A Share Financial Data (Baostock)

🚀 Quantitative Trading System

This project provides a quantitative trading system including a server and a client. It enables users to obtain real - time and historical trading data, run back - testing strategies, and execute trades.

🚀 Quick Start

Example: Get Real - time Data

In a Python environment, enter the following code:

from quant_client import QuantClient

# Initialize the client
qc = QuantClient('http://localhost:5000', 'your_api_key')

# Get historical stock data
data = qc.get_historical_k_data('AAPL', '1D')
print(data)

Example: Run a Back - testing Strategy

from quant_backtester import Backtester

bt = Backtester()
bt.add_strategy(MovingAverageStrategy)
bt.run('2020-01-01', '2023-12-31', 'AAPL')
bt.plot_results()

📦 Installation

Server - side Installation

Install Dependencies

1. Python Environment: Use Python 3.6 or higher.
2. Jinja2: Used to generate configuration files. Installation command: pip install jinja2
3. Flask: Used to develop a local Web service. Installation command: pip install flask

Download Source Code

Download the code from the GitHub repository:

git clone https://github.com/yourusername/quantitative-trading-server.git
cd quantitative-trading-server

Configure the Server

1. Generate Configuration Files: Use the provided script to generate default configuration files:

   python3 server.py init
   

   This will create the config.ini and data/ directories.

2. Modify Configuration: Edit config.ini according to actual needs, setting parameters such as database connection information and API keys.

3. Prepare Data:

   • Download historical trading data and place it in the data/historical_data/ directory.
   • Ensure that financial statement data and market data files are located in the specified paths.

Start the Server

python3 server.py start

The default binding address is http://localhost:5000

Client - side Installation

Download the Client

Download the client from the GitHub repository:

git clone https://github.com/yourusername/quantitative-trading-client.git
cd quantitative-trading-client
pip install requirements.txt

Configure the Client

1. Create a Configuration File:

   python3 client.py init
   

   This will generate client_config.json.

2. Edit Configuration: Fill in information such as the server address, port, and API key in client_config.json.

Use the Command - line Tool

python3 client.py --help

View available commands and their options.

💻 Usage Examples

Basic Usage

The above quick - start examples show the basic usage of getting real - time data and running back - testing strategies.

Advanced Usage

You can customize strategies by inheriting and modifying existing strategy classes, and use more functions provided by the system to meet different trading needs.

📚 Documentation

Tool List

🤝 Contribution Guidelines

Submit an Issue

• Create a new issue in the GitHub repository: Issues
• Please describe the problem in detail, attaching logs and steps to reproduce.

Submit Code

1. Fork the repository to your personal account.
2. Create a feature branch:

   git checkout -b feature/new-feature-name
   

3. Commit and push to GitHub:

   git add .
   git commit -m 'Add a new feature'
   git push origin feature/new-feature-name
   

4. Submit a Pull Request to the main repository.

Code Style

• Follow the PEP8 code style.
• Each PR should only submit one feature or fix.
• Add detailed documentation and comments.

📄 License

This project follows the MIT license agreement. For details, see the LICENSE file.

📞 Contact Information

Project Maintainers

• Zhang San (Project Leader): zhangsan@example.com
• Li Si (Technical Expert): lisi@example.com

Community Support

Join our Discord community: https://discord.gg/quant-trading

Or post in the Reddit discussion area: reddit.com/r/quant_trading_support