How to Build and Backtest Systematic Trading Strategies with AWS Batch and Airflow

This article demonstrates how to build and backtest systematic trading strategies using AWS services, combining AWS Batch for parallel computation and Apache Airflow for workflow orchestration.

• AWS Batch enables scalable parallel backtesting of multiple strategy parameter combinations simultaneously
• Amazon MWAA orchestrates complex backtesting workflows with task dependencies and monitoring
• ClickHouse on EC2 stores and processes large volumes of market data and backtest results
• Streamlit application provides user-friendly interface for strategy configuration and results visualization
• Backtrader framework validates trading strategy feasibility with flexible Python API
• Three-layer architecture: Configuration, Orchestration, and Execution layers simplify backtesting
• Solution supports local testing before cloud deployment via local_backtest.py
• Comprehensive deployment scripts automate infrastructure setup and containerization
• Results dashboard displays performance metrics, Sharpe ratio, drawdown, and trade statistics

The framework accelerates quantitative strategy development by combining cloud scalability with user-friendly interfaces, reducing time-to-market and infrastructure management overhead.