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.

1. || 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

2. ||
§ 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

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

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

5. || 18.09.2018COSS 5

6. || 18.09.2018COSS 6
Market & blockchains

7. || 18.09.2018COSS 7
News

8. || 18.09.2018COSS 8
Blockchain analysis

9. ||
§ 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

10. ||
§ 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

11. || 18.09.2018COSS 11

12. || 18.09.2018COSS 12

13. || 18.09.2018COSS 13

14. || 18.09.2018COSS 14
Reconstruction

15. || 18.09.2018COSS 15

16. || 18.09.2018COSS 16

17. || 18.09.2018COSS 17

18. || 18.09.2018COSS 18

19. || 18.09.2018COSS 19

20. || 18.09.2018COSS 20

21. || 18.09.2018COSS 21

22. || 18.09.2018COSS 22
Market (orderbook) analysis

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

24. || 18.09.2018COSS 24

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

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

27. || 18.09.2018COSS 27

28. || 18.09.2018COSS 28

29. || 18.09.2018COSS 29
Results

30. || 18.09.2018COSS 30

31. || 18.09.2018COSS 31

32. || 18.09.2018COSS 32
CryptoNews analysis

33. ||
§ 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

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

35. || 18.09.2018COSS 35

36. || 18.09.2018COSS 36

37. || 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

38. ||
§ 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

39. ||
§ 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
References