financial-machine-learning

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

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.

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.

Python library for building financial machine learning models.

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

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.

An open-source, lightweight, and blazing-fast financial machine learning library built with Numba. Process raw trades, generate advanced bars, features, and labels for quantitative research.

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

Implementations of Genetic Methods for Financial Machine Learning Applications

Financial Machine Learning Repository

Pytorch implementation of TABL from Temporal Attention Augmented Bilinear Network for Financial Time Series Data Analysis

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

2024학년도 1학기 MLfinLab Project Team repository

A set of financial analytics projects with python.

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

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