for test make 4h to 1h
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@ -172,55 +172,55 @@ class FreqaiPrimer(IStrategy):
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avg_stochrsi = dataframe['stochrsi_k'].rolling(window=period).mean()
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return avg_stochrsi > threshold
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def is_bearish_market(self, dataframe: DataFrame, metadata: dict, timeframe: str = "4h") -> pd.Series:
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def is_bearish_market(self, dataframe: DataFrame, metadata: dict, timeframe: str = "1h") -> pd.Series:
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pair = metadata.get('pair', 'Unknown')
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logger.debug(f"[{pair}] 开始计算 4h 熊市信号")
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logger.debug(f"[{pair}] 开始计算 1h 熊市信号")
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# 防御性检查
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required_columns = ['close_4h', 'high_4h', 'low_4h', 'open_4h', 'stochrsi_k_4h', 'stochrsi_d_4h', 'bb_middle_4h']
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required_columns = ['close_1h', 'high_1h', 'low_1h', 'open_1h', 'stochrsi_k_1h', 'stochrsi_d_1h', 'bb_middle_1h']
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missing = [col for col in required_columns if col not in dataframe.columns]
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if missing:
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logger.error(f"[{pair}] 缺少必要列：{missing}，返回全 False")
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return pd.Series([False] * len(dataframe), index=dataframe.index)
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# 检查 volume_z_score_4h 是否存在
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has_volume_z_score = 'volume_z_score_4h' in dataframe.columns and not dataframe['volume_z_score_4h'].isna().all()
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# 检查 volume_z_score_1h 是否存在
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has_volume_z_score = 'volume_z_score_1h' in dataframe.columns and not dataframe['volume_z_score_1h'].isna().all()
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# 条件 a：价格远低于布林带中轨（低于中轨 1% 以上）
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cond_a = dataframe['close_4h'] < dataframe['bb_middle_4h'] * 0.99
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cond_a = dataframe['close_1h'] < dataframe['bb_middle_1h'] * 0.99
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# 条件 b：STOCHRSI 超过90
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cond_b = dataframe['stochrsi_k_4h'] > 90
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cond_b = dataframe['stochrsi_k_1h'] > 90
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# 条件 c：看跌蜡烛图形态
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open_4h = dataframe['open_4h']
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close_4h = dataframe['close_4h']
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high_4h = dataframe['high_4h']
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low_4h = dataframe['low_4h']
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prev_open = open_4h.shift(1)
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prev_close = close_4h.shift(1)
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open_1h = dataframe['open_1h']
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close_1h = dataframe['close_1h']
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high_1h = dataframe['high_1h']
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low_1h = dataframe['low_1h']
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prev_open = open_1h.shift(1)
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prev_close = close_1h.shift(1)
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cond_engulfing = (
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(prev_close > prev_open) &
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(close_4h < open_4h) &
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(close_4h < prev_open) &
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(open_4h > prev_close)
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(close_1h < open_1h) &
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(close_1h < prev_open) &
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(open_1h > prev_close)
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)
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cond_dark_cloud_cover = (
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(prev_close > prev_open) &
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(open_4h > prev_close) &
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(close_4h < (prev_open + prev_close) / 2) &
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(close_4h < open_4h)
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(open_1h > prev_close) &
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(close_1h < (prev_open + prev_close) / 2) &
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(close_1h < open_1h)
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)
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body = abs(open_4h - close_4h)
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upper_wick = high_4h - np.maximum(open_4h, close_4h)
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lower_wick = np.minimum(open_4h, close_4h) - low_4h
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cond_shooting_star = (upper_wick > 2 * body) & (lower_wick < body) & (close_4h < open_4h)
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body = abs(open_1h - close_1h)
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upper_wick = high_1h - np.maximum(open_1h, close_1h)
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lower_wick = np.minimum(open_1h, close_1h) - low_1h
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cond_shooting_star = (upper_wick > 2 * body) & (lower_wick < body) & (close_1h < open_1h)
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cond_stochrsi_high = self.is_stochrsi_overbought(dataframe, period=10, threshold=85)
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cond_c = cond_engulfing | cond_dark_cloud_cover | cond_shooting_star | cond_stochrsi_high
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# 条件 d：成交量显著下降
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cond_d = dataframe['volume_z_score_4h'] < -1.0 if has_volume_z_score else pd.Series([False] * len(dataframe), index=dataframe.index)
