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Black-Litterman模型，结合市场均衡收益与投资者主观观点计算后验预期收益。适用于多资产组合配置与观点融合的。逆方差矩阵为简化计算，未考虑观点置信度校准与交易成本，不构成投资建议。
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| name | black-litterman |
| description | Black-Litterman模型，结合市场均衡收益与投资者主观观点计算后验预期收益。适用于多资产组合配置与观点融合的。逆方差矩阵为简化计算，未考虑观点置信度校准与交易成本，不构成投资建议。 |
| version | 1.0.0 |
| author | wangyi |
Black-Litterman 模型工具，结合市场均衡收益与投资者主观观点，计算后验预期收益，仅使用 Python 标准库。
📌 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。
risk_aversion_coefficient(market_return, risk_free_rate, market_volatility)计算风险厌恶系数。 必填: market_return, risk_free_rate, market_volatility
implied_equilibrium_returns(cov_matrix, market_weights, risk_aversion)计算隐含均衡收益（逆优化）。 必填: cov_matrix, market_weights, risk_aversion
compute_omega(cov_matrix, P, tau, method='proportional')计算观点不确定性矩阵 Ω。 必填: cov_matrix, P, tau
black_litterman_posterior(cov_matrix, implied_returns, P, Q, tau=0.05, omega=None)计算 Black-Litterman 后验预期收益和协方差。 必填: cov_matrix, implied_returns, P, Q
bl_optimal_weights(posterior_returns, posterior_cov, risk_aversion)基于 BL 后验计算最优权重。 必填: posterior_returns, posterior_cov, risk_aversion
run_black_litterman(market_weights, cov_matrix, P, Q, risk_free_rate=0.02, market_risk_premium=0.06, market_volatility=0.15, tau=0.05, omega_method='proportional')Black-Litterman 模型完整流程。 必填: market_weights, cov_matrix, P, Q
python scripts/main.py \
--market-weights <market_weights> \
--cov-matrix <cov_matrix> \
--P <P> \
--Q <Q> \
--risk-free-rate <risk_free_rate> \
--market-risk-premium <market_risk_premium> \
--market-volatility <market_volatility> \
--tau <tau> \
--omega-method <omega_method>