fix ADX calculation and trade imbalance factor implementation
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sam
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0bd015b525
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77e5b93297
@ -88,11 +88,11 @@ EXTENDED_FACTORS: List[FactorSpec] = [
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# 趋势跟踪因子
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FactorSpec("trend_ma_cross", 20), # 均线交叉信号
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FactorSpec("trend_price_channel", 20), # 价格通道突破
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FactorSpec("trend_adx", 14), # 平均趋向指标
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FactorSpec("trend_adx", 14), # 简化版平均趋向指标（近似）
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# 市场微观结构因子
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FactorSpec("micro_tick_direction", 5), # 逐笔方向
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FactorSpec("micro_trade_imbalance", 10), # 交易失衡
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FactorSpec("micro_trade_imbalance", 10), # 交易不平衡度（基于签名成交量）
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# 波动率预测因子
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FactorSpec("vol_garch", 20), # GARCH波动率
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@ -199,9 +199,11 @@ class ExtendedFactors:
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return rsi(close_series, 14)
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elif factor_name == "tech_macd_signal":
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# MACD柱状图值（histogram）
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return macd(close_series, 12, 26, 9)
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elif factor_name == "tech_bb_position":
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# 价格在布林带中的位置(-1到1)
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return bollinger_bands(close_series, 20)
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elif factor_name == "tech_obv_momentum":
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@ -212,9 +214,13 @@ class ExtendedFactors:
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# 趋势跟踪因子
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elif factor_name == "trend_ma_cross":
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# 修复：返回均线交叉的比例而不是差值
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ma_5 = rolling_mean(close_series, 5)
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ma_20 = rolling_mean(close_series, 20)
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return ma_5 - ma_20
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if ma_20 is not None and ma_20 != 0:
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return (ma_5 - ma_20) / ma_20 if ma_5 is not None else None
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else:
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return 0.0 if ma_5 is not None else None
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elif factor_name == "trend_price_channel":
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# 价格通道突破因子：当前价格相对于通道的位置
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@ -226,39 +232,36 @@ class ExtendedFactors:
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return 0.0
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elif factor_name == "trend_adx":
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# 标准ADX计算实现
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# 修复：标准ADX计算实现（需要high/low序列）
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# 注意：当前仅使用close序列作为high/low的近似，这是一个简化实现
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window = 14
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if len(close_series) < window + 1:
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return None
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# 计算+DI和-DI
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plus_di = 0
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minus_di = 0
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tr_sum = 0
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# 计算TR、+DM、-DM序列
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tr_series = []
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plus_dm_series = []
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minus_dm_series = []
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# 计算初始TR、+DM、-DM
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for i in range(window):
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if i + 1 >= len(close_series):
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break
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# 计算真实波幅(TR)
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for i in range(len(close_series) - 1):
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# 使用close作为high/low的近似（简化实现）
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today_high = close_series[i]
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today_low = close_series[i]
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prev_high = close_series[i + 1]
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prev_low = close_series[i + 1]
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prev_close = close_series[i + 1]
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# 计算真实波幅(TR)
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tr = max(
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abs(today_high - today_low),
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abs(today_high - prev_close),
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abs(today_low - prev_close)
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)
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tr_sum += tr
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tr_series.append(tr)
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# 计算方向运动
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prev_high = close_series[i + 1] if i + 1 < len(close_series) else close_series[i]
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prev_low = close_series[i + 1] if i + 1 < len(close_series) else close_series[i]
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plus_dm = max(0, close_series[i] - prev_high)
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minus_dm = max(0, prev_low - close_series[i])
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plus_dm = max(today_high - prev_high, 0)
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minus_dm = max(prev_low - today_low, 0)
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# 确保只有一项为正值
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if plus_dm > minus_dm:
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@ -268,23 +271,67 @@ class ExtendedFactors:
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else:
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plus_dm = minus_dm = 0
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plus_di += plus_dm
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minus_di += minus_dm
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plus_dm_series.append(plus_dm)
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minus_dm_series.append(minus_dm)
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if len(tr_series) < window:
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return None
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# 计算+DI和-DI（使用Wilder平滑方法）
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plus_di_series = []
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minus_di_series = []
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# 初始化第一个值
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tr_sum = sum(tr_series[:window])
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plus_dm_sum = sum(plus_dm_series[:window])
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minus_dm_sum = sum(minus_dm_series[:window])
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# 计算+DI和-DI
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if tr_sum > 0:
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plus_di = (plus_di / tr_sum) * 100
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minus_di = (minus_di / tr_sum) * 100
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plus_di = 100 * (plus_dm_sum / tr_sum)
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minus_di = 100 * (minus_dm_sum / tr_sum)
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else:
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plus_di = minus_di = 0
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# 计算DX
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dx = 0
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if plus_di + minus_di > 0:
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dx = (abs(plus_di - minus_di) / (plus_di + minus_di)) * 100
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plus_di_series.append(plus_di)
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minus_di_series.append(minus_di)
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# ADX是DX的移动平均，这里简化为直接返回DX值，确保在0-100范围内
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return max(0, min(100, dx))
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# 计算后续值（使用Wilder平滑）
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for i in range(1, len(tr_series) - window + 1):
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# Wilder平滑：当前值 = (前一个平滑值 * (n-1) + 当前值) / n
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tr_sum = (tr_sum * (window - 1) + tr_series[i + window - 1]) / window
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plus_dm_sum = (plus_dm_sum * (window - 1) + plus_dm_series[i + window - 1]) / window
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minus_dm_sum = (minus_dm_sum * (window - 1) + minus_dm_series[i + window - 1]) / window
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if tr_sum > 0:
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plus_di = 100 * (plus_dm_sum / tr_sum)
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minus_di = 100 * (minus_dm_sum / tr_sum)
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else:
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plus_di = minus_di = 0
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plus_di_series.append(plus_di)
