MlFinLab helps portfolio managers and traders who want to leverage the power of machine learning by providing reproducible, interpretable, and easy to use tools.

Quant/Algorithm trading resources with an emphasis on Machine Learning

algorithmic trading using machine learning

A collection of awesome papers, articles and various resources on credit and credit risk modeling

🪁 A fast Adaptive Machine Learning library for Time-Series, that lets you build, deploy and update composite models easily. An order of magnitude speed-up, combined with flexibility and rigour. This is an internal project - documentation is not updated anymore and substantially differ from the current API.

A series of interactive labs we prepared for the Chartered Financial Data Scientist Certification. The content of the series is based on Python, IPython Notebook, and PyTorch.

Package based on the work of Dr Marcos Lopez de Prado regarding his research with respect to Advances in Financial Machine Learning

Python library for building financial machine learning models.

A series of interactive labs we prepared for the Chartered Financial Data Scientist Certification. The content of the series is based on Python, IPython Notebook, and PyTorch.

Implementations of Genetic Methods for Financial Machine Learning Applications

Pricing Financial Options contracts using LightGBM, Deep Learning, and Support Vector Machines.

A tool to detect whether numerals present in Financial Texts are in-claim or out-of-claim

Two ensemble models made from ensembles of LightGBM and CNN for a multiclass classification problem.

Implementations for Adances In Financial Machine Learning

Numerai's Next Top Model

Financial Machine Learning Repository

Comprehensive analysis of various ML models to detect fraud in financial transactions.

It is a Jupyter notebook that compares different trading strategies using technical analysis, machine learning, and deep learning methods.

실전 금융 머신러닝 완벽 분석 / Advances in Financial Machine Learning