Double Deep Q-Learning for Optimal Execution
This addresses the problem of optimal execution for traders, but it is incremental as it applies an existing reinforcement learning method to a specific domain.
The paper tackled optimal trade execution by developing a model-free Deep Q-Learning variation, which outperformed standard benchmarks on most of nine stocks in measures like mean out-performance and gain-loss ratios.
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.