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跟踪组合优化，计算跟踪误差与信息比率并优化构建跟踪组合。适用于指数跟踪与增强指数策略的。优化采用随机采样近似，未考虑成分股调整与交易成本，不适用于实盘指数跟踪，不构成投资建议。
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| name | benchmark-tracking |
| description | 跟踪组合优化，计算跟踪误差与信息比率并优化构建跟踪组合。适用于指数跟踪与增强指数策略的。优化采用随机采样近似，未考虑成分股调整与交易成本，不适用于实盘指数跟踪，不构成投资建议。 |
| version | 1.0.0 |
| author | wangyi |
基准跟踪工具，计算跟踪误差和信息比率，优化构建跟踪组合，仅使用 Python 标准库。
tracking_error(portfolio_returns, benchmark_returns, annualization=1)计算跟踪误差。 必填: portfolio_returns, benchmark_returns
information_ratio(portfolio_returns, benchmark_returns, annualization=1)计算信息比率。 必填: portfolio_returns, benchmark_returns
tracking_quality(portfolio_returns, benchmark_returns, annualization=12)综合跟踪质量评估。 必填: portfolio_returns, benchmark_returns
optimize_tracking_portfolio(cov_matrix, benchmark_weights, asset_alphas=None, max_weight=1.0, min_weight=0.0, target_te=None, lambda_risk=1.0, n_iter=10000, seed=42)优化跟踪组合。 必填: cov_matrix, benchmark_weights
calc_te(weights)计算跟踪误差²。 必填: weights
python scripts/main.py \
--cov-matrix <cov_matrix> \
--benchmark-weights <benchmark_weights> \
--asset-alphas <asset_alphas> \
--max-weight <max_weight> \
--min-weight <min_weight> \
--target-te <target_te> \
--lambda-risk <lambda_risk> \
--n-iter <n_iter> \
--seed <seed>
Based on SOC occupation classification
