day-trading-models 0.1.0

SQL models for day trading

Day Trading Models

This repository contains a collection of SQLAlchemy models for day trading analysis and data management. These models are designed to be used in various projects related to stock market analysis, particularly focusing on the Chinese A-share market.

Features

• Comprehensive set of models covering various aspects of day trading:
  • Opening dates
  • Fund flows
  • Limit up (涨停) and limit down (跌停) stocks
  • Large orders (龙虎榜)
  • Quotations
  • Strong stocks
  • Sub-new stocks
  • And more...
• SQLAlchemy ORM for easy database integration
• Designed for flexibility and reusability across different projects

Installation

You can install this package using pip:

pip install day_trading_models

Usage

Here's a basic example of how to use these models:

from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker
from day_trading_models import OpeningDate, Dt, Zt, Lhb, Fund

# Create an engine and session
engine = create_engine('your_database_url')
Session = sessionmaker(bind=engine)
session = Session()

# Create tables
OpeningDate.__table__.create(engine, checkfirst=True)
Dt.__table__.create(engine, checkfirst=True)
Zt.__table__.create(engine, checkfirst=True)
Lhb.__table__.create(engine, checkfirst=True)
Fund.__table__.create(engine, checkfirst=True)

# Example: Add an opening date
new_date = OpeningDate(date='2023-05-01')
session.add(new_date)

# Example: Query Zt (limit up) stocks
zt_stocks = session.query(Zt).filter(Zt.date == '2023-05-01').all()

# Example: Add a new fund flow record
new_fund = Fund(
    date='2023-05-01',
    sse_close_price=3000.0,
    sse_change=0.02,
    # ... other fields ...
)
session.add(new_fund)

session.commit()

Models

• OpeningDate: Trading calendar dates
• Dt: Stocks hitting lower price limits
• Fund: Fund flow information
• Lhb: Large order ("龙虎榜") data
• LhbHyyyb: Active brokerage branches in large orders
• LhbPlayer: Individual players in large orders
• Quotation: Stock quotations
• Strong: Strong performing stocks
• Subnew: Newly listed stocks
• Zb: Stocks that hit the upper price limit but failed to maintain it until market close (炸板股)
• Zt: Stocks hitting upper price limits

Development

To set up the development environment:

1. Clone the repository
2. Install development dependencies:

   pip install -r requirements-dev.txt
   

3. Run tests:

   pytest
   

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License.