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

config_examples/config_freqai.okx.json

@@ -31,14 +31,17 @@
         },
         "ccxt_async_config": {
             "enableRateLimit": true,
-	          "rateLimit": 1000,
+	          "rateLimit": 500,
             "timeout": 20000
         },
         "pair_whitelist": [
             "BTC/USDT",
             "SOL/USDT"
         ],
-        "pair_blacklist": []
+        "pair_blacklist": [
+            "LAYER/USD",
+            "LAYER/USDT"
+        ]
     },
     "entry_pricing": {
         "price_side": "same",
@@ -67,7 +70,7 @@
         },
         "freqaimodel": "CatboostClassifier",
         "purge_old_models": 2,
-        "identifier": "test130",
+        "identifier": "test131",
         "train_period_days": 30,
         "backtest_period_days": 10,
         "live_retrain_hours": 0,

docker-compose.yml

@@ -23,6 +23,7 @@ services:
       - "./config_examples:/freqtrade/config_examples"
       - "./freqtrade/templates:/freqtrade/templates"
       - "./freqtrade/exchange/:/freqtrade/exchange"
+      - "./ccxt/async_support/okx.py:/home/ftuser/.local/lib/python3.12/site-packages/ccxt/async_support/okx.py" 
     # Expose api on port 8080 (localhost only)
     # Please read the https://www.freqtrade.io/en/stable/rest-api/ documentation
     # for more information.

freqtrade/templates/FreqaiExampleStrategy.py

@@ -11,9 +11,9 @@ logger = logging.getLogger(__name__)
 
 class FreqaiExampleStrategy(IStrategy):
     minimal_roi = {
-        "0": 0.076,
+        "0": 0.02,
-        "7": 0.034,
+        "7": 0.01,
-        "13": 0.007,
+        "13": 0.005,
         "60": 0
     }
 
@@ -24,29 +24,25 @@ class FreqaiExampleStrategy(IStrategy):
     startup_candle_count: int = 40
     can_short = False
 
-    # Hyperopt 参数
     buy_rsi = IntParameter(low=10, high=50, default=27, space="buy", optimize=True, load=True)
     sell_rsi = IntParameter(low=50, high=90, default=59, space="sell", optimize=True, load=True)
     roi_0 = DecimalParameter(low=0.01, high=0.2, default=0.038, space="roi", optimize=True, load=True)
     roi_15 = DecimalParameter(low=0.005, high=0.1, default=0.027, space="roi", optimize=True, load=True)
     roi_30 = DecimalParameter(low=0.001, high=0.05, default=0.009, space="roi", optimize=True, load=True)
     stoploss_param = DecimalParameter(low=-0.25, high=-0.05, default=-0.1, space="stoploss", optimize=True, load=True)
-    trailing_stop_positive_offset = DecimalParameter(low=0.01, high=0.5, default=0.02, space="trailing", optimize=True, load=True)
+    trailing_stop_positive_offset = DecimalParameter(low=0.005, high=0.5, default=0.01, space="trailing", optimize=True, load=True)
 
-    protections = [
+    protections = []
-        {"method": "StoplossGuard", "stop_duration": 60, "lookback_period": 120},
-        {"method": "MaxDrawdown", "lookback_period": 120, "max_allowed_drawdown": 0.05}
-    ]
 
     freqai_info = {
         "model": "LightGBMRegressor",
         "feature_parameters": {
             "include_timeframes": ["5m"],
-            "include_corr_pairlist": ["SOL/USDT", "BTC/USDT"],
+            "include_corr_pairlist": ["SOL/USDT"],
             "label_period_candles": 12,
             "include_shifted_candles": 0,
-            "include_periods": [10, 20],
+            "include_periods": [20],
-            "DI_threshold": 3.0
+            "DI_threshold": 5.0
         },
         "data_split_parameters": {
             "test_size": 0.2,
@@ -62,14 +58,20 @@ class FreqaiExampleStrategy(IStrategy):
     }
 
