CryptoAnalytics: Cryptocoins Price Forecasting with Machine Learning Techniques
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912017313431552 |
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| author | De Rosa, Pasquale Felber, Pascal Schiavoni, Valerio |
| author_facet | De Rosa, Pasquale Felber, Pascal Schiavoni, Valerio |
| contents | This paper introduces CryptoAnalytics, a software toolkit for cryptocoins price forecasting with machine learning (ML) techniques. Cryptocoins are tradable digital assets exchanged for specific trading prices. While history has shown the extreme volatility of such trading prices, the ability to efficiently model and forecast the time series resulting from the exchange price volatility remains an open research challenge. Good results can been achieved with state-of-the-art ML techniques, including Gradient-Boosting Machines (GBMs) and Recurrent Neural Networks (RNNs). CryptoAnalytics is a software toolkit to easily train these models and make inference on up-to-date cryptocoin trading price data, with facilities to fetch datasets from one of the main leading aggregator websites, i.e., CoinMarketCap, train models and infer the future trends. This software is implemented in Python. It relies on PyTorch for the implementation of RNNs (LSTM and GRU), while for GBMs, it leverages on XgBoost, LightGBM and CatBoost. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_04106 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CryptoAnalytics: Cryptocoins Price Forecasting with Machine Learning Techniques De Rosa, Pasquale Felber, Pascal Schiavoni, Valerio Computational Engineering, Finance, and Science This paper introduces CryptoAnalytics, a software toolkit for cryptocoins price forecasting with machine learning (ML) techniques. Cryptocoins are tradable digital assets exchanged for specific trading prices. While history has shown the extreme volatility of such trading prices, the ability to efficiently model and forecast the time series resulting from the exchange price volatility remains an open research challenge. Good results can been achieved with state-of-the-art ML techniques, including Gradient-Boosting Machines (GBMs) and Recurrent Neural Networks (RNNs). CryptoAnalytics is a software toolkit to easily train these models and make inference on up-to-date cryptocoin trading price data, with facilities to fetch datasets from one of the main leading aggregator websites, i.e., CoinMarketCap, train models and infer the future trends. This software is implemented in Python. It relies on PyTorch for the implementation of RNNs (LSTM and GRU), while for GBMs, it leverages on XgBoost, LightGBM and CatBoost. |
| title | CryptoAnalytics: Cryptocoins Price Forecasting with Machine Learning Techniques |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2409.04106 |