[GitHub Trending] stefan-jansen/machine-learning-for-trading
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Book code for ML trading, low novelty but moderate relevance.
Stefan Jansen's second edition of 'Machine Learning for Trading' provides an end-to-end workflow for building algorithmic trading strategies, spanning data sourcing, feature engineering, supervised/unsupervised learning, deep reinforcement learning, and backtesting. The accompanying GitHub repository contains over 150 executed Jupyter notebooks that replicate published research and demonstrate practical strategy design, evaluation, and performance simulation. This resource emphasizes the ML4T iterative process—from idea generation to real-market execution—with new chapters on backtesting and over 100 alpha factor definitions.
- Explore the 150+ notebooks to understand how end-to-end ML pipelines are structured for financial time-series, then adapt the feature engineering and backtesting patterns to your own data-intensive systems.
As a solutions architect focused on production ML pipelines, this repo offers a concrete, open-source blueprint for integrating data engineering, feature stores, model training, and backtesting into a coherent system—directly applicable to any time-series or event-driven ML workload.
stefan-jansen