     plot_config = {
-        "main_plot": {},
+        "main_plot": {
+            "close": {"color": "blue"},
+            "bb_lowerband": {"color": "purple"}
+        },
         "subplots": {
             "&-buy_rsi": {"&-buy_rsi": {"color": "green"}},
             "&-sell_rsi": {"&-sell_rsi": {"color": "red"}},
-            "&-stoploss": {"&-stoploss": {"color": "purple"}},
+            "rsi": {"rsi": {"color": "black"}},
-            "&-roi_0": {"&-roi_0": {"color": "orange"}},
             "do_predict": {"do_predict": {"color": "brown"}},
-        },
+            "trade_signals": {
+                "enter_long": {"color": "green", "type": "scatter"},
+                "exit_long": {"color": "red", "type": "scatter"}
+            }
+        }
     }
 
     def feature_engineering_expand_all(self, dataframe: DataFrame, period: int, metadata: dict, **kwargs) -> DataFrame:
@@ -130,12 +132,10 @@ class FreqaiExampleStrategy(IStrategy):
         logger.info(f"DataFrame rows: {len(dataframe)}")
         logger.info(f"Columns before freqai.start: {list(dataframe.columns)}")
 
-        # 验证输入数据
         if "close" not in dataframe.columns or dataframe["close"].isna().all():
             logger.error(f"DataFrame missing 'close' column or all NaN for pair: {metadata['pair']}")
             raise ValueError("DataFrame missing valid 'close' column")
 
-        # 生成 RSI
         if len(dataframe) < 14:
             logger.warning(f"DataFrame too short ({len(dataframe)} rows), cannot compute rsi")
             dataframe["rsi"] = 50
@@ -143,7 +143,6 @@ class FreqaiExampleStrategy(IStrategy):
             dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
             logger.info(f"rsi stats: {dataframe['rsi'].describe().to_string()}")
 
-        # 生成 %-volatility
         if len(dataframe) < 20 or dataframe["close"].isna().any():
             logger.warning(f"DataFrame too short ({len(dataframe)} rows) or contains NaN in close, cannot compute %-volatility")
             dataframe["%-volatility"] = 0
@@ -154,7 +153,6 @@ class FreqaiExampleStrategy(IStrategy):
                 dataframe["%-volatility"] = (dataframe["%-volatility"] - dataframe["%-volatility"].mean()) / dataframe["%-volatility"].std()
             logger.info(f"%-volatility stats: {dataframe['%-volatility'].describe().to_string()}")
 
-        # 生成 TEMA
         if len(dataframe) < 9:
             logger.warning(f"DataFrame too short ({len(dataframe)} rows), cannot compute tema")
             dataframe["tema"] = dataframe["close"]
@@ -165,7 +163,6 @@ class FreqaiExampleStrategy(IStrategy):
                 dataframe["tema"] = dataframe["tema"].fillna(dataframe["close"])
             logger.info(f"tema stats: {dataframe['tema'].describe().to_string()}")
 
-        # 生成 Bollinger Bands
         if len(dataframe) < 20:
             logger.warning(f"DataFrame too short ({len(dataframe)} rows), cannot compute bb_lowerband")
             dataframe["bb_lowerband"] = dataframe["close"]
@@ -177,21 +174,6 @@ class FreqaiExampleStrategy(IStrategy):
                 dataframe["bb_lowerband"] = dataframe["bb_lowerband"].fillna(dataframe["close"])
             logger.info(f"bb_lowerband stats: {dataframe['bb_lowerband'].describe().to_string()}")
 
