Awesome Quant

Qlib is an AI-oriented Quant investment platform that aims to use AI tech to empower Quant Research, from exploring ideas to implementing productions. Qlib supports diverse ML modeling paradigms, including supervised learning, market dynamics modeling, and RL, and is now equipped with https://github.com/microsoft/RD-Agent to automate R&D process.

Code for Machine Learning for Trading, 3rd edition — from data sourcing to live execution.

Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations

Probabilistic time series modeling in Python

:boar: :bear: Deep Learning based Python Library for Stock Market Prediction and Modelling

Repository containing notebooks of my posts on Medium

Deep Learning and Machine Learning stocks represent promising opportunities for both long-term and short-term investors and traders.

Python AutoML for Trading Systems and Sports Betting

Portfolio optimization with deep learning.

Tutorials about Quantitative Finance in Python and QuantLib: Pricing, xVAs, Hedging, Portfolio Optimisation, Machine Learning and Deep Learning

Implement, demonstrate, reproduce and extend the results of the Risk articles 'Differential Machine Learning' (2020) and 'PCA with a Difference' (2021) by Huge and Savine, and cover implementation details left out from the papers.

Quant trading framework by OctoBot. Write, backtest & automate Python trading strategies like TradingView Pine Script. Work in progress.

AI crypto trading bot with deep neural network (84.9% accuracy, 25 coins). BiLSTM + Attention trained on GPU. Bybit, Binance, OKX, Gate.io. Free cloud or self-hosted.