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统计套利模型，利用多元线性回归构建对冲组合并基于残差均值回归生成套利信号。适用于量化金融教学与统计套利概念演示；基于简化回归模型，不适用于实盘交易。
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| name | statistical-arbitrage |
| description | 统计套利模型，利用多元线性回归构建对冲组合并基于残差均值回归生成套利信号。适用于量化金融教学与统计套利概念演示；基于简化回归模型，不适用于实盘交易。 |
| version | 1.0.0 |
| author | wangyi |
统计套利策略：利用多因子线性回归构建对冲组合，基于回归残差的均值回归特性生成套利交易信号。
📌 说明：本工具基于简化模型实现，仅使用 Python 标准库。结果仅供学习参考，不构成投资建议或交易依据。
matrix_transpose(matrix)矩阵转置。 必填: matrix
matrix_multiply(a, b)矩阵乘法。 必填: a, b
matrix_inverse(matrix)使用高斯消元法求矩阵逆。 必填: matrix
ols_regression(y, x)多元线性回归（OLS）。 必填: y, x
simplified_adf_test(series)简化版 ADF 检验（检验序列平稳性）。 必填: series
calc_rolling_zscore(series, window=60)计算滚动 Z-score。 必填: series
generate_signals(z_scores, entry_z=2.0, exit_z=0.5)生成统计套利交易信号。 必填: z_scores
run_statistical_arbitrage(target_prices, factor_prices, window=60, entry_z=2.0, exit_z=0.5)运行完整统计套利策略。 必填: target_prices, factor_prices
python scripts/main.py \
--target-prices <target_prices> \
--factor-prices <factor_prices> \
--window <window> \
--entry-z <entry_z> \
--exit-z <exit_z>