综合3m,15m,1h的各个指标的入场出场条件
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@ -103,6 +103,17 @@ class FreqaiPrimer(IStrategy):
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dataframe['bb_upper_3m'] = bb_3m[f'BBU_{self.bb_length}_{self.bb_std}']
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dataframe['rsi_3m'] = ta.rsi(dataframe['close'], length=self.rsi_length)
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# 新增 StochRSI 指标
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stochrsi_3m = ta.stochrsi(dataframe['close'], length=14, rsi_length=14)
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dataframe['stochrsi_k_3m'] = stochrsi_3m['STOCHRSIk_14_14_3_3']
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dataframe['stochrsi_d_3m'] = stochrsi_3m['STOCHRSId_14_14_3_3']
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# 新增 MACD 指标
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macd_3m = ta.macd(dataframe['close'], fast=12, slow=26, signal=9)
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dataframe['macd_3m'] = macd_3m['MACD_12_26_9']
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dataframe['macd_signal_3m'] = macd_3m['MACDs_12_26_9']
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dataframe['macd_hist_3m'] = macd_3m['MACDh_12_26_9']
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# 计算3m时间框架的EMA50和EMA200
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dataframe['ema_50_3m'] = ta.ema(dataframe['close'], length=50)
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dataframe['ema_200_3m'] = ta.ema(dataframe['close'], length=200)
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@ -120,6 +131,17 @@ class FreqaiPrimer(IStrategy):
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df_15m['ema_50_15m'] = ta.ema(df_15m['close'], length=50)
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df_15m['ema_200_15m'] = ta.ema(df_15m['close'], length=200)
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# 新增 StochRSI 指标
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stochrsi_15m = ta.stochrsi(df_15m['close'], length=14, rsi_length=14)
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df_15m['stochrsi_k_15m'] = stochrsi_15m['STOCHRSIk_14_14_3_3']
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df_15m['stochrsi_d_15m'] = stochrsi_15m['STOCHRSId_14_14_3_3']
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# 新增 MACD 指标
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macd_15m = ta.macd(df_15m['close'], fast=12, slow=26, signal=9)
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df_15m['macd_15m'] = macd_15m['MACD_12_26_9']
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df_15m['macd_signal_15m'] = macd_15m['MACDs_12_26_9']
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df_15m['macd_hist_15m'] = macd_15m['MACDh_12_26_9']
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# 手动合并 15m 数据
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df_15m = df_15m[['date', 'rsi_15m', 'ema_50_15m', 'ema_200_15m']]
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df_15m = df_15m.rename(columns={'date': 'date_15m'})
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@ -133,6 +155,17 @@ class FreqaiPrimer(IStrategy):
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df_1h['ema_200_1h'] = ta.ema(df_1h['close'], length=200) # 1h 200周期EMA
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df_1h['trend_1h'] = df_1h['close'] > df_1h['ema_50_1h'] # 1h上涨趋势
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# 新增 StochRSI 指标
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stochrsi_1h = ta.stochrsi(df_1h['close'], length=14, rsi_length=14)
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df_1h['stochrsi_k_1h'] = stochrsi_1h['STOCHRSIk_14_14_3_3']
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df_1h['stochrsi_d_1h'] = stochrsi_1h['STOCHRSId_14_14_3_3']
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# 新增 MACD 指标
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macd_1h = ta.macd(df_1h['close'], fast=12, slow=26, signal=9)
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df_1h['macd_1h'] = macd_1h['MACD_12_26_9']
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df_1h['macd_signal_1h'] = macd_1h['MACDs_12_26_9']
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df_1h['macd_hist_1h'] = macd_1h['MACDh_12_26_9']
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# 手动合并 1h 数据
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df_1h = df_1h[['date', 'rsi_1h', 'trend_1h', 'ema_50_1h', 'ema_200_1h']]
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df_1h = df_1h.rename(columns={'date': 'date_1h'})
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@ -195,6 +228,33 @@ class FreqaiPrimer(IStrategy):
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return dataframe
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def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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# 入场信号主要依据1小时周期
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# 条件1: 价格接近布林带下轨
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close_to_bb_lower_1h = (dataframe['close'] <= dataframe['bb_lower_1h'] * 1.02)
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# 条件2: RSI 不高于阈值
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rsi_condition_1h = dataframe['rsi_1h'] < self.rsi_oversold
