quantitative-trading

Quantitative analysis, algorithmic trading strategies, financial modeling, portfolio risk management, and backtesting

Installation

npx claude-plugins install @wshobson/claude-code-workflows/quantitative-trading

Folders: agents, skills

Included Skills

This plugin includes 2 skill definitions:

backtesting-frameworks

Build robust backtesting systems for trading strategies with proper handling of look-ahead bias, survivorship bias, and transaction costs. Use when developing trading algorithms, validating strategies, or building backtesting infrastructure.

View skill definition

Backtesting Frameworks

Build robust, production-grade backtesting systems that avoid common pitfalls and produce reliable strategy performance estimates.

When to Use This Skill

Developing trading strategy backtests
Building backtesting infrastructure
Validating strategy performance
Avoiding common backtesting biases
Implementing walk-forward analysis
Comparing strategy alternatives

Core Concepts

1. Backtesting Biases

Bias	Description	Mitigation
Look-ahead	Using future information	Point-in-time data
Survivorship	Only testing on survivors	Use delisted securities
Overfitting	Curve-fitting to history	Out-of-sample testing
Selection	Cherry-picking strategies	Pre-registration
Transaction	Ignoring trading costs	Realistic cost models

2. Proper Backtest Structure

Historical Data
      │
      ▼
┌─────────────────────────────────────────┐
│              Training Set               │
│  (Strategy Development & Optimization)  │
└─────────────────────────────────────────┘
      │
      ▼
┌─────────────────────────────────────────┐
│             Validation Set              │
│  (Parameter Selection, No Peeking)      │
└─────────────────────────────────────────┘
      │
      ▼
┌─────────────────────────────────────────┐
│               Test Set 

...(truncated)

</details>

### risk-metrics-calculation

> Calculate portfolio risk metrics including VaR, CVaR, Sharpe, Sortino, and drawdown analysis. Use when measuring portfolio risk, implementing risk limits, or building risk monitoring systems.

<details>
<summary>View skill definition</summary>

# Risk Metrics Calculation

Comprehensive risk measurement toolkit for portfolio management, including Value at Risk, Expected Shortfall, and drawdown analysis.

## When to Use This Skill

- Measuring portfolio risk
- Implementing risk limits
- Building risk dashboards
- Calculating risk-adjusted returns
- Setting position sizes
- Regulatory reporting

## Core Concepts

### 1. Risk Metric Categories

| Category          | Metrics         | Use Case             |
| ----------------- | --------------- | -------------------- |
| **Volatility**    | Std Dev, Beta   | General risk         |
| **Tail Risk**     | VaR, CVaR       | Extreme losses       |
| **Drawdown**      | Max DD, Calmar  | Capital preservation |
| **Risk-Adjusted** | Sharpe, Sortino | Performance          |

### 2. Time Horizons

Intraday: Minute/hourly VaR for day traders Daily: Standard risk reporting Weekly: Rebalancing decisions Monthly: Performance attribution Annual: Strategic allocation


## Implementation

### Pattern 1: Core Risk Metrics

```python
import numpy as np
import pandas as pd
from scipy import stats
from typing import Dict, Optional, Tuple

class RiskMetrics:
    """Core risk metric calculations."""

    def __init__(self, returns: pd.Series, rf_rate: float = 0.02):
        """
        Args:
            returns: Series of periodic returns
            rf_rate: Annual risk-free rate
        """
        self.returns = returns
        self.rf_rate = rf_rate
        self.a



</details>

## Source

[View on GitHub](https://github.com/wshobson/agents)