Description
#Python #Stock Analysis #A-share Quantitative #Quantitative Trading #Stock Selection Tool #Backtesting System
InStock is a Python quantitative analysis and automated trading tool aimed at the A-share market, corresponding to the GitHub repository myhhub/stock. It integrates market data fetching, technical indicator calculation, candlestick pattern recognition, chip analysis, strategy stock selection, historical backtesting, and automated trading into a single system, suitable for studying A-share technical analysis, building personal stock selection strategies, or learning the complete quantitative trading process.
The project also provides a Web management page that can be accessed from both computers and mobile devices, and supports one-click Docker deployment. According to the project description, a regular laptop takes about 4 minutes to process a day's data, and the overall entry barrier is not high. It is important to note that the results of quantitative analysis can only serve as research references and do not represent actual returns, nor do they constitute investment advice.
30+ Technical Indicators: Built-in common technical indicators such as MACD, KDJ, BOLL, etc., with the project emphasizing that some calculation results can be consistent with mainstream market software.
Candlestick Pattern Recognition: Can recognize 61 types of candlestick patterns and generate corresponding buy and sell signal references based on the pattern results.
Chip Distribution Analysis: Supports CYQ chip distribution calculation, useful for observing market holding costs, chip concentration areas, and potential support and resistance levels.
Comprehensive Stock Selection System: Provides 200+ stock selection dimensions, allowing stocks to be filtered based on technical indicators, market characteristics, and custom conditions.
Strategies and Backtesting: Built-in multiple classic trading strategies and provides historical backtesting functionality, facilitating the verification of strategies' performance in different market phases.
Custom Strategies: Supports writing stock selection and trading logic based on personal research ideas, suitable for further learning Python quantitative development.
Automated Trading and IPO Subscription: Supports integration with broker clients to execute some automated operations, including automatic IPO subscriptions, but requires prior configuration of the broker environment.
Web Management Page: Data, indicators, and stock selection results can be viewed through a browser, accessible from both mobile and computer.
Docker Deployment: Provides a containerized deployment method, allowing for quick setup of the running environment, reducing dependency installation and environment configuration work.
Free and Open Source: Project code is hosted on GitHub, available for learning, research, and secondary development, with specific usage scope subject to the repository's open-source license.
The project also provides a Web management page that can be accessed from both computers and mobile devices, and supports one-click Docker deployment. According to the project description, a regular laptop takes about 4 minutes to process a day's data, and the overall entry barrier is not high. It is important to note that the results of quantitative analysis can only serve as research references and do not represent actual returns, nor do they constitute investment advice.
Software Features
Real-time Market Data Fetching: Supports obtaining real-time and historical market data for A-shares, providing a data foundation for indicator analysis, stock selection, and backtesting.30+ Technical Indicators: Built-in common technical indicators such as MACD, KDJ, BOLL, etc., with the project emphasizing that some calculation results can be consistent with mainstream market software.
Candlestick Pattern Recognition: Can recognize 61 types of candlestick patterns and generate corresponding buy and sell signal references based on the pattern results.
Chip Distribution Analysis: Supports CYQ chip distribution calculation, useful for observing market holding costs, chip concentration areas, and potential support and resistance levels.
Comprehensive Stock Selection System: Provides 200+ stock selection dimensions, allowing stocks to be filtered based on technical indicators, market characteristics, and custom conditions.
Strategies and Backtesting: Built-in multiple classic trading strategies and provides historical backtesting functionality, facilitating the verification of strategies' performance in different market phases.
Custom Strategies: Supports writing stock selection and trading logic based on personal research ideas, suitable for further learning Python quantitative development.
Automated Trading and IPO Subscription: Supports integration with broker clients to execute some automated operations, including automatic IPO subscriptions, but requires prior configuration of the broker environment.
Web Management Page: Data, indicators, and stock selection results can be viewed through a browser, accessible from both mobile and computer.
Docker Deployment: Provides a containerized deployment method, allowing for quick setup of the running environment, reducing dependency installation and environment configuration work.
Free and Open Source: Project code is hosted on GitHub, available for learning, research, and secondary development, with specific usage scope subject to the repository's open-source license.
