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

This article discusses how synthetic data generated through agent-based models (ABMs) can enhance equity strategy backtesting by overcoming limitations of traditional historical data analysis.

• Synthetic data helps address challenges like insufficient historical data and limited market scenario representation
• Agent-based models simulate market interactions by modeling different types of traders and their behaviors
• Benefits include generating diverse market scenarios, controlled experimentation, and innovative strategy development
• The approach allows testing investment strategies across multiple market conditions, including extreme but plausible events
• Key objectives include simulating long-term market dynamics and enabling performance evaluation of custom strategies

The methodology provides a more robust approach to backtesting by generating performance distributions that are harder to overfit, offering deeper insights into potential investment strategy performance across various market scenarios.