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