Comparing Actual and Simulated HFT Traders' Behavior for Agent Design

Abstract

Recently financial markets have shown significant risks and levels of volatility. Understanding the sources of these risks require simulation models capable of representing adequately the real mechanisms of markets. In this paper, we compared data of the high-frequency-tradermarket-making (HFT-MM) strategy from both the real financialmarket andour simulation. Regarding the former,weextracted trader clusters and identified one cluster whose statistical indexes indicated HFT-MM features. We then analyzed the di erence between these traders’ orders and themarket price. In our simulation, we built an artificial market model with a continuous double auction system, stylized trader agents, and HFT-MM trader agents based on prior research. As an experiment, we compared the distribution of the order placements of HFT-MM traders in the real and simulated financial data. We found that the order placement distribution near the market or best price in both the real data and the simulations were similar. However, the orders far from the market or best price di ered significantly when the real data exhibited a wider range of orders. This indicates that in order to build more realistic simulation of financial markets, integrating fine-grained data is essential.