Deep Reinforcement Learning for Trading
This work addresses the problem of automated trading for investors and traders, but it is incremental as it applies existing DRL methods to a specific financial domain.
The authors tackled the problem of designing trading strategies for continuous futures contracts using Deep Reinforcement Learning, and demonstrated that their method outperforms classical time series momentum strategies, delivering positive profits despite heavy transaction costs.
We adopt Deep Reinforcement Learning algorithms to design trading strategies for continuous futures contracts. Both discrete and continuous action spaces are considered and volatility scaling is incorporated to create reward functions which scale trade positions based on market volatility. We test our algorithms on the 50 most liquid futures contracts from 2011 to 2019, and investigate how performance varies across different asset classes including commodities, equity indices, fixed income and FX markets. We compare our algorithms against classical time series momentum strategies, and show that our method outperforms such baseline models, delivering positive profits despite heavy transaction costs. The experiments show that the proposed algorithms can follow large market trends without changing positions and can also scale down, or hold, through consolidation periods.