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Deep convolutional autoencoder for cryptocurrency market analysis

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  • Vladimir Puzyrev

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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  • Vladimir Puzyrev, 2019. "Deep convolutional autoencoder for cryptocurrency market analysis," Papers 1910.12281, arXiv.org.
    • Handle: RePEc:arx:papers:1910.12281
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    References listed on IDEAS

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    Cited by:

    1. Kumar Yashaswi, 2021. "Deep Reinforcement Learning for Portfolio Optimization using Latent Feature State Space (LFSS) Module," Papers 2102.06233, arXiv.org.

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