diff --git a/freqtrade/templates/freqaiprimer.py b/freqtrade/templates/freqaiprimer.py index b90cb787..72ec3b1d 100644 --- a/freqtrade/templates/freqaiprimer.py +++ b/freqtrade/templates/freqaiprimer.py @@ -8,16 +8,16 @@ import pandas_ta as ta 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) + "0": 0.03, # 3% ROI (10 分钟内) + "60": 0.015, # 1.5% ROI (1 小时) + "180": 0.005, # 0.5% ROI (3 小时) "360": 0.0 # 0% ROI (6 小时) } - stoploss = -0.015 # 初始止损 -1.5% + stoploss = -0.01 # 固定止损 -1% trailing_stop = True - 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% + trailing_stop_positive = 0.005 # 价格上涨 0.5% 后开始追踪 + trailing_stop_positive_offset = 0.008 # 追踪止损偏移量 0.8% timeframe = "3m" # 主时间框架为 3 分钟 can_short = False # 禁用做空 @@ -26,8 +26,8 @@ class FreqaiPrimer(IStrategy): bb_length = 20 bb_std = 2.0 rsi_length = 14 - rsi_overbought = 65 # 收紧超买阈值到 65 - rsi_oversold = 30 # 收紧超卖阈值到 30 + rsi_overbought = 58 # 收紧超买阈值到 60 + rsi_oversold = 39 # 放宽超卖阈值到 45 def informative_pairs(self): pairs = self.dp.current_whitelist() @@ -48,26 +48,22 @@ class FreqaiPrimer(IStrategy): # 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) # 手动合并 15m 数据 - df_15m = df_15m[['date', 'bb_lower_15m', 'bb_upper_15m', 'rsi_15m']] + df_15m = df_15m[['date', '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') # 获取 1h 数据 df_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe='1h') - bb_1h = ta.bbands(df_1h['close'], length=self.bb_length, std=self.bb_std) - df_1h['bb_lower_1h'] = bb_1h[f'BBL_{self.bb_length}_{self.bb_std}'] - df_1h['bb_upper_1h'] = bb_1h[f'BBU_{self.bb_length}_{self.bb_std}'] df_1h['rsi_1h'] = ta.rsi(df_1h['close'], length=self.rsi_length) + df_1h['ema_50_1h'] = ta.ema(df_1h['close'], length=50) # 1h 50周期EMA + df_1h['trend_1h'] = df_1h['close'] > df_1h['ema_50_1h'] # 1h上涨趋势 # 手动合并 1h 数据 - df_1h = df_1h[['date', 'bb_lower_1h', 'bb_upper_1h', 'rsi_1h']] + df_1h = df_1h[['date', 'rsi_1h', 'trend_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') @@ -82,57 +78,57 @@ class FreqaiPrimer(IStrategy): # 调试：打印指标值（最后 5 行） print(f"Pair: {metadata['pair']}, Last 5 rows:") - print(dataframe[['date', 'close', 'bb_lower_3m', 'rsi_3m', 'rsi_15m', 'rsi_1h', 'bullish_engulfing', 'volume', 'volume_ma']].tail(5)) + print(dataframe[['date', 'close', 'bb_lower_3m', 'rsi_3m', 'rsi_15m', 'rsi_1h', 'trend_1h', 'bullish_engulfing', 'volume', 'volume_ma']].tail(5)) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: - # 做多条件（收紧条件以减少交易量） + # 做多条件（放宽以达到每月150单） conditions = [ - (dataframe['close'] <= dataframe['bb_lower_3m']), # 严格要求价格低于 3m 布林带下轨 - (dataframe['rsi_3m'] < self.rsi_oversold), # 3m RSI < 30 - (dataframe['rsi_15m'] < self.rsi_oversold), # 15m RSI < 30 - (dataframe['close'] <= dataframe['bb_lower_15m']), # 价格低于 15m 布林带下轨 - (dataframe['bullish_engulfing']), # 强制要求看涨吞没 - (dataframe['volume'] > dataframe['volume_ma'] * 