Real-Time Stock Alert System

A scalable microservices-based - Git** - Version control with proper gitignore configuration

Getting Started monitoring and alert system built with Go, Apache Kafka, and PostgreSQL. The system tracks stock prices, analyzes market data, and sends real-time alerts to users based on their portfolio preferences.

Project Overview

This project implements a distributed stock alert system that:

• Monitors stock prices in real-time
• Analyzes portfolio performance and market trends
• Sends automated alerts based on user-defined criteria
• Provides scalable microservices architecture for high availability

Architecture

The system follows a microservices architecture with event-driven communication:

┌─────────────┐    ┌─────────────┐    ┌─────────────┐
│   API       │    │   Alert     │    │ Persistence │
│  Service    │    │  Consumer   │    │  Consumer   │
└─────────────┘    └─────────────┘    └─────────────┘
       │                   │                   │
       └───────────────────┼───────────────────┘
                           │
                   ┌─────────────┐
                   │   Apache    │
                   │   Kafka     │
                   └─────────────┘
                           │
                   ┌─────────────┐
                   │ Analytics   │
                   │ Consumer    │
                   └─────────────┘

Tech Stack

Backend

• Go 1.25.1 - Core application language
• Gin Framework - HTTP web framework for REST APIs
• GORM - ORM for database operations
• PostgreSQL - Primary database for data persistence

Messaging & Communication

• Apache Kafka - Event streaming platform for real-time data processing
• Kafka Producers - For publishing stock events and user actions
• Kafka Consumers - For processing alerts, persistence, and analytics

Microservices

• API Service - REST API for user interactions and portfolio management
• Alert Consumer - Processes and sends stock price alerts
• Persistence Consumer - Handles data storage and retrieval operations
• Analytics Consumer - Performs market analysis and trend calculations

Infrastructure & DevOps

• Docker - Containerization of all microservices
• Docker Compose - Local development environment orchestration
• GitHub Actions - CI/CD pipeline for automated testing and deployment
• Docker Hub - Container registry for image distribution

Development & Testing

• Go Testing - Comprehensive unit test suite (28+ tests)
• Environment Configuration - Secure environment variable management
• Git - Version control with proper gitignore configuration

🚀 Getting Started

Prerequisites

• Go 1.25.1 or higher
• Docker and Docker Compose
• PostgreSQL
• Apache Kafka

Local Development Setup

1. Clone the repository

   git clone https://github.com/Anubhav200311/Real-Time-Stock-Alert.git
   cd Real-Time-Stock-Alert

2. Set up environment variables

   cp .env.example .env
   # Edit .env with your configuration:
   # - ALPHA_VANTAGE_API_KEY=your_api_key
   # - Database credentials
   # - Kafka broker configuration

3. Start infrastructure services --> Basically the Entire Project

   docker-compose up --build -d 

Testing

The project includes comprehensive test coverage:

# Run all tests
go test ./... -v

# Run specific package tests
go test ./models -v     # Data model tests
go test ./services -v   # Business logic tests  
go test ./routes -v     # API endpoint tests
go test ./db -v         # Database tests

API Endpoints

User Management

• POST /users - Create new user
• GET /users - List all users
• GET /users/:id/portfolio - Get user portfolio

Portfolio Management

• POST /portfolio - Create/update portfolio
• GET /portfolio/:id - Get portfolio details

Stock Operations

• GET /stocks/:symbol - Get stock information
• POST /alerts - Create price alerts

CI/CD Pipeline

The project uses GitHub Actions for automated CI/CD:

Build Process

1. Code Checkout - Latest code from main branch
2. Go Setup - Configure Go 1.25.1 environment
3. Dependency Caching - Cache Go modules for faster builds
4. Testing - Run comprehensive test suite
5. Build Verification - Compile all microservices
6. Docker Build - Create optimized container images
7. Registry Push - Publish to Docker Hub

Deployment

• Automated staging deployment on main branch pushes
• Production-ready Docker images with multi-stage builds
• Environment-specific configuration management

Future Roadmap

Short-term Improvements

• WebSocket Integration - Replace polling with real-time WebSocket connections for live price feeds
• Enhanced Error Handling - Implement circuit breakers and retry mechanisms
• Monitoring & Observability - Add Prometheus metrics and distributed tracing

Medium-term Goals

• AI-Powered Analytics - Integrate machine learning models for:
  • Predictive price analysis
  • Market sentiment analysis
  • Automated trading strategies
  • Risk assessment algorithms

Long-term Vision

• Kubernetes Orchestration - Migrate to Kubernetes for:

  • Auto-scaling based on market volatility
  • Rolling deployments with zero downtime
  • Advanced service mesh with Istio
  • Multi-region deployment capabilities

• Advanced Features

  • Real-time portfolio optimization
  • Social trading and copy-trading features
  • Integration with multiple stock exchanges
  • Mobile application with push notifications
  • Advanced charting and technical analysis tools

Infrastructure Enhancements

• Message Queue Optimization - Implement Kafka Streams for complex event processing
• Database Scaling - Add read replicas and implement database sharding
• Security Hardening - Implement OAuth 2.0, rate limiting, and API encryption
• Performance Optimization - Redis caching layer and CDN integration

Performance Metrics

System design targets and expectations:

• Designed to handle high concurrent user loads through microservices architecture
• Event-driven processing with Kafka enables scalable message throughput
• Asynchronous alert processing for responsive user experience
• Containerized deployment ready for horizontal scaling

Note: Performance benchmarks will be established during production deployment and load testing phases.

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Acknowledgments

• Alpha Vantage API for real-time stock data
• Apache Kafka community for excellent event streaming platform
• Go community for robust ecosystem and libraries

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Built with Go, Kafka, and Cloud-Native technologies