)
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roi_0 = DecimalParameter(low=0.01, high=0.2, default=0.038, space="roi", optimize=True, load=True)
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roi_15 = DecimalParameter(low=0.005, high=0.1, default=0.027, space="roi", optimize=True, load=True)
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roi_30 = DecimalParameter(low=0.001, high=0.05, default=0.009, space="roi", optimize=True, load=True)
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stoploss_param = DecimalParameter(low=-0.25, high=-0.05, default=-0.1, space="stoploss", optimize=True, load=True)
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trailing_stop_positive_offset = DecimalParameter(low=0.01, high=0.5, default=0.02, space="trailing", optimize=True, load=True)
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trailing_stop_positive_offset = DecimalParameter(low=0.005, high=0.5, default=0.01, space="trailing", optimize=True, load=True)
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protections = [
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{"method": "StoplossGuard", "stop_duration": 60, "lookback_period": 120},
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{"method": "MaxDrawdown", "lookback_period": 120, "max_allowed_drawdown": 0.05}
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]
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protections = []
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freqai_info = {
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"model": "LightGBMRegressor",
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"feature_parameters": {
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"include_timeframes": ["5m"],
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"include_corr_pairlist": ["SOL/USDT", "BTC/USDT"],
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"include_corr_pairlist": ["SOL/USDT"],
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"label_period_candles": 12,
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"include_shifted_candles": 0,
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"include_periods": [10, 20],
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"DI_threshold": 3.0
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"include_periods": [20],
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"DI_threshold": 5.0
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},
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"data_split_parameters": {
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"test_size": 0.2,
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@ -62,14 +58,20 @@ class FreqaiExampleStrategy(IStrategy):
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}
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plot_config = {
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"main_plot": {},
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"main_plot": {
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"close": {"color": "blue"},
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"bb_lowerband": {"color": "purple"}
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},
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"subplots": {
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"&-buy_rsi": {"&-buy_rsi": {"color": "green"}},
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"&-sell_rsi": {"&-sell_rsi": {"color": "red"}},
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"&-stoploss": {"&-stoploss": {"color": "purple"}},
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"&-roi_0": {"&-roi_0": {"color": "orange"}},
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"rsi": {"rsi": {"color": "black"}},
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"do_predict": {"do_predict": {"color": "brown"}},
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},
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"trade_signals": {
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"enter_long": {"color": "green", "type": "scatter"},
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"exit_long": {"color": "red", "type": "scatter"}
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}
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}
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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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@ -130,12 +132,10 @@ class FreqaiExampleStrategy(IStrategy):
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logger.info(f"DataFrame rows: {len(dataframe)}")
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logger.info(f"Columns before freqai.start: {list(dataframe.columns)}")
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# 验证输入数据
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if "close" not in dataframe.columns or dataframe["close"].isna().all():
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logger.error(f"DataFrame missing 'close' column or all NaN for pair: {metadata['pair']}")
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raise ValueError("DataFrame missing valid 'close' column")
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# 生成 RSI
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if len(dataframe) < 14:
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logger.warning(f"DataFrame too short ({len(dataframe)} rows), cannot compute rsi")
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dataframe["rsi"] = 50
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@ -143,7 +143,6 @@ class FreqaiExampleStrategy(IStrategy):
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dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
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logger.info(f"rsi stats: {dataframe['rsi'].describe().to_string()}")
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# 生成 %-volatility
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if len(dataframe) < 20 or dataframe["close"].isna().any():
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logger.warning(f"DataFrame too short ({len(dataframe)} rows) or contains NaN in close, cannot compute %-volatility")
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dataframe["%-volatility"] = 0
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@ -154,7 +153,6 @@ class FreqaiExampleStrategy(IStrategy):
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dataframe["%-volatility"] = (dataframe["%-volatility"] - dataframe["%-volatility"].mean()) / dataframe["%-volatility"].std()
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logger.info(f"%-volatility stats: {dataframe['%-volatility'].describe().to_string()}")
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# 生成 TEMA
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if len(dataframe) < 9:
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logger.warning(f"DataFrame too short ({len(dataframe)} rows), cannot compute tema")
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dataframe["tema"] = dataframe["close"]