1.2) # 成交量高于均量的 1.2 倍 + (dataframe['close'] <= dataframe['bb_lower_3m'] * 1.03), # 价格接近3m布林带下轨（3%偏差） + (dataframe['rsi_3m'] < self.rsi_oversold), # 3m RSI < 45 + (dataframe['rsi_15m'] < self.rsi_oversold), # 15m RSI < 45 + (dataframe['trend_1h']), # 1h上涨趋势 + (dataframe['volume'] > dataframe['volume_ma']) # 成交量高于均量 ] + # 看涨吞没或极低RSI（可选） + conditions.append(dataframe['bullish_engulfing'] | (dataframe['rsi_3m'] < self.rsi_oversold - 5)) dataframe.loc[conditions[0] & conditions[1] & conditions[2] & conditions[3] & conditions[4] & conditions[5], 'enter_long'] = 1 # 调试：检查每个条件的触发情况 print(f"Pair: {metadata['pair']}, Entry condition checks:") - print(f" - Close <= bb_lower_3m: {(dataframe['close'] <= dataframe['bb_lower_3m']).sum()} candles") + print(f" - Close <= bb_lower_3m * 1.03: {(dataframe['close'] <= dataframe['bb_lower_3m'] * 1.03).sum()} candles") print(f" - RSI_3m < {self.rsi_oversold}: {(dataframe['rsi_3m'] < self.rsi_oversold).sum()} candles") print(f" - RSI_15m < {self.rsi_oversold}: {(dataframe['rsi_15m'] < self.rsi_oversold).sum()} candles") - print(f" - Close <= bb_lower_15m: {(dataframe['close'] <= dataframe['bb_lower_15m']).sum()} candles") - print(f" - Bullish Engulfing: {(dataframe['bullish_engulfing']).sum()} candles") - print(f" - Volume > Volume_MA * 1.2: {(dataframe['volume'] > dataframe['volume_ma'] * 1.2).sum()} candles") + print(f" - Trend_1h (Close > EMA_50_1h): {(dataframe['trend_1h']).sum()} candles") + print(f" - Volume > Volume_MA: {(dataframe['volume'] > dataframe['volume_ma']).sum()} candles") + print(f" - Bullish Engulfing or RSI_3m < {self.rsi_oversold - 5}: {(dataframe['bullish_engulfing'] | (dataframe['rsi_3m'] < self.rsi_oversold - 5)).sum()} candles") if dataframe['enter_long'].sum() > 0: print(f"Entry signals found at:") - print(dataframe[dataframe['enter_long'] == 1][['date', 'close', 'rsi_3m', 'rsi_15m', 'bullish_engulfing']]) + print(dataframe[dataframe['enter_long'] == 1][['date', 'close', 'rsi_3m', 'rsi_15m', 'trend_1h', 'bullish_engulfing']]) else: print("No entry signals generated.") return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: - # 做多退出（添加 ATR 动态退出） + # 做多退出（优化以减少亏损） dataframe.loc[ ( (dataframe['close'] >= dataframe['bb_upper_3m']) | (dataframe['rsi_3m'] > self.rsi_overbought) | - (dataframe['close'] > dataframe['open'] + dataframe['atr'] * 2) # 获利 2 倍 ATR + (dataframe['close'] > dataframe['open'] + dataframe['atr'] * 2) # 获利2倍ATR ), 'exit_long'] = 1 return dataframe def custom_stoploss(self, pair: str, trade: 'Trade', current_time, current_rate: float, current_profit: float, **kwargs) -> float: - # 动态止损基于 ATR + # 动态止损基于ATR dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1] atr = last_candle['atr'] if atr > 0: - return -1.5 * atr / current_rate + return -1.0 * atr / current_rate # 收紧到1倍ATR return self.stoploss -