# portfolio-analytics

Computes return, risk and risk-adjusted metrics from an equity curve and trade log. Useful after a backtest when you need to compare strategies or ship a report.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 489 installs · ★ 443
- Author: agiprolabs
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/agiprolabs/claude-trading-skills/tree/HEAD/skills/portfolio-analytics/SKILL.md
- Updated: 2026-09-03
- HTML: https://affpapa.org/en/nevibecode/skills/portfolio-analytics

## What it does

The skill starts from an equity curve — a time-indexed series of portfolio values. From it you get total return, CAGR, daily mean return, annualized volatility, historical VaR and CVaR, maximum drawdown and time underwater. Then come the ratios: Sharpe, Sortino, Calmar, Omega and Information Ratio. Each one ships as a ready pandas and numpy function, so you are not retyping formulas.

Rolling analysis covers rolling Sharpe and rolling max drawdown. Trade-level statistics pull win rate, average win and average loss out of a PnL series. Benchmark comparison and HTML report generation via quantstats are covered too. What is inside is Python code, not prompt templates.

Install the dependencies first: `uv pip install pandas numpy quantstats`. Without quantstats the HTML report will not build, while the rest of the metrics run on pandas and numpy. The input is backtest data, so a short or empty equity curve produces meaningless ratios — check that the series is longer than the window you pick.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add agiprolabs/claude-trading-skills --skill portfolio-analytics -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/portfolio-analytics/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/portfolio-analytics/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add agiprolabs/claude-trading-skills --skill portfolio-analytics -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/portfolio-analytics/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add agiprolabs/claude-trading-skills --skill portfolio-analytics -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/portfolio-analytics/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install agiprolabs/claude-trading-skills portfolio-analytics
```

2. **Manually: put the files in place**
- File: `.github/skills/portfolio-analytics/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add agiprolabs/claude-trading-skills --skill portfolio-analytics -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/agiprolabs/claude-trading-skills --path skills/portfolio-analytics
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/portfolio-analytics/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add agiprolabs/claude-trading-skills --skill portfolio-analytics -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/agiprolabs/claude-trading-skills/tree/HEAD/skills/portfolio-analytics/SKILL.md

## When to use it
- measure return and risk from an equity curve
- compare strategies by Sharpe and Sortino
- size up maximum drawdown and time underwater
- build an HTML performance report with quantstats
- get win rate and average trade result from a PnL log

## Example prompts
- Compute portfolio metrics from this equity curve: total return, CAGR, volatility and max drawdown
- Compare the two strategies on Sharpe, Sortino and Calmar and tell me which is steadier
- Generate an HTML backtest report with quantstats

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 26c3b7942ce15b32.

BEGIN THIRD-PARTY FILE 26c3b7942ce15b32
````text
---
name: portfolio-analytics
description: Portfolio-level performance measurement including return metrics, risk metrics, risk-adjusted ratios, rolling analysis, and HTML reports
---

# 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.

    Args:
        returns: Daily return series.
        confidence: Confidence level (e.g., 0.95 for 95%).

    Returns:
        VaR as a positive number representing potential loss.
    """
    return -np.percentile(returns, (1 - confidence) * 100)
```

### Conditional VaR (CVaR / Expected Shortfall)

```python
def historical_cvar(returns: pd.Series, confidence: float = 0.95) -> float:
    """Mean of returns below the VaR threshold."""
````
END THIRD-PARTY FILE 26c3b7942ce15b32

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