Analyzing Blockchain Data with Deep Learning

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Dr. Gyires-Tóth Bálint Pál
Department of Telecommunications and Media Informatics

Blockchain is a distributed peer to peer network, which allows clients to anonymously and securely transfer digital currencies without the intervention of a centralized authority. Blockchain technology is also called public ledger, because the network’s transactions are public.

Bitcoin is the most valuable digital asset, which is traded on cryptocurrency exchanges. The publicity of the Bitcoin ledger creates an opportunity to combine blockchain data and deep learning algorithms in order to leverage possible new sources of information for automated trading. In this thesis, in the first place I introduce the basic definitions, mathematical formulas and operations of public-key cryptographic methods that facilitate blockchain technology to operate without a central authority and establish so called decentralised trust between anonymous parties. Then I discuss in detail the data structure and the operation of the Bitcoin blockchain. The second half of this thesis represents the process, through which I collected, transformed and analyzed data about Bitcoin, and utilized the effectiveness of deep learning algorithms in order to predict future properties of Bitcoin and its network. At the end of the document I mention a possible use case of a prediction system and some future investigation opportunities that the thesis leaves behind.

Traditional stock market and cryptocurrency trading are mostly based on algorithmic trading in today’s world. The trading algorithms exploit pattern recognition, stickiness to precise trading strategies and rapid information processing in order to beat human traders.


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