加入risk_rate freqai属性
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zhangkun9038@dingtalk.com
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189add64f9
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4c5f444693
@ -130,7 +130,7 @@ class FreqaiPrimer(IStrategy):
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}
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freqai_info = {
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"identifier": "freqai_primer_mixed_v4", # 更新标识符以强制重新训练
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"identifier": "freqai_primer_mixed_v5", # 更新标识符以强制重新训练
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"model": "LightGBMRegressor", # 默认回归模型
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"feature_parameters": {
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"include_timeframes": ["3m", "15m", "1h"], # 3个时间框架
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@ -165,6 +165,18 @@ class FreqaiPrimer(IStrategy):
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"verbose": -1,
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}
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},
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"risk_rating": { # 新增：智能风险评级预测
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"model": "LightGBMClassifier",
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"model_params": {
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"n_estimators": 160,
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"learning_rate": 0.08,
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"num_leaves": 20,
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"max_depth": 6,
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"min_child_samples": 8,
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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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},
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"fit_live_predictions_candles": 100,
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"live_retrain_candles": 100,
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@ -227,11 +239,21 @@ class FreqaiPrimer(IStrategy):
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dataframe["ema200"] = ta.EMA(dataframe, timeperiod=200)
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dataframe["%-price_value_divergence"] = (dataframe["close"] - dataframe["ema200"]) / dataframe["ema200"]
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# 新增：风险评级相关特征
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dataframe["%-atr-period"] = ta.ATR(dataframe, timeperiod=period)
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dataframe["%-volatility_ratio"] = dataframe["%-atr-period"] / dataframe["close"]
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dataframe["%-price_range"] = (dataframe["high"] - dataframe["low"]) / dataframe["close"]
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dataframe["%-volume_volatility"] = dataframe["volume"].pct_change().rolling(period).std()
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dataframe["%-price_velocity"] = dataframe["close"].pct_change().abs().rolling(period).mean()
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dataframe["%-rsi_velocity"] = dataframe["%-rsi-period"].diff().abs().rolling(3).mean()
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columns_to_clean = [
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"%-rsi-period", "%-mfi-period", "%-sma-period", "%-ema-period", "%-adx-period",
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"bb_lowerband-period", "bb_middleband-period", "bb_upperband-period",
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"%-bb_width-period", "%-relative_volume-period", "%-price_value_divergence"
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"%-bb_width-period", "%-relative_volume-period", "%-price_value_divergence",
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"%-atr-period", "%-volatility_ratio", "%-price_range", "%-volume_volatility",
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"%-price_velocity", "%-rsi_velocity"
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]
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for col in columns_to_clean:
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dataframe[col] = dataframe[col].replace([np.inf, -np.inf], 0).ffill().fillna(0)
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@ -263,6 +285,46 @@ class FreqaiPrimer(IStrategy):
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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：智能风险评级分类目标
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# 基于未来波动率、成交量异常、价格动量等多维度评估风险等级
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# 0: 低风险(绿色) 1: 中低风险(蓝色) 2: 中等风险(黄色) 3: 中高风险(橙色) 4: 高风险(红色)
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# 计算未来波动率作为风险指标
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future_atr = ta.ATR(dataframe, timeperiod=14).shift(-6)
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future_volatility = (future_atr / dataframe['close']).replace([np.inf, -np.inf], 0)
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# 成交量异常检测
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volume_zscore = (dataframe['volume'] - dataframe['volume'].rolling(20).mean()) / dataframe['volume'].rolling(20).std()
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volume_anomaly = abs(volume_zscore) > 2
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# 价格动量强度
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price_momentum = abs(dataframe['close'].pct_change(5)).rolling(3).mean()
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# 综合风险评分
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risk_score = (
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future_volatility * 0.4 + # 未来波动率权重40%
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(abs(volume_zscore) / 5) * 0.3 + # 成交量异常权重30%
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price_momentum * 0.3 # 价格动量权重30%
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)
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# 风险等级分类
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risk_conditions = [
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risk_score < 0.01, # 低风险
