Agent Based Modeling for CryptoFinance in Splunk [Splunk Enterprise, Splunk Machine Learning Toolkit]

Cryptocurrency ecosystems are highly complex, distributed, and rapidly evolving, rendering many existing financial models ineffective. By aggregating the heterogeneous data streams that are produced by distinct groups within crypto (blockchains, mining pools, exchanges, etc.), we have built a unified analytical platform called Nakamoto Terminal (NTerminal) using Splunk. By leveraging NTerminal, we are creating an adapted agent-based modeling (ABM) system; agents monitor the state of the ecosystem by consuming real time updates from the individual data sources that modulate their state and connectivity. Different heuristic models are called upon to facilitate data transformations and agent interactions. Within this ecosystem, collective agent activity reveals emergent properties and patterns of behavior. With Splunk as the centerpiece, integrated reports, dashboards, or searches allow you to better navigate the ecosystem of interest.

Speaker(s)

Nick Gans, Research and Development Lead, Inca Digital Securities

Zach Finzi, Research & Software Director, Inca Digital Securities

Slides PDF link - https://conf.splunk.com/files/2019/slides/FN1408.pdf?podcast=1577146256