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Stream/Area:
-
Subject:
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Paper ID:
IJSREM65590
Volume:
10 |
6 |
Jun 2026
Pages:
1-10
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Computer Science and Engineering
Suresh Gyan Vihar University, India
Department of Electrical Engineering,
Suresh Gyan Vihar University, India
Department of Electronics and Communication Engineering,
Suresh Gyan Vihar University, India
Department of EXTC,
Bharti Vidyapeeth College of Engineering
The rapid growth and high volatility of cryptocurrency markets have created a strong demand for intelligent and automated trading systems capable of making accurate and adaptive decisions. This paper presents the design and performance evaluation of an intelligent cryptocurrency trading bot based on advanced machine learning techniques. The proposed system integrates multiple machine learning and deep learning models to predict short-term price movements and market trends, enabling data-driven trading decisions under diverse market conditions. A hybrid deep learning architecture combining convolutional and recurrent neural networks is employed to capture both spatial feature representations and temporal dependencies in cryptocurrency price data. The trading bot is evaluated using extensive historical backtesting across bull, bear, and sideways market regimes. Performance is assessed using standard predictive metrics, financial indicators, and computational efficiency measures, including accuracy, F1-score, cumulative return, Sharpe ratio, drawdown, and inference latency. Experimental results demonstrate that the proposed trading bot consistently outperforms traditional technical indicator–based strategies and baseline machine learning models, achieving higher profitability, improved risk-adjusted returns, and enhanced stability under volatile market conditions. The findings confirm the effectiveness of advanced machine learning–driven automation for real-time cryptocurrency trading and highlight the proposed system’s suitability for practical deployment in dynamic financial environments.
KEYWORDS
This paper presented the design and performance evaluation of an intelligent automated cryptocurrency trading bot using advanced machine learning techniques. By integrating predictive modeling, automated decision-making, and risk-aware trading logic, the proposed system addresses key challenges associated with cryptocurrency market volatility and complexity. Experimental results demonstrate that the proposed trading bot achieves superior predictive accuracy, improved profitability, and enhanced risk-adjusted returns compared to conventional strategies and baseline models. Furthermore, the system exhibits robust performance across diverse market conditions while maintaining computational efficiency suitable for real-time trading. The findings highlight the effectiveness of advanced machine learning approaches in developing reliable and adaptive cryptocurrency trading systems. Future work may explore reinforcement learning-based strategy optimization, multi-asset portfolio management, and deployment in live trading environments.
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Publication Date
6/28/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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