恢复之前的config
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zhangkun9038@dingtalk.com
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42ecd2bbcd
commit
fe14f21626
@ -113,32 +113,15 @@
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"shuffle": false
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},
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"model_training_parameters": {
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"price_value_divergence": {
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"model": "LightGBMRegressor",
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"model_params": {
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"n_estimators": 400,
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"learning_rate": 0.03,
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"num_leaves": 40,
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"max_depth": 10,
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"min_data_in_leaf": 20,
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"feature_fraction": 0.8,
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"bagging_fraction": 0.8,
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"bagging_freq": 5,
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"verbose": -1
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}
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},
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"optimal_first_length": {
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"model": "LightGBMClassifier",
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"model_params": {
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"n_estimators": 150,
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"learning_rate": 0.1,
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"num_leaves": 15,
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"max_depth": 8,
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"min_data_in_leaf": 10,
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"class_weight": "balanced",
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"verbose": -1
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}
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}
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"n_estimators": 400,
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"learning_rate": 0.03,
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"num_leaves": 40,
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"max_depth": 10,
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"min_data_in_leaf": 20,
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"feature_fraction": 0.8,
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"bagging_fraction": 0.8,
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"bagging_freq": 5,
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"verbose": -1
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}
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},
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"api_server": {
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@ -9,17 +9,27 @@
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"max_open_trades": 5
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},
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"buy": {
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"trend_final_bearish_threshold": 13,
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"trend_final_bullish_threshold": 55
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"ADD_POSITION_THRESHOLD": -0.021,
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"BUY_THRESHOLD_MAX": -0.001,
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"BUY_THRESHOLD_MIN": -0.035,
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"COOLDOWN_PERIOD_MINUTES": 9,
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"MAX_ENTRY_POSITION_ADJUSTMENT": 3
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},
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"sell": {
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"EXIT_POSITION_RATIO": 0.472,
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"SELL_THRESHOLD_MAX": 0.065,
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"SELL_THRESHOLD_MIN": 0.002,
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"TRAILING_STOP_DISTANCE": 0.015,
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"TRAILING_STOP_START": 0.016
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},
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"sell": {},
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"protection": {},
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"trailing": {
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"trailing_stop": true,
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"trailing_stop_positive": 0.046,
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"trailing_stop_positive_offset": 0.14600000000000002,
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"trailing_stop_positive": 0.106,
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"trailing_stop_positive_offset": 0.196,
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"trailing_only_offset_is_reached": false
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}
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},
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"ft_stratparam_v": 1,
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"export_time": "2025-08-14 06:30:51.352733+00:00"
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@ -243,7 +243,14 @@ class FreqaiPrimer(IStrategy):
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dataframe["volume_z_score"] = (dataframe["volume"] - dataframe["volume_mean_20"]) / dataframe["volume_std_20"]
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dataframe["volume_z_score"] = dataframe["volume_z_score"].replace([np.inf, -np.inf], 0).ffill().fillna(0)
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dataframe["&-price_value_divergence"] = dataframe["&-price_value_divergence"].replace([np.inf, -np.inf], 0).ffill().fillna(0)
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# 严格清理price_value_divergence，确保数据类型正确
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price_divergence = dataframe["&-price_value_divergence"].replace([np.inf, -np.inf], 0).ffill().fillna(0)
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dataframe["&-price_value_divergence"] = price_divergence.astype(np.float64)
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# 验证数据类型和范围
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if not np.isfinite(dataframe["&-price_value_divergence"]).all():