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@ -165,7 +163,6 @@ class FreqaiExampleStrategy(IStrategy):
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dataframe["tema"] = dataframe["tema"].fillna(dataframe["close"])
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logger.info(f"tema stats: {dataframe['tema'].describe().to_string()}")
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# 生成 Bollinger Bands
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if len(dataframe) < 20:
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logger.warning(f"DataFrame too short ({len(dataframe)} rows), cannot compute bb_lowerband")
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dataframe["bb_lowerband"] = dataframe["close"]
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@ -177,21 +174,6 @@ class FreqaiExampleStrategy(IStrategy):
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dataframe["bb_lowerband"] = dataframe["bb_lowerband"].fillna(dataframe["close"])
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logger.info(f"bb_lowerband stats: {dataframe['bb_lowerband'].describe().to_string()}")
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# 生成 up_or_down
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label_period = self.freqai_info["feature_parameters"]["label_period_candles"]
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if len(dataframe) < label_period + 1:
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logger.warning(f"DataFrame too short ({len(dataframe)} rows), cannot compute up_or_down")
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dataframe["up_or_down"] = 0
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else:
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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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if dataframe["up_or_down"].isna().any():
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logger.warning("up_or_down contains NaN, filling with 0")
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dataframe["up_or_down"] = dataframe["up_or_down"].fillna(0)
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logger.info(f"up_or_down stats: {dataframe['up_or_down'].describe().to_string()}")
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# 生成其他特征
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if "date" in dataframe.columns:
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dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
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dataframe["%-hour_of_day"] = dataframe["date"].dt.hour
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@ -200,7 +182,6 @@ class FreqaiExampleStrategy(IStrategy):
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dataframe["%-day_of_week"] = 0
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dataframe["%-hour_of_day"] = 0
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# 调用 FreqAI
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try:
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dataframe = self.freqai.start(dataframe, metadata, self)
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logger.info(f"Columns after freqai.start: {list(dataframe.columns)}")
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@ -210,26 +191,23 @@ class FreqaiExampleStrategy(IStrategy):
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dataframe["sell_rsi_pred"] = 80
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dataframe["do_predict"] = 1
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# 检查预测列
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for col in ["buy_rsi_pred", "sell_rsi_pred"]:
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if col not in dataframe.columns:
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logger.error(f"Error: {col} column not generated for pair: {metadata['pair']}")
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dataframe[col] = 50 if col == "buy_rsi_pred" else 80
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logger.info(f"{col} stats: {dataframe[col].describe().to_string()}")
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# 调试特征分布
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if "%-bb_width-period_10_SOL/USDT_5m" in dataframe.columns:
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if dataframe["%-bb_width-period_10_SOL/USDT_5m"].std() > 0:
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dataframe["%-bb_width-period_10_SOL/USDT_5m"] = (
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dataframe["%-bb_width-period_10_SOL/USDT_5m"] - dataframe["%-bb_width-period_10_SOL/USDT_5m"].mean()
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) / dataframe["%-bb_width-period_10_SOL/USDT_5m"].std()
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logger.info(f"%-bb_width-period_10 stats: {dataframe['%-bb_width-period_10_SOL/USDT_5m'].describe().to_string()}")
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if "%-bb_width-period_20_SOL/USDT_5m" in dataframe.columns:
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if dataframe["%-bb_width-period_20_SOL/USDT_5m"].std() > 0:
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dataframe["%-bb_width-period_20_SOL/USDT_5m"] = (
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dataframe["%-bb_width-period_20_SOL/USDT_5m"] - dataframe["%-bb_width-period_20_SOL/USDT_5m"].mean()
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) / dataframe["%-bb_width-period_20_SOL/USDT_5m"].std()
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logger.info(f"%-bb_width-period_20 stats: {dataframe['%-bb_width-period_20_SOL/USDT_5m'].describe().to_string()}")
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# 动态生成期望的特征列
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def get_expected_columns(freqai_config: dict) -> list:
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indicators = ["rsi", "bb_width", "pct-change"]
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periods = freqai_config.get("feature_parameters", {}).get("include_periods", [10, 20])
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pairs = freqai_config.get("include_corr_pairlist", ["SOL/USDT", "BTC/USDT"])
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periods = freqai_config.get("feature_parameters", {}).get("include_periods", [20])
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pairs = freqai_config.get("include_corr_pairlist", ["SOL/USDT"])
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timeframes = freqai_config.get("include_timeframes", ["5m"])
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shifts = [0]
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expected_columns = ["%-volatility", "%-day_of_week", "%-hour_of_day"]
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@ -248,50 +226,47 @@ class FreqaiExampleStrategy(IStrategy):
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expected_columns = get_expected_columns(self.freqai_info)
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logger.info(f"Expected feature columns ({len(expected_columns)}): {expected_columns[:10]}...")
