AI Trading & Analytics Platform

The Challenge

Build a fully automated trading system that could operate 24/7 across multiple cryptocurrency exchanges, making data-driven decisions faster and more consistently than manual trading. The system needed to process real-time market data, run ML predictions, execute trades, and manage risk - all while keeping infrastructure costs minimal.

Our Role

Served as Product Architect and Lead Engineer, responsible for the full stack from ML models to production infrastructure:

• ML pipeline: Built TensorFlow models for price prediction and PyTorch models for sentiment analysis, with automated retraining as market conditions changed
• Architecture: Designed a serverless AWS architecture (Lambda + DynamoDB + SQS) that kept operational costs under $200/month despite processing millions of data points
• Trading engine: Built multi-exchange integration with sophisticated risk management rules, automated portfolio rebalancing, and performance tracking
• Product thinking: Made pragmatic decisions about which ML approaches were worth the complexity and which were better solved with simpler heuristics

Results & Impact

The platform achieved a 45% ROI improvement through ML-powered trading strategies. Fully automated 24/7 operation eliminated emotional decision-making - one of the biggest sources of loss in trading. The serverless architecture kept infrastructure costs negligible while handling real money in production.

Why this matters for our clients: This project demonstrates two things we bring to every engagement: first, the ability to apply AI/ML practically (not every prediction model worked - knowing when to simplify was as important as knowing when to use ML). Second, architecture decisions that optimize for operational cost and reliability, not just technical elegance.