Boost High Frequency Trading with Deep Reinforcement Learning and Transformer

High-frequency trading on the short-term base is extremely challenging as it seems to disobey the tenet of long-term value investment lauded by gurus like Warren Buffet. At the same time, it has become a crucial mechanism in modern financial markets for providing liquidity and enhancing market efficiency, which underscores the importance of understanding and developing effective trading algorithms. Motivated by the need to uncover effective approaches in such complex and volatile environments, this paper focuses on analyzing the potential of advanced machine learning techniques in short-term trading. So the paper would explore the most probable weapons that could efficiently help investors glean profits in the volatile and risky markets, the deep reinforcement learning, the transformer, deep residual networks. Readers might find the analysis "far-fetched" because most of the chosen work had no financial root. Rather, they came from fields like gaming or language synthesis. However, this is the magic of algorithms' generalizing ability: after proving its power in its "hometown", it could revolutionize another field. So if the ancient game Go and the modern data center could be treated as close relatives, then readers might find that the explorations are actually very relevant to the future of high-frequency trading. Ultimately, this work aims to provide a conceptual foundation for the application of cross-domain machine learning techniques in financial markets and to highlight promising directions for future research in high-frequency trading strategies.