From a46895526c2c734dc08151e452a3d1f31303a285 Mon Sep 17 00:00:00 2001
From: "zhangkun9038@dingtalk.com" <zhangkun9038@dingtalk.com>
Date: Mon, 28 Apr 2025 20:51:06 +0800
Subject: [PATCH] stable1 11

---
 freqtrade/templates/FreqaiExampleStrategy.py | 243 ++++++++++++++++---
 1 file changed, 213 insertions(+), 30 deletions(-)

diff --git a/freqtrade/templates/FreqaiExampleStrategy.py b/freqtrade/templates/FreqaiExampleStrategy.py
index c83c3ab..4683c0a 100644
--- a/freqtrade/templates/FreqaiExampleStrategy.py
+++ b/freqtrade/templates/FreqaiExampleStrategy.py
@@ -1,38 +1,222 @@
+import logging
+import numpy as np
+from functools import reduce
+import talib.abstract as ta
+from pandas import DataFrame
+from technical import qtpylib
+from freqtrade.strategy import IStrategy, IntParameter, DecimalParameter
+
+logger = logging.getLogger(__name__)
+
+class FreqaiExampleStrategy(IStrategy):
+    minimal_roi = {}
+    stoploss = 0.0
+    trailing_stop = True
+    process_only_new_candles = True
+    use_exit_signal = True
+    startup_candle_count: int = 40
+    can_short = False
+
+    # 参数定义
+    buy_rsi = IntParameter(low=10, high=50, default=30, space="buy", optimize=True, load=True)
+    sell_rsi = IntParameter(low=50, high=90, default=70, space="sell", optimize=True, load=True)
+    roi_0 = DecimalParameter(low=0.01, high=0.1, default=0.05, space="roi", optimize=True, load=True)
+    roi_15 = DecimalParameter(low=0.005, high=0.05, default=0.03, space="roi", optimize=True, load=True)
+    roi_30 = DecimalParameter(low=0.001, high=0.03, default=0.01, space="roi", optimize=True, load=True)
+    stoploss_param = DecimalParameter(low=-0.2, high=-0.05, default=-0.1, space="stoploss", optimize=True, load=True)
+
+    # FreqAI 配置
+    freqai_info = {
+        "model": "XGBoostRegressor",
+        "return_type": "raw",  # 确保使用原始回归输出
+        "feature_parameters": {
+            "include_timeframes": ["15m", "1h", "4h"],
+            "include_corr_pairlist": ["BTC/USDT", "SOL/USDT"],
+            "label_period_candles": 20,  # 增加预测周期
+            "include_shifted_candles": 2,
+            "weight_factor": 0.9,
+            "principal_component_analysis": False,
+            "use_SVM_to_remove_outliers": True,
+            "SVM_parameters": {"nu": 0.1},
+        },
+        "data_split_parameters": {
+            "test_size": 0.2,
+            "shuffle": True,
+        },
+        "model_training_parameters": {
+            "n_estimators": 300,  # 增加迭代次数
+            "learning_rate": 0.03,  # 降低学习率
+            "max_depth": 6,
+            "subsample": 0.8,
+            "colsample_bytree": 0.8,
+            "reg_lambda": 1.0,
+            "objective": "reg:squarederror",
+            "eval_metric": "rmse",
+            "early_stopping_rounds": 20,
+            "verbose": 0,
+        },
+        "data_kitchen": {
+            "feature_parameters": {
+                "DI_threshold": 0  # 禁用 DI 过滤
+            }
+        }
+    }
+
+    def calculate_macd(self, dataframe: DataFrame) -> DataFrame:
+        try:
+            macd = ta.MACD(dataframe, fastperiod=12, slowperiod=26, signalperiod=9)
+            dataframe["macd"] = macd["macd"].replace([np.inf, -np.inf], np.nan).ffill().fillna(0)
+            dataframe["macdsignal"] = macd["macdsignal"].replace([np.inf, -np.inf], np.nan).ffill().fillna(0)
+            logger.info("MACD calculated successfully.")
+        except Exception as e:
+            logger.error(f"Error calculating MACD: {str(e)}")
+            dataframe["macd"] = np.nan
+            dataframe["macdsignal"] = np.nan
         return dataframe
-    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
-        # 改进买入信号条件
-        # 使用 centralized 方法计算 MACD
+
+    def feature_engineering_expand_all(self, dataframe: DataFrame, period: int, metadata: dict, **kwargs) -> DataFrame:
+        # 增加趋势性特征
+        dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
+        dataframe["sma20"] = ta.SMA(dataframe["close"], timeperiod=20)
+        dataframe["ema50"] = ta.EMA(dataframe["close"], timeperiod=50)
+        dataframe["atr"] = ta.ATR(dataframe["high"], dataframe["low"], dataframe["close"], timeperiod=14)
+        dataframe["obv"] = ta.OBV(dataframe["close"], dataframe["volume"])
+        dataframe["adx"] = ta.ADX(dataframe["high"], dataframe["low"], dataframe["close"], timeperiod=14)
