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We explore deep Reinforcement Learning(RL) algorithms for scalping tradingand knew that there is no appropriate trading gym and agent examples. Thus wepropose gym and agent like Open AI gym in finance. Not only that, we introducenew RL framework based on our hybrid algorithm which leverages betweensupervised learning and RL algorithm and uses meaningful observations suchorder book and settlement data from experience watching scalpers trading. Thatis very crucial information for traders behavior to be decided. To feed thesedata into our model, we use spatio-temporal convolution layer, called Conv3Dfor order book data and temporal CNN, called Conv1D for settlement data. Thoseare preprocessed by episode filter we developed. Agent consists of four subagents divided to clarify their own goal to make best decision. Also, weadopted value and policy based algorithm to our framework. With these features,we could make agent mimic scalpers as much as possible. In many fields, RLalgorithm has already begun to transcend human capabilities in many domains.This approach could be a starting point to beat human in the financial stockmarket, too and be a good reference for anyone who wants to design RL algorithmin real world domain. Finally, weexperiment our framework and gave youexperiment progress.

Cooperative Multi-Agent Reinforcement Learning Framework for Scalping Trading