Risk-Sensitive Compact Decision Trees for Autonomous Execution in Presence of Simulated Market Response

Understand

We demonstrate an application of risk-sensitive reinforcement learning to optimizing execution in limit order book markets.

• We represent taking order execution decisions based on limit order book knowledge by a Markov Decision Process; and train a trading agent in a market simulator, which emulates multi-agent interaction by synthesizing market response to our agent's execution decisions from historical data.
• Due to market impact, executing high volume orders can incur significant cost.
• We learn trading signals from market microstructure in presence of simulated market response and derive explainable decision-tree-based execution policies using risk-sensitive Q-learning to minimize execution cost subject to constraints on cost variance.