Research on Optimization of High Frequency Trading Strategies and Market Impact Based on Artificial Intelligence

With the development of artificial intelligence technology, the high-frequency trading strategy has been significantly optimized in the financial market. This paper first reviews the basic concepts of high-frequency trading and its application status in financial markets, and deeply discusses the application of artificial intelligence (including machine learning, deep learning and reinforcement learning) in the optimization of high-frequency trading strategies, especially how to achieve higher returns and stronger market adaptability through algorithm improvement. Furthermore, this paper analyzes the impact of HFT on market microstructure and constructs a corresponding market impact model based on market impact and liquidity. Through empirical research, we verify the performance of different AI trading strategies in a variety of market environments, and reveal the effectiveness and potential risks of the strategies in real transactions. The research in this paper not only provides a new perspective for strategy optimization in HFT, but also provides theoretical support for future financial market participants to understand and manage the market impact.