综合3m,15m,1h的各个指标的入场出场
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@ -388,40 +388,40 @@ 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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# 获取当前市场状态
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current_state = dataframe['market_state'].iloc[-1] if 'market_state' in dataframe.columns else 'neutral'
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# 条件2: RSI 不高于阈值（更严格）
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rsi_condition_1h = dataframe['rsi_1h'] < 40 # 进一步收紧 RSI 阈值
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# 条件1: 价格接近布林带下轨（允许一定偏差）
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close_to_bb_lower_1h = (dataframe['close'] <= dataframe['bb_lower_1h'] * 1.03) # 放宽到3%偏差
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# 条件3: StochRSI 处于超卖区域（更严格）
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stochrsi_condition_1h = (dataframe['stochrsi_k_1h'] < 20) & (dataframe['stochrsi_d_1h'] < 20) # 进一步收紧 StochRSI 阈值
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# 条件2: RSI 不高于阈值（根据市场状态动态调整）
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rsi_threshold = 45 if current_state in ['strong_bull', 'weak_bull'] else 40
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rsi_condition_1h = dataframe['rsi_1h'] < rsi_threshold
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# 条件3: StochRSI 处于超卖区域（根据市场状态动态调整）
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stochrsi_threshold = 30 if current_state in ['strong_bull', 'weak_bull'] else 20
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stochrsi_condition_1h = (dataframe['stochrsi_k_1h'] < stochrsi_threshold) & (dataframe['stochrsi_d_1h'] < stochrsi_threshold)
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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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# 条件5: 成交量显著放大
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# 条件5: 成交量显著放大（可选条件）
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volume_spike = dataframe['volume'] > dataframe['volume_ma'] * 1.5
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# 条件6: 布林带宽度过滤（避免窄幅震荡）
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bb_width = (dataframe['bb_upper_1h'] - dataframe['bb_lower_1h']) / dataframe['close']
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bb_width_condition = bb_width > 0.02 # 布林带宽度大于2%
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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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# 辅助条件: 3m 和 15m 趋势确认（允许部分时间框架不一致）
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trend_confirmation = (dataframe['trend_3m'] == 1) | (dataframe['trend_15m'] == 1)
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# 增加布林带宽度过滤器，避免窄幅震荡市场
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bb_width_condition = (dataframe['bb_upper_1h'] - dataframe['bb_lower_1h']) / dataframe['close'] > 0.03 # 布林带宽度大于3%
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# 合并所有条件（增加成交量和布林带宽度过滤）
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# 合并所有条件（减少强制性条件）
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final_condition = (
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close_to_bb_lower_1h &
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rsi_condition_1h & # 严格要求RSI满足条件
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stochrsi_condition_1h & # 严格要求StochRSI满足条件
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rsi_condition_1h &
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stochrsi_condition_1h &
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macd_condition_1h &
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volume_spike & # 成交量显著放大
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bb_width_condition & # 布林带宽度过滤
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(volume_spike | bb_width_condition) & # 成交量或布林带宽度满足其一即可
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trend_confirmation
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)
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@ -434,6 +434,7 @@ class FreqaiPrimer(IStrategy):
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logger.info(f" - RSI 超卖: {rsi_condition_1h.sum()} 次")
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logger.info(f" - StochRSI 超卖: {stochrsi_condition_1h.sum()} 次")
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logger.info(f" - MACD 上升趋势: {macd_condition_1h.sum()} 次")
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logger.info(f" - 成交量或布林带宽度: {(volume_spike | bb_width_condition).sum()} 次")
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logger.info(f" - 趋势确认: {trend_confirmation.sum()} 次")
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logger.info(f" - 最终条件: {final_condition.sum()} 次")
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@ -442,84 +443,6 @@ class FreqaiPrimer(IStrategy):
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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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# 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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# 基础退出条件
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basic_exit = (
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(dataframe['close'] >= dataframe['bb_upper_3m']) |
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(dataframe['rsi_3m'] > self.rsi_overbought)
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)
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# 强劲趋势条件：1h趋势向上 + 熊牛得分>70
