Build a Java Stock Trading Monitoring System: From Design to Deployment

Introduction

Hello, I am Snowball. This article shares a Java implementation of stock‑trading related functions. If you are not familiar with stock or derivatives trading but are interested, you can look up related materials yourself.

The article introduces two low‑frequency strategies: a high‑sell low‑buy grid trading strategy and an intraday T‑strategy.

Requirement Analysis & Implementation Ideas

Each trading day a stock moves up or down. To monitor price changes or automate trades, existing broker platforms often require paid services or lack flexibility. Two approaches are proposed:

Use an existing quantitative platform to write strategies and back‑test, then execute via broker software.

Develop custom code to monitor valuations and handle price movements according to specific needs.

The first approach has lower cost but limited functionality; the second costs more but offers full control.

Interval Trading Monitoring System

1) Architecture

The system consists of four main modules:

Stock pool – list of stocks to fetch data for.

Real‑time data collection service – pulls data from various sources with retry mechanisms.

Portfolio, strategy configuration, and execution – see interface documentation for details.

Notification service – supports email and enterprise WeChat bot notifications (configured in application.yml).

2) Code Implementation

The project uses a single‑machine architecture with SpringBoot, Spring Data JPA, Redis, and MySQL.

Source code repository:

https://gitee.com/snowball2dev/stock-trade-strategy

Project structure:

stock-back-test          // strategy back‑testing module (not yet implemented)
stock-base               // common utilities
stock-data               // stock data module (pool, real‑time collection, updates, notifications)
stock-platform           // backend platform startup module
stock-trade              // trading strategy module (grid strategy, intraday T strategy, etc.)
stock-user               // user management module

3) Interface Documentation

Example endpoint (full list available in the documentation):

POST /stock/tradePlan/grid/save   // create or update a grid‑trading plan

4) Deployment Steps

Create database tables using stock_sql.sql located in the data directory.

Modify application.yml to set your email and WeChat bot addresses.

Package the stock-platform module with Maven and upload the generated JAR to a server, then run it.

5) Running Results

1. Database schema

2. Local IDE logs

3. Server directory layout

4. Server runtime logs

6) Summary

The article described the design and implementation of a stock interval‑trading monitoring system, including user management, simulated portfolio, arbitrage strategies, and common stock utilities. The backend was deployed on a single server and validated on A‑share stocks for several months without major bugs. Readers can download the code, run it in IntelliJ IDEA, and experiment with the two strategies.

Conclusion

Based on personal trading experience and quantitative‑trading knowledge, the author built a backend that supports a high‑sell low‑buy grid strategy and an intraday T strategy, with notification mechanisms. Although not feature‑complete, it satisfies the core arbitrage functions and serves as a practical reference for anyone interested in automated stock‑trading projects.