分段长度去掉freqai优化
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zhangkun9038@dingtalk.com
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2673588121
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0332a1f9f3
@ -1300,15 +1300,15 @@ class FreqaiPrimer(IStrategy):
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规则：
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- 将最近N个trend_score分为3段：first, second, third
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- 长度比例为：first:second:third = 1:3:5
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- 例如：1,3,5；2,6,10；3,9,15；4,12,20；5,15,25
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- 例如：2,6,10（固定使用段长2）
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- 权重：first(权重10)，second(权重7)，third(权重3)
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- 变量：first段长度(范围1-5)，由FreqAI模型学习得到
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- 使用静态段长：first段长度固定为2
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"""
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pair = metadata.get('pair', 'Unknown')
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try:
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# 从FreqAI模型获取第一段长度预测值
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first_length = self.get_trend_segment_length_from_freqai(dataframe, metadata)
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# 直接使用静态值2作为第一段长度
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first_length = 2
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# 根据比例计算第二段和第三段长度
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second_length = first_length * 3
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@ -1415,224 +1415,3 @@ class FreqaiPrimer(IStrategy):
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except Exception as e:
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logger.error(f"[{pair}] 趋势状态检测失败: {e}")
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return "ranging"
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def get_trend_segment_length_from_freqai(self, dataframe: DataFrame, metadata: dict) -> int:
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"""
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从FreqAI模型获取趋势分段的第一段长度预测值
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范围：1-5，比例关系：first:second:third = 1:3:5
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"""
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pair = metadata.get('pair', 'Unknown')
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try:
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# 检查是否启用了FreqAI
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if not hasattr(self, 'freqai') or self.freqai is None:
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logger.warning(f"[{pair}] FreqAI未启用，使用默认段长2")
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return 2
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# 准备特征数据
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features_df = dataframe.tail(50).copy() # 使用最近50个周期的数据作为特征
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# 确保有足够的特征数据
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if len(features_df) < 10:
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logger.warning(f"[{pair}] 特征数据不足，使用默认段长2")
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return 2
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# 构建特征
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features = self.build_trend_segment_features(features_df)
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# 使用FreqAI模型预测
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predictions = self.freqai.predict(features, metadata)
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# 调试：记录预测结果的类型和结构
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logger.debug(f"[{pair}] FreqAI预测结果类型: {type(predictions)}, 内容: {predictions}")
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# 获取第一段长度预测值
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predicted_length = 2 # 默认值
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try:
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if isinstance(predictions, dict):
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if 'trend_segment_length' in predictions:
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predicted_length = int(predictions['trend_segment_length'])
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elif 'prediction' in predictions:
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predicted_length = int(predictions['prediction'])
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else:
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# 尝试获取字典中的第一个数值
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for key, value in predictions.items():
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if isinstance(value, (int, float, np.number)):
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predicted_length = int(value)
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break
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elif isinstance(predictions, (list, tuple)) and len(predictions) > 0:
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# 如果是列表或元组，使用第一个预测值
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first_pred = predictions[0]
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if isinstance(first_pred, dict):
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if 'trend_segment_length' in first_pred:
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predicted_length = int(first_pred['trend_segment_length'])
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else:
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predicted_length = int(list(first_pred.values())[0])
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else:
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predicted_length = int(first_pred)
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elif hasattr(predictions, 'trend_segment_length'):
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# 如果是对象属性
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predicted_length = int(predictions.trend_segment_length)
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else:
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logger.warning(f"[{pair}] 无法识别的预测格式，使用默认段长2")
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except (ValueError, IndexError, TypeError) as e:
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logger.error(f"[{pair}] 解析预测结果失败: {e}, 使用默认段长2")
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predicted_length = 2
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# 确保在有效范围内
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predicted_length = max(1, min(5, predicted_length))
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logger.info(f"[{pair}] FreqAI预测段长: {predicted_length} (比例: {predicted_length}:{predicted_length*3}:{predicted_length*5})")
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logger.info(f"[{pair}] FreqAI预测详情 - "
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f"第一段长度: {predicted_length}, "
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f"第二段长度: {predicted_length*3}, "
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f"第三段长度: {predicted_length*5}, "
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f"总分析周期: {predicted_length + predicted_length*3 + predicted_length*5}")
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return predicted_length
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else:
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logger.warning(f"[{pair}] FreqAI未返回段长预测，使用默认段长2")
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logger.info(f"[{pair}] 使用默认段长 - 第一段长度: 2, 第二段长度: 6, 第三段长度: 10, 总分析周期: 18")
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return 2
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except Exception as e:
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logger.error(f"[{pair}] FreqAI段长预测失败: {e}")
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return 2
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def build_trend_segment_features(self, dataframe: DataFrame) -> pd.DataFrame:
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"""
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构建用于预测趋势分段长度的特征
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"""
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features = pd.DataFrame(index=dataframe.index)
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# 基础价格特征
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features['close'] = dataframe['close']
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features['volume'] = dataframe['volume']
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# 技术指标特征
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features['rsi'] = ta.RSI(dataframe['close'], timeperiod=14)
