The Premier Event for Quantitative Investment Thought Leaders
Alexander Unterrainer
KDB Enthusiast DefconQAlexander Unterrainer is a seasoned KDB/Q developer with nearly a decade of experience in the financial industry. He currently works as a consultant for Data Intellect, supporting a leading buy-side client. Alexander is also the founder of DefconQ, a dedicated KDB tech blog focused on knowledge sharing and community growth. Over the years, he has worked with some of the world’s top investment banks, developing deep expertise in time-series data, market data systems, and quantitative finance. His passion for the KDB ecosystem and quant tech is reflected in his active involvement in the community, for which he was recognized with a KXpert Award by KX.
Day 1: 14 October
11:45 AM Data to Deployment – End-to-End Coding for Quant Analysts
This hands-on workshop guides quant analysts through the complete workflow of utilizing KDB and q, the high-performance time-series database and programming language widely adopted in financial institutions. Participants will learn the fundamentals of KDB and q for handling real-time market data at scale, build and test end-to-end workflows for analysis, signal generation, and execution, and gain practical experience in writing, optimizing, and deploying production-ready code in a quant environment.
Session Objectives:
- Understand the core concepts and syntax of KDB and q for efficient data handling.
- Develop and test complete workflows for quantitative analysis and trading strategies.
- Acquire practical skills in optimizing and deploying code suitable for production environments in finance.
Session Breakdown (90 Minutes):
1. Introduction to KDB and q (15 minutes)
- Overview of KDB and q: architecture, features, and advantages in financial data processing.
- Discussion on the role of KDB/q in high-frequency trading and real-time analytics.
2. Hands-On Coding: Data Ingestion and Processing (25 minutes)
- Demonstration of ingesting real-time market data into KDB.
- Writing q scripts for data parsing, cleaning, and transformation.
- Best practices for handling large-scale time-series data.
3. Workflow Development: Analysis to Execution (25 minutes)
- Building analytical models and signal generation using q.
- Integrating analysis with execution systems.
- Testing and validating workflows in a simulated environment.
4. Optimization and Deployment (20 minutes)
- Techniques for optimizing q code for performance and scalability.
- Deploying workflows into production environments.
- Monitoring and maintaining deployed applications.
5. Q&A and Discussion (5 minutes)
- Open floor for participant questions
- Discussion on advanced topics and real-world applications.
Key Takeaways:
- Proficiency in using KDB and q for real-time data processing and analysis.
- Ability to develop end-to-end workflows from data ingestion to execution.
- Knowledge of best practices for optimizing and deploying quant applications in production settings.
Check out the incredible speaker line-up to see who will be joining Alexander.
Download The Latest Agenda
