Optimize asset allocation with Markowitz, Black-Litterman, and hierarchical clustering methods.
npx skills add https://github.com/gauss314/skills --skill portfolio-gauss314
Please help me install this Agent Skill. Skill: portfolio Source: https://github.com/gauss314/skills/tree/main/skills/portfolio Command: npx skills add https://github.com/gauss314/skills --skill portfolio-gauss314
This skill solves the challenge of constructing robust, diversified investment portfolios that are resistant to market noise and instability, moving beyond simple mean-variance models.
Run the portfolio skill to calculate the optimal asset weights for a maximum Sharpe ratio using the provided sample returns file.
High-intent search queries and answers about installing and using this skill.
Black-Litterman portfolio optimization improves on Markowitz by applying a Bayesian framework that incorporates your specific market views, producing asset allocations that are more resistant to market noise and instability than simple mean-variance outputs.
You can calculate optimal asset weights for a maximum Sharpe ratio by running quantitative optimization with numpy and scipy, processing sample returns data to find the risk-efficient Capital Market Line allocation.
Yes, you can compute over 30 performance and risk metrics including VaR, CVaR, and Drawdown analysis using only numpy, pandas, and scipy, avoiding external heavy dependencies entirely.
Hierarchical risk parity is a clustering-based asset allocation method that groups correlated assets to reduce noise. You need it when constructing diversified portfolios that resist instability better than standard mean-variance models.
Yes, the portfolio optimization approach works with pandas DataFrames, utilizing pandas alongside numpy and scipy to execute the mathematical computations required for asset allocation and risk measurement.
Hierarchical clustering for portfolio risk management is limited by its reliance on historical return data quality; while it reduces noise through clustering, it cannot predict unprecedented market shocks that fall outside the Capital Market Line analysis.
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