Run daily analysis on Chinese A-share stocks to get automated buy and sell signals from 30+ technical indicators.
Backtest built-in stock-selection strategies like volume surge breakout or platform consolidation against historical data.
Browse and filter stock analysis results on a mobile-friendly web dashboard.
Set up automated trading based on the system's generated signals, with an awareness of financial risk.
Requires a running MySQL database, processing a full day of data takes about 4 minutes on a standard laptop.
This is a stock market analysis system called InStock, focused on Chinese A-share stocks and ETFs. It is written in Python and designed to run on Windows, Linux, or macOS. A Docker image is also provided for easier setup. The system pulls daily market data, then runs a series of calculations and pattern-detection steps on that data. It computes over 30 technical indicators, including common ones like MACD, RSI, KDJ, Bollinger Bands, and many others. These indicators are used to flag stocks that may be overbought or oversold, and to produce buy or sell signals based on specific numerical thresholds defined in the README. Beyond indicators, the system can recognize 61 candlestick chart patterns. Candlestick patterns are shapes formed by price movements over one or more trading sessions, and traders use them to guess future price direction. Users can also choose which patterns to watch for. A chip distribution feature shows how purchase costs are spread across investors over a given period, which is a technique used in Chinese technical analysis to understand where most buyers are sitting. The system includes several built-in stock-selection strategies with names like "volume surge breakout," "platform consolidation," and "annual line pullback." You can backtest these strategies against historical data to see how they would have performed. There is also support for automated trading, though the README notes that this involves real money and carries risk. All results are stored in a MySQL database, and the system serves a web interface so you can browse and filter the output visually on a PC, tablet, or phone. Processing one full day of data across all steps takes about four minutes on a regular laptop. Logs are written to separate files for data jobs, the web server, and the trading service, making it easier to track what happened.
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