Research on Financial Data Analysis Based on Applied Deep Learning in Quantitative Trading

This research aiming at the situation of traditional quantitative trading that cannot be effectively identified in the case of major stock fluctuations, a shock index is designed to calculate the current market shock. This index can more accurately capture the characteristics of the financial market and greatly increase the risk of the agent. Resistance. This article applies deep learning quantitative trading to the stock market. Algorithms and trading strategies can dig out potential laws in complex and high-latitude stock information and select the optimal strategy to obtain the maximum profit. In addition to the method of this article, great returns have been achieved in both single-share investment and mixed investment. Therefore, the deep reinforcement learning strategy provided in this article can help stock researchers achieve higher returns.