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A multi-layer machine learning approach for cryptocurrency trading utilizing technical indicators and sentiment index

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This study demonstrates a machine learning framework that enhances cryptocurrency prediction using technical indicators and sentiment analysis.

Complex models like DQN and LSTM improve prediction accuracy in volatile cryptocurrency markets.
The research identifies 37 key features including the Fear and Greed Index and technical indicators for effective modeling.
A six-stage framework is proposed, integrating various machine learning techniques for enhanced computational efficiency.
The comparative analysis offers guidance for investors, highlighting which models work best under different market conditions.

Vahidpour et al. (2025) studied this question.

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This study demonstrates a machine learning framework that enhances cryptocurrency prediction using technical indicators and sentiment analysis.