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minus_di_series.append(minus_di)
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# 计算DX序列
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dx_series = []
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for i in range(len(plus_di_series)):
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plus_di_val = plus_di_series[i]
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minus_di_val = minus_di_series[i]
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if plus_di_val + minus_di_val > 0:
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dx = 100 * (abs(plus_di_val - minus_di_val) / (plus_di_val + minus_di_val))
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else:
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dx = 0
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dx_series.append(dx)
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# 计算ADX（DX的移动平均）
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if len(dx_series) < window:
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return None
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# ADX是DX的移动平均（使用简单移动平均）
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adx = sum(dx_series[:window]) / window
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# 确保在0-100范围内
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return max(0, min(100, adx))
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# 市场微观结构因子
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elif factor_name == "micro_tick_direction":
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@ -298,18 +345,36 @@ class ExtendedFactors:
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return sum(directions) / window
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elif factor_name == "micro_trade_imbalance":
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# 交易失衡：基于价格和成交量的联合分析
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# 修复：交易失衡（按常规定义实现）
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# 基于签名成交量的归一化差值
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window = 10
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if len(close_series) < window + 1 or len(volume_series) < window + 1:
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return None
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# 计算价格变动和成交量变动
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price_changes = [close_series[i] - close_series[i+1] for i in range(window)]
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volume_changes = [volume_series[i] - volume_series[i+1] for i in range(window)]
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# 计算签名成交量：volume_t * sign(close_t - close_{t-1})
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signed_volumes = []
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total_volume = 0
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# 计算交易失衡指标
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imbalance = sum(price_changes[i] * volume_changes[i] for i in range(window))
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return imbalance / (window * np.mean(volume_series[:window]) + 1e-8)
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for i in range(window):
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if i + 1 < len(close_series):
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# 计算价格变动符号
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price_change = close_series[i] - close_series[i+1]
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sign = 1 if price_change > 0 else (-1 if price_change < 0 else 0)
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# 计算签名成交量
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signed_volume = volume_series[i] * sign
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signed_volumes.append(signed_volume)
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total_volume += volume_series[i]
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if total_volume == 0:
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return 0.0
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# 计算交易不平衡度：signed_vol / total_volume
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signed_vol_sum = sum(signed_volumes)
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imbalance = signed_vol_sum / (total_volume + 1e-8)
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# 确保结果在[-1, 1]范围内
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return max(-1.0, min(1.0, imbalance))
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# 波动率预测因子
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elif factor_name == "vol_garch":
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@ -77,6 +77,11 @@ def validate_factor_value(
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如果因子值有效则返回原值，否则返回 None
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"""
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if value is None:
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LOGGER.warning(
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"因子值非数值 factor=%s value=%s ts_code=%s date=%s",
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name, value, ts_code, trade_date,
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extra=LOG_EXTRA
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)
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return None
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# 检查是否为有限数值
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@ -98,8 +103,8 @@ def validate_factor_value(
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"tech_pv_trend": (-1.0, 1.0), # 量价趋势相关性
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# 趋势指标精确范围
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"trend_adx": (0, 100.0), # ADX趋势强度 0-100
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"trend_ma_cross": (-1.0, 1.0), # 均线交叉
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"trend_adx": (0, 100.0), # 简化版 ADX（近似）趋势强度 0-100
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"trend_ma_cross": (-1.0, 1.0), # 均线交叉比例
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"trend_price_channel": (-1.0, 1.0), # 价格通道位置
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# 波动率指标精确范围
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@ -109,7 +114,7 @@ def validate_factor_value(
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# 微观结构精确范围
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"micro_tick_direction": (-1.0, 1.0), # 买卖方向比例
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"micro_trade_imbalance": (-100.0, 100.0), # 交易不平衡度
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"micro_trade_imbalance": (-1.0, 1.0), # 交易不平衡度，范围在[-1, 1]之间
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# 情绪指标精确范围
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"sent_impact": (0, 1.0), # 情绪影响度
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@ -1,55 +0,0 @@
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#!/usr/bin/env python3
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"""测试因子值范围验证功能"""
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from app.features.factors import compute_factors
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from datetime import date
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def test_factor_ranges():
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"""测试因子值范围验证功能"""
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print('测试改进后的因子值范围验证功能...')
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try:
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results = compute_factors(
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date(2024, 1, 15),
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ts_codes=['000001.SZ', '000002.SZ'],
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skip_existing=False,
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batch_size=10
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)
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print(f'因子计算完成，共计算 {len(results)} 个结果')
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# 检查每个因子的值范围
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valid_count = 0
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invalid_count = 0
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for result in results:
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print(f'\n证券 {result.ts_code} 的因子值:')
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for factor_name, value in result.values.items():
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if value is not None:
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# 检查值是否在合理范围内
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if -10 <= value <= 10: # 放宽检查范围，主要看验证逻辑
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print(f' ✓ {factor_name}: {value:.6f}')
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valid_count += 1
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else:
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print(f' ✗ {factor_name}: {value:.6f} (超出范围!)')
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invalid_count += 1
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print(f'\n验证统计:')
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print(f' 有效因子值: {valid_count}')
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print(f' 无效因子值: {invalid_count}')
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print(f' 总因子值: {valid_count + invalid_count}')
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if invalid_count == 0:
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print('\n✅ 所有因子值都在合理范围内，验证通过!')
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else:
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print(f'\n⚠️ 发现 {invalid_count} 个超出范围的因子值，需要进一步优化')
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print('\n✅ 因子值范围验证测试完成')
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except Exception as e:
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print(f'❌ 测试失败: {e}')
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import traceback
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traceback.print_exc()
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if __name__ == "__main__":
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test_factor_ranges()
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