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We employ deep reinforcement learning (RL) to train an agent to successfullytranslate a high-frequency trading signal into a trading strategy that placesindividual limit orders. Based on the ABIDES limit order book simulator, webuild a reinforcement learning OpenAI gym environment and utilise it tosimulate a realistic trading environment for NASDAQ equities based on historicorder book messages. To train a trading agent that learns to maximise itstrading return in this environment, we use Deep Duelling Double Q-learning withthe APEX (asynchronous prioritised experience replay) architecture. The agentobserves the current limit order book state, its recent history, and ashort-term directional forecast. To investigate the performance of RL foradaptive trading independently from a concrete forecasting algorithm, we studythe performance of our approach utilising synthetic alpha signals obtained byperturbing forward-looking returns with varying levels of noise. Here, we findthat the RL agent learns an effective trading strategy for inventory managementand order placing that outperforms a heuristic benchmark trading strategyhaving access to the same signal.

Asynchronous Deep Double Duelling Q-Learning for Trading-Signal Execution in Limit Order Book Markets