全新的策略

freqtrade/templates/freqaiprimer.py

@@ -1,3 +1,57 @@
+import warnings
+warnings.filterwarnings("ignore", category=UserWarning, module="pandas_ta")
+
+from freqtrade.strategy import IStrategy
+from pandas import DataFrame
+import pandas_ta as ta
+from freqtrade.exchange import timeframe_to_minutes
+
+class FreqaiPrimer(IStrategy):
+    # 策略参数
+    minimal_roi = {
+        "0": 0.04,    # 4% ROI (10 分钟内)
+        "60": 0.02,   # 2% ROI (1 小时)
+        "180": 0.01,  # 1% ROI (3 小时)
+        "360": 0.0    # 0% ROI (6 小时)
+    }
+    
+    stoploss = -0.015  # 初始止损 -1.5%
+    trailing_stop = True  # 启用追踪止损
+    trailing_stop_positive = 0.008  # 价格上涨 0.8% 后开始追踪
+    trailing_stop_positive_offset = 0.01  # 追踪止损偏移量 1%
+    
+    timeframe = "3m"  # 主时间框架为 3 分钟
+    can_short = False  # 禁用做空
+
+    # 自定义指标参数
+    bb_length = 20  # 布林带周期
+    bb_std = 2.0    # 布林带标准差
+    rsi_length = 14 # RSI 周期
+    rsi_overbought = 70  # RSI 超买阈值
+    rsi_oversold = 30    # RSI 超卖阈值
+
+    def informative_pairs(self):
+        # 定义辅助时间框架
+        pairs = self.dp.current_whitelist()
+        return [(pair, '15m') for pair in pairs] + [(pair, '1h') for pair in pairs]
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        # 计算 3m 周期的指标
+        bb_3m = ta.bbands(dataframe['close'], length=self.bb_length, std=self.bb_std)
+        dataframe['bb_lower_3m'] = bb_3m[f'BBL_{self.bb_length}_{self.bb_std}']
+        dataframe['bb_upper_3m'] = bb_3m[f'BBU_{self.bb_length}_{self.bb_std}']
+        dataframe['rsi_3m'] = ta.rsi(dataframe['close'], length=self.rsi_length)
+
+        # 成交量过滤
+        dataframe['volume_ma'] = dataframe['volume'].rolling(20).mean()
+        
+        # 计算 ATR 用于动态止损
+        dataframe['atr'] = ta.atr(dataframe['high'], dataframe['low'], dataframe['close'], length=14)
+
+        # 获取 15m 数据
+        df_15m = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe='15m')
+        bb_15m = ta.bbands(df_15m['close'], length=self.bb_length, std=self.bb_std)
+        df_15m['bb_lower_15m'] = bb_15m[f'BBL_{self.bb_length}_{self.bb_std}']
         df_15m['bb_upper_15m'] = bb_15m[f'BBU_{self.bb_length}_{self.bb_std}']
         df_15m['rsi_15m'] = ta.rsi(df_15m['close'], length=self.rsi_length)
         
@@ -5,7 +59,7 @@
         df_15m = df_15m[['date', 'bb_lower_15m', 'bb_upper_15m', 'rsi_15m']]
         df_15m = df_15m.rename(columns={'date': 'date_15m'})
         dataframe = dataframe.merge(df_15m, how='left', left_on='date', right_on='date_15m')
-        dataframe = dataframe.fillna(method='ffill')  # 前向填充缺失值
+        dataframe = dataframe.fillna(method='ffill')
 
         # 获取 1h 数据
         df_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe='1h')
@@ -18,7 +72,7 @@
         df_1h = df_1h[['date', 'bb_lower_1h', 'bb_upper_1h', 'rsi_1h']]
         df_1h = df_1h.rename(columns={'date': 'date_1h'})
         dataframe = dataframe.merge(df_1h, how='left', left_on='date', right_on='date_1h')
-        dataframe = dataframe.fillna(method='ffill')  # 前向填充缺失值
+        dataframe = dataframe.fillna(method='ffill')
 
         # K线形态：看涨吞没
         dataframe['bullish_engulfing'] = (
@@ -42,7 +96,6 @@
                 (dataframe['volume'] > dataframe['volume_ma'])
             ),
             'enter_long'] = 1
-
         return dataframe
 
     def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
@@ -53,7 +106,6 @@
                 (dataframe['rsi_3m'] > self.rsi_overbought)
             ),
             'exit_long'] = 1
-
         return dataframe
 
     def custom_stoploss(self, pair: str, trade: 'Trade', current_time, current_rate: float,
@@ -63,6 +115,5 @@
         last_candle = dataframe.iloc[-1]
         atr = last_candle['atr']
         if atr > 0:
-            return -1.5 * atr / current_rate  # 动态止损为 1.5 倍 ATR
-        return self.stoploss  # 回退到固定止损
-
+            return -1.5 * atr / current_rate
+        return self.stoploss