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.

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

algorithmic trading using machine learning

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.

🪁 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.

End-to-end RL trading framework with PPO agent, self-attention neural network, custom Gym environment, and advanced backtesting.

A community-curated vault of openly available resources that replicates the rigorous syllabus of top MFE / Quant Finance programs

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.

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

Financial Machine Learning Repository

Implementations of Genetic Methods for Financial Machine Learning Applications

실전 금융 머신러닝 완벽 분석 / Advances in Financial Machine Learning

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

2024학년도 1학기 MLfinLab Project Team repository

Advanced stock forecasting system using LSTM neural networks with real-time sentiment analysis. Predicts price movements and volatility by combining technical indicators, news sentiment from Finnhub API, and multivariate analysis. Features dual LSTM models, intelligent alerts, and comprehensive risk assessment for informed trading decisions.