notebooks

Isaac Flath predicts RLM will revive notebooks by enabling agents to drive REPLs with interleaved prose.

The article introduces the GitHub repository for the book 'Machine Learning for Trading' (2nd edition), which provides over 150 Jupyter notebooks covering ML techniques for algorithmic trading, including feature engineering, supervised/unsupervised learning, deep learning, and reinforcement learning.