Quantitative trading replaces human intuition with mathematical models, statistical analysis, and computational power. Instead of a portfolio manager reading earnings reports and making gut calls, quant strategies use algorithms that process massive datasets — price history, fundamental data, alternative data like satellite imagery or credit card transactions — to identify patterns and execute trades systematically. Firms like Renaissance Technologies, Two Sigma, DE Shaw, and Citadel have built empires on this approach, consistently outperforming traditional managers by removing human emotion from the equation.

Factor-based strategies are the foundation of institutional quant trading. The Fama-French model identified that three factors — market risk, company size, and value — explain most of the variation in stock returns. Modern quant models have expanded to hundreds of factors including momentum, quality, low volatility, earnings revisions, and sentiment. A typical quant strategy ranks all stocks in a universe by a factor score, goes long the top decile and short the bottom decile, and rebalances at regular intervals. The edge isn't in any single trade — it's in the law of large numbers applied across thousands of positions.

Machine learning has transformed quant trading in the past decade. Traditional quant models use linear regressions and fixed rules. Modern approaches use neural networks, random forests, and gradient boosting to find non-linear patterns that humans and simple models miss. But ML in finance is treacherous — markets are non-stationary (the rules keep changing), data is noisy, and overfitting (finding patterns that only exist in historical data) is the biggest risk. The most successful quant firms spend more time preventing overfitting than finding signals.

The practical barrier to quant trading is infrastructure. You need clean data (Bloomberg terminal costs $24,000/year), computing power (backtesting across thousands of stocks requires significant hardware), programming skills (Python and R are standard), and risk management frameworks. But understanding quant concepts benefits every investor — knowing that factor exposures drive most returns helps you understand why your portfolio performs the way it does and whether your active manager is actually adding value or just harvesting well-known factors.