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

uv pip install pandas numpy quantstats

Input Format

All analytics start from an equity curve, a time-indexed Series of portfolio values:

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

total_return = (equity.iloc[-1] / equity.iloc[0]) - 1

CAGR (Compound Annual Growth Rate)

days = (equity.index[-1] - equity.index[0]).days
cagr = (equity.iloc[-1] / equity.iloc[0]) ** (365.25 / days) - 1

Daily Mean Return

daily_mean = returns.mean()
annualized_mean = daily_mean * 252  # trading days

Cumulative Returns

cumulative = (1 + returns).cumprod() - 1

Risk Metrics

Annualized Volatility

daily_vol = returns.std()
annual_vol = daily_vol * np.sqrt(252)

Value at Risk (VaR)

Historical VaR at a given confidence level:

def historical_var(returns: pd.Series, confidence: float = 0.95) -> float:
    """Compute historical VaR.