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features['macd'] = ta.MACD(dataframe['close'])['macd']
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features['bb_upper'], features['bb_middle'], features['bb_lower'] = ta.BBANDS(dataframe['close'])
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features['bb_width'] = features['bb_upper'] - features['bb_lower']
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features['bb_position'] = (dataframe['close'] - features['bb_lower']) / features['bb_width']
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# 波动率特征
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features['atr'] = ta.ATR(dataframe['high'], dataframe['low'], dataframe['close'], timeperiod=14)
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features['volatility'] = dataframe['close'].pct_change().rolling(window=20).std()
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# 趋势特征
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features['sma_20'] = ta.SMA(dataframe['close'], timeperiod=20)
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features['sma_50'] = ta.SMA(dataframe['close'], timeperiod=50)
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features['trend_strength'] = abs(features['sma_20'] - features['sma_50']) / features['sma_50']
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# 成交量特征
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features['volume_sma'] = ta.SMA(dataframe['volume'], timeperiod=20)
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features['volume_ratio'] = dataframe['volume'] / features['volume_sma']
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# 价格动量特征
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features['price_momentum'] = dataframe['close'].pct_change(periods=10)
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features['price_acceleration'] = features['price_momentum'].diff()
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# 清理NaN值
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features = features.fillna(0)
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return features
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def calculate_optimal_segment_length(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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基于未来价格表现计算最优的趋势分段长度
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评估不同段长组合在历史数据上的表现，选择最优的段长
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"""
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pair = metadata.get('pair', 'Unknown')
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# 创建目标变量列
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dataframe["&-trend_segment_length"] = 2 # 默认值
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if len(dataframe) < 50:
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logger.warning(f"[{pair}] 数据量不足，无法计算最优段长")
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return dataframe
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# 为每个时间点计算最优段长
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for i in range(30, len(dataframe) - 25): # 留出足够的未来数据
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current_idx = i
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# 评估不同段长组合
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best_length = 2
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best_score = -np.inf
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for test_length in range(1, 6): # 1-5
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# 计算段长
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first_len = test_length
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second_len = test_length * 3
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third_len = test_length * 5
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total_len = first_len + second_len + third_len
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if current_idx + total_len + 5 >= len(dataframe):
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continue # 数据不足，跳过
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# 计算加权趋势得分
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try:
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# 获取历史数据段
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hist_data = dataframe.iloc[current_idx-total_len:current_idx]
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if len(hist_data) < total_len:
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continue
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# 模拟计算趋势得分（简化版本）
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close_prices = hist_data['close'].values
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# 第一段：最近价格
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segment1 = close_prices[-first_len:]
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segment2 = close_prices[-(first_len+second_len):-first_len]
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segment3 = close_prices[-total_len:-(first_len+second_len)]
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# 计算价格变化趋势
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trend1 = (segment1[-1] - segment1[0]) / segment1[0] if len(segment1) > 1 else 0
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trend2 = (segment2[-1] - segment2[0]) / segment2[0] if len(segment2) > 1 else 0
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trend3 = (segment3[-1] - segment3[0]) / segment3[0] if len(segment3) > 1 else 0
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# 加权得分
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weighted_score = (trend1 * 10 + trend2 * 7 + trend3 * 3) / 20
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# 评估未来表现
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future_data = dataframe.iloc[current_idx:current_idx+5] # 未来5个周期
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if len(future_data) < 5:
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continue
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future_return = (future_data['close'].iloc[-1] - future_data['close'].iloc[0]) / future_data['close'].iloc[0]
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# 计算得分：趋势得分与未来收益的相关性
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# 正趋势应该对应正未来收益，负趋势对应负未来收益
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correlation_score = weighted_score * future_return
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if correlation_score > best_score:
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best_score = correlation_score
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best_length = test_length
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except Exception as e:
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logger.debug(f"[{pair}] 段长评估失败: {e}")
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continue
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# 设置最优段长
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dataframe.loc[dataframe.index[current_idx], "&-trend_segment_length"] = float(best_length)
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# 输出具体的最优段长结果
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logger.info(f"[{pair}] 最优趋势分段长度计算完成 - "
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f"第一段长度: {best_length}, "
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f"第二段长度: {best_length*3}, "
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f"第三段长度: {best_length*5}, "
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f"总长度: {best_length + best_length*3 + best_length*5}, "
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f"最佳得分: {best_score:.4f}")
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# 计算统计信息并输出
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segment_lengths = dataframe["&-trend_segment_length"].dropna()
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if len(segment_lengths) > 0:
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length_counts = segment_lengths.value_counts()
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logger.info(f"[{pair}] 趋势分段长度统计:")
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for length in sorted(length_counts.index):
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count = length_counts[length]
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percentage = (count / len(segment_lengths)) * 100
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logger.info(f"[{pair}] 段长{int(length)}: {count}次 ({percentage:.1f}%) - 比例 {int(length)}:{int(length*3)}:{int(length*5)}")
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avg_length = segment_lengths.mean()
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logger.info(f"[{pair}] 平均段长: {avg_length:.2f}")
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logger.info(f"[{pair}] 最优趋势分段长度计算完成，共计算 {len(segment_lengths)} 个数据点")
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return dataframe
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