aszerW
796a695eef
核心组件： - FactorBase: 因子抽象基类（compute方法 + 数据验证） - FactorRegistry: 因子注册器（注册/获取/按类别筛选） - FactorCombiner: 因子组合器（加权组合4种方法） 已实现因子： - MomentumFactor: 加权动量因子（含崩盘过滤） - TrendFactor: 趋势因子（MA交叉/MACD） - ReversalFactor: 反转因子（RSI/KDJ） - VolatilityFactor: 波动率因子（ATR/标准差） 测试覆盖：18个测试全部通过
312 lines
8.8 KiB
Python
312 lines
8.8 KiB
Python
"""
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动量因子实现
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基于加权线性回归动量的因子
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"""
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import pandas as pd
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import numpy as np
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import math
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from typing import Optional
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from framework.factors import FactorBase
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class MomentumFactor(FactorBase):
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"""
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动量因子
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计算加权线性回归动量得分：
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得分 = 年化收益率 × R²
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参数：
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- n_days: 动量窗口（默认25）
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- weighted: 是否加权（默认True）
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- crash_filter: 是否启用崩盘过滤（默认True）
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"""
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name = "momentum"
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category = "momentum"
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def __init__(
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self,
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n_days: int = 25,
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weighted: bool = True,
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crash_filter: bool = True
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):
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super().__init__(n_days=n_days, weighted=weighted, crash_filter=crash_filter)
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self.n_days = n_days
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self.weighted = weighted
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self.crash_filter = crash_filter
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def compute(self, data: pd.DataFrame) -> pd.Series:
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"""计算动量因子值"""
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if 'close' not in data.columns:
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raise ValueError("data must contain 'close' column")
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prices = data['close']
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if self.weighted:
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# 加权动量得分
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factor_values = prices.rolling(self.n_days).apply(
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lambda x: self._weighted_momentum_score(x.values),
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raw=False
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)
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else:
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# 简单动量
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factor_values = prices.pct_change(self.n_days)
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# 应用崩盘过滤
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if self.crash_filter:
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factor_values = self._apply_crash_filter(prices, factor_values)
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return factor_values
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def _weighted_momentum_score(self, prices: np.ndarray) -> float:
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"""计算加权动量得分"""
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if len(prices) < 5:
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return 0.0
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y = np.log(prices)
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x = np.arange(len(y))
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weights = np.linspace(1, 2, len(y))
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# 加权线性回归
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slope, intercept = np.polyfit(x, y, 1, w=weights)
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annualized_returns = math.exp(slope * 250) - 1
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# 加权R²
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y_pred = slope * x + intercept
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ss_res = np.sum(weights * (y - y_pred) ** 2)
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ss_tot = np.sum(weights * (y - np.average(y, weights=weights)) ** 2)
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r2 = 1 - ss_res / ss_tot if ss_tot > 0 else 0
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return annualized_returns * r2
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def _apply_crash_filter(
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self,
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prices: pd.Series,
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factor_values: pd.Series
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) -> pd.Series:
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"""崩盘过滤：连续3天跌>5%清零"""
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result = factor_values.copy()
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for i in range(3, len(prices)):
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r1 = prices.iloc[i] / prices.iloc[i-1]
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r2 = prices.iloc[i-1] / prices.iloc[i-2]
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r3 = prices.iloc[i-2] / prices.iloc[i-3]
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# 条件1：任一天跌>5%
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con1 = min(r1, r2, r3) < 0.95
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# 条件2：连续下跌且累计跌>5%
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con2 = (r1 < 1) and (r2 < 1) and (r3 < 1) and (prices.iloc[i] / prices.iloc[i-3] < 0.95)
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if con1 or con2:
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result.iloc[i] = 0.0
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return result
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class TrendFactor(FactorBase):
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"""
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趋势因子
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计算趋势强度：
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- MA交叉偏离度
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- MACD趋势
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参数：
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- method: 趋势方法（'ma_cross', 'macd'）
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- fast: 快线周期（默认5）
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- slow: 慢线周期（默认20）
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"""
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name = "trend"
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category = "trend"
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def __init__(
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self,
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method: str = 'ma_cross',
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fast: int = 5,
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slow: int = 20
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):
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super().__init__(method=method, fast=fast, slow=slow)
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self.method = method
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self.fast = fast
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self.slow = slow
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def compute(self, data: pd.DataFrame) -> pd.Series:
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"""计算趋势因子值"""
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if 'close' not in data.columns:
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raise ValueError("data must contain 'close' column")
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prices = data['close']
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if self.method == 'ma_cross':
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# MA交叉偏离度
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fast_ma = prices.rolling(self.fast).mean()
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slow_ma = prices.rolling(self.slow).mean()
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trend_strength = (fast_ma - slow_ma) / slow_ma
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return trend_strength
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elif self.method == 'macd':
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# MACD趋势
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ema12 = prices.ewm(span=12).mean()
