Cryptocurrency Trading Agent Using Deep Reinforcement Learning

DOI: 10.1109/ISCMI56532.2022.10068485

ABSTRACT: Cryptocurrencies are peer-to-peer digital assets monitored and organised by a blockchain network. Price has been a significant focus point with various algorithms, especially concerning cryptocurrency. This work addresses the challenge faced by traders of short-term profit maximisation. The study presents a deep reinforcement learning algorithm to trade in cryptocurrency markets, Duelling DQN. The environment has been designed to simulate actual behaviour, observing historical price movements and taking action on real-time prices. The proposed algorithm was tested with Bitcoin, Ethereum, and Litecoin. The respective portfolio returns are used as a metric to measure the algorithm's performance against the -and-hold benchmark, with the buy-and-hold outperforming the results produced by the Duelling DQN agent.

– The paper presents a deep reinforcement learning algorithm for cryptocurrency trading.
– The algorithm's performance was outperformed by the buy-and-hold benchmark.

– The paper presents a deep reinforcement learning algorithm for cryptocurrency trading.
– The algorithm's performance was outperformed by the buy-and-hold benchmark.

– The paper presents a deep reinforcement learning algorithm for cryptocurrency trading.
– The algorithm's performance was outperformed by the buy-and-hold benchmark.

– The proposed algorithm underperformed compared to the buy-and-hold benchmark.
– No other limitations are mentioned in the given information.

Methods used: – The proposed algorithm underperformed compared to the buy-and-hold benchmark.
– No other limitations are mentioned in the given information.

– The proposed algorithm did not outperform the buy-and-hold benchmark.
– Traders may not achieve short-term profit maximization using the Duelling DQN agent.

– Duelling DQN agent underperformed the buy-and-hold benchmark.
– No specific details about the performance metrics provided.

– Deep reinforcement learning algorithm used for cryptocurrency trading.
– Algorithm tested with Bitcoin, Ethereum, and Litecoin, but underperformed buy-and-hold strategy.

– Paper focuses on short-term profit maximization in cryptocurrency trading.
– Presents a deep reinforcement learning algorithm, Duelling DQN, for trading.

In this article , a deep reinforcement learning algorithm to trade in cryptocurrency markets, Duelling DQN, is presented. But the proposed algorithm was tested with Bitcoin, Ethereum, and Litecoin.

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