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cond_d = dataframe['volume_z_score_1h'] < -1.0 if has_volume_z_score else pd.Series([False] * len(dataframe), index=dataframe.index)
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# 综合熊市信号（至少满足两个条件）
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bearish_signal = (cond_a & cond_b) | (cond_a & cond_c) | (cond_b & cond_c) | (cond_a & cond_d)
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@ -250,8 +250,8 @@ class FreqaiPrimer(IStrategy):
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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计算主时间框架（3m）和 4h 时间框架的指标，并映射到主 dataframe。
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包含 FreqAI 预测、布林带、RSI、成交量 Z 分数等，并确保 4h 数据列完整性。
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计算主时间框架（3m）和 1h 时间框架的指标，并映射到主 dataframe。
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包含 FreqAI 预测、布林带、RSI、成交量 Z 分数等，并确保 1h 数据列完整性。
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"""
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pair = metadata.get('pair', 'Unknown')
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logger.info(f"[{pair}] 当前可用列（调用FreqAI前）：{list(dataframe.columns)}")
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@ -272,46 +272,46 @@ class FreqaiPrimer(IStrategy):
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dataframe["stochrsi_k"] = stochrsi["fastk"]
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dataframe["stochrsi_d"] = stochrsi["fastd"]
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# 获取 4h 时间框架数据
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dataframe_4h = self.dp.get_pair_dataframe(pair=pair, timeframe="4h")
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if dataframe_4h.empty or len(dataframe_4h) < 50:
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logger.warning(f"[{pair}] 4h 数据为空或不足（{len(dataframe_4h)} 根K线），初始化空列")
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for col in ['open_4h', 'high_4h', 'low_4h', 'close_4h', 'stochrsi_k_4h', 'stochrsi_d_4h',
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'bb_upper_4h', 'bb_middle_4h', 'bb_lower_4h', 'volume_z_score_4h']:
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# 获取 1h 时间框架数据
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dataframe_1h = self.dp.get_pair_dataframe(pair=pair, timeframe="1h")
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if dataframe_1h.empty or len(dataframe_1h) < 50:
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logger.warning(f"[{pair}] 1h 数据为空或不足（{len(dataframe_1h)} 根K线），初始化空列")
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for col in ['open_1h', 'high_1h', 'low_1h', 'close_1h', 'stochrsi_k_1h', 'stochrsi_d_1h',
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'bb_upper_1h', 'bb_middle_1h', 'bb_lower_1h', 'volume_z_score_1h']:
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dataframe[col] = np.nan
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else:
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# 计算 4h 指标
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if len(dataframe_4h) >= 20: # 确保有足够数据计算 rolling(20)
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stochrsi_4h = ta.STOCHRSI(dataframe_4h, timeperiod=14, fastk_period=3, fastd_period=3)
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dataframe_4h['stochrsi_k'] = stochrsi_4h['fastk']
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dataframe_4h['stochrsi_d'] = stochrsi_4h['fastd']
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real = ta.TYPPRICE(dataframe_4h)
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# 计算 1h 指标
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if len(dataframe_1h) >= 20: # 确保有足够数据计算 rolling(20)
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stochrsi_1h = ta.STOCHRSI(dataframe_1h, timeperiod=14, fastk_period=3, fastd_period=3)
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dataframe_1h['stochrsi_k'] = stochrsi_1h['fastk']
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dataframe_1h['stochrsi_d'] = stochrsi_1h['fastd']
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real = ta.TYPPRICE(dataframe_1h)
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upperband, middleband, lowerband = ta.BBANDS(real, timeperiod=20, nbdevup=2.0, nbdevdn=2.0)
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dataframe_4h['bb_upper_4h'] = upperband
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dataframe_4h['bb_middle_4h'] = middleband
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dataframe_4h['bb_lower_4h'] = lowerband
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dataframe_4h['volume_mean_20_4h'] = dataframe_4h["volume"].rolling(20).mean()
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dataframe_4h['volume_std_20_4h'] = dataframe_4h["volume"].rolling(20).std()
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dataframe_4h['volume_z_score_4h'] = (dataframe_4h["volume"] - dataframe_4h['volume_mean_20_4h']) / dataframe_4h['volume_std_20_4h']
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dataframe_1h['bb_upper_1h'] = upperband
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dataframe_1h['bb_middle_1h'] = middleband
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dataframe_1h['bb_lower_1h'] = lowerband
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dataframe_1h['volume_mean_20_1h'] = dataframe_1h["volume"].rolling(20).mean()
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dataframe_1h['volume_std_20_1h'] = dataframe_1h["volume"].rolling(20).std()
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dataframe_1h['volume_z_score_1h'] = (dataframe_1h["volume"] - dataframe_1h['volume_mean_20_1h']) / dataframe_1h['volume_std_20_1h']
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# 清理 NaN 和无穷值
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dataframe_4h['volume_z_score_4h'] = dataframe_4h['volume_z_score_4h'].replace([np.inf, -np.inf], 0).ffill().fillna(0)
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dataframe_1h['volume_z_score_1h'] = dataframe_1h['volume_z_score_1h'].replace([np.inf, -np.inf], 0).ffill().fillna(0)
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else:
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logger.warning(f"[{pair}] 4h 数据不足以计算 volume_z_score_4h（{len(dataframe_4h)} 根K线，需至少20根）")
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dataframe_4h['volume_z_score_4h'] = np.nan
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logger.warning(f"[{pair}] 1h 数据不足以计算 volume_z_score_1h（{len(dataframe_1h)} 根K线，需至少20根）")
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dataframe_1h['volume_z_score_1h'] = np.nan
