Autor: |
Peer Nagy, Jan-Peter Calliess, Stefan Zohren |
Jazyk: |
angličtina |
Rok vydání: |
2023 |
Předmět: |
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Zdroj: |
Frontiers in Artificial Intelligence, Vol 6 (2023) |
Druh dokumentu: |
article |
ISSN: |
2624-8212 |
DOI: |
10.3389/frai.2023.1151003 |
Popis: |
We employ deep reinforcement learning (RL) to train an agent to successfully translate a high-frequency trading signal into a trading strategy that places individual limit orders. Based on the ABIDES limit order book simulator, we build a reinforcement learning OpenAI gym environment and utilize it to simulate a realistic trading environment for NASDAQ equities based on historic order book messages. To train a trading agent that learns to maximize its trading return in this environment, we use Deep Dueling Double Q-learning with the APEX (asynchronous prioritized experience replay) architecture. The agent observes the current limit order book state, its recent history, and a short-term directional forecast. To investigate the performance of RL for adaptive trading independently from a concrete forecasting algorithm, we study the performance of our approach utilizing synthetic alpha signals obtained by perturbing forward-looking returns with varying levels of noise. Here, we find that the RL agent learns an effective trading strategy for inventory management and order placing that outperforms a heuristic benchmark trading strategy having access to the same signal. |
Databáze: |
Directory of Open Access Journals |
Externí odkaz: |
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