Double Deep Q-Learning for Optimal Execution

Abstract

Optimal trade execution is an important problem faced by essentially all traders. Much research into optimal execution uses stringent model assumptions and applies continuous time stochastic control to solve them. Here, we instead take a model free approach and develop a variation of Deep Q-Learning to estimate the optimal actions of a trader. The model is a fully connected Neural Network trained using Experience Replay and Double DQN with input features given by the current state of the limit order book, other trading signals, and available execution actions, while the output is the Q-value function estimating the future rewards under an arbitrary action. We apply our model to nine different stocks and find that it outperforms the standard benchmark approach on most stocks using the measures of (i) mean and median out-performance, (ii) probability of out-performance, and (iii) gain-loss ratios.

arXiv comment20 pages, 7 figures, 1 table. Updated minor typos

References in corpus (2)

• Playing Atari with Deep Reinforcement Learning
• A reinforcement learning extension to the Almgren-Chriss model for optimal trade execution

Cited by in corpus (4)

• Learning Multi-Agent Intention-Aware Communication for Optimal Multi-Order Execution in Finance
• Reinforcement Learning for Quantitative Trading
• An overall view of key problems in algorithmic trading and recent progress
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