  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.
