microservices

TradeX

A scalable paper-trading platform built with Spring Boot microservices and a self-contained market-data system.

Jun 2026 - Present

Overview

TradeX is a production-inspired full-stack paper trading platform built using Java (Spring Boot) and React. The application simulates a modern stock trading platform similar to Groww or INDmoney without performing any real financial transactions. Unlike traditional demo trading applications, TradeX is completely self-contained and does not rely on any third-party market data providers. Instead, it contains its own market simulation engine capable of generating realistic historical and live stock market data.

Key Features

•Microservices architecture with API Gateway and Eureka Service Discovery
•Self-contained market data system generating price history for 15 Indian stocks/ETFs
•10 years of generated OHLCV historical data
•Real-time price streaming using Kafka and WebSockets
•Redis caching for low-latency market price access
•PostgreSQL for transactional persistence
•JWT-based authentication and authorization
•Portfolio management with virtual trading
•Transaction history and watchlists
•Interactive TradingView Lightweight Charts

Microservices Architecture & Event Streaming

Interactive system architecture flow showing ingress, service discovery, pub/sub streaming, and dual-layer persistence

Spring Boot Microservices Cluster

Client Layer

React Frontend

Trading UI & Dashboards

ReactTypeScriptRedux ToolkitTailwind CSSWebSockets

Interactive trading interface featuring TradingView Lightweight Charts, real-time ticker quotes, portfolio tracking, and order placement.

Key Engineering Challenges

•Building a realistic market simulation engine without third-party APIs
•Ensuring consistent price data generation across 15 stocks over 10 years
•Real-time price streaming at low latency using Kafka and WebSockets
•Managing inter-service communication in a microservices architecture

Architectural Solutions

•Implemented stochastic price movement algorithms (Random Walk, Geometric Brownian Motion, Mean Reversion)
•Used deterministic random seeds for consistent historical data generation
•Kafka event streaming with WebSocket consumers for real-time price delivery
•Spring Cloud Eureka and API Gateway for service discovery and routing

Technologies Used

JavaSpring BootSpring CloudAPI GatewayEurekaKafkaRedisPostgreSQLWebSocketsDockerNginxReactTypeScriptRedux ToolkitTailwind CSS

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