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# 比较特征集
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actual_columns = list(dataframe.columns)
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missing_columns = [col for col in expected_columns if col not in actual_columns]
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extra_columns = [col for col in actual_columns if col not in expected_columns and col.startswith("%-")]
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logger.info(f"Missing columns ({len(missing_columns)}): {missing_columns}")
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logger.info(f"Extra columns ({len(extra_columns)}): {extra_columns}")
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# 调试 DI 丢弃预测
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if "DI_values" in dataframe.columns:
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logger.info(f"DI_values stats: {dataframe['DI_values'].describe().to_string()}")
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logger.info(f"DI discarded predictions: {len(dataframe[dataframe['do_predict'] == 0])}")
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# 清理数据
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dataframe = dataframe.replace([np.inf, -np.inf], 0).ffill().fillna(0)
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logger.info(f"Final columns in populate_indicators: {list(dataframe.columns)}")
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return dataframe
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def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
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enter_long_conditions = [
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qtpylib.crossed_above(df["rsi"], df["buy_rsi_pred"] + (5 if metadata["pair"] == "BTC/USDT" else 0)),
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qtpylib.crossed_above(df["rsi"], df["buy_rsi_pred"]),
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df["tema"] > df["tema"].shift(1),
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df["volume"] > 0,
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df["do_predict"] == 1,
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df["up_or_down"] == 1
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df["do_predict"] == 1
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]
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if enter_long_conditions:
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df.loc[
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reduce(lambda x, y: x & y, enter_long_conditions),
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["enter_long", "enter_tag"]
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] = (1, "long")
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df["entry_signal"] = reduce(lambda x, y: x & y, enter_long_conditions)
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df["entry_signal"] = df["entry_signal"].rolling(window=2, min_periods=1).max().astype(bool)
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df.loc[
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df["entry_signal"],
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["enter_long", "enter_tag"]
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] = (1, "long")
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if df["entry_signal"].iloc[-1]:
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logger.info(f"Entry signal triggered for {metadata['pair']}: rsi={df['rsi'].iloc[-1]}, buy_rsi_pred={df['buy_rsi_pred'].iloc[-1]}, do_predict={df['do_predict'].iloc[-1]}")
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return df
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def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
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exit_long_conditions = [
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(qtpylib.crossed_above(df["rsi"], df["sell_rsi_pred"])) |
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(qtpylib.crossed_above(df["rsi"], df["sell_rsi_pred"] - 5)) |
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(df["close"] < df["close"].shift(1) * 0.98) |
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(df["close"] < df["bb_lowerband"]),
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df["volume"] > 0,
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df["do_predict"] == 1,
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df["up_or_down"] == 0
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df["do_predict"] == 1
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]
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time_exit = (df["date"] >= df["date"].shift(1) + pd.Timedelta(days=1))
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df.loc[
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(reduce(lambda x, y: x & y, exit_long_conditions)) | time_exit,
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reduce(lambda x, y: x & y, exit_long_conditions),
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"exit_long"
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] = 1
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return df
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@ -300,9 +275,16 @@ class FreqaiExampleStrategy(IStrategy):
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self, pair: str, order_type: str, amount: float, rate: float,
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time_in_force: str, current_time, entry_tag, side: str, **kwargs
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) -> bool:
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logger.info(f"Confirming trade entry for {pair}, order_type: {order_type}, rate: {rate}, current_time: {current_time}")
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df, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
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last_candle = df.iloc[-1].squeeze()
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if side == "long":
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if rate > (last_candle["close"] * (1 + 0.001)):
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return False
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if order_type == "market":
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logger.info(f"Order confirmed for {pair}, rate: {rate} (market order)")
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return True
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if rate <= (last_candle["close"] * (1 + 0.01)):
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logger.info(f"Order confirmed for {pair}, rate: {rate}")
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return True
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logger.info(f"Order rejected: rate {rate} exceeds threshold {last_candle['close'] * 1.01}")
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return False
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return True
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Reference in New Issue
Block a user