Comparing Reinforcement Learning approaches in financial trading systems


Prometeia organizes training sessions on economic, financial and methodological issues open to whom can be interested (the opportunity to participate is subject to availability of seats).

Topic: Comparing Reinforcement Learning approaches in financial trading systems
Speaker: Marco Corazza (Università di Venezia)
Where: Training Room, Bologna Headquarters (Piazza Trento e Trieste 3)
When: 23/01/2020; from 14:30 to 16:30

In this talk, we analyze and implement different Machine Learning algorithms belonging to the family of the Reinforcement Learning (RL) ones in financial trading system applications. 

RL-based algorithms applied to financial systems aim to find an optimal policy, that is an optimal mapping between the variables describing the state of the system and the actions available to an agent, by interacting with the system itself in order to maximize a cumulative return. 

In this contribution, we compare the results obtained considering different on-policy (SARSA) and off-policy (Q-Learning, Greedy-GQ) RL algorithms applied to daily trading in the Italian stock market. 

We consider both computational issues related to the implementation of the algorithms, and issues originating from practical application to real stock markets, in an effort to improve previous results while keeping a simple and understandable structure of the used models.

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