Algorithmic Trading with Fitted Q Iteration and Heston Model

2018/05/18 by Son Le, Le, Son
Decision Sciences · Economics, Econometrics and Finance · #Complex Systems and Time Series Analysis #Computational Finance (q-fin.CP) #FOS: Economics and business #Stochastic processes and financial applications #Stock Market Forecasting Methods #Trading and Market Microstructure (q-fin.TR)

paper · pdf · doi:10.48550/arxiv.1805.07478

openalex publication_date 2018/05/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• The Pricing of Options and Corporate Liabilities

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