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

Shamima Nasrin Tumpa, Kehelwala Dewage Gayan Maduranga
11/5/2024
q-fin.STcs.AIcs.LG

No official code found. Best implementation: shamima08/Cryptocurrency-Price-Prediction-using-RNN (6 stars, LGPL-2.1, last commit 2025-04-04).

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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Code Implementations(1)

shamima08/Cryptocurrency-Price-Prediction-using-RNNCommunity100%
LGPL-2.1

Recurrent Neural Networks (RNNs) are used for real-time cryptocurrency price prediction, aiming to outperform traditional methods in the volatile crypto market. Here, three DL methods are compared to find out which RNN model gives the best result.

63Jupyter NotebookJun 26, 20241 years agoLGPL-2.1

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Cite this paper

@article{tumpa2024utilizing,
  title  = {Utilizing RNN for Real-time Cryptocurrency Price Prediction and Trading Strategy Optimization},
  author = {Shamima Nasrin Tumpa and Kehelwala Dewage Gayan Maduranga},
  year   = {2024},
  eprint = {2411.05829},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2411.05829v1}
}

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