up

config_examples/config_freqai.okx.json

@@ -35,7 +35,7 @@
             "timeout": 20000
         },
         "pair_whitelist": [
-            "OKB/USDT",
+            "BTC/USDT",
             "SOL/USDT"
         ]
     },
@@ -66,7 +66,7 @@
         },
         "freqaimodel": "CatboostClassifier",
         "purge_old_models": 2,
-        "identifier": "test176",
+        "identifier": "test178",
         "train_period_days": 30,
         "backtest_period_days": 10,
         "live_retrain_hours": 0,
@@ -80,7 +80,7 @@
             ],
             "include_corr_pairlist": [
                 "BTC/USDT",
-                "ETH/USDT"
+                "SOL/USDT"
             ],
             "label_period_candles": 12,
             "include_shifted_candles": 3,

freqtrade/templates/FreqaiExampleStrategy.py

@@ -24,7 +24,7 @@ class FreqaiExampleStrategy(IStrategy):
     startup_candle_count: int = 40
     can_short = False
 
-    buy_rsi = IntParameter(low=10, high=50, default=27, space="buy", optimize=True, load=True)
+    buy_rsi = IntParameter(low=10, high=40, default=20, 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)
@@ -38,11 +38,11 @@ class FreqaiExampleStrategy(IStrategy):
         "model": "LightGBMRegressor",
         "feature_parameters": {
             "include_timeframes": ["5m"],
-            "include_corr_pairlist": ["SOL/USDT"],
+            "include_corr_pairlist": ["OKB/USDT", "SOL/USDT"],  # 与白名单一致
             "label_period_candles": 12,
             "include_shifted_candles": 0,
             "include_periods": [20],
-            "DI_threshold": 5.0
+            "DI_threshold": 1.5  # 提高以减少丢弃预测
         },
         "data_split_parameters": {
             "test_size": 0.2,
@@ -128,9 +128,9 @@ class FreqaiExampleStrategy(IStrategy):
         return dataframe
 
     def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
-        logger.info(f"Processing pair: {metadata['pair']}")
+        logger.info(f"populate_indicators Processing pair: {metadata['pair']}")
-        logger.info(f"DataFrame rows: {len(dataframe)}")
+        logger.info(f"populate_indicators DataFrame rows: {len(dataframe)}")
-        logger.info(f"Columns before freqai.start: {list(dataframe.columns)}")
+        logger.info(f"populate_indicators Columns before freqai.start: {list(dataframe.columns)}")
 
         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']}")
@@ -173,19 +173,6 @@ class FreqaiExampleStrategy(IStrategy):
                 logger.warning("bb_lowerband contains NaN, filling with close")
                 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()}")
 
         if "date" in dataframe.columns:
             dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
@@ -211,17 +198,23 @@ class FreqaiExampleStrategy(IStrategy):
             logger.info(f"{col} stats: {dataframe[col].describe().to_string()}")
 
         # 调试特征分布
+        if "%-bb_width-period_10_OKB/USDT_5m" in dataframe.columns:
+            if dataframe["%-bb_width-period_10_OKB/USDT_5m"].std() > 0:
+                dataframe["%-bb_width-period_10_OKB/USDT_5m"] = (
+                    dataframe["%-bb_width-period_10_OKB/USDT_5m"] - dataframe["%-bb_width-period_10_OKB/USDT_5m"].mean()
+                ) / dataframe["%-bb_width-period_10_OKB/USDT_5m"].std()
+            logger.info(f"%-bb_width-period_10_OKB stats: {dataframe['%-bb_width-period_10_OKB/USDT_5m'].describe().to_string()}")
         if "%-bb_width-period_10_SOL/USDT_5m" in dataframe.columns:
             if dataframe["%-bb_width-period_10_SOL/USDT_5m"].std() > 0:
                 dataframe["%-bb_width-period_10_SOL/USDT_5m"] = (
                     dataframe["%-bb_width-period_10_SOL/USDT_5m"] - dataframe["%-bb_width-period_10_SOL/USDT_5m"].mean()
                 ) / dataframe["%-bb_width-period_10_SOL/USDT_5m"].std()
-            logger.info(f"%-bb_width-period_10 stats: {dataframe['%-bb_width-period_10_SOL/USDT_5m'].describe().to_string()}")
+            logger.info(f"%-bb_width-period_10_SOL stats: {dataframe['%-bb_width-period_10_SOL/USDT_5m'].describe().to_string()}")
 
