portfolio-analyticslisted

# Portfolio Analytics Compute portfolio-level performance metrics from equity curves and trade logs. Covers return metrics, risk metrics, risk-adjusted ratios, drawdown analysis, rolling windows, benchmark comparison, trade-level statistics, and automated HTML report generation via quantstats. ## When to Use This Skill - After backtesting a strategy (e.g., from `vectorbt` or `strategy-framework`) - Comparing multiple strategies or parameter sets side-by-side - Generating investor-ready performance reports - Evaluating live trading performance against benchmarks - Assessing risk-adjusted returns for portfolio allocation decisions ## Prerequisites ```bash uv pip install pandas numpy quantstats ``` ## Input Format All analytics start from an **equity curve** — a time-indexed Series of portfolio values: ```python import pandas as pd import numpy as np # From a backtest equity = pd.Series( [10000, 10150, 10080, 10320, 10510, 10440, 10680], index=pd.date_range("2025-01-01", periods=7, freq="D"), name="strategy_equity" ) # Convert to returns returns = equity.pct_change().dropna() ``` ## Return Metrics ### Total Return ```python total_return = (equity.iloc[-1] / equity.iloc[0]) - 1 ``` ### CAGR (Compound Annual Growth Rate) ```python days = (equity.index[-1] - equity.index[0]).days cagr = (equity.iloc[-1] / equity.iloc[0]) ** (365.25 / days) - 1 ``` ### Daily Mean Return ```python daily_mean = returns.mean() annualized_mean = daily_mean * 252 # trading d