Hands-On Machine Learning for Algorithmic Trading

ML tools

We covered many libraries of the Python ecosystem in this book. Python has evolved to become the language of choice for data science and ML and the set of open-source libraries continues to both diversify and mature, built on the robust core of scientific computing libraries NumPy and SciPy. The popular pandas library that has contributed significantly to popularizing the use of Python for data science is planning its 1.0 release. The scikit-learn interface has become the standard for modern ML libraries like xgboost or lightgbm that often interface with the various workflow automation tools like GridSearchCV and Pipeline that we used repeatedly throughout the book.

There are several providers that aim to facilitate the ML workflow: ...