Crypto Trading Using AI

Abstract

The rapid growth of crypto currency markets has led to increased interest in automated trading systems that can adapt to their high volatility and unpredictability. Traditional trading methods often rely on manual decision-making or static rule-based algorithms, which can be inefficient in reacting to real-time market fluctuations. This paper proposes an intelligent crypto trading system utilizing Artificial Intelligence (AI) techniques to enhance prediction accuracy and optimize decision-making in cryptocurrency trading. By leveraging machine learning models and real-time data analysis, the system aims to forecast price movements, perform risk assessments, and execute trades dynamically based on predefined objectives. The AI model is trained on historical crypto datasets, incorporating technical indicators and sentiment analysis to improve the robustness of predictions. Experimental results demonstrate improved performance over conventional strategies in terms of profit maximization and risk minimization. This research emphasizes the potential of AI-driven trading system store volutionize financial markets, particularly in decentralized and highly volatile domains like cryptocurrency. With its growing influence, cryptocurrency is increasingly playing an essential role in the reformation of the financial system popular appeal and merchant acceptance. While many people are making investments in Cryptocurrency, the dynamical features, uncertainty, and the predictability of Cryptocurrency are still mostly unknown, which dramatically risk the investments. It is a matter to try and understand the factors that influence the value. In the present work, advanced artificial intelligence frameworks of fully connected Artificial Neural Network-ANN and Long Short-Term Memory-LSTM to analyse the price dynamics of Bitcoin, Ethereum etc. We observe that ANN is more inclined to depend on longer-term history, whereas LSTM relies more on the short-term dynamics, that illustrate the efficiency of LSTM to make use of useful information hidden in historical memory is stronger than ANN. However, given enough historical information, ANN can achieve a similar accuracy compared with LSTM. This study provides a unique demonstrate that the Cryptocurrency market price is predictable. However, the explanation of the predictability might vary due to the nature of the machine-learning model involved.