# Portfolio Analytics

Portfolio-level performance measurement including return metrics, risk metrics...

- Repository: https://github.com/agiprolabs/claude-trading-skills (507 stars)
- Category: Engineering
- License: MIT
- Last updated: 2026-09-03
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add agiprolabs/claude-trading-skills --skill portfolio-analytics -g -a claude-code -y`
Codex: `npx skills add agiprolabs/claude-trading-skills --skill portfolio-analytics -g -a codex -y`
Cursor: `npx skills add agiprolabs/claude-trading-skills --skill portfolio-analytics -g -a cursor -y`
Gemini CLI: `npx skills add agiprolabs/claude-trading-skills --skill portfolio-analytics -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add agiprolabs/claude-trading-skills`

## Description

Portfolio-level performance measurement including return metrics, risk metrics, risk-adjusted ratios, rolling analysis, and HTML reports

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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 days
```

### Cumulative Returns

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

## Risk Metrics

### Annualized Volatility

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

### Value at Risk (VaR)

Historical VaR at a given confidence level:

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

The rest: https://github.com/agiprolabs/claude-trading-skills/blob/HEAD/skills/portfolio-analytics/SKILL.md