         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])
-            pairs = freqai_config.get("include_corr_pairlist", ["SOL/USDT", "BTC/USDT"])
+            pairs = freqai_config.get("include_corr_pairlist", ["OKB/USDT", "SOL/USDT"])
             timeframes = freqai_config.get("include_timeframes", ["5m"])
             shifts = [0]
             expected_columns = ["%-volatility", "%-day_of_week", "%-hour_of_day"]
@@ -256,11 +249,10 @@ class FreqaiExampleStrategy(IStrategy):
 
     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["close"] > df["tema"],
             df["volume"] > 0,
-            df["do_predict"] == 1,
+            df["do_predict"] == 1
-            df["up_or_down"] == 1
         ]
         if enter_long_conditions:
             df.loc[
@@ -274,7 +266,9 @@ class FreqaiExampleStrategy(IStrategy):
             ["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]}")
+            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]}, close={df['close'].iloc[-1]}, tema={df['tema'].iloc[-1]}")
+        logger.info(f"Entry conditions: RSI_cross={qtpylib.crossed_above(df['rsi'], df['buy_rsi_pred']).iloc[-1]}, Close_above_tema={df['close'].iloc[-1] > df['tema'].iloc[-1]}, Volume={df['volume'].iloc[-1] > 0}, Do_predict={df['do_predict'].iloc[-1] == 1}")
+        logger.info(f"Last candle: rsi={df['rsi'].iloc[-1]}, buy_rsi_pred={df['buy_rsi_pred'].iloc[-1]}, close={df['close'].iloc[-1]}, tema={df['tema'].iloc[-1]}, do_predict={df['do_predict'].iloc[-1]}")
         return df
 
     def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
@@ -283,8 +277,7 @@ class FreqaiExampleStrategy(IStrategy):
             (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[

