An Analytical Study of the Cryptocurrency Trading Robot
Abstract This paper provides an in-depth analysis of a cryptocurrency trading bot designed for the Kraken exchange. The bot employs a combination of reinforcement learning, deep learning, and technical indicators to optimize trading decisions. The system includes a robust reward function, a dual-mode trading mechanism (real and simulated), and advanced risk management techniques. This research aims to evaluate the design, architecture, and performance of the bot, focusing on its decision-making process, reinforcement learning strategy, and reward function effectiveness. Furthermore, we delve into the mathematical framework behind the reward function, explore the deep Q-network (DQN) in depth, and provide quantitative evaluations based on empirical trading results.
