只要当前EMA5在EMA20之上就满足条件
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zhangkun9038@dingtalk.com
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80acbac9f4
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38241423b2
@ -440,6 +440,20 @@ class FreqaiPrimer(IStrategy):
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df_1h['bb_lower_1h'] = bb_ma_1h - (bb_std_value * bb_std_1h)
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df_1h['bb_upper_1h'] = bb_ma_1h + (bb_std_value * bb_std_1h)
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# 添加 EMA5 和 EMA20 用于趋势过滤（方案2：宽松条件）
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df_1h['ema_5_1h'] = df_1h['close'].ewm(span=5, adjust=False).mean()
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df_1h['ema_20_1h'] = df_1h['close'].ewm(span=20, adjust=False).mean()
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# 检测 EMA5 向上穿越 EMA20（添加安全检查）
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if len(df_1h) >= 2:
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df_1h['ema5_cross_above_ema20'] = (
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(df_1h['ema_5_1h'] > df_1h['ema_20_1h']) &
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(df_1h['ema_5_1h'].shift(1) <= df_1h['ema_20_1h'].shift(1))
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)
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else:
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# 数据不足时，默认为False
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df_1h['ema5_cross_above_ema20'] = False
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# 使用 rolling 计算 RSI（减少看前偏差）
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delta_1h = df_1h['close'].diff()
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gain_1h = delta_1h.where(delta_1h > 0, 0).rolling(window=rsi_length_value).mean()
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@ -470,13 +484,13 @@ class FreqaiPrimer(IStrategy):
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# 将 1h 数据重新索引到主时间框架 (3m)，并填充缺失值
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df_1h = df_1h.set_index('date').reindex(dataframe['date']).ffill().bfill().reset_index()
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df_1h = df_1h.rename(columns={'index': 'date'})
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# Include macd_1h and macd_signal_1h in the column selection
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df_1h = df_1h[['date', 'rsi_1h', 'trend_1h', 'ema_50_1h', 'ema_200_1h', 'bb_lower_1h', 'bb_upper_1h', 'stochrsi_k_1h', 'stochrsi_d_1h', 'macd_1h', 'macd_signal_1h']].ffill()
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# Include macd_1h, macd_signal_1h, ema_5_1h, ema_20_1h, ema5_cross_above_ema20 in the column selection
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df_1h = df_1h[['date', 'rsi_1h', 'trend_1h', 'ema_50_1h', 'ema_200_1h', 'bb_lower_1h', 'bb_upper_1h', 'stochrsi_k_1h', 'stochrsi_d_1h', 'macd_1h', 'macd_signal_1h', 'ema_5_1h', 'ema_20_1h', 'ema5_cross_above_ema20']].ffill()
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# Validate that all required columns are present
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required_columns = ['date', 'rsi_1h', 'trend_1h', 'ema_50_1h', 'ema_200_1h',
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'bb_lower_1h', 'bb_upper_1h', 'stochrsi_k_1h', 'stochrsi_d_1h',
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'macd_1h', 'macd_signal_1h']
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'macd_1h', 'macd_signal_1h', 'ema_5_1h', 'ema_20_1h', 'ema5_cross_above_ema20']
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missing_columns = [col for col in required_columns if col not in df_1h.columns]
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if missing_columns:
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logger.error(f"[{metadata['pair']}] 缺少以下列: {missing_columns}")
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@ -485,7 +499,7 @@ class FreqaiPrimer(IStrategy):
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# 确保所有需要的列都被合并
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required_columns = ['date', 'rsi_1h', 'trend_1h', 'ema_50_1h', 'ema_200_1h',
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'bb_lower_1h', 'bb_upper_1h', 'stochrsi_k_1h', 'stochrsi_d_1h',
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'macd_1h', 'macd_signal_1h']
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'macd_1h', 'macd_signal_1h', 'ema_5_1h', 'ema_20_1h', 'ema5_cross_above_ema20']
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# 验证所需列是否存在
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missing_columns = [col for col in required_columns if col not in df_1h.columns]
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@ -493,7 +507,7 @@ class FreqaiPrimer(IStrategy):
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logger.error(f"[{metadata['pair']}] 缺少以下列: {missing_columns}")
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raise KeyError(f"缺少以下列: {missing_columns}")
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df_1h = df_1h[required_columns] # 确保包含 macd_1h 和 macd_signal_1h
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df_1h = df_1h[required_columns] # 确保包含所有必需的列（包括EMA过滤相关列）
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# 合并 1h 数据
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dataframe = dataframe.merge(df_1h, how='left', on='date').ffill()
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@ -640,8 +654,29 @@ class FreqaiPrimer(IStrategy):
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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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# 新增：EMA趋势过滤条件（方案2：宽松版本）
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# 条件1：EMA5保持在EMA20之上 或 条件2：最近20根1h K线内发生过向上穿越
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# 这样既能捕捉趋势启动，又能在趋势延续时继续入场
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if 'ema_5_1h' in dataframe.columns and 'ema_20_1h' in dataframe.columns:
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# 条件1：EMA5保持在EMA20之上
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ema5_above_ema20 = dataframe['ema_5_1h'] > dataframe['ema_20_1h']
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# 条件2：最近20根1h K线内发生过向上穿越
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if 'ema5_cross_above_ema20' in dataframe.columns:
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# 使用rolling.max检查最近20根K线内是否有True值
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recent_cross = dataframe['ema5_cross_above_ema20'].rolling(window=20, min_periods=1).max() == 1