freqtrade/templates/celuo.diff

@@ -0,0 +1,104 @@
+diff --git a/freqtrade/templates/FreqaiExampleStrategy.py b/freqtrade/templates/FreqaiExampleStrategy.py
+index 1d7ed33..245f771 100644
+--- a/freqtrade/templates/FreqaiExampleStrategy.py
++++ b/freqtrade/templates/FreqaiExampleStrategy.py
+@@ -128,9 +128,9 @@ class FreqaiExampleStrategy(IStrategy):
+         return dataframe
+ 
+     def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+-        logger.info(f"Processing pair: {metadata['pair']}")
+-        logger.info(f"DataFrame rows: {len(dataframe)}")
+-        logger.info(f"Columns before freqai.start: {list(dataframe.columns)}")
++        logger.info(f"populate_indicators Processing pair: {metadata['pair']}")
++        logger.info(f"populate_indicators DataFrame rows: {len(dataframe)}")
++        logger.info(f"populate_indicators Columns before freqai.start: {list(dataframe.columns)}")
+ 
+         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']}")
+@@ -173,6 +173,19 @@ class FreqaiExampleStrategy(IStrategy):
+                 logger.warning("bb_lowerband contains NaN, filling with close")
+                 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()}")
+ 
+         if "date" in dataframe.columns:
+             dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
+@@ -197,17 +210,18 @@ class FreqaiExampleStrategy(IStrategy):
+                 dataframe[col] = 50 if col == "buy_rsi_pred" else 80
+             logger.info(f"{col} stats: {dataframe[col].describe().to_string()}")
+ 
+-        if "%-bb_width-period_20_SOL/USDT_5m" in dataframe.columns:
+-            if dataframe["%-bb_width-period_20_SOL/USDT_5m"].std() > 0:
+-                dataframe["%-bb_width-period_20_SOL/USDT_5m"] = (
+-                    dataframe["%-bb_width-period_20_SOL/USDT_5m"] - dataframe["%-bb_width-period_20_SOL/USDT_5m"].mean()
+-                ) / dataframe["%-bb_width-period_20_SOL/USDT_5m"].std()
+-            logger.info(f"%-bb_width-period_20 stats: {dataframe['%-bb_width-period_20_SOL/USDT_5m'].describe().to_string()}")
++        # 调试特征分布
++        if "%-bb_width-period_10_SOL/USDT_5m" in dataframe.columns:
++            if dataframe["%-bb_width-period_10_SOL/USDT_5m"].std() > 0:
++                dataframe["%-bb_width-period_10_SOL/USDT_5m"] = (
++                    dataframe["%-bb_width-period_10_SOL/USDT_5m"] - dataframe["%-bb_width-period_10_SOL/USDT_5m"].mean()
++                ) / dataframe["%-bb_width-period_10_SOL/USDT_5m"].std()
++            logger.info(f"%-bb_width-period_10 stats: {dataframe['%-bb_width-period_10_SOL/USDT_5m'].describe().to_string()}")
+ 
+         def get_expected_columns(freqai_config: dict) -> list:
+             indicators = ["rsi", "bb_width", "pct-change"]
+-            periods = freqai_config.get("feature_parameters", {}).get("include_periods", [20])
+-            pairs = freqai_config.get("include_corr_pairlist", ["SOL/USDT"])
++            periods = freqai_config.get("feature_parameters", {}).get("include_periods", [10, 20])
++            pairs = freqai_config.get("include_corr_pairlist", ["SOL/USDT", "BTC/USDT"])
+             timeframes = freqai_config.get("include_timeframes", ["5m"])
+             shifts = [0]
+             expected_columns = ["%-volatility", "%-day_of_week", "%-hour_of_day"]
+@@ -242,11 +256,17 @@ class FreqaiExampleStrategy(IStrategy):
+ 
+     def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
+         enter_long_conditions = [
+-            qtpylib.crossed_above(df["rsi"], df["buy_rsi_pred"]),
++            qtpylib.crossed_above(df["rsi"], df["buy_rsi_pred"] + (5 if metadata["pair"] == "BTC/USDT" else 0)),
+             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.loc[
++                reduce(lambda x, y: x & y, enter_long_conditions),
++                ["enter_long", "enter_tag"]
++            ] = (1, "long")
+         df["entry_signal"] = reduce(lambda x, y: x & y, enter_long_conditions)
+         df["entry_signal"] = df["entry_signal"].rolling(window=2, min_periods=1).max().astype(bool)
+         df.loc[
+@@ -259,14 +279,16 @@ class FreqaiExampleStrategy(IStrategy):
+ 
+     def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
+         exit_long_conditions = [
+-            (qtpylib.crossed_above(df["rsi"], df["sell_rsi_pred"] - 5)) |
++            (qtpylib.crossed_above(df["rsi"], df["sell_rsi_pred"])) |
+             (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),
++            (reduce(lambda x, y: x & y, exit_long_conditions)) | time_exit,
+             "exit_long"
+         ] = 1
+         return df

nohup.out

@@ -2,3 +2,4 @@
 实时监控 freqtrade_freqtrade_run_ef258891294d 的日志，过滤 'but got Index'...
 开始过滤日志，输出到 freqtrade_error_logs.txt ...
 实时监控 freqtrade_freqtrade_run_ef258891294d 的日志，过滤 'but got Index'...
+未捕获到包含 'but got Index' 的日志，文件 freqtrade_error_logs.txt 为空