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Technical Analysis and Feature Engineering
Feature engineering and feature importance for financial ML
What it is
A project on feature engineering and feature importance for machine learning in financial markets. It builds technical indicators as features, generates feature sets and evaluates how much each feature contributes to a model. Quant researchers and machine learning enthusiasts who want worked examples of preparing market data for models will find it useful. Feature importance results depend on the model and data used, so they should not be generalised without retesting.
At a glance
Research onlyOur rating, based on popularity, maintenance and how ready it is for real use.
| Best for | Professional quants |
|---|---|
| Used for | Strategy research, Data analysis |
| Markets | Multi-market |
| Stack | Python |
| Learning curve | Moderate learning curve |
| Practical value | Low practical value |
| Cost | Free and open source |
| Hardware | No GPU needed |
| Maintenance | No commits in over six months |
GitHub stars, last 30 days
Daily snapshots since 2026-09-28 (up to 30 days): 0 over the period, now 202. Gaps mean no snapshot was taken that day.
Feature Engineering and Feature Importance of Machine Learning in Financial Market.
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