second

2025-04-22 15:43:04 +08:00 · 2025-04-22 15:43:04 +08:00 · b816a42eab
commit b816a42eab
parent 17199e9a44
4 changed files with 214 additions and 80 deletions
--- a/.gitignore
+++ b/.gitignore
@ -114,6 +114,7 @@ target/
 !config_examples/config_full.example.json
 !config_examples/config_kraken.example.json
 !config_examples/config_freqai.example.json
+!config_examples/*.json

 docker-compose-*.yml
 data/
--- a/config_examples/config_freqai.okx.json
+++ b/config_examples/config_freqai.okx.json
@ -0,0 +1,130 @@
+{
+    "$schema": "https://schema.freqtrade.io/schema.json",
+    "trading_mode": "spot",
+    "margin_mode": "isolated",
+    "max_open_trades": 4,
+    "stake_currency": "USDT",
+    "stake_amount": 150,
+    "tradable_balance_ratio": 1,
+    "fiat_display_currency": "USD",
+    "dry_run": true,
+    "timeframe": "3m",
+    "dry_run_wallet": 1000,
+    "cancel_open_orders_on_exit": true,
+    "stoploss": -0.09,
+    "unfilledtimeout": {
+        "entry": 5,
+        "exit": 15
+    },
+    "exchange": {
+        "name": "okx",
+        "key": "eca767d4-fda5-4a1b-bb28-49ae18093307",
+        "secret": "8CA3628A556ED137977DB298D37BC7F3",
+        "enable_ws": false,
+        "ccxt_config": {
+            "enableRateLimit": true,
+            "rateLimit": 500,
+            "options": {
+                "defaultType": "spot"
+            }
+        },
+        "ccxt_async_config": {
+            "enableRateLimit": true,
+	          "rateLimit": 500,
+            "timeout": 20000
+        },
+        "pair_whitelist": [
+            "BTC/USDT",
+            "SOL/USDT"
+        ],
+        "pair_blacklist": []
+    },
+    "entry_pricing": {
+        "price_side": "same",
+        "use_order_book": true,
+        "order_book_top": 1,
+        "price_last_balance": 0.0,
+        "check_depth_of_market": {
+            "enabled": false,
+            "bids_to_ask_delta": 1
+        }
+    },
+    "exit_pricing": {
+        "price_side": "other",
+        "use_order_book": true,
+        "order_book_top": 1
+    },
+    "pairlists": [
+        {
+            "method": "StaticPairList"
+        }
+    ],
+    "freqai": {
+        "enabled": true,
+        "data_kitchen": {
+            "fillna": "ffill"
+        },
+        "freqaimodel": "CatboostClassifier",
+        "purge_old_models": 2,
+        "train_period_days": 15,
+        "identifier": "test3",
+        "train_period_days": 30,
+        "backtest_period_days": 10,
+        "live_retrain_hours": 0,
+        "feature_selection": {
+          "method": "recursive_elimination"
+        },
+        "feature_parameters": {
+            "include_timeframes": [
+                "3m",
+                "15m",
+                "1h"
+            ],
+            "include_corr_pairlist": [
+                "BTC/USDT",
+                "SOL/USDT"
+            ],
+            "label_period_candles": 20,
+            "include_shifted_candles": 2,
+            "DI_threshold": 0.9,
+            "weight_factor": 0.9,
+            "principal_component_analysis": false,
+            "use_SVM_to_remove_outliers": false,
+            "indicator_periods_candles": [
+                10,
+                20,
+                50
+            ],
+            "plot_feature_importances": 0
+        },
+        "data_split_parameters": {
+            "test_size": 0.2
+        },
+        "model_training_parameters": {
+            "n_estimators": 100,
+            "learning_rate": 0.05,
+            "max_depth": 5,
+            "num_leaves": 31
+        }
+    },
+    "api_server": {
+        "enabled": true,
+        "listen_ip_address": "0.0.0.0",
+        "listen_port": 8080,
+        "verbosity": "error",
+        "enable_openapi": false,
+        "jwt_secret_key": "6a599ab046dbb419014807dffd7b8823bfa7e5df56b17d545485deb87331b4ca",
+        "ws_token": "6O5pBDiRigiZrmIsofaE2rkKMJtf9h8zVQ",
+        "CORS_origins": [],
+        "username": "freqAdmin",
+        "password": "admin"
+    },
+    "bot_name": "freqtrade",
+    "initial_state": "running",
+    "force_entry_enable": false,
+    "internals": {
+        "process_throttle_secs": 5,
+        "heartbeat_interval": 20,
+        "loglevel": "DEBUG"
+    }
+}
--- a/docker-compose.yml
+++ b/docker-compose.yml
@ -22,6 +22,7 @@ services:
      - "./user_data:/freqtrade/user_data"
      - "./config_examples:/freqtrade/config_examples"
      - "./freqtrade/templates:/freqtrade/templates"
+      - "./freqtrade/exchange/:/freqtrade/exchange"
    # Expose api on port 8080 (localhost only)
    # Please read the https://www.freqtrade.io/en/stable/rest-api/ documentation
    # for more information.
@ -30,12 +31,23 @@ services:
    # Default command used when running `docker compose up`
      