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strong_trend = (dataframe['trend_1h_ema'] == 1) & (dataframe['market_score'] > 70)
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# 一般趋势条件：熊牛得分50-70
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normal_trend = (dataframe['market_score'] >= 50) & (dataframe['market_score'] <= 70)
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# 极端强劲趋势条件：熊牛得分>95
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extreme_strong_trend = (dataframe['market_score'] > 95)
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# 获取15m数据进行趋势确认
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df_15m = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe='15m')
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# 确保df_15m中包含rsi_15m列，如果没有则计算
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try:
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import pandas_ta as ta
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if 'rsi_15m' not in df_15m.columns:
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# 使用pandas_ta计算14期RSI
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rsi_series = ta.rsi(df_15m['close'], length=14)
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df_15m['rsi_15m'] = rsi_series
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except Exception as e:
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# 如果计算失败，创建一个默认值列
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logger.warning(f"[{metadata['pair']}] 计算15m RSI时出错: {e}")
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df_15m['rsi_15m'] = 50 # 设置默认值为中性
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# 重命名date列并保留必要的列
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df_15m = df_15m[['date', 'rsi_15m']].rename(columns={'date': 'date_15m'})
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# 合并数据
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merged_data = dataframe.copy()
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# 创建合并列
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merged_data['rsi_15m'] = 50 # 设置默认值
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# 尝试合并数据
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try:
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# 使用更简单的方式：直接将df_15m的rsi_15m列映射到dataframe
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# 创建一个日期到rsi的映射字典
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date_rsi_map = df_15m.set_index('date_15m')['rsi_15m'].to_dict()
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# 使用映射填充rsi_15m列
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merged_data['rsi_15m'] = merged_data['date'].map(date_rsi_map).ffill().bfill()
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except Exception as e:
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logger.warning(f"[{metadata['pair']}] 合并15m数据时出错: {e}")
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# 趋势反转信号：15m RSI超买
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trend_reversal = merged_data['rsi_15m'] > 75
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# 动态调整退出条件 - 提高ATR倍数以增加单笔盈利潜力
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# 强劲趋势中：大幅提高ATR倍数到5.5倍，充分利用趋势利润
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# 但在极端强劲趋势(market_score>95)下不给出退出信号
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dataframe.loc[strong_trend & ~extreme_strong_trend & ((dataframe['close'] > dataframe['open'] + dataframe['atr'] * 5.5) | (basic_exit & trend_reversal)), 'exit_long'] = 1
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# 一般趋势中：提高到4倍ATR退出
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# 但在极端强劲趋势(market_score>95)下不给出退出信号
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dataframe.loc[normal_trend & ~extreme_strong_trend & ((dataframe['close'] > dataframe['open'] + dataframe['atr'] * 4) | basic_exit), 'exit_long'] = 1
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# 非趋势或弱势：保持2倍ATR退出
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# 但在极端强劲趋势(market_score>95)下不给出退出信号
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# 记录退出决策日志
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if len(dataframe[dataframe['exit_long'] == 1]) > 0:
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last_exit = dataframe[dataframe['exit_long'] == 1].iloc[-1]
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current_state = dataframe['market_state'].iloc[-1]
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logger.info(f"[{metadata['pair']}] 触发退出信号，市场状态: {current_state}, 价格: {last_exit['close']:.2f}")
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# 记录因极端强劲趋势阻止的退出信号
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extreme_strong_prevented = dataframe[extreme_strong_trend & ((strong_trend & ((dataframe['close'] > dataframe['open'] + dataframe['atr'] * 5.5) | (basic_exit & trend_reversal))) | (normal_trend & ((dataframe['close'] > dataframe['open'] + dataframe['atr'] * 4) | basic_exit)))]
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if len(extreme_strong_prevented) > 0:
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logger.info(f"[{metadata['pair']}] 由于极端强劲趋势(market_score>95)，阻止了 {len(extreme_strong_prevented)} 个潜在的退出信号")
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return dataframe
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def detect_h1_rapid_rise(self, pair: str, dataframe: DataFrame, metadata: dict) -> tuple[bool, float]:
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"""
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