Quantitative trading (or “quant trading”) applies mathematical models and computational techniques to financial markets. While algorithmic trading focuses on the automation of execution, quantitative trading encompasses the entire research-to-execution pipeline: hypothesis formation, data analysis, model building, backtesting, and deployment.
Quant vs. Discretionary Trading
| Aspect | Quantitative | Discretionary |
|---|---|---|
| Decision basis | Data and models | Judgment and experience |
| Emotions | Eliminated | A constant factor |
| Scalability | High (many strategies simultaneously) | Limited by attention |
| Reproducibility | Fully reproducible | Hard to replicate |
The Quant Workflow
- Hypothesis: “Stocks with high ROE and low PE outperform”
- Data Collection: Gather historical fundamental and price data
- Model Building: Define selection criteria, ranking, and weighting rules
- Backtesting: Validate the hypothesis against out-of-sample data
- Deployment: Run the strategy live with automated execution
- Monitoring: Track performance metrics and detect model decay