Deep reinforcement learning on a multi-asset environment for trading - CORE Reader
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Reinforcement learning (RL) is an area of machine learning concerned with how agents ought to take actions inan environment to maximize the notion of cumulative reward. Deep reinforcement learning(DRL), a recently reinvigorated method with significant success in multiple domains, still has toshow it
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- Reinforcement learning (RL) is an area of machine learning concerned with how agents ought to take actions inan environment to maximize the notion of cumulative reward.
- Deep reinforcement learning(DRL), a recently reinvigorated method with significant success in multiple domains, still has toshow its benefit in the financial markets.
- We use a deep Q-network (DQN) to design long-shorttrading strategies for futures contracts.
- The state space consists of volatility-normalized daily returns,with buying or selling being the reinforcement learning action and the total reward defined as thecumulative profits from our actions.
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