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Feature-Rich Long-term Bitcoin Trading Assistant

Author

Listed:
  • Jatin Nainani

    (K. J. Somaiya College of Engineering)

  • Nirman Taterh

    (K. J. Somaiya College of Engineering)

  • Md Ausaf Rashid

    (K. J. Somaiya College of Engineering)

  • Ankit Khivasara

    (K. J. Somaiya College of Engineering)

Abstract

For a long time predicting, studying and analyzing financial indices has been of major interest for the financial community. Recently, there has been a growing interest in the Deep-Learning community to make use of reinforcement learning which has surpassed many of the previous benchmarks in a lot of fields. Our method provides a feature rich environment for the reinforcement learning agent to work on. The aim is to provide long term profits to the user so, we took into consideration the most reliable technical indicators. We have also developed a custom indicator which would provide better insights of the Bitcoin market to the user. The Bitcoin market follows the emotions and sentiments of the traders, so another element of our trading environment is the overall daily Sentiment Score of the market on Twitter. The agent is tested for a period of 685 days which also included the volatile period of Covid-19. It has been capable of providing reliable recommendations which give an average profit of about 69%. Finally, the agent is also capable of suggesting the optimal actions to the user through a website. Users on the website can also access the visualizations of the indicators to help fortify their decisions.

Suggested Citation

  • Jatin Nainani & Nirman Taterh & Md Ausaf Rashid & Ankit Khivasara, 2022. "Feature-Rich Long-term Bitcoin Trading Assistant," Papers 2209.12664, arXiv.org.
    • Handle: RePEc:arx:papers:2209.12664
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    References listed on IDEAS

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    1. JoonBum Leem & Ha Young Kim, 2020. "Action-specialized expert ensemble trading system with extended discrete action space using deep reinforcement learning," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-39, July.
    2. Pieter de Jong & Sherif Elfayoumy & Oliver Schnusenberg, 2017. "From Returns to Tweets and Back: An Investigation of the Stocks in the Dow Jones Industrial Average," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 18(1), pages 54-64, January.
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