Developing a Data-Driven Decision-Making Model for Algorithmic Trading Strategies (Cryptocurrency Market)
Keywords:
Algorithmic trading, Banking industry, Machine learning, Cryptocurrency market, Price predictionAbstract
The rapid growth of the cryptocurrency market in recent years has created both lucrative opportunities and unprecedented risks for investors. High volatility, the decentralized nature of exchanges, and the massive volume of trading data have challenged human decision-making, highlighting the necessity of employing intelligent and automated systems. In this study, a data-driven model is proposed for decision-making and the implementation of algorithmic trading strategies in the cryptocurrency market. The proposed framework integrates historical price data analysis, technical indicators, and market microstructure data with machine learning methods to forecast price trends and generate buy and sell signals. Subsequently, optimization algorithms are employed to adjust trading parameters in a way that maximizes returns while minimizing risk and transaction costs. Experimental results based on real data from selected cryptocurrencies (such as Bitcoin and Ethereum) demonstrate that the proposed model outperforms traditional strategies in terms of risk-adjusted returns and result stability. Finally, the article provides recommendations for the practical implementation of this framework in intelligent trading systems and suggests future research directions.
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Copyright (c) 2026 Alireza Gharibshahian; Mohammad Reza Dehghani (Author)
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