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# 映射 4h 数据到主时间框架
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for col in ['open', 'high', 'low', 'close', 'stochrsi_k', 'stochrsi_d', 'bb_upper_4h', 'bb_middle_4h', 'bb_lower_4h', 'volume_z_score_4h']:
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if col in dataframe_4h.columns:
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dataframe[col if col.endswith('_4h') else f"{col}_4h"] = dataframe_4h[col].reindex(dataframe.index, method='ffill').bfill()
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# 映射 1h 数据到主时间框架
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for col in ['open', 'high', 'low', 'close', 'stochrsi_k', 'stochrsi_d', 'bb_upper_1h', 'bb_middle_1h', 'bb_lower_1h', 'volume_z_score_1h']:
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if col in dataframe_1h.columns:
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dataframe[col if col.endswith('_1h') else f"{col}_1h"] = dataframe_1h[col].reindex(dataframe.index, method='ffill').bfill()
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else:
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logger.warning(f"[{pair}] 4h 数据缺少列 {col}，初始化为空")
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dataframe[col if col.endswith('_4h') else f"{col}_4h"] = np.nan
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logger.warning(f"[{pair}] 1h 数据缺少列 {col}，初始化为空")
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dataframe[col if col.endswith('_1h') else f"{col}_1h"] = np.nan
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# 数据清理：处理 NaN 和无穷值
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for col in ["ema200", "bb_upperband", "bb_middleband", "bb_lowerband", "rsi", "volume_z_score",
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"&-price_value_divergence", "price_value_divergence", "open_4h", "high_4h", "low_4h",
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"close_4h", "stochrsi_k_4h", "stochrsi_d_4h", "bb_upper_4h", "bb_middle_4h", "bb_lower_4h",
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"volume_z_score_4h"]:
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"&-price_value_divergence", "price_value_divergence", "open_1h", "high_1h", "low_1h",
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"close_1h", "stochrsi_k_1h", "stochrsi_d_1h", "bb_upper_1h", "bb_middle_1h", "bb_lower_1h",
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"volume_z_score_1h"]:
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if col in dataframe.columns:
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dataframe[col] = dataframe[col].replace([np.inf, -np.inf], 0).ffill().fillna(0)
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@ -385,9 +385,9 @@ class FreqaiPrimer(IStrategy):
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f"&-price_value_divergence: {dataframe['&-price_value_divergence'].iloc[-1]:.6f}, "
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f"volume_z_score: {dataframe['volume_z_score'].iloc[-1]:.2f}, "
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f"bb_lowerband: {dataframe['bb_lowerband'].iloc[-1]:.6f}, "
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f"close_4h: {dataframe['close_4h'].iloc[-1] if 'close_4h' in dataframe else 'N/A'}, "
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f"stochrsi_k_4h: {dataframe['stochrsi_k_4h'].iloc[-1] if 'stochrsi_k_4h' in dataframe else 'N/A'}, "
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f"volume_z_score_4h: {dataframe['volume_z_score_4h'].iloc[-1] if 'volume_z_score_4h' in dataframe else 'N/A'}")
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f"close_1h: {dataframe['close_1h'].iloc[-1] if 'close_1h' in dataframe else 'N/A'}, "
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f"stochrsi_k_1h: {dataframe['stochrsi_k_1h'].iloc[-1] if 'stochrsi_k_1h' in dataframe else 'N/A'}, "
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f"volume_z_score_1h: {dataframe['volume_z_score_1h'].iloc[-1] if 'volume_z_score_1h' in dataframe else 'N/A'}")
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if not self.stats_logged:
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logger.info("===== 所有币对的 labels_mean 和 labels_std 汇总 =====")
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@ -419,7 +419,7 @@ class FreqaiPrimer(IStrategy):
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stochrsi_threshold = self.linear_map(trend_score, 0, 100, stochrsi_max, stochrsi_min)
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# 计算熊市信号和 STOCHRSI 超买信号
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bearish_signal = self.is_bearish_market(dataframe, metadata, timeframe="4h")
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bearish_signal = self.is_bearish_market(dataframe, metadata, timeframe="1h")
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bearish_signal_aligned = bearish_signal.reindex(dataframe.index, method='ffill').fillna(False)
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stochrsi_overbought = self.is_stochrsi_overbought(dataframe, period=10, threshold=85)
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stochrsi_overbought_aligned = stochrsi_overbought.reindex(dataframe.index, method='ffill').fillna(True)
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@ -8,7 +8,7 @@ def analyze_candlestick_data(file_path):
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# 查看数据集行数和列数
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rows, columns = df.shape
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if rows < 500:
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if rows < 1000:
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# 短表数据（行数少于500）查看全量数据信息
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print('数据全部内容信息：')
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print(df.to_csv(sep='\t', na_rep='nan'))
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@ -17,6 +17,10 @@ def analyze_candlestick_data(file_path):
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print('数据前几行内容信息：')
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print(df.head().to_csv(sep='\t', na_rep='nan'))
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# 查看数据最后几行信息
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print('数据最后几行内容信息：')
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print(df.tail().to_csv(sep='\t', na_rep='nan'))
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# 查看数据的基本信息
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print('数据基本信息：')
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df.info()
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