Deep Reinforcement Learning on a Multi-Asset Environment for Trading

@article{Hirsa2021DeepRL,
  title={Deep Reinforcement Learning on a Multi-Asset Environment for Trading},
  author={Ali Hirsa and Branka Hadji Misheva and Joerg Osterrieder and J. A. Posth},
  journal={International Political Economy: Investment \& Finance eJournal},
  year={2021}
}
Financial trading has been widely analyzed for decades with market participants and academics always looking for advanced methods to improve trading performance. Deep reinforcement learning (DRL), a recently reinvigorated method with significant success in multiple domains, still has to show its benefit in the financial markets. We use a deep Q-network (DQN) to design long-short trading strategies for futures contracts. The state space consists of volatility-normalized daily returns, with… Expand

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