Open-source Python framework for training and backtesting financial trading agents with deep reinforcement learning.
Python and Jupyter reproductions of quant strategies from Chinese brokerage research reports, covering A-share timing, factors, value and portfolio optimization.
A beginner curriculum teaching the Model Context Protocol (MCP) with hands-on code examples in C#, Java, JavaScript, Rust, Python, and TypeScript.
Stock AI Trader: 1-stop platform for learning, sim & live trading. Covers: stock basics, strategies, LLMs, factor mining, ML/DL/RL, graph nets, HFT, C++ deploy & JoinQuant code. 股票AI操盘手:一站式学习、模拟、实盘平台。涵盖:股票基础、策略、大模型、因子挖掘、机器学习/深度学习/强化学习、图网络、高频交易、C++部署及聚宽代码。
Comprehensive resources on Generative AI, including a detailed roadmap, projects, use cases, interview preparation, and coding preparation.
Collection of notebooks about quantitative finance, with interactive python code.
Samples using the Agent2Agent (A2A) Protocol
A collection of scripts and notebooks to help you get started quickly.
Independent quantitative finance research project comparing U.S. and Kazakhstan equity markets using Python and portfolio theory.
Course Materials for Analytics in Stock Markets Zoomcamp
This repository introduces PIXIU, an open-source resource featuring the first financial large language models (LLMs), instruction tuning data, and evaluation benchmarks to holistically assess financial LLMs. Our goal is to continually push forward the open-source development of financial artificial intelligence (AI).
Segment Anything for Microscopy
WFGY is heading toward WFGY 5.0 Polaris Protocol, a major open-source release for AI reasoning, RAG, agents, and real-world workflows. Includes Problem Map, Global Debug Card, WFGY 4.0, and the CFV Easter Egg.