Feature-Rich Long-term Bitcoin Trading Assistant
Jatin Nainani (1), Nirman Taterh (1), Md Ausaf Rashid (1), Ankit, Khivasara (1) ((1) K. J. Somaiya College of Engineering)

TL;DR
This paper presents a feature-rich reinforcement learning-based Bitcoin trading assistant that integrates technical indicators and sentiment analysis to achieve an average profit of 69% over 685 days, including volatile periods.
Contribution
It introduces a novel environment combining technical indicators and sentiment scores for long-term Bitcoin trading using reinforcement learning.
Findings
Achieved an average profit of 69% over 685 days.
Successfully incorporated sentiment analysis into trading decisions.
Provided a user-friendly website with visualizations and recommendations.
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…
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Taxonomy
TopicsBlockchain Technology Applications and Security · Stock Market Forecasting Methods
