Build a Go-Powered Automated Stock Screener with SQLite and Vue3 Visualization

Overview

The project is a Go‑based stock analysis system (version 2.0) that replaces CSV storage with SQLite , automatically computes technical indicators (MA5/10/20/30, MACD), modularizes the strategy engine, schedules daily updates at 15:00, and provides a Vue3 front‑end for visualizing results.

System Architecture

The system is divided into the following modules:

Fetcher : pulls K‑line data from the Sina Finance API.

Indicator : calculates moving averages and MACD.

Storage : persists raw data and analysis results in SQLite.

Strategy : modular engine supporting MA, MACD, composite, and DSL strategies.

Scheduler : runs the update job daily at 15:00.

Web : exposes a RESTful API.

Frontend : Vue3 UI for result display and DSL testing.

Project Structure

backend/
├── main.go
├── config/          # configuration files
│   └── config.yaml
├── fetcher/         # data fetching
├── indicator/       # technical indicators
├── storage/         # SQLite handling
├── strategy/        # strategy engine
├── scheduler/       # scheduled tasks
└── web/             # API service

frontend/
├── src/
│   ├── views/
│   │   ├── ResultView.vue
│   │   └── DSLTester.vue
│   └── components/
│       └── StockChart.vue

Configuration‑Driven Strategies

All strategies are enabled via a YAML file:

# backend/config/config.yaml
database: "stock.db"
update_time: "15:00"

strategies:
  - type: "MA"
    ma: 20
    hold_days: 3
  - type: "MACD"
  - type: "Composite"
    hold_days: 3
  - type: "DSL"
    expr: "close > ma20 and macd_dif > macd_dea"

Data Fetching and Storage

Fetch Historical Data

// fetcher/fetcher.go
url := fmt.Sprintf(
    "http://money.finance.sina.com.cn/quotes_service/api/json_v2.php/CN_MarketData.getKLineData?symbol=%s&scale=240&ma=5&datalen=120",
    stockCode,
)

Sample JSON response from the API:

[{ "day":"2025-04-10", "open":"10.18", "high":"10.18", "low":"9.91", "close":"10.00", "volume":"78108087" }]

SQLite Table Schema

CREATE TABLE IF NOT EXISTS kline (
    code TEXT,
    day TEXT,
    open REAL, high REAL, low REAL, close REAL, volume INTEGER,
    ma5 REAL, ma10 REAL, ma20 REAL, ma30 REAL,
    dif REAL, dea REAL, macd REAL,
    PRIMARY KEY(code, day)
);

CREATE TABLE IF NOT EXISTS results (
    code TEXT,
    day TEXT,
    strategy TEXT,
    PRIMARY KEY(code, day, strategy)
);

Strategy Engine

All strategies implement a common interface:

type Strategy interface {
    Name() string
    Match(code string, klines []storage.KLine) bool
}

MA Strategy

// Continuous N‑day closing price > MA20
ma := strategy.NewMAStrategy(20, 3)

MACD Strategy

// Detect DIF crossing above DEA (golden cross)
macd := strategy.NewMACDStrategy()

Composite Strategy

// Require both MA20 and MACD golden cross
combo := strategy.NewCompositeStrategy(3)

DSL Strategy

// User‑defined expression for flexible conditions
dsl := strategy.NewDSLStrategy("close > ma20 and macd_dif > macd_dea")

Scheduling and Tasks

// scheduler/scheduler.go
func ScheduleDailyUpdate(stocks []string) {
    gocron.Every(1).Day().At("15:00").Do(func() {
        UpdateAndAnalyze(stocks)
    })
}

// At startup, fetch 120 days of history and then run incremental updates daily at 15:00.

Web API

Return Screening Results

r.GET("/api/results", func(c *gin.Context) {
    results := storage.LoadResults()
    c.JSON(200, results)
})

Test DSL Strategy

r.POST("/api/dsl/test", func(c *gin.Context) {
    var req struct { Expr string }
    c.BindJSON(&req)
    strat := strategy.NewDSLStrategy(req.Expr)
    matched := strat.Match("sz000001", storage.GetKLines("sz000001"))
    c.JSON(200, gin.H{"match": matched})
})

Frontend Visualization (Vue3)

ResultView.vue : displays the latest screening results in a table or list.

StockChart.vue : renders K‑line charts using ECharts.

DSLTester.vue : allows users to input a DSL expression and see real‑time matching results.

Deployment

# Start backend
cd backend
go run main.go

# Start frontend
cd ../frontend
npm install
npm run dev

Open http://localhost:5173 in a browser to view the UI.

Project Highlights

Configuration‑driven, modular strategy engine.

Automated daily scheduling for hands‑free operation.

SQLite storage enables easy back‑testing and analysis.

Front‑end/back‑end separation with Vue3 visualization.

GitHub Repository

https://github.com/louis-xie-programmer/go-stock-analyzer