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# 两个条件满足其一即可
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ema_trend_filter = ema5_above_ema20 | recent_cross
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else:
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# 如果没有交叉列，只用保持在上方的条件
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ema_trend_filter = ema5_above_ema20
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else:
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# 如果列不存在，创建一个全False的Series（不允许入场）
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self.strategy_log(f"[{metadata['pair']}] 警告：ema_5_1h或ema_20_1h列不存在，过滤条件设为False")
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ema_trend_filter = pd.Series(False, index=dataframe.index)
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# 合并所有条件（减少强制性条件）
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# 至少满足多个条件中的一定数量
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# 至少满足多个条件中的一定数量，并且必须满足EMA趋势过滤
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condition_count = (
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close_to_bb_lower_1h.astype(int) +
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rsi_condition_1h.astype(int) +
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@ -650,7 +685,9 @@ class FreqaiPrimer(IStrategy):
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(volume_spike | bb_width_condition).astype(int) + # 成交量或布林带宽度满足其一即可
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trend_confirmation.astype(int)
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)
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final_condition = condition_count >= self.min_condition_count.value
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# 最终条件：基本条件 + EMA趋势过滤（方案2：宽松版）
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basic_condition = condition_count >= self.min_condition_count.value
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final_condition = basic_condition & ema_trend_filter
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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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@ -724,14 +761,29 @@ class FreqaiPrimer(IStrategy):
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dataframe.loc[final_condition_updated, 'enter_price'] = dataframe.loc[final_condition_updated, 'close'] * 0.9833
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# 增强调试信息
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#self.strategy_log(f"[{metadata['pair']}] 入场条件检查:")
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#self.strategy_log(f" - 价格接近布林带下轨: {close_to_bb_lower_1h.sum()} 次")
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#self.strategy_log(f" - RSI 超卖: {rsi_condition_1h.sum()} 次")
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#self.strategy_log(f" - StochRSI 超卖: {stochrsi_condition_1h.sum()} 次")
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#self.strategy_log(f" - MACD 上升趋势: {macd_condition_1h.sum()} 次")
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#self.strategy_log(f" - 成交量或布林带宽度: {(volume_spike | bb_width_condition).sum()} 次")
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#self.strategy_log(f" - 趋势确认: {trend_confirmation.sum()} 次")
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#self.strategy_log(f" - 最终条件: {final_condition.sum()} 次")
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# 确保ema_trend_filter是Series类型才能调用sum()
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if isinstance(ema_trend_filter, pd.Series):
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ema_trend_count = ema_trend_filter.sum()
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else:
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ema_trend_count = 0
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basic_condition_count = basic_condition.sum()
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final_condition_count = final_condition.sum()
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self.strategy_log(f"[{metadata['pair']}] 入场条件检查:")
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self.strategy_log(f" - 价格接近布林带下轨: {close_to_bb_lower_1h.sum()} 次")
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self.strategy_log(f" - RSI 超卖: {rsi_condition_1h.sum()} 次")
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self.strategy_log(f" - StochRSI 超卖: {stochrsi_condition_1h.sum()} 次")
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self.strategy_log(f" - MACD 上升趋势: {macd_condition_1h.sum()} 次")
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self.strategy_log(f" - 成交量或布林带宽度: {(volume_spike | bb_width_condition).sum()} 次")
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self.strategy_log(f" - 趋势确认: {trend_confirmation.sum()} 次")
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self.strategy_log(f" - EMA趋势过滤(在上方或20根K线内穿越): {ema_trend_count} 次")
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self.strategy_log(f" - 基本条件满足: {basic_condition_count} 次")
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self.strategy_log(f" - 最终条件(基本+EMA过滤): {final_condition_count} 次")
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# 如果EMA条件满足但最终条件未满足，输出详细信息
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if ema_trend_count > 0 and final_condition_count == 0:
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self.strategy_log(f"[{metadata['pair']}] 注意：检测到 {ema_trend_count} 次EMA趋势过滤满足，但由于其他条件不足未能生成入场信号")
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# 在populate_entry_trend方法末尾添加
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# 计算条件间的相关性
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conditions = DataFrame({
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@ -740,7 +792,8 @@ class FreqaiPrimer(IStrategy):
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'stochrsi': stochrsi_condition_1h,
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'macd': macd_condition_1h,
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'vol_bb': (volume_spike | bb_width_condition),
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'trend': trend_confirmation
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'trend': trend_confirmation,
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'ema_trend': ema_trend_filter
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})
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correlation = conditions.corr().mean().mean()
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#self.strategy_log(f"[{metadata['pair']}] 条件平均相关性: {correlation:.2f}")
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