Enhancing Equity Strategy Backtesting with Synthetic Data: An Agent-Based Model Approach – part 2

The article discusses a comprehensive approach to enhancing equity strategy backtesting using synthetic data generated through an agent-based modeling (ABM) approach. Key highlights include:

• Developed a simulation with 1,180 stocks and up to 5,040 agents representing different investment strategies
• Created an agent-based model to generate realistic market data beyond limited historical information
• Simulated various agent types including growth, value, momentum, and ESG investors
• Used AWS infrastructure (Batch, ECR, S3, DynamoDB) to run complex simulations at scale
• Demonstrated ability to generate synthetic market data that closely mirrors real-world market characteristics

The research aims to overcome backtesting limitations by creating more diverse and comprehensive market scenario simulations, potentially improving investment strategy development and testing.