Loading...

Algorithmic Trading Using Deep Reinforcement Learning

Majidi, Naseh | 2022

605 Viewed
  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 55226 (05)
  4. University: Sharif University of Technology
  5. Department: Electrical Engineering
  6. Advisor(s): Marvasti, Farohk
  7. Abstract:
  8. Price movement prediction has always been one of the traders’ concerns in the field of financial market prediction. In order to increase the profit of the trades, the traders can process the historical data and predict the movement. The large size of the data and complex relations between them lead us to use algorithmic trading and artificial intelligence.The stock and Cryptocurrency markets are two common markets attracting traders. This thesis aims to offer an approach using Twin-Delayed DDPG (TD3) and daily close price in order to achieve a trading strategy. Unlike the previous studies using a discrete action space reinforcement learning algorithm, TD3 is a continuous one offering both position and bet size. Both stock (Amazon) and cryptocurrency (Bitcoin) markets are addressed in the research to evaluate the performance of the algorithm. The achieved strategy using TD3 is compared with some algorithms using technical analysis, reinforcement learning, stochastic and deterministic strategies through two standard metrics Return and Sharpe ratio. The results indicate that employing both the position and bet size can improve the performance of a trading system.
  9. Keywords:
  10. Algorithmic Trading ; Deep Reinforcement Learning ; Bitcoin ; Stock Market ; Financial Market ; Cryptocurrency ; Continuous Action Space Deep Reinforcement Learning

 Digital Object List