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 (AI / ML)
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.
A community-curated vault of openly available resources that replicates the rigorous syllabus of top MFE / Quant Finance programs
Python library for building financial machine learning models.
End-to-end RL trading framework with PPO agent, self-attention neural network, custom Gym environment, and advanced backtesting.
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.
Pricing Financial Options contracts using LightGBM, Deep Learning, and Support Vector Machines.
Agent skills for Machine Learning for Algorithmic Trading: leakage-safe quant ML, backtesting, validation, and autonomous research workflows.
Market-guided Transformer research framework for A-share cross-sectional return forecasting with intraday price-volume features.
Financial Machine Learning Repository
An experimental JEV-powered framework for forecasting short-term stock price direction from structured market data.
Implementations of Genetic Methods for Financial Machine Learning Applications
A financial forecasting research prototype containing multiple competing forecasting approaches, with an LSTM price model currently being used by the Streamlit application.
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