wealth-portfoliolisted

Portfolio drift monitoring and rebalancing, parametric VaR and expected shortfall, tax-loss harvesting screens with a wash-sale look-back, cost basis by lot, time- and money-weighted returns, Sharpe ratio reliability, and small-universe historical and forward simulations - plus guidance for accounting, attribution and risk-model workflows that the engines do not implement.
ai-theories/wealth-skills · ★ 0 · AI & Automation · score 63

Install: claude install-skill ai-theories/wealth-skills

# Wealth Portfolio & Risk Skill Pack (`wealth-portfolio`) This skill pack equips AI agents (**Claude Code, Devin, Cursor, Antigravity, OpenAI Codex**) to monitor allocation drift, size rebalance trades, estimate parametric risk, screen for tax-loss harvesting, and run backtests and forward simulations with local, zero-dependency Node.js engines. --- ## When to Activate Trigger this skill when the user asks to: - Evaluate portfolio asset allocation against targets and monitor drift. - Run historical backtests (CAGR, Sharpe, Sortino, max drawdown) or forward Monte Carlo simulations. - Calculate Value at Risk and expected shortfall. - Find tax-loss harvesting opportunities across taxable lots. --- ## Engine-Backed Capabilities | Capability | Engine function | CLI | |---|---|---| | Drift monitoring and rebalance trade sizing | `monitorPortfolioDrift`, `calculatePortfolioRebalance` | `portfolio drift-monitor`, `portfolio rebalance` | | Parametric VaR and expected shortfall at any confidence level | `calculatePortfolioVar` | `portfolio var` | | Tax-loss harvesting screen with wash-sale look-back and cross-index replacement candidates | `scanTaxLossHarvesting` | `portfolio tlh` | | Historical backtest (VTI, BND, VXUS, VNQ; 2015–2024 annual returns) | `backtestPortfolio` | `quant backtest` | | Forward Monte Carlo under macro regimes | `forwardTestSimulation` | `quant forward-test` | | Asset-class mix, and factor or duration averages of values you supply | `analyzePortfolioFact