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logger.warning(f"[{pair}] price_value_divergence包含非有限值，进行清理")
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dataframe["&-price_value_divergence"] = dataframe["&-price_value_divergence"].replace([np.inf, -np.inf, np.nan], 0.0)
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# 新增：分类模型优化 first_length
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# 基于市场状态预测最优的 first_length 分类（2, 4, 6, 8, 10）
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@ -265,9 +272,22 @@ class FreqaiPrimer(IStrategy):
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]
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choices = [0, 1, 2, 3]
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# 确保分类目标列是整数类型
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optimal_length_class = np.select(conditions, choices, default=4)
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dataframe["&*-optimal_first_length"] = optimal_length_class.astype(int)
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# 确保分类目标列是整数类型，添加严格验证
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try:
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optimal_length_class = np.select(conditions, choices, default=4)
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# 确保所有值都是有效的整数
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optimal_length_class = np.clip(optimal_length_class, 0, 4) # 限制在0-4范围内
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dataframe["&*-optimal_first_length"] = optimal_length_class.astype(np.int32)
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# 验证分类值的有效性
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unique_values = np.unique(optimal_length_class)
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if not all(0 <= val <= 4 for val in unique_values):
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logger.warning(f"[{pair}] 发现无效的first_length分类值: {unique_values}")
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dataframe["&*-optimal_first_length"] = np.clip(optimal_length_class, 0, 4).astype(np.int32)
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except Exception as e:
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logger.error(f"[{pair}] 计算分类目标时出错: {e}")
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dataframe["&*-optimal_first_length"] = np.full(len(dataframe), 2, dtype=np.int32) # 默认值为2
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# 添加调试信息
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logger.info(f"[{pair}] 📈 分类目标统计: {dict(pd.Series(optimal_length_class).value_counts())}")
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@ -428,20 +448,14 @@ class FreqaiPrimer(IStrategy):
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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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# 调用 FreqAI 预测 - 分别处理回归和分类模型
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# 调用 FreqAI 预测 - 使用单一回归模型
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if not hasattr(self, 'freqai') or self.freqai is None:
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logger.error(f"[{pair}] FreqAI 未初始化，回退到规则计算")
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dataframe["&-price_value_divergence"] = dataframe["price_value_divergence"]
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dataframe["&*-optimal_first_length"] = 2.0 # 默认分类值
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else:
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logger.info(f"[{pair}] 调用 FreqAI 预测，类型：{type(self.freqai)}")
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# 先处理回归模型
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original_targets = list(self.freqai_info.get("model_training_parameters", {}).keys())
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logger.info(f"[{pair}] 原始目标列: {original_targets}")
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try:
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# 只保留回归目标进行预测
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dataframe = self.freqai.start(dataframe, metadata, self)
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# 检查 FreqAI 生成的列
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@ -455,28 +469,6 @@ class FreqaiPrimer(IStrategy):
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except Exception as e:
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logger.error(f"[{pair}] FreqAI 预测失败: {e}")
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dataframe["&-price_value_divergence"] = dataframe["price_value_divergence"]
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# 分类模型预测值将通过训练数据直接生成，这里创建默认值
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if "&*-optimal_first_length" not in dataframe.columns:
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# 使用规则计算分类值
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atr = ta.ATR(dataframe, timeperiod=14)
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volatility_ratio = atr / dataframe['close']
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trend_strength = abs(dataframe['close'] - dataframe['ema200']) / dataframe['ema200']
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volume_anomaly = abs(dataframe['volume_z_score']) > 2
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conditions = [
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(volatility_ratio > 0.02) & (trend_strength > 0.05),
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(volatility_ratio > 0.015) & (trend_strength > 0.03),
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(volatility_ratio > 0.01) | (volume_anomaly),
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(volatility_ratio < 0.008) & (trend_strength < 0.02),
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]
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choices = [0, 1, 2, 3]
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optimal_length_class = np.select(conditions, choices, default=4)
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dataframe["&*-optimal_first_length"] = optimal_length_class.astype(float)
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logger.info(f"[{pair}] ✅ 使用规则计算分类值: {dict(pd.Series(optimal_length_class).value_counts())}")
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else:
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logger.info(f"[{pair}] ✅ FreqAI 生成了 &*-optimal_first_length: {dataframe['&*-optimal_first_length'].value_counts().to_dict()}")
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# 计算 labels_mean 和 labels_std
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labels_mean = None
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