# Risk Analysis

Risk measurement and stress testing, VaR/CVaR/max drawdown calculation, Monte Carlo...

- Repository: https://github.com/HKUDS/Vibe-Trading (37375 stars)
- Category: Testing
- License: MIT
- Last updated: 2026-11-01
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add HKUDS/Vibe-Trading --skill risk-analysis -g -a claude-code -y`
Codex: `npx skills add HKUDS/Vibe-Trading --skill risk-analysis -g -a codex -y`
Cursor: `npx skills add HKUDS/Vibe-Trading --skill risk-analysis -g -a cursor -y`
Gemini CLI: `npx skills add HKUDS/Vibe-Trading --skill risk-analysis -g -a gemini-cli -y`

## Description

Risk measurement and stress testing, VaR/CVaR/max drawdown calculation, Monte Carlo simulation, extreme-value tail-risk analysis, and historical scenario stress testing.

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

# Risk Measurement and Stress Testing

## Overview

Systematic risk-measurement methodology covering VaR/CVaR calculation, Monte Carlo simulation, stress-test design, and tail-risk analysis. It provides risk evaluation for backtest results and risk-control constraints for asset allocation.

The measures below are implemented once, with tests, in `src/quantlib/risk.py`. Call them; do not retype the formulas, because a hand-retyped VaR is where the sign convention silently flips.

```python
from src.quantlib.risk import (
    historical_var, parametric_var, historical_cvar,
    max_drawdown_analysis, monte_carlo_gbm, analyze_mc_results, fit_gpd_tail,
)
```

### Sign convention

**A loss is a positive number**, uniformly, across every function in the module:

| Value | Reads as |
|------|------|
| `historical_var(...) == 0.028` | a 2.8% loss |
| `historical_cvar(...) == 0.042` | a 4.2% average loss in the tail |
| `max_drawdown_analysis(...)["max_drawdown"] == 0.325` | a 32.5% peak-to-trough decline |
| `analyze_mc_results(...)["var"] == 0.224` | a 22.4% loss |

Quantities that are *returns* rather than *losses* keep their natural sign and are named `*_return` (`mean_return`, `worst_5pct_return`, `best_5pct_return`), so a bad outcome there is negative. Report VaR to the user with the sign the user expects, but never re-derive it, flip it at the presentation layer only.

`cvar >= var` holds by construction whenever both come from the same sample at the same confidence level. If you ever compute a CVaR below its VaR, the tail mask is wrong.

This is *not* `cvar >= var >= 0`. The magnitudes are never clipped, so a sample whose tail contains no actual loss reports a **negative** loss, a gain. That is deliberate and informative; do not assert non-negativity on a VaR and do not clip it, or you destroy the distinction between "small loss" and "no loss at all".

## Risk Measurement Methods

### 1. VaR (Value at Risk)

The rest: https://github.com/HKUDS/Vibe-Trading/blob/HEAD/agent/src/skills/risk-analysis/SKILL.md