⚠️ Please don't forget to provide your full name and an identification document (ID, passport, etc.) in the designated field during registration. It will be mandatory to present this document on the day of the event to gain access to the ILab.
Join us on May 28th for a deep dive into the world of kdb+ and the q programming language, the fastest technologies for handling time-series data in finance. In this session, Miguel and Christian will tell us about how they use these technologies in their work, exploring Insights Enterprise and the Tick Architecture to power real-time analytics and decision-making.
Agenda
18:20 Registration and Welcome!
18:30 Miguel Zaballa Pardo (BBVA) From Exploration to Dashboard: Building an End-to-End Data Pipeline with Insights.
19:15 Christian Aberturas López (Habla Computing) Evolving Kdb Tick Architecture.
20:00 Networking
Abstracts & Speakers
Miguel Zaballa (BBVA) From Exploration to Dashboard: Building an End-to-End Data Pipeline with Insights. In this talk, Miguel will present a model to characterize customer flow value, offering a hands-on walkthrough of the main KDB IE tools. He will demonstrate how to:
- Explore data quickly using the Scratchpad.
- Transition to Visual Studio Code for streamlined development.
- Build an efficient data pipeline from start to finish.
- Visualize and interpret results using KDB Views.
Attendees will come away with a clear end-to-end understanding of how to build robust data pipelines that deliver actionable insights.
Miguel is a Data Scientist in the Advanced Analytics and Algorithmic Trading team at BBVA. Sitting next to the FX desks, he builds large-scale data pipelines for FX trading, develops machine learning solutions, and creates models that inform critical business decisions. Prior to this role, Miguel served in various data scientist positions at both BBVA and Amadeus. He is currently pursuing a PhD in algorithmic trading, where he explores new ways to scientifically enhance market making models.
Christian Aberturas (Habla Computing) Evolving Kdb Tick Architecture. This talk will focus on how to evolve and enhance the KDB Tick architecture by integrating a real-time machine learning module. We will begin with a brief overview of the core components of KDB Tick, providing a foundational understanding for those new to the architecture. This will set the stage for the main focus: improving the system with cutting-edge capabilities. The second part will dive into the implementation of a new module designed to run machine learning models in real time. Specifically, we’ll explore a Pair Trading model, showcasing how to leverage predictive analytics within the KDB Tick framework. By the end of the session, attendees will understand how to extend KDB Tick’s functionalities to drive more sophisticated and dynamic decision-making in financial applications.
Christian is a software developer with a solid background in mathematics, having completed his studies before transitioning into the tech world. For more than two and a half years, he has been an essential part of Habla Computing, where his interest in efficient, problem-solving technologies quickly led him to the kdb+ language. Known for its conciseness and ability to offer elegant solutions, kdb+ became a core focus for Christian, influencing much of his work.