-        # 生成 up_or_down
-        label_period = self.freqai_info["feature_parameters"]["label_period_candles"]
-        if len(dataframe) < label_period + 1:
-            logger.warning(f"DataFrame too short ({len(dataframe)} rows), cannot compute up_or_down")
-            dataframe["up_or_down"] = 0
-        else:
-            dataframe["up_or_down"] = np.where(
-                dataframe["close"].shift(-label_period) > dataframe["close"], 1, 0
-            )
-            if dataframe["up_or_down"].isna().any():
-                logger.warning("up_or_down contains NaN, filling with 0")
-                dataframe["up_or_down"] = dataframe["up_or_down"].fillna(0)
-            logger.info(f"up_or_down stats: {dataframe['up_or_down'].describe().to_string()}")
-
-        # 生成其他特征
         if "date" in dataframe.columns:
             dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
             dataframe["%-hour_of_day"] = dataframe["date"].dt.hour
@@ -200,7 +182,6 @@ class FreqaiExampleStrategy(IStrategy):
             dataframe["%-day_of_week"] = 0
             dataframe["%-hour_of_day"] = 0
 
-        # 调用 FreqAI
         try:
             dataframe = self.freqai.start(dataframe, metadata, self)
             logger.info(f"Columns after freqai.start: {list(dataframe.columns)}")
@@ -210,26 +191,23 @@ class FreqaiExampleStrategy(IStrategy):
             dataframe["sell_rsi_pred"] = 80
             dataframe["do_predict"] = 1
 
-        # 检查预测列
         for col in ["buy_rsi_pred", "sell_rsi_pred"]:
             if col not in dataframe.columns:
                 logger.error(f"Error: {col} column not generated for pair: {metadata['pair']}")
                 dataframe[col] = 50 if col == "buy_rsi_pred" else 80
             logger.info(f"{col} stats: {dataframe[col].describe().to_string()}")
 
-        # 调试特征分布
+        if "%-bb_width-period_20_SOL/USDT_5m" in dataframe.columns:
-        if "%-bb_width-period_10_SOL/USDT_5m" in dataframe.columns:
+            if dataframe["%-bb_width-period_20_SOL/USDT_5m"].std() > 0:
-            if dataframe["%-bb_width-period_10_SOL/USDT_5m"].std() > 0:
+                dataframe["%-bb_width-period_20_SOL/USDT_5m"] = (
-                dataframe["%-bb_width-period_10_SOL/USDT_5m"] = (
+                    dataframe["%-bb_width-period_20_SOL/USDT_5m"] - dataframe["%-bb_width-period_20_SOL/USDT_5m"].mean()
-                    dataframe["%-bb_width-period_10_SOL/USDT_5m"] - dataframe["%-bb_width-period_10_SOL/USDT_5m"].mean()
+                ) / dataframe["%-bb_width-period_20_SOL/USDT_5m"].std()
-                ) / dataframe["%-bb_width-period_10_SOL/USDT_5m"].std()
+            logger.info(f"%-bb_width-period_20 stats: {dataframe['%-bb_width-period_20_SOL/USDT_5m'].describe().to_string()}")
-            logger.info(f"%-bb_width-period_10 stats: {dataframe['%-bb_width-period_10_SOL/USDT_5m'].describe().to_string()}")
 
-        # 动态生成期望的特征列
         def get_expected_columns(freqai_config: dict) -> list:
             indicators = ["rsi", "bb_width", "pct-change"]
-            periods = freqai_config.get("feature_parameters", {}).get("include_periods", [10, 20])
+            periods = freqai_config.get("feature_parameters", {}).get("include_periods", [20])
-            pairs = freqai_config.get("include_corr_pairlist", ["SOL/USDT", "BTC/USDT"])
+            pairs = freqai_config.get("include_corr_pairlist", ["SOL/USDT"])
             timeframes = freqai_config.get("include_timeframes", ["5m"])
             shifts = [0]
             expected_columns = ["%-volatility", "%-day_of_week", "%-hour_of_day"]
@@ -248,50 +226,47 @@ class FreqaiExampleStrategy(IStrategy):
         expected_columns = get_expected_columns(self.freqai_info)
         logger.info(f"Expected feature columns ({len(expected_columns)}): {expected_columns[:10]}...")
 
