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Algorithmic Trading with Fitted Q Iteration and Heston Model

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  • Son Le

Abstract

We present the use of the fitted Q iteration in algorithmic trading. We show that the fitted Q iteration helps alleviate the dimension problem that the basic Q-learning algorithm faces in application to trading. Furthermore, we introduce a procedure including model fitting and data simulation to enrich training data as the lack of data is often a problem in realistic application. We experiment our method on both simulated environment that permits arbitrage opportunity and real-world environment by using prices of 450 stocks. In the former environment, the method performs well, implying that our method works in theory. To perform well in the real-world environment, the agents trained might require more training (iteration) and more meaningful variables with predictive value.

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  • Son Le, 2018. "Algorithmic Trading with Fitted Q Iteration and Heston Model," Papers 1805.07478, arXiv.org.
  • Handle: RePEc:arx:papers:1805.07478
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    Cited by:

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