Popular GitHub Repositories for Quantitative Finance

Investment Research for Everyone, Everywhere.

MlFinLab helps portfolio managers and traders who want to leverage the power of machine learning by providing reproducible, interpretable, and easy to use tools.

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

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

Using python and scikit-learn to make stock predictions

Use unsupervised and supervised learning to predict stocks

Machine Learning in Asset Management (by @firmai)

fastquant — Backtest and optimize your ML trading strategies with only 3 lines of code!

This code accompanies the the paper Trading with the Momentum Transformer: An Intelligent and Interpretable Architecture (https://arxiv.org/pdf/2112.08534.pdf).

Cloud-native Financial Reinforcement Learning

请访问 ?www.jieyu.ai和公众号?量化风云

powerful ai-copilot for quant traders and researchers

Deep learning for forecasting company fundamental data

Build your autonomous hedge fund in minutes. AutoHedge harnesses the power of swarm intelligence and AI agents to automate market analysis, risk management, and trade execution.

This project studies the intrinsic relationship between the stocks’ multiple factors and the investment value of the stocks listed in China Securities Index 800 Index through the machine method. The investment system pipeline has been implemented including data acquirement, data preprocessing, model tuning and selection based on the XGBoost boosted tree model.

Unsupervised machine learning Principal Component Analysis (PCA) on the Dow Jones Industrial Average index and it's respective 30 stocks to construct an optimized diversified intelligent portfolio.

Augmento sentiment data quickstart and research

Reimplementation of Paper: (Re-)Imag(in)ing Price Trends

金融研报分析小助手

A deep reinforcement learning framework for generating formulaic alpha factors for quantitative investment, powered by GFlowNet, implemented in Python&PyTorch.