Test Project

Designed and developed a high-performance algorithmic trading platform capable of processing real-time market data from multiple exchanges and executing automated trades based on quantitative strategies. The system ingests live price feeds, calculates technical indicators, evaluates trading signals, and places orders with minimal latency. It includes a backtesting engine for evaluating historical trading performance, a risk management module to enforce position limits and stop-loss rules, and a monitoring dashboard for real-time portfolio analytics.

Key Responsibilities

Developed low-latency market data processing services.
Implemented algorithmic trading strategies based on quantitative models.
Built a backtesting engine using historical market data.
Designed a real-time risk management system to monitor positions and exposure.
Optimized database queries and memory usage for high-throughput data processing.
Developed REST APIs for strategy management and trade reporting.
Created monitoring dashboards displaying live trades, P&L, and system health.
Wrote unit and integration tests to ensure trading engine reliability.

Technology Stack

Languages: C++, Python
Backend: FastAPI / Flask
Database: PostgreSQL, Redis
Messaging: Kafka
Data Analysis: NumPy, Pandas
Deployment: Docker, Linux
Monitoring: Grafana, Prometheus
Version Control: Git

Key Features

Real-time market data streaming
Automated order execution
Historical backtesting
Portfolio and P&L tracking
Risk management and stop-loss controls
Performance analytics dashboard
Multi-strategy support
Low-latency architecture