Evaluate mutual funds and ETFs using performance metrics and style-drift detection.
npx skills add https://github.com/loanntc/Paave --skill mutual-fund-analysis
Please help me install this Agent Skill. Skill: mutual-fund-analysis Source: https://github.com/loanntc/Paave/tree/main/skills/fund-analysis Command: npx skills add https://github.com/loanntc/Paave --skill mutual-fund-analysis
It helps you stop relying on “past-best performance” by systematically evaluating mutual funds using performance, risk-adjusted metrics, style-box regression, and style-drift detection, then guiding FOF (fund-of-funds) construction and ETF selection.
Use the mutual-fund-analysis skill to generate a fund screening report that includes performance ranking, style-box regression results with R², drift assessment over rolling windows, and a recommended FOF allocation plan.
High-intent search queries and answers about installing and using this skill.
Mutual fund screening evaluates annualized returns, Alpha, Sharpe, Sortino, Treynor, drawdown, and Calmar ratios to rank funds against quality thresholds, preventing reliance on past-best performance. It systematically compares funds using documented metric thresholds and benchmark assumptions.
Sharpe style-box regression determines whether a fund’s realized exposure matches its declared style using a nine-grid value/balance/growth and size mapping. It validates fund exposure by calculating R²-based clarity to ensure the actual portfolio aligns with the stated investment strategy.
Style drift detection uses rolling-window regression to flag significant style shifts by monitoring |Δβ| thresholds and R² trends. This identifies closet-to-index behavior or manager-driven style changes over specified rolling window parameters, ensuring portfolio style consistency.
FOF portfolio construction builds diversified allocations across asset classes and selects complementary style exposures. It applies rebalancing rules based on deviation thresholds across market cycles, generating a recommended allocation plan with structured portfolio selection.
ETF selection evaluates tracking error, fee structure, liquidity, and size to choose efficient passive or strategy vehicles. The framework assesses ETFs against documented metric thresholds to identify tradable passive products that align with portfolio construction goals.
Fund screening report generation requires YAML-defined metadata containing the fund name and description. This YAML input provides the baseline operational metadata needed to execute performance ranking, style-box regression, and drift assessment.
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