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

Shamima Nasrin Tumpa and Kehelwala Dewage Gayan Maduranga

Papers from arXiv.org

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.

Date: 2024-11
New Economics Papers: this item is included in nep-big, nep-cmp, nep-for, nep-inv and nep-pay
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