    # --freqaimodel XGBoostRegressor
+    # commangd: >
+    #      # trade
+      # --logfile /freqtrade/user_data/logs/freqtrade.log
+      # --db-url sqlite:////freqtrade/user_data/tradesv3.sqlite
+      # --freqaimodel LightGBMRegressor
+      # --config /freqtrade/config_examples/config_freqai.okx.json
+      # --strategy FreqaiExampleStrategy
+      #  --strategy FreqaiExampleHybridStrategy
+      # --strategy-path /freqtrade/templates
    command: >
-      trade
-      --logfile /freqtrade/user_data/logs/freqtrade.log
-      --db-url sqlite:////freqtrade/user_data/tradesv3.sqlite
-      --freqaimodel LightGBMRegressor
-      --config /freqtrade/config_examples/config_freqai.okx.json
-      --strategy FreqaiExampleStrategy
-      --strategy-path /freqtrade/templates
+      backtesting
+        --logfile /freqtrade/user_data/logs/freqtrade.log
+        --freqaimodel XGBoostRegressor
+        --config /freqtrade/config_examples/config_freqai.okx.json
+        --strategy-path /freqtrade/templates
+        --strategy FreqaiExampleStrategy
+        --timerange 20250320-20250420
+        --export trades
+

--- a/freqtrade/templates/FreqaiExampleHybridStrategy.py
+++ b/freqtrade/templates/FreqaiExampleHybridStrategy.py
@ -88,13 +88,11 @@ class FreqaiExampleHybridStrategy(IStrategy):
    stoploss = -0.05
    use_exit_signal = True
    startup_candle_count: int = 30
-    can_short = True
+    can_short = False

    # Hyperoptable parameters
    buy_rsi = IntParameter(low=1, high=50, default=30, space="buy", optimize=True, load=True)
    sell_rsi = IntParameter(low=50, high=100, default=70, space="sell", optimize=True, load=True)
-    short_rsi = IntParameter(low=51, high=100, default=70, space="sell", optimize=True, load=True)
-    exit_short_rsi = IntParameter(low=1, high=50, default=30, space="buy", optimize=True, load=True)

    def feature_engineering_expand_all(
        self, dataframe: DataFrame, period: int, metadata: dict, **kwargs
@ -210,35 +208,65 @@ class FreqaiExampleHybridStrategy(IStrategy):
        dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
        dataframe["%-hour_of_day"] = dataframe["date"].dt.hour
        return dataframe
-
    def set_freqai_targets(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame:
-        """
-        *Only functional with FreqAI enabled strategies*
-        Required function to set the targets for the model.
-        All targets must be prepended with `&` to be recognized by the FreqAI internals.
-
-        More details about feature engineering available:
-
-        https://www.freqtrade.io/en/latest/freqai-feature-engineering
-
-        :param dataframe: strategy dataframe which will receive the targets
-        :param metadata: metadata of current pair
-        usage example: dataframe["&-target"] = dataframe["close"].shift(-1) / dataframe["close"]
-        """
-        self.freqai.class_names = ["down", "up"]
-        dataframe["&s-up_or_down"] = np.where(
-            dataframe["close"].shift(-50) > dataframe["close"], "up", "down"
-        )
-
+        logger.info(f"Setting FreqAI targets for pair: {metadata['pair']}")
+        logger.info(f"DataFrame shape: {dataframe.shape}")
+        logger.info(f"Available columns: {list(dataframe.columns)}")
+        logger.info(f"First few rows:\n{dataframe[['date', 'close']].head().to_string()}")
+        
+        if "close" not in dataframe.columns:
+            logger.error("Required 'close' column missing in dataframe")
+            raise ValueError("Required 'close' column missing in dataframe")
+        
+        if len(dataframe) < 50:
+            logger.error(f"Insufficient data: {len(dataframe)} rows, need at least 50 for shift(-50)")
+            raise ValueError("Insufficient data for target calculation")
+        
+        try:
+            # 生成数值型标签：1 表示上涨，0 表示下跌
+            dataframe["&-up_or_down"] = np.where(
+                dataframe["close"].shift(-50) > dataframe["close"],
+                1.0,  # 数值型标签
+                0.0
+            )
+        except Exception as e:
+            logger.error(f"Failed to create &-up_or_down column: {str(e)}")
+            raise
+        
+        logger.info(f"Target column head:\n{dataframe[['&-up_or_down']].head().to_string()}")
+        
+        if "&-up_or_down" not in dataframe.columns:
+            logger.error("FreqAI failed to generate the &-up_or_down column")
+            raise KeyError("FreqAI failed to generate the &-up_or_down column")
+        
+        logger.info("FreqAI targets set successfully")
        return dataframe

