Self-Operating Stock Exchange – A Deep Reinforcement Learning Approach
Abstract
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Stock trading approaches play an important role in equity. However, it is tough to create a financially beneficial approach in a complicated and evolving stock market. In this manuscript, we suggest an epsilon greedy policy in our DQN prototype that allows you to get effective policy for the agent this could optimize the predicted values of the total reward across any sequential steps ranging from the present state i. E. To maximize the state-action-value function through engaging with the environment q (s, a) to recommend when to buy, sell or hold. In this prototype, the state depends on routine principles of buy, sell or hold of existing data and the state alter as the buying and selling session alters. The prototype is able to grow rapidly the responses to market on reward signals but by agents which will allow us to understand about the holding and buying of the stocks.
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