CryptoAnalytics: Cryptocoins Price Forecasting with Machine Learning Techniques

TL;DR

CryptoAnalytics is a Python toolkit that uses machine learning models like GBMs and RNNs to forecast cryptocoin prices, helping users analyze volatile digital asset markets efficiently.

Contribution

The paper presents a comprehensive software toolkit integrating multiple ML models for cryptocoin price forecasting with data fetching and inference capabilities.

Findings

Effective modeling of cryptocoin price volatility using ML techniques

Toolkit supports real-time data fetching and prediction

Demonstrates practical application of RNNs and GBMs in finance

Abstract

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.