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risk_score < 0.02, # 中低风险
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risk_score < 0.035, # 中等风险
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risk_score < 0.05, # 中高风险
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]
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risk_choices = [0, 1, 2, 3]
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dataframe["&*-risk_rating"] = np.select(risk_conditions, risk_choices, default=4).astype(np.int32)
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# 清理风险评级值
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if not np.isfinite(dataframe["&*-risk_rating"]).all():
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logger.warning(f"[{pair}] risk_rating包含非有限值，进行清理")
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dataframe["&*-risk_rating"] = dataframe["&*-risk_rating"].replace([np.inf, -np.inf, np.nan], 2).astype(np.int32)
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logger.info(f"[{pair}] 风险评级分布: {dict(pd.Series(dataframe['&*-risk_rating']).value_counts())}")
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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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# 使用技术指标和市场波动率作为特征
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@ -583,6 +645,32 @@ class FreqaiPrimer(IStrategy):
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self.sell_threshold = max(self.sell_threshold, self.SELL_THRESHOLD_MIN)
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# 调试日志
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# 处理风险评级预测
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if "&*-risk_rating" in dataframe.columns:
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risk_rating = dataframe["&*-risk_rating"].iloc[-1] if len(dataframe) > 0 else 2
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# 基于风险评级调整仓位和止损
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risk_multipliers = {
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0: {"position": 1.5, "stoploss": 0.8, "label": "🟢低风险"}, # 低风险：加大仓位，收紧止损
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1: {"position": 1.2, "stoploss": 0.9, "label": "🔵中低风险"}, # 中低风险：适中仓位
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2: {"position": 1.0, "stoploss": 1.0, "label": "🟡中等风险"}, # 中等风险：标准参数
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3: {"position": 0.7, "stoploss": 1.2, "label": "🟠中高风险"}, # 中高风险：减小仓位，放宽止损
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4: {"position": 0.5, "stoploss": 1.5, "label": "🔴高风险"}, # 高风险：最小仓位，最宽止损
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}
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risk_config = risk_multipliers.get(risk_rating, risk_multipliers[2])
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# 应用到策略参数
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self.risk_position_multiplier = risk_config["position"]
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self.risk_stoploss_multiplier = risk_config["stoploss"]
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logger.info(f"[{pair}] {risk_config['label']} - 仓位倍数: {self.risk_position_multiplier}, "
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f"止损倍数: {self.risk_stoploss_multiplier}")
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else:
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# 默认值
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self.risk_position_multiplier = 1.0
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self.risk_stoploss_multiplier = 1.0
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logger.info(f"[{pair}] 市场趋势得分：{market_trend_score}, labels_mean：{labels_mean:.4f}, labels_std：{labels_std:.4f}")
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logger.info(f"[{pair}] k_buy：{k_buy:.2f}, k_sell：{k_sell:.2f}")
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logger.info(f"[{pair}] 动态买入阈值：{self.buy_threshold:.4f}, 卖出阈值：{self.sell_threshold:.4f}")
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@ -591,9 +679,7 @@ 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_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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f"risk_rating: {dataframe['&*-risk_rating'].iloc[-1] if '&*-risk_rating' 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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@ -642,6 +728,22 @@ class FreqaiPrimer(IStrategy):
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open_trades = len(self.active_trades) if hasattr(self, 'active_trades') else 0
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is_green_channel = (trend_status == "bullish" and open_trades <= 2 and self.GREEN_CHANNEL_ENABLED)
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# 获取风险评级并调整入场条件
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risk_rating = dataframe["&*-risk_rating"].iloc[-1] if "&*-risk_rating" in dataframe.columns else 2
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# 风险评级影响入场严格程度
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risk_adjustments = {
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0: {"threshold_mult": 1.5, "strict_mult": 0.7}, # 低风险：更宽松入场
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1: {"threshold_mult": 1.2, "strict_mult": 0.8}, # 中低风险：较宽松
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2: {"threshold_mult": 1.0, "strict_mult": 1.0}, # 中等风险：标准
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3: {"threshold_mult": 0.8, "strict_mult": 1.2}, # 中高风险：更严格
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4: {"threshold_mult": 0.6, "strict_mult": 1.5}, # 高风险：最严格
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}
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risk_adj = risk_adjustments.get(risk_rating, risk_adjustments[2])
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logger.info(f"[{pair}] 风险评级: {risk_rating}, 阈值调整: {risk_adj['threshold_mult']}, 严格度调整: {risk_adj['strict_mult']}")
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if is_green_channel:
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# 🟢 牛市绿色通道：持仓≤2个，25USDT入场，5条件需要满足4个
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cond1 = (dataframe["&-price_value_divergence"] < self.buy_threshold * 1.8) # 超宽松偏离度
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