+        dataframe["price_sma_diff"] = (dataframe["close"] - dataframe["sma20"]) / dataframe["sma20"]
+        dataframe["momentum"] = ta.MOM(dataframe["close"], timeperiod=14)
+        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
+        dataframe["bb_lowerband"] = bollinger["lower"]
+        dataframe["bb_middleband"] = bollinger["mid"]
+        dataframe["bb_upperband"] = bollinger["upper"]
+        dataframe["bb_width"] = (bollinger["upper"] - bollinger["lower"]) / bollinger["mid"]
+
+        # 添加相关交易对特征
+        if metadata["pair"] == "SOL/USDT":
+            btc_data = self.dp.get_pair_dataframe(pair="BTC/USDT", timeframe=self.timeframe)
+            if not btc_data.empty:
+                dataframe["btc_rsi"] = ta.RSI(btc_data, timeperiod=14)
+                dataframe["btc_price_change"] = btc_data["close"].pct_change()
+
+        # 数据清理
+        for col in dataframe.columns:
+            if dataframe[col].dtype in ["float64", "int64"]:
+                dataframe[col] = dataframe[col].replace([np.inf, -np.inf], np.nan).ffill().fillna(dataframe[col].median())
+        logger.info(f"Feature engineering completed, features: {len(dataframe.columns)}")
+        return dataframe
+
+    def feature_engineering_expand_basic(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame:
+        dataframe["%-pct-change"] = dataframe["close"].pct_change()
+        dataframe["%-raw_volume"] = dataframe["volume"]
+        dataframe["%-raw_price"] = dataframe["close"]
+        for col in dataframe.columns:
+            if dataframe[col].dtype in ["float64", "int64"]:
+                dataframe[col] = dataframe[col].replace([np.inf, -np.inf], np.nan).ffill().fillna(dataframe[col].median())
+        return dataframe
+
+    def feature_engineering_standard(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame:
+        dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek
+        dataframe["%-hour_of_day"] = dataframe["date"].dt.hour
+        for col in dataframe.columns:
+            if dataframe[col].dtype in ["float64", "int64"]:
+                dataframe[col] = dataframe[col].replace([np.inf, -np.inf], np.nan).ffill().fillna(dataframe[col].median())
+        return dataframe
+
+    def set_freqai_targets(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame:
+        logger.info(f"Setting FreqAI targets for pair: {metadata['pair']}")
+        if "close" not in dataframe.columns:
+            logger.error("DataFrame missing 'close' column")
+            raise ValueError("DataFrame missing 'close' column")
+
+        try:
+            label_period = self.freqai_info["feature_parameters"]["label_period_candles"]
+            dataframe["&-up_or_down"] = (
+                dataframe["close"].shift(-label_period) - dataframe["close"]
+            ) / dataframe["close"]
+            dataframe["&-up_or_down"] = dataframe["&-up_or_down"].replace([np.inf, -np.inf], np.nan).ffill().fillna(0)
+            dataframe["%-volatility"] = dataframe["close"].pct_change().rolling(20).std()
+            dataframe["&-buy_rsi"] = ta.RSI(dataframe, timeperiod=14)
+
+            for col in ["&-buy_rsi", "&-up_or_down", "%-volatility"]:
+                dataframe[col] = dataframe[col].replace([np.inf, -np.inf], np.nan).ffill().fillna(dataframe[col].median())
+        except Exception as e:
+            logger.error(f"Failed to create FreqAI targets: {str(e)}")
+            raise
+        logger.info(f"Target column shape: {dataframe['&-up_or_down'].shape}")
+        logger.info(f"Target preview:\n{dataframe[['&-up_or_down', '&-buy_rsi']].head().to_string()}")
+        return dataframe
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        logger.info(f"Processing pair: {metadata['pair']}")
+        dataframe = self.freqai.start(dataframe, metadata, self)
+
+        # 计算传统指标
+        dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
+        dataframe["atr"] = ta.ATR(dataframe["high"], dataframe["low"], dataframe["close"], timeperiod=14)
+        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
+        dataframe["bb_lowerband"] = bollinger["lower"]
+        dataframe["bb_middleband"] = bollinger["mid"]
+        dataframe["bb_upperband"] = bollinger["upper"]
         dataframe = self.calculate_macd(dataframe)
 
