#4545th of 88 in Learning and cookbooksFor finance and business, trading, stocks and crypto, finance and accounting

ai_quant_trade

Quant research workspace and example collection for strategies, backtests and LLM agents

Stars
6.7k
License
Apache-2.0
Last commit
Nov 2026
Last release
Feb 2025
Language
Python
Stars, last 29 weeks+1,323
21 Apr 2026: 5,418 stars28 Apr 2026: 5,453 stars5 May 2026: 5,489 stars12 May 2026: 5,535 stars19 May 2026: 5,564 stars26 May 2026: 5,606 stars2 Jun 2026: 5,644 stars9 Jun 2026: 5,685 stars16 Jun 2026: 5,721 stars23 Jun 2026: 5,774 stars30 Jun 2026: 5,814 stars7 Jul 2026: 5,857 stars14 Jul 2026: 5,904 stars21 Jul 2026: 5,943 stars28 Jul 2026: 5,972 stars4 Aug 2026: 6,024 stars11 Aug 2026: 6,098 stars18 Aug 2026: 6,249 stars25 Aug 2026: 6,332 stars1 Sept 2026: 6,406 stars8 Sept 2026: 6,450 stars15 Sept 2026: 6,484 stars22 Sept 2026: 6,530 stars29 Sept 2026: 6,557 stars6 Oct 2026: 6,581 stars13 Oct 2026: 6,604 stars20 Oct 2026: 6,627 stars27 Oct 2026: 6,659 stars3 Nov 2026: 6,712 stars10 Nov 2026: 6,741 stars

5,418 on 21 Apr 20266,741 on 10 Nov 2026

From GitHub's daily star counts.

Checked on GitHub.

Overview

A Python repo combining a Gradio-based quant workspace (src/, launched with start.py) and many standalone examples for strategies, alpha mining, RL and financial NLP. The workspace covers market research, rule and ML strategy composition, fee-aware backtests, local RAG over documents, and LLM agent sessions through OpenAI/LiteLLM interfaces. Much of the repo is tutorials and curated resource lists.

Who it is for: Quant traders and researchers learning or prototyping AI trading strategies

Strengths

  • Workspace backtests set commission, tax and slippage, with REST API and CLI access
  • Wide strategy examples: double MA, FinRL reinforcement learning, alpha mining, LightGBM/XGBoost
  • Curated awesome_projects lists with per-project license and maintenance notes
  • Local document RAG with cited references, plus MCP and Skills management

Weaknesses

  • Legacy egs_* examples each have separate dependencies and do not share one runtime
  • README demo GIFs use mock data and show no investment performance
  • Live broker execution needs a separately configured channel and checks
  • Some tutorials and video content sit behind a Knowledge Planet community

What it needs

  • GPU optional
  • Docker + Compose
  • Needs Python 3.10-3.14, Gradio, LiteLLM, Qlib (optional), Langfuse (optional)
  • Models: OpenAI-compatible APIs, LiteLLM, LightGBM, XGBoost, CPU MLP
  • port 8000

Quick answers

Is ai_quant_trade open source?

Yes. Its code is on GitHub under the Apache-2.0 license.

Can you run ai_quant_trade with Docker?

Yes. Its repository has a Docker setup: a Dockerfile, a Compose file or a published image.

Does ai_quant_trade need a GPU?

No. It runs on a CPU and can use a GPU when you have one.

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