Abstract
Reinforcement learning has been applied to various types of financial assets trading, such as stocks, futures, and cryptocurrencies. Options, as a novel kind of derivative, have their characteristics. Because there are too many option contracts for one underlying asset and their price behavior is different. Besides, the validity period of an option contract is relatively short. To apply reinforcement learning to options trading, we propose the options trading reinforcement learning (OTRL) framework. We use options’ underlying asset data to train the reinforcement learning model. Candle data in different time intervals are utilized, respectively. The protective closing strategy is added to the model to prevent unbearable losses. Our experiments demonstrate that the most stable algorithm for obtaining high returns is proximal policy optimization (PPO) with the protective closing strategy. The deep Q network (DQN) can exceed the buy and hold strategy in options trading, as can soft actor critic (SAC). The OTRL framework is verified effectively.
Highlights
In recent years, reinforcement learning (RL) emerges as an effective way to trade financial assets, such as stocks, futures, and cryptocurrencies
Reinforcement learning is an area of machine learning concerned with how intelligent agents should take actions in an environment to maximize the notion of cumulative reward [17]
PPO is motivated by the same question of trust region policy optimization (TRPO) [32]
Summary
Reinforcement learning (RL) emerges as an effective way to trade financial assets, such as stocks, futures, and cryptocurrencies. Reference [9] uses a time-series momentum and technical indicators to train DQN, policy gradient (PG), and advantage actor-critic (A2C) model They tested the methods on 50 liquid futures contracts and found that the RL algorithms deliver profits even under high transaction costs. Our study has research significance and application meaning in options algorithmic trading It has a reference value for other financial assets trading and training data augmentation problems. The experimental results showed that the proposed model, with the protective close strategy, can get decent returns, compared to the Buy and Hold strategy in options trading, which indicates that our model learns ways to make profit from underlying asset data and applies it to options trading.
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