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ema26 = prices.ewm(span=26).mean()
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macd = ema12 - ema26
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signal = macd.ewm(span=9).mean()
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return macd - signal
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else:
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raise ValueError(f"Unknown method: {self.method}")
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class ReversalFactor(FactorBase):
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"""
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反转因子
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计算超买超卖信号：
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- RSI偏离度
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- KDJ
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参数：
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- method: 反转方法（'rsi', 'kdj'）
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- period: 周期（默认14）
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- overbought: 超买阈值（默认70）
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- oversold: 超卖阈值（默认30）
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"""
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name = "reversal"
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category = "reversal"
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def __init__(
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self,
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method: str = 'rsi',
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period: int = 14,
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overbought: float = 70,
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oversold: float = 30
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):
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super().__init__(method=method, period=period, overbought=overbought, oversold=oversold)
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self.method = method
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self.period = period
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self.overbought = overbought
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self.oversold = oversold
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def compute(self, data: pd.DataFrame) -> pd.Series:
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"""计算反转因子值"""
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if 'close' not in data.columns:
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raise ValueError("data must contain 'close' column")
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prices = data['close']
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if self.method == 'rsi':
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# RSI反转信号
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rsi = self._compute_rsi(prices, self.period)
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# 超买超卖偏离度
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# 超买 → 负值（反转向下信号）
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# 超卖 → 正值（反转向上信号）
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reversal_signal = pd.Series(index=prices.index, dtype=float)
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reversal_signal = np.where(
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rsi > self.overbought,
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-(rsi - self.overbought) / (100 - self.overbought), # 超买：负值
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np.where(
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rsi < self.oversold,
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(self.oversold - rsi) / self.oversold, # 超卖：正值
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0 # 正常区间：0
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)
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)
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return pd.Series(reversal_signal, index=prices.index)
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elif self.method == 'kdj':
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# KDJ反转信号
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return self._compute_kdj(data)
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else:
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raise ValueError(f"Unknown method: {self.method}")
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def _compute_rsi(self, prices: pd.Series, period: int) -> pd.Series:
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"""计算RSI"""
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delta = prices.diff()
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gain = delta.where(delta > 0, 0)
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loss = (-delta).where(delta < 0, 0)
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avg_gain = gain.rolling(period).mean()
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avg_loss = loss.rolling(period).mean()
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rs = avg_gain / avg_loss
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rsi = 100 - (100 / (1 + rs))
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return rsi
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def _compute_kdj(self, data: pd.DataFrame) -> pd.Series:
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"""计算KDJ反转信号"""
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low = data['low']
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high = data['high']
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close = data['close']
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# 计算K、D、J
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low_min = low.rolling(self.period).min()
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high_max = high.rolling(self.period).max()
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rsv = (close - low_min) / (high_max - low_min) * 100
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k = rsv.ewm(alpha=1/3).mean()
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d = k.ewm(alpha=1/3).mean()
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j = 3 * k - 2 * d
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# J值偏离度作为反转信号
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return j
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class VolatilityFactor(FactorBase):
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"""
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波动率因子
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计算价格波动率：
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- ATR
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- 标准差
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参数：
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- method: 波动率方法（'atr', 'std'）
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- period: 周期（默认20）
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"""
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name = "volatility"
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category = "volatility"
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def __init__(
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self,
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method: str = 'std',
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period: int = 20
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):
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super().__init__(method=method, period=period)
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self.method = method
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self.period = period
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def compute(self, data: pd.DataFrame) -> pd.Series:
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"""计算波动率因子值"""
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if self.method == 'std':
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# 标准差波动率
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return data['close'].rolling(self.period).std()
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elif self.method == 'atr':
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# ATR波动率
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return self._compute_atr(data)
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else:
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raise ValueError(f"Unknown method: {self.method}")
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def _compute_atr(self, data: pd.DataFrame) -> pd.Series:
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"""计算ATR"""
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high = data['high']
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low = data['low']
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close = data['close']
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prev_close = close.shift(1)
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tr = pd.concat([
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high - low,
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(high - prev_close).abs(),
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(low - prev_close).abs()
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], axis=1).max(axis=1)
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return tr.rolling(self.period).mean() |