Utilizing RNN for Real-time Cryptocurrency Price Prediction and Trading Strategy Optimization
Fuente:
arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866913575175454720 |
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| author | Tumpa, Shamima Nasrin Maduranga, Kehelwala Dewage Gayan |
| author_facet | Tumpa, Shamima Nasrin Maduranga, Kehelwala Dewage Gayan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_05829 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Utilizing RNN for Real-time Cryptocurrency Price Prediction and Trading Strategy Optimization Tumpa, Shamima Nasrin Maduranga, Kehelwala Dewage Gayan Statistical Finance Artificial Intelligence Machine Learning 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. |
| title | Utilizing RNN for Real-time Cryptocurrency Price Prediction and Trading Strategy Optimization |
| topic | Statistical Finance Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2411.05829 |