Free module · Systematic strategy development
Trading Algorithms: A Practical Selection Guide
Choose a method by its job, assumptions and failure modes—not by its popularity.
EWMA · Kalman · ARIMA/GARCH · boosting · allocation · TWAP/VWAP/POV
The building blocks
An algorithm is a sequence of instructions. Different algorithms solve different jobs: estimating a noisy quantity, forecasting, allocating capital or executing an already chosen trade.
- Identify the job and a simple baseline
- Follow one numerical update
- Compare more complex methods at the same task
Lessons in this module
- Moving averages, EWMA and momentum/reversion rules
- Kalman filtering: combine a prediction with a noisy observation
- ARIMA for conditional means and GARCH for conditional variance
- Linear models, random forests and gradient boosting
- PCA, clustering, risk parity and quadratic programming
- TWAP, VWAP and percentage-of-volume execution
- Optimal execution: impact versus waiting risk
Practice and apply
- Update an EWMA — Old estimate 100, new observation 104, α=.25.
- Compute a Kalman gain — Predicted uncertainty 2, observation uncertainty 2.
Work through the practice exercises · Quant development tools