The role of the stock market across the overall financial market is indispensable.
The way to acquire practical trading signals in the transaction process to maximize the benefits is a problem that has been studied for a long time. Profitable automated stock trading strategy is vital to investment companies and hedge funds. It is applied to optimize capital allocation and maximize investment performance, such as expected return. Return maximization can be based on the estimates of potential return and risk. However, it is challenging to design a profitable strategy in a complex and dynamic stock market. Every player wants a winning strategy. Needless to say, a profitable strategy in such a complex and dynamic stock market is not easy to design.
For the purpose of this project, we are making use of a deep reinforcement learning approach, more specifically Q learning, that automatically learns the optimal trading strategy by maximizing investment return.