ML for Trading - 2nd Edition This book aims to show how ML can add value to algorithmic trading strategies in a practical yet comprehensive way. It covers a broad range of ML techniques from linear regression to deep reinforcement learning and demonstrates how to build, backtest, and evaluate a trading strategy driven by model predictions.
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MachineLearningStocks is designed to be an intuitive and highly extensible template project applying machine learning to making stock predictions. Source from: https://github.com/robertmartin8/MachineLearningStocks Caution!!! As of Feb 2021: MachineLearningStocks is no longer actively maintained
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notebooks for pawarbi.github.io/blog/
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It is an utility library for Kaggle and offline competitions. It is particularly focused on experiment tracking, feature engineering, and validation.
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An Ultimate Compilation of AI Resources for Mathematics, Machine Learning and Deep Learning. Highly recommended!
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Jupyter notebooks from the scikit-learn video series Youtube link: https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A
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