Algorithmic Trading with Fitted Q Iteration and Heston Model

TL;DR

This paper applies fitted Q iteration to algorithmic trading, addressing high-dimensionality issues and data scarcity, demonstrating promising results in simulated environments and potential in real-world stock trading.

Contribution

It introduces a fitted Q iteration approach combined with model fitting and data simulation to improve algorithmic trading in high-dimensional and data-limited settings.

Findings

Performs well in simulated arbitrage environments

Shows potential in real-world stock trading with further training needed

Addresses high-dimensionality and data scarcity challenges

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.