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# 条件3: StochRSI 处于超卖区域
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stochrsi_condition_1h = (dataframe['stochrsi_k_1h'] < 20) & (dataframe['stochrsi_d_1h'] < 20)
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# 条件4: MACD 上升趋势
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macd_condition_1h = dataframe['macd_1h'] > dataframe['macd_signal_1h']
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# 辅助条件: 3m 和 15m 趋势确认
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trend_confirmation = (dataframe['trend_3m'] == 1) & (dataframe['trend_15m'] == 1)
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# 合并所有条件
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final_condition = close_to_bb_lower_1h & rsi_condition_1h & stochrsi_condition_1h & macd_condition_1h & trend_confirmation
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# 设置入场信号
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dataframe.loc[final_condition, 'enter_long'] = 1
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# 日志记录
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if dataframe['enter_long'].sum() > 0:
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logger.info(f"[{metadata['pair']}] 发现入场信号数量: {dataframe['enter_long'].sum()}")
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return dataframe
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# 做多条件（放宽以增加入场信号）
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# 1. 价格接近3m布林带下轨（放宽到5%偏差）
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close_to_bb_lower = (dataframe['close'] <= dataframe['bb_lower_3m'] * 1.05)
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@ -245,6 +305,30 @@ class FreqaiPrimer(IStrategy):
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return dataframe
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def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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# 出场信号基于趋势和量价关系
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# 条件1: 价格突破布林带上轨
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breakout_condition = dataframe['close'] >= dataframe['bb_upper_1h']
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# 条件2: 成交量显著放大
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volume_spike = dataframe['volume'] > dataframe['volume_ma'] * 2
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# 条件3: MACD 下降趋势
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macd_downward = dataframe['macd_1h'] < dataframe['macd_signal_1h']
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# 条件4: RSI 进入超买区域
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rsi_overbought = dataframe['rsi_1h'] > self.rsi_overbought
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# 合并所有条件
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final_condition = breakout_condition | volume_spike | macd_downward | rsi_overbought
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# 设置出场信号
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dataframe.loc[final_condition, 'exit_long'] = 1
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# 日志记录
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if dataframe['exit_long'].sum() > 0:
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logger.info(f"[{metadata['pair']}] 触发出场信号数量: {dataframe['exit_long'].sum()}")
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return dataframe
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# 缓存数据框以便在trailing_stop_positive属性中使用
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self._dataframe_cache = dataframe
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@ -190,7 +190,6 @@ echo "docker-compose run --rm freqtrade backtesting $PAIRS_FLAG \
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--freqaimodel LightGBMRegressorMultiTarget \
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--config /freqtrade/config_examples/$CONFIG_FILE \
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--config /freqtrade/templates/freqaiprimer.json \
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--enable-protections \
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--strategy-path /freqtrade/templates \
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--strategy $STRATEGY_NAME \
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--timerange $START_DATE-$END_DATE \
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@ -203,8 +202,8 @@ docker-compose run --rm freqtrade backtesting $PAIRS_FLAG \
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--freqaimodel LightGBMRegressorMultiTarget \
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--config /freqtrade/config_examples/$CONFIG_FILE \
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--config /freqtrade/templates/freqaiprimer.json \
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--enable-protections \
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--strategy-path /freqtrade/templates \
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--enable-protections \
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--strategy $STRATEGY_NAME \
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--timerange $START_DATE-$END_DATE \
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--fee 0.0008 \
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