quant-analyst

Q: How do I install quant-analyst?

Run npx skills add https://github.com/404kidwiz/claude-supercode-skills --skill quant-analyst in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/quant-analyst/ (personal) or .claude/skills/quant-analyst/ (project).

by 404kidwizBusiness & Finance

Expert in quantitative finance, algorithmic trading, and financial data analysis using Python (Pandas/NumPy), statistical modeling, and machine learning.

Installs: 1.1K
Last 24h: —
Trending rank: —
Security: Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/404kidwiz/claude-supercode-skills --skill quant-analyst

01
Install
Run the command in your project. The CLI asks which agents to install for.
02
Describe the task
Ask your agent for something this skill covers. It loads quant-analyst automatically when relevant.
03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

Quantitative Analyst

Purpose

Provides expertise in quantitative finance, algorithmic trading strategies, and financial data analysis. Specializes in statistical modeling, risk analytics, and building data-driven trading systems using Python scientific computing stack.

When to Use

Building algorithmic trading strategies or backtesting frameworks
Performing statistical analysis on financial time series data
Implementing risk models (VaR, CVaR, Greeks calculations)
Creating portfolio optimization algorithms
Developing quantitative pricing models for derivatives
Analyzing market microstructure and order book dynamics
Building factor models for asset returns
Implementing Monte Carlo simulations for financial instruments

Quick Start

Invoke this skill when:

Building algorithmic trading strategies or backtesting frameworks
Performing statistical analysis on financial time series data
Implementing risk models (VaR, CVaR, Greeks calculations)
Creating portfolio optimization algorithms
Developing quantitative pricing models for derivatives

Do NOT invoke when:

Building general web applications → use fullstack-developer
Creating data visualizations without financial context → use data-analyst
Implementing payment processing → use payment-integration
Building generic ML models → use ml-engineer

Decision Framework

Financial Analysis Task?
├── Trading Strategy → Backtesting framework + signal generation
├── Risk Management → VaR/CVaR models + stress testing
├── Portfolio Optimization → Mean-variance, Black-Litterman, risk parity
├── Derivatives Pricing → Monte Carlo, finite difference, analytical
└── Time Series Analysis → ARIMA, GARCH, cointegration tests

Core Workflows

1. Algorithmic Trading Strategy Development

Define trading hypothesis and signal generation logic
Implement strategy using vectorized Pandas operations
Build backtesting engine with realistic execution simulation
Calculate performance metrics (Sharpe, Sortino, max drawdown)
Perform walk-forward optimization to avoid overfitting
Implement live trading hooks with proper risk controls

2. Risk Model Implementation

Gather historical price/returns data
Select appropriate risk metric (VaR, CVaR, Greeks)
Implement calculation using parametric, historical, or Monte Carlo methods
Validate model with backtesting and stress scenarios
Build monitoring dashboard for real-time risk exposure

3. Portfolio Optimization

Define investment universe and constraints
Calculate expected returns and covariance matrix
Implement optimization (scipy.optimize or cvxpy)
Apply regularization to prevent concentration
Rebalance periodically with transaction cost consideration

Best Practices

Use vectorized NumPy/Pandas operations for performance on large datasets
Always account for transaction costs, slippage, and market impact in backtests
Implement proper cross-validation (walk-forward) to prevent lookahead bias
Use log returns for statistical properties, simple returns for aggregation
Store financial data with timezone-aware timestamps (UTC preferred)
Validate models with out-of-sample testing before deployment

Anti-Patterns

Overfitting to historical data → Use walk-forward validation and regularization
Ignoring transaction costs → Include realistic costs in all backtests
Using future data in signals → Ensure strict point-in-time correctness
Assuming normal distributions → Use fat-tailed distributions for risk models
Hardcoding market assumptions → Parameterize and stress test assumptions

FAQ

What does the quant-analyst skill do?

Expert in quantitative finance, algorithmic trading, and financial data analysis using Python (Pandas/NumPy), statistical modeling, and machine learning.

How do I install quant-analyst?

Run npx skills add https://github.com/404kidwiz/claude-supercode-skills --skill quant-analyst in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/quant-analyst/ (personal) or .claude/skills/quant-analyst/ (project).

Which AI agents can use quant-analyst?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is quant-analyst safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains quant-analyst?

It is published by 404kidwiz in 404kidwiz/claude-supercode-skills. It has 1,105 recorded installs.