2019 GDRR: Blockchain Data Analytics - Cryptocurrency and blockchain analysis — Complexity and Data Science perspective - Nino Antulov-Fantulin, October 6, 2019

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The Statistical and Applied Mathematical Sciences Institute

In this talk, we present the research around “Cryptocurrency and blockchain systems”. In particular, we analyse, three different sources of data originating from (i) blockchains, (ii) exchange office, and (iii) news data. In the first part, we study the possibility of inferring early warning indicators for periods of extreme bitcoin price volatility using features obtained from the non-negative decomposition of Bitcoin daily transaction graphs. In the second part, we show the temporal mixture models capable of adaptively exploiting both volatility history and order book features. Our temporal mixture model enables to decipher time-varying effect of order book features on the volatility. In the last part, we focus on cryptocurrency news. In order to track popular news in real-time, we (a) match news from the web with tweets from social media,(b) track their intraday tweet activity and (c) explore different machine learning models for predicting the number of article mentions on Twitter after its publication.

Sciences

|| 06.10.2019 1
Cryptocurrency and blockchain analysis — Complexity and
Data Science perspective
Foundations for Blockchain Data Analytics, SAMSI, Research Triangle Park, NC, USA.
Nino Antulov-Fantulin, ETH Zurich
ETH Zurich, COSS – Computational Social Science

||
§ Senior scientist @ ETH Zurich, Computational Social Science group
§ Modeling dynamics and structure of agents in complex socio-
technical systems
§ EU Projects related to blockchains & cryptomarkets
§ SoBigData project
§ FuturICT 2.0
18.09.2018COSS 2
Intro

||
§ Interactions
§ Heterogenous agents
§ Nonlinearity
§ Feedback loops
§ Emergent behavior
18.09.2018COSS 3
Complex system – Crypto&Blockchain systems

||
§ Exchange office (timeseries)
§ Social media (text)
§ News (text)
§ Blockchain (events)
18.09.2018COSS 4
Observations from different sources and formats

|| 18.09.2018COSS 6
Market & blockchains

||
§ COMPLEX NETWORKS 2018. Studies in Computational Intelligence, vol 813.
Springer, https://doi.org/10.1007/978-3-030-05414-4_41
18.09.2018COSS 9

||
§ Whole transaction history is available in blockchain
§ We are able to analyze the BTC evolution
§ In this study, we have used the period 2014 – 2017
§ We have processed cca 300 GB binary files
18.09.2018COSS 10
Data analyzed

|| 18.09.2018COSS 22
Market (orderbook) analysis

||
§ IEEE International Conference on Data Mining 2018,
https://doi.org/10.1109/ICDM.2018.00123
18.09.2018COSS 23

|| 18.09.2018COSS 25
§ OB features:
§ Spread
§ Ask/bid depth
§ Ask/bid volume
§ Volume difference
§ Weighted spread
§ Ask/bid slope

|| 18.09.2018COSS 26
The joint density of volatility observations is:
Temporal mixture model

|| 18.09.2018COSS 32
CryptoNews analysis

||
§ ACM WWW '19 Companion Proceedings of The 2019 World Wide Web
Conference, Pages 1051-1054, https://doi.org/10.1145/3308560.3316706
18.09.2018COSS 33

|| 18.09.2018COSS 34
Task: predict the importance (number
of mentions) of certain cryptocurrency
news.
Prediction horizon: 24 hours after the
publication time.

|| 18.09.2018COSS 37
New research
*With Qianqian Qiao & Tian Guo
Other collaborations for cryptomarkets: Tian Guo, Qianqian Qiao, Irena Barjasic, Fabrizio Lillo,
Petter Kolm, Zhang Ce, Kevin Primicerio, Claudio Tessone, Data Science lab 2019 ETH

||
§ Disclosure: The opinions expressed in this talk are those of the authors.
§ Some insights for this talk are also coming from my connection (cofounder) with
§ Aisot GmbH
c/o F10 Fintech Incubator & Accelerator Förrlibuckstrasse 10
8005 Zürich
stefank@gmx.ch (CEO)
§ Real-time signals for cryptomarkets (e.g. volatility predictions).
18.09.2018COSS 38

||
§ Beck, Huang, Lindner, Guo, Zhang, Helbing, Antulov-Fantulin, Sensing Social Media Signals
for Cryptocurrency News, ACM WWW '19 Conference, MSM'19 Workshop
§ Nino Antulov-Fantulin, Dijana Tolic, Matija Piskorec, Zhang Ce, Irena Vodenska, Inferring short-
term volatility indicators from Bitcoin blockchain, Complex Networks and Their Applications
VII. COMPLEX NETWORKS 2018. Studies in Computational Intelligence, vol 813. Springer,
https://doi.org/10.1007/978-3-030-05414-4_41
§ Tian Guo, Albert Bifet, and Nino Antulov-Fantulin, Bitcoin Volatility Forecasting with a
Glimpse into Buy and Sell Orders, IEEE International Conference on Data Mining 2018,
https://doi.org/10.1109/ICDM.2018.00123
§ Tian Guo, Tao Lin, Nino Antulov-Fantulin, Exploring Interpretable LSTM Neural Networks over
Multi-Variable Data, ICML 2019
18.09.2018COSS 39
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