Financial Market Data Simulation Using Deep Intelligence Agents

Trading strategies are often assessed against historical financial data in an effort to predict the profits and losses a strategy would generate in future. However, using only data from the past ignores the evolution of market microstructure and does not account for market conditions outside historical bounds. Simulations provide an effective supplement. We present an agent-based model to simulate financial market prices both under steady-state conditions and stress situations. Our new class of agents utilize recent advances in deep learning to make trading decisions and employ different trading objectives to ensure diversity in outcomes. The model supports various what-if scenarios such as sudden price crash, bearish or bullish market sentiment and shock contagion. We conduct evaluations on multiple asset classes including portfolio of assets and illustrate that the proposed agent decision mechanism outperforms other techniques. Our simulation model also successfully replicates the empirical stylized facts of financial markets.