-        # 比较特征集
         actual_columns = list(dataframe.columns)
         missing_columns = [col for col in expected_columns if col not in actual_columns]
         extra_columns = [col for col in actual_columns if col not in expected_columns and col.startswith("%-")]
         logger.info(f"Missing columns ({len(missing_columns)}): {missing_columns}")
         logger.info(f"Extra columns ({len(extra_columns)}): {extra_columns}")
 
-        # 调试 DI 丢弃预测
         if "DI_values" in dataframe.columns:
             logger.info(f"DI_values stats: {dataframe['DI_values'].describe().to_string()}")
             logger.info(f"DI discarded predictions: {len(dataframe[dataframe['do_predict'] == 0])}")
 
-        # 清理数据
         dataframe = dataframe.replace([np.inf, -np.inf], 0).ffill().fillna(0)
         logger.info(f"Final columns in populate_indicators: {list(dataframe.columns)}")
         return dataframe
 
     def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
         enter_long_conditions = [
-            qtpylib.crossed_above(df["rsi"], df["buy_rsi_pred"] + (5 if metadata["pair"] == "BTC/USDT" else 0)),
+            qtpylib.crossed_above(df["rsi"], df["buy_rsi_pred"]),
             df["tema"] > df["tema"].shift(1),
             df["volume"] > 0,
-            df["do_predict"] == 1,
+            df["do_predict"] == 1
-            df["up_or_down"] == 1
         ]
-        if enter_long_conditions:
+        df["entry_signal"] = reduce(lambda x, y: x & y, enter_long_conditions)
-            df.loc[
+        df["entry_signal"] = df["entry_signal"].rolling(window=2, min_periods=1).max().astype(bool)
-                reduce(lambda x, y: x & y, enter_long_conditions),
+        df.loc[
-                ["enter_long", "enter_tag"]
+            df["entry_signal"],
-            ] = (1, "long")
+            ["enter_long", "enter_tag"]
+        ] = (1, "long")
+        if df["entry_signal"].iloc[-1]:
+            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]}")
         return df
 
     def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
         exit_long_conditions = [
-            (qtpylib.crossed_above(df["rsi"], df["sell_rsi_pred"])) |
+            (qtpylib.crossed_above(df["rsi"], df["sell_rsi_pred"] - 5)) |
             (df["close"] < df["close"].shift(1) * 0.98) |
             (df["close"] < df["bb_lowerband"]),
             df["volume"] > 0,
-            df["do_predict"] == 1,
+            df["do_predict"] == 1
-            df["up_or_down"] == 0
         ]
-        time_exit = (df["date"] >= df["date"].shift(1) + pd.Timedelta(days=1))
         df.loc[
-            (reduce(lambda x, y: x & y, exit_long_conditions)) | time_exit,
+            reduce(lambda x, y: x & y, exit_long_conditions),
             "exit_long"
         ] = 1
         return df
@@ -300,9 +275,16 @@ class FreqaiExampleStrategy(IStrategy):
         self, pair: str, order_type: str, amount: float, rate: float,
         time_in_force: str, current_time, entry_tag, side: str, **kwargs
     ) -> bool:
+        logger.info(f"Confirming trade entry for {pair}, order_type: {order_type}, rate: {rate}, current_time: {current_time}")
         df, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
         last_candle = df.iloc[-1].squeeze()
         if side == "long":
-            if rate > (last_candle["close"] * (1 + 0.001)):
+            if order_type == "market":
-                return False
+                logger.info(f"Order confirmed for {pair}, rate: {rate} (market order)")
+                return True
+            if rate <= (last_candle["close"] * (1 + 0.01)):
+                logger.info(f"Order confirmed for {pair}, rate: {rate}")
+                return True
+            logger.info(f"Order rejected: rate {rate} exceeds threshold {last_candle['close'] * 1.01}")
+            return False
         return True