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.