Deep convolutional autoencoder for cryptocurrency market analysis
Vladimir Puzyrev
TL;DR
This paper uses a deep convolutional autoencoder to analyze and classify cryptocurrency market patterns over time, revealing insights into market maturity and potential trading strategies.
Contribution
It introduces a novel application of convolutional autoencoders for classifying cryptocurrency market states across multiple periods.
Findings
Cryptocurrencies are classified into distinct market behavior classes.
Transitions between classes correlate with cryptocurrency maturity.
The method offers potential for improved trading strategies.
Abstract
This study attempts to analyze patterns in cryptocurrency markets using a special type of deep neural networks, namely a convolutional autoencoder. The method extracts the dominant features of market behavior and classifies the 40 studied cryptocurrencies into several classes for twelve 6-month periods starting from 15th May 2013. Transitions from one class to another with time are related to the maturement of cryptocurrencies. In speculative cryptocurrency markets, these findings have potential implications for investment and trading strategies.
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Taxonomy
TopicsBlockchain Technology Applications and Security · Complex Systems and Time Series Analysis · Stock Market Forecasting Methods