-        # 再次检查 MACD 列是否存在
-        if "macd" not in dataframe.columns or "macdsignal" not in dataframe.columns:
-            logger.error("MACD 或 MACD 信号列仍然缺失，无法生成买入信号。")
-            raise ValueError("dataframe 缺少必要的 MACD 列且无法重新计算。")
+        # 动态参数设置
+        if "&-buy_rsi" in dataframe.columns:
+            dataframe["&-sell_rsi"] = dataframe["&-buy_rsi"] + 20
+            dataframe["%-volatility"] = dataframe["close"].pct_change().rolling(20).std()
+            dataframe["&-stoploss"] = -2 * dataframe["atr"] / dataframe["close"]
+            dataframe["&-roi_0"] = (dataframe["close"] / dataframe["close"].shift(10) - 1).clip(0, 0.1).fillna(0)
 
-        enter_long_conditions = [
-            (dataframe["rsi"] < dataframe["buy_rsi_pred"]),  # RSI 低于买入阈值
-            (
-                (dataframe["volume"] > dataframe["volume"].rolling(window=10).mean() * 1.2) |  # 成交量高于近期均值20%
-                (dataframe["volume"] > dataframe["volume"].shift(1) * 1.1)  # 当前成交量高于上一周期的10%
-            ),
-            (
-                (dataframe["close"] > dataframe["bb_middleband"]) |  # 价格高于布林带中轨
-                (dataframe["close"] > dataframe["bb_upperband"].shift(1))  # 当前价格高于上一周期的布林带上轨
-            )
-        ]
+            # 动态预测值
+            dataframe["buy_rsi_pred"] = dataframe["rsi"].rolling(window=10).mean().clip(20, 50).fillna(dataframe["rsi"].median())
+            dataframe["sell_rsi_pred"] = dataframe["buy_rsi_pred"] + 20
+            dataframe["stoploss_pred"] = -2 * dataframe["atr"] / dataframe["close"].clip(-0.2, -0.05)
+            dataframe["roi_0_pred"] = dataframe["&-roi_0"].clip(0.01, 0.1).fillna(dataframe["&-roi_0"].mean())
 
-        # 如果 MACD 列存在，则添加 MACD 金叉条件
-        if "macd" in dataframe.columns and "macdsignal" in dataframe.columns:
-            enter_long_conditions.append((dataframe["macd"] > dataframe["macdsignal"]))
-        
-        # 确保模型预测为买入
-        enter_long_conditions.append((dataframe["do_predict"] == 1))
-        if enter_long_conditions:
-            dataframe.loc[
-                reduce(lambda x, y: x & y, enter_long_conditions),
-                ["enter_long", "enter_tag"]
-            ] = (1, "long")
+            for col in ["&-stoploss", "&-roi_0", "buy_rsi_pred", "sell_rsi_pred", "stoploss_pred", "roi_0_pred"]:
+                dataframe[col] = dataframe[col].replace([np.inf, -np.inf], np.nan).ffill().fillna(dataframe[col].mean())
+
+            # 设置策略参数
+            self.buy_rsi.value = float(dataframe["buy_rsi_pred"].iloc[-1])
+            self.sell_rsi.value = float(dataframe["sell_rsi_pred"].iloc[-1])
+            self.stoploss = float(dataframe["stoploss_pred"].iloc[-1])
+            self.minimal_roi = {
+                0: float(self.roi_0.value),
+                15: float(self.roi_15.value),
+                30: float(self.roi_30.value),
+                60: 0
+            }
+            # 动态追踪止损
+            self.trailing_stop_positive = float(2 * dataframe["atr"].iloc[-1] / dataframe["close"].iloc[-1])
+            self.trailing_stop_positive_offset = float(3 * dataframe["atr"].iloc[-1] / dataframe["close"].iloc[-1])
+
+        dataframe.ffill(inplace=True)
+        dataframe.fillna(dataframe.mean(numeric_only=True), inplace=True)
+        logger.info(f"&-up_or_down stats:\n{dataframe['&-up_or_down'].describe().to_string()}")
         return dataframe
+
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        dataframe = self.calculate_macd(dataframe)
+        enter_long_conditions = [
+            (dataframe["rsi"] < dataframe["buy_rsi_pred"]),
+            (dataframe["volume"] > dataframe["volume"].rolling(window=10).mean() * 1.05),
+            (dataframe["close"] > dataframe["bb_middleband"]),
+            (dataframe["macd"] > dataframe["macdsignal"]),
+            (dataframe["&-up_or_down"] > 0.003)  # 降低阈值
+        ]
+        if enter_long_conditions:
+            dataframe.loc[reduce(lambda x, y: x & y, enter_long_conditions), ["enter_long", "enter_tag"]] = (1, "long")
+        return dataframe
+
+    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        exit_long_conditions = [
+            (dataframe["rsi"] > dataframe["sell_rsi_pred"]),
+            (dataframe["close"] < dataframe["bb_middleband"]),
+            (dataframe["macd"] < dataframe["macdsignal"]),
+            (dataframe["&-up_or_down"] < -0.003)
+        ]
+        if exit_long_conditions:
+            dataframe.loc[reduce(lambda x, y: x | y, exit_long_conditions), "exit_long"] = 1
+        return dataframe
+
     def confirm_trade_entry(
         self, pair: str, order_type: str, amount: float, rate: float,
         time_in_force: str, current_time, entry_tag, side: str, **kwargs
@@ -44,4 +228,3 @@
                 return False
         return True
 
-