Popular GitHub Repositories for Investment

Investment Research for Everyone, Everywhere.

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.

Code for Machine Learning for Algorithmic Trading, 2nd edition.

[?updating ...] AI 自动量化交易机器人(完全本地部署) AI-powered Quantitative Investment Research Platform. ? online docs: https://ufund-me.github.io/Qbot ? :news: qbot-mini: https://github.com/Charmve/iQuant

Algorithmic Trading in Python with Machine Learning

Providing the solutions for high-frequency trading (HFT) strategies using data science approaches (Machine Learning) on Full Orderbook Tick Data.

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

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

Applying Machine Learning and AI Algorithms applied to Trading for better performance and low Std.

? The all-in-one tool, for keeping track of your domain name portfolio. Got domain names? Get Domain Locker!

A sentiment analyzer package for financial assets and securities utilizing GPT models.

A Large-Scale Company Relation Graph for Investment Industry

Scrape, analyze & visualize stock market data for the S&P500 using Python. Build a basic trading strategy using machine learning to assess company performance and determine buy, sell, hold. Read me & instructions available in Spanish. This is a working repo, with plans to expand the project from technical analysis to fundamental analysis.

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.

A curated list of awesome resources for quantitative investment and trading strategies focusing on artificial intelligence and machine learning applications in finance.

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

Financial and Investment Data Science: FinDS Python library and examples for applying quantitative and machine learning methods on structured and unstructured financial data sets

Rallies is an AI powered investment research tool

My quant portfolio leverages quantitative finance and data-driven insights to optimize investment strategies. Using advanced models, statistical analysis, and machine learning, I develop systematic trading strategies to capitalize on market inefficiencies and generate alpha.

ESG-investment AI