aszerW
988c2335fb
- 新增 .env.example，包含 Tushare API、钉钉机器人和PostgreSQL数据库配置模板 - 更新.gitignore，忽略本地配置文件如 .env.local 和 config_local.py - 添加对报表文件命名规则的支持，保留示例文件不忽略 - 删除废弃的 chart.py 及相关图表模块代码 - 新增 config/settings.py，实现从环境变量读取配置的统一接口 - 设置数据目录及缓存目录，确保目录存在，提高配置管理规范性
138 lines
3.4 KiB
Python
138 lines
3.4 KiB
Python
"""
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动量因子计算模块
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支持两种动量因子：
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1. N日涨幅（简单动量）
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2. 斜率×R²趋势得分（改进版）
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"""
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import numpy as np
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import pandas as pd
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from sklearn.linear_model import LinearRegression
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def calculate_momentum(price_series: pd.Series, n: int) -> pd.Series:
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"""
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计算 N 日涨幅（简单动量）
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Args:
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price_series: 价格序列
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n: 动量窗口天数
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Returns:
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Series: N日涨幅
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"""
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return price_series / price_series.shift(n + 1) - 1.0
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def _slope_r2_score(srs: pd.Series, n: int = 25) -> float:
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"""
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单次计算斜率×R²趋势得分
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Args:
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srs: 价格窗口序列（长度为 n）
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n: 窗口长度
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Returns:
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float: 斜率 × R² × 10000
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"""
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if srs.shape[0] < n:
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return np.nan
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x = np.arange(1, n + 1).reshape(-1, 1)
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y = srs.values / srs.values[0] # 归一化
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lr = LinearRegression().fit(x, y)
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slope = lr.coef_[0]
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r_squared = lr.score(x, y)
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score = 10000 * slope * r_squared
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return score
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def calculate_slope_r2(price_series: pd.Series, n: int = 25) -> pd.Series:
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"""
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计算斜率×R²趋势得分序列
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Args:
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price_series: 价格序列
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n: 滚动窗口天数
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Returns:
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Series: 趋势得分序列
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"""
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return price_series.rolling(n).apply(
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lambda x: _slope_r2_score(x, n), raw=False
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)
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def calculate_daily_return(price_series: pd.Series) -> pd.Series:
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"""
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计算日收益率
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Args:
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price_series: 价格序列
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Returns:
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Series: 日收益率
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"""
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return price_series / price_series.shift(1) - 1
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def compute_factors(
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etf_data: pd.DataFrame,
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code_list: list,
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n: int = 25,
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factor_type: str = "slope_r2",
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) -> tuple[pd.DataFrame, list]:
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"""
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计算所有指数的因子和日收益率
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Args:
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etf_data: DataFrame, 宽表格式的收盘价
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code_list: 指数代码列表
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n: 动量/趋势窗口
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factor_type: 'momentum' 或 'slope_r2'
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Returns:
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tuple: (result_df, valid_codes)
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"""
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result = etf_data.copy()
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# 过滤掉缺失值过多的指数
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total_rows = len(result)
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valid_codes = []
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for code in code_list:
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if code not in result.columns:
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print(f" ⚠ 跳过 {code}: 不在数据中")
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continue
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null_pct = result[code].isnull().sum() / total_rows
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if null_pct > 0.2:
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print(f" ⚠ 剔除 {code}: 缺失率 {null_pct:.1%} 过高")
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result = result.drop(columns=[code])
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else:
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valid_codes.append(code)
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# 对有效指数计算因子
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for code in valid_codes:
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result[f"日收益率_{code}"] = calculate_daily_return(result[code])
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if factor_type == "momentum":
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result[f"得分_{code}"] = calculate_momentum(result[code], n)
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elif factor_type == "slope_r2":
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result[f"得分_{code}"] = calculate_slope_r2(result[code], n)
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else:
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raise ValueError(f"不支持的因子类型: {factor_type}")
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# 按得分列做 dropna
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score_cols = [f"得分_{code}" for code in valid_codes]
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result = result.dropna(subset=score_cols)
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print("\n因子计算完成:")
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print(f" 因子类型: {factor_type}")
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print(f" 窗口天数: {n}")
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print(f" 有效指数: {len(valid_codes)}/{len(code_list)}")
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print(f" 有效数据: {len(result)} 行")
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return result, valid_codes
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