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Prediction of financial market data with deep learning models has achieved some level of recent success. However, historical financial data suffer from an unknowable state space, limited observations, and the inability to model the impact of your own actions on the market can often be prohibitive when trying to find investment strategies using deep reinforcement learning. One way to overcome these limitations is to augment real market data with agent based artificial market simulation. Artificial market simulations designed to reproduce realistic market features may be used to create unobserved market states, to model the impact of your own investment actions on the market itself, and train models with as much data as necessary. In this study we propose a framework for training deep reinforcement learning models in agent based artificial price-order-book simulations that yield non-trivial policies under diverse conditions with market impact. Our simulations confirm that the proposed deep reinforcement learning model with unique task-specific reward function was able to learn a robust investment strategy with an attractive risk-return profile.

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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-trader market-making (HFT-MM) strategy from both the real financial market and our simulation. Regarding the former, we extracted trader clusters and identified one cluster whose statistical indexes indicated HFT-MM features. We then analyzed the di erence between these traders' orders and the market 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.