-    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:  # noqa: C901
-        # User creates their own custom strat here. Present example is a supertrend
-        # based strategy.
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        logger.info(f"Processing pair: {metadata['pair']}")
+        logger.info(f"Input DataFrame shape: {dataframe.shape}")
+        logger.info(f"Input DataFrame columns: {list(dataframe.columns)}")
+        logger.info(f"Input DataFrame head:\n{dataframe[['date', 'close', 'volume']].head().to_string()}")
+        
+        # Ensure FreqAI processing
+        logger.info("Calling self.freqai.start")
+        try:
+            dataframe = self.freqai.start(dataframe, metadata, self)
+        except Exception as e:
+            logger.error(f"self.freqai.start failed: {str(e)}")
+            raise
+        logger.info("self.freqai.start completed")
+        
+        logger.info(f"Output DataFrame shape: {dataframe.shape}")
+        logger.info(f"Output DataFrame columns: {list(dataframe.columns)}")
+        # Safely log columns that exist
+        available_columns = [col for col in ['date', 'close', '&-up_or_down'] if col in dataframe.columns]
+        logger.info(f"Output DataFrame head:\n{dataframe[available_columns].head().to_string()}")
+        
+        if "&-up_or_down" not in dataframe.columns:
+            logger.error("FreqAI did not generate the required &-up_or_down column")
+            raise KeyError("FreqAI did not generate the required &-up_or_down column")

-        dataframe = self.freqai.start(dataframe, metadata, self)
-
-        # TA indicators to combine with the Freqai targets
        # RSI
        dataframe["rsi"] = ta.RSI(dataframe)

@ -254,67 +282,30 @@ class FreqaiExampleHybridStrategy(IStrategy):
            "bb_middleband"
        ]

-        # TEMA - Triple Exponential Moving Average
+        # TEMA
        dataframe["tema"] = ta.TEMA(dataframe, timeperiod=9)

        return dataframe
-
    def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
        df.loc[
            (
-                # Signal: RSI crosses above 30
                (qtpylib.crossed_above(df["rsi"], self.buy_rsi.value))
-                & (df["tema"] <= df["bb_middleband"])  # Guard: tema below BB middle
-                & (df["tema"] > df["tema"].shift(1))  # Guard: tema is raising
-                & (df["volume"] > 0)  # Make sure Volume is not 0
-                & (df["do_predict"] == 1)  # Make sure Freqai is confident in the prediction
-                &
-                # Only enter trade if Freqai thinks the trend is in this direction
-                (df["&s-up_or_down"] == "up")
+                & (df["tema"] <= df["bb_middleband"])
+                & (df["tema"] > df["tema"].shift(1))
+                & (df["volume"] > 0)
            ),
            "enter_long",
        ] = 1
-
-        df.loc[
-            (
-                # Signal: RSI crosses above 70
-                (qtpylib.crossed_above(df["rsi"], self.short_rsi.value))
-                & (df["tema"] > df["bb_middleband"])  # Guard: tema above BB middle
-                & (df["tema"] < df["tema"].shift(1))  # Guard: tema is falling
-                & (df["volume"] > 0)  # Make sure Volume is not 0
-                & (df["do_predict"] == 1)  # Make sure Freqai is confident in the prediction
-                &
-                # Only enter trade if Freqai thinks the trend is in this direction
-                (df["&s-up_or_down"] == "down")
-            ),
-            "enter_short",
-        ] = 1
-
        return df

    def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
        df.loc[
            (
-                # Signal: RSI crosses above 70
                (qtpylib.crossed_above(df["rsi"], self.sell_rsi.value))
-                & (df["tema"] > df["bb_middleband"])  # Guard: tema above BB middle
-                & (df["tema"] < df["tema"].shift(1))  # Guard: tema is falling
-                & (df["volume"] > 0)  # Make sure Volume is not 0
+                & (df["tema"] > df["bb_middleband"])
+                & (df["tema"] < df["tema"].shift(1))
+                & (df["volume"] > 0)
            ),
            "exit_long",
        ] = 1
-
-        df.loc[
-            (
-                # Signal: RSI crosses above 30
-                (qtpylib.crossed_above(df["rsi"], self.exit_short_rsi.value))
-                &
-                # Guard: tema below BB middle
-                (df["tema"] <= df["bb_middleband"])
-                & (df["tema"] > df["tema"].shift(1))  # Guard: tema is raising
-                & (df["volume"] > 0)  # Make sure Volume is not 0
-            ),
-            "exit_short",
-        ] = 1
-
-        return df
+        return df