ai-agent

ai_quant_trade: Master AI‑Driven Stock Trading from Theory to Live Execution

A comprehensive Jupyter‑based platform for founders to learn stock concepts, test strategies, and launch AI‑powered live trades—backed by 6.6k+ GitHub stars.

From the repo · “股票AI操盘手:从学习、模拟到实盘,一站式平台。包含股票知识、策略实例、大模型、因子挖掘、传统策略、机器学习、深度学习、强化学习、图网络、高频交易、C++部署和聚宽实例代码等,可以方便学习、模拟及实盘交易”

6,649 stars+29★ this week1,242 forksJupyter NotebookGuide quality 8/10 · reviewed Oct 13Updated 10/22/2026100% free · open source
What it does

Ai_quant_trade is a one-stop platform for learning, simulating, and executing stock trading strategies using AI, including traditional strategies, machine learning, and deep learning.

When to use it
  • •When you need to automate stock trading decisions with data-driven insights
  • •When you want to experiment with different trading strategies and models
  • •When you need to deploy and execute trading strategies in a production-ready environment
Ready-to-paste prompt
Run the `traditional_strategy.ipynb` example with a specific stock symbol: `python traditional_strategy.py --stock-symbol AAPL`
Heads up: Make sure you have the necessary dependencies installed, including `pandas`, `numpy`, and `jupyter`, and that you have a compatible version of Python (3.8 or later) to run the Jupyter Notebook
Saves to your device
Use with Claude
New

Skip the builder — one click puts this in Claude, Cursor, Antigravity and more.

🛠️ Technical setup

Expect 20–40 minutes in a terminal — or let your AI agent drive it.

Try it instantly — no install
Claude Code
mkdir -p ~/.claude/skills/ai-quant-trade && curl -fsSL https://workflowstacks.com/api/skills/ai-quant-trade/claude-skill -o ~/.claude/skills/ai-quant-trade/SKILL.md
Open in another AI app

Opens the app with this repo with the prompt ready to go — no copy-paste needed.

Connect the whole catalog (MCP)
claude mcp add --transport http workflowstacks https://workflowstacks.com/api/mcp

Adds a WorkflowStacks connector to Claude Code: search and load any skill here by chatting.

How ai_quant_trade: Master AI‑Driven Stock Trading from Theory to Live Execution works
Codeflow
Free to inspect

Ai_quant_trade: Master AI‑Driven Stock Trading from Theory to Live Execution is a large Jupyter Notebook project (~29k lines across 172 code files, plus 14 test files). It is a full software project: use it through its install path rather than reading it end to end. Last commit this month, Apache-2.0 license, has a test suite.

Size
Large codebase
~29k lines · 172 code files · ~4 h to skim
Setup
Developer setup
A real software project. Use it via its install path; don't expect to read it all.
Runs on
Python
No API keys detected
Jupyter Notebook 64%Python 36%
Where to start reading
  1. 1
    README.md
    Start here — what it does and how to install it
  2. 2
    requirements.txt
    Dependencies and the commands it exposes
  3. 3
    egs_skill/broker-research-analyst/README.md
    Inside egs_skill/ — an example of what the AI is told to do
What's in each folder
egs_skill/Prompts, skills & agent definitions30 files
src/Core code — the actual logic5 files
docs/Documentation61 files
a_全网优秀资源/Folder527 files
egs_trade/Folder158 files
egs_data/Folder92 files
egs_aide/Folder62 files
egs_llm/Folder25 files
READMEHas testsDocumentedCI checksApache-2.0 licenseUpdated this month
Quick Actions
Details
Creator
charliedream1
Language
Jupyter Notebook
Category
ai-agent
Published
6/9/2022

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