Applications of Reinforcement Learning in Finance -- Trading with a Double Deep Q-Network

TL;DR

This paper develops a Double Deep Q-Network trading agent for financial markets, demonstrating its ability to adapt to various assets and conditions, outperform benchmarks, and provide insights into reinforcement learning applications in finance.

Contribution

It introduces a DDQN-based trading framework that adapts to multiple assets and market conditions, with comprehensive testing and analysis of its performance.

Findings

The trading agent outperformed the market benchmark.

It adjusted its policy based on trading costs and environmental factors.

The agent demonstrated consistent in-sample and out-of-sample performance.

Abstract

This paper presents a Double Deep Q-Network algorithm for trading single assets, namely the E-mini S&P 500 continuous futures contract. We use a proven setup as the foundation for our environment with multiple extensions. The features of our trading agent are constantly being expanded to include additional assets such as commodities, resulting in four models. We also respond to environmental conditions, including costs and crises. Our trading agent is first trained for a specific time period and tested on new data and compared with the long-and-hold strategy as a benchmark (market). We analyze the differences between the various models and the in-sample/out-of-sample performance with respect to the environment. The experimental results show that the trading agent follows an appropriate behavior. It can adjust its policy to different circumstances, such as more extensive use of the neutral position when trading costs are present. Furthermore, the net asset value exceeded that of the benchmark, and the agent outperformed the market in the test set. We provide initial insights into the behavior of an agent in a financial domain using a DDQN algorithm. The results of this study can be used for further development.