Popular GitHub Repositories for Stock Return Predictions

Qlib is an AI-oriented quantitative investment platform that aims to realize the potential, empower research, and create value using AI technologies in quantitative investment, from exploring ideas to implementing productions. Qlib supports diverse machine learning modeling paradigms. including supervised learning, market dynamics modeling, and RL.

阿布量化交易系统(股票，期权，期货，比特币，机器学习) 基于python的开源量化交易，量化投资架构

FinRL: Financial Reinforcement Learning. ?

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

ValueCell is a community-driven, multi-agent platform for financial applications.

Real-time Claude Code usage monitor with predictions and warnings

? A curated list of awesome LLMs & deep learning strategies & tools in financial market.

Quant/Algorithm trading resources with an emphasis on Machine Learning

A library for debugging/inspecting machine learning classifiers and explaining their predictions

Stock market analyzer and predictor using Elasticsearch, Twitter, News headlines and Python natural language processing and sentiment analysis

TradeMaster is an open-source platform for quantitative trading empowered by reinforcement learning :fire: :zap: :rainbow:

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

Predict stock market prices using RNN model with multilayer LSTM cells + optional multi-stock embeddings.

Using python and scikit-learn to make stock predictions

The AI Datastore for Schemas, BLOBs, and Predictions. Use with your apps or integrate built-in Human Supervision, Data Workflow, and UI Catalog to get the most value out of your AI Data.

Use unsupervised and supervised learning to predict stocks

Find big moving stocks before they move using machine learning and anomaly detection

List of awesome resources for machine learning-based algorithmic trading

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

A selection of state-of-the-art research materials on trajectory prediction