ai-agent

AI Quant Trade

Name: AI Quant Trade
Author: charliedream1

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

6,016 stars1,147 forksJupyter NotebookUpdated 7/26/2026100% 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.

Install / run

git clone https://github.com/charliedream1/ai_quant_trade.git

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

Quick start

1Navigate to the cloned repository and open the Jupyter Notebook: `cd ai_quant_trade` and `jupyter notebook`
2Import the required libraries and load the sample data: `import pandas as pd` and `df = pd.read_csv('stock_data.csv')`
3Explore the provided examples and tutorials in the `examples` folder, such as `traditional_strategy.ipynb`
4Modify and run the example code to suit your trading strategy, such as changing the `strategy` variable in `machine_learning.ipynb`
5Deploy your strategy to a production environment using the provided C++ deployment code or AkShare instance

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

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