Utilizing RNN for Real-time Cryptocurrency Price Prediction and Trading Strategy Optimization

Shamima Nasrin Tumpa; Kehelwala Dewage Gayan Maduranga

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arXiv · 2411.05829

Utilizing RNN for Real-time Cryptocurrency Price Prediction and Trading Strategy Optimization

Shamima Nasrin Tumpa·Kehelwala Dewage Gayan Maduranga

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Abstract

This study explores the use of Recurrent Neural Networks (RNN) for real-time cryptocurrency price prediction and optimized trading strategies. Given the high volatility of the cryptocurrency market, traditional forecasting models often fall short. By leveraging RNNs' capability to capture long-term patterns in time-series data, this research aims to improve accuracy in price prediction and develop effective trading strategies. The project follows a structured approach involving data collection, preprocessing, and model refinement, followed by rigorous backtesting for profitability and risk assessment. This work contributes to both the academic and practical fields by providing a robust predictive model and optimized trading strategies that address the challenges of cryptocurrency trading.

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q-fin.ST cs.AI cs.LG stat.ML

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BibTeX RIS

Shamima Nasrin Tumpa, Kehelwala Dewage Gayan Maduranga. 2024-11-05. Utilizing RNN for Real-time Cryptocurrency Price Prediction and Trading Strategy Optimization. https://arxiv.org/abs/2411.05829

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