Machine Learning and Cryptocurrency Markets

Invited talk in Special Session on Advances in Machine Learning for Finance

Nino Antulov-Fantulin, PhD

ETH Zurich, Switzerland

Abstract

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 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.

Short bio

Nino Antulov-Fantulin is a senior researcher at ETH Zurich, COSS group and visiting research associate at Courant Institute of Mathematical Sciences, NYU. He works at the interface of complexity and data science. His main interests include dynamical processes on networks, predictive analytics for FinTech (cryptocurrency & blockchain markets), machine learning, social network analysis and Monte-Carlo algorithms. He is a co-founder and head of research at Aisot GmbH Zurich, which delivers real-time (crypto)financial predictive analytics signals. Prior to ETH Zurich, he worked at the Rudjer Boskovic Institute and Faculty of Electrical Engineering and Computing, Croatia and he was a visiting scientist at Robert Koch Institute, Berlin. He also works as Supervisor & Panel member of PhD Program in Data Science, Scuola Normale Superiore, Pisa. He worked on several EU projects: SoBigData — “Social Mining & Big Data Ecosystems”, Multiplex — “Foundational Research on MULTI-level comPLEX networks and systems”, FOC — “Forecasting Financial Crisis” and eLico — “An e-Laboratory for Interdisciplinary Collaborative Research in Data Mining and Data Intensive Science”.

Advancing optical communication and measurement systems using machine learning

Darko Zibar, PhD

DTU Fotonik, Technical University of Denmark, Denmark

Abstract

According to the recent data traffic predictions, current optical communication systems, operating in the C–band only, will not be able to satisfy future data rate demands. A viable and long–term solution would be to employ systems operating in multiple bands (O+E+S+C+L) and make usage of the spatial division multiplexing (SDM) (multi–core and multi–mode). Designing optimal signaling, amplification and detection schemes, for such systems, will be challenging due to the high system complexity. Finally, performing system optimization in terms of channel power and bandwidth allocation, as well as modulation format selection, will become difficult using standard tools that rely on analytical or semi–analytical models. What will complicate the matter even further is the focus on providing a secure way of transmitting information using quantum communication. This will require a coexistence and management of classical and quantum channels in the same optical network. As quantum signals have in general significantly, lower powers compared to the signals in classical communication, the reception of quantum signals is more challenging, making a strong case for having intelligent optical receivers that can receive and even distinguish between classical and quantum signals. The field of machine learning (ML) can provide useful tools to address the aforementioned challenges. This is because ML techniques excel at: 1) learning highly–complex input–output mappings which allows for system optimization, 2) learning signaling and detection schemes for complex channels or for channels where analytical models are not available and 3) performing ultra–sensitive signal detection. In this talk, it will be shown how machine learning can enable design of ultrawide-band optical amplifiers, perform constellation shaping over the nonlinear fibre optic channel and enable ultra-sensitive measurements of optical phase that approach the quantum limit.

Short bio

Darko Zibar is Associate Professor at the Department of Photonics Engineering, Technical University of Denmark and the group leader of Machine Learning in Photonics Systems (M-LiPS) group. He received M.Sc. degree in telecommunication and the Ph.D. degree in optical communications from the Technical University of Denmark, in 2004 and 2007, respectively. He has been on several occasions (2006, 2008 and 2019) visiting researcher with the Optoelectronic Research Group led by Prof. John E. Bowers at the University of California, Santa Barbara, (UCSB). At UCSB, he has been working on topics ranging from analog and digital demodulation techniques for microwave photonic links, coherent detection to machine learning enabled ultra-sensitive laser phase noise measurements techniques. In 2009, he was a visiting researcher with Nokia-Siemens Networks, working on clock recovery techniques for 112 Gb/s polarization multiplexed optical communication systems. In 2018, he was visiting Professor with Optical Communication (Prof. Andrea Carena, OptCom) group, Dipartimento di Elettronica e Telecomunicazioni, Politecnico di Torino working on the topic of machine learning based Raman amplifier design. His resrearch efforts are currently focused on the application of machine learning technqiues to advance classical and quantum optical communication and measurement systems. Some of his major scientific contributions include: record-capacity hybrid optical-wireless link (2011), record-sensitive optical phase noise measurement technique that approaches the quantum limit (2019) and demonstration of first ultra-wide band programmable gain Raman amplifier (2019). He is a recipient of Best Student paper award at Microwave Photonics Conference (2006), Villum Young Investigator Programme (2012), Young Researcher Award by University of Erlangen-Nurnberg (2016) and European Research Council (ERC) Consolidator Grant (2017). Finally, he was a part of the team that won the HORIZON 2020 prize for breaking the optical transmission barriers (2016).