Description

Introduction

This training provides a comprehensive foundation in kdb+ and the q programming language, purpose-built for high-performance time-series and financial data analytics. Developed by KX, kdb+ is widely adopted across global investment banks, hedge funds, exchanges, and fintech firms for handling massive volumes of tick, trade, and market data with ultra-low latency.

Prerequisites

1. Basic understanding of financial markets (trades, quotes, order books, OHLC, etc.)
2. Familiarity with databases (SQL knowledge is helpful but not mandatory)
3. Basic programming knowledge (Python, R, Java, or similar)
4. Understanding of time-series data concepts (recommended but not required)

Table of Contents

Module 1: Overview of kdb+ & q

1. Introduction to kdb+ Ecosystem
2. Why kdb+ in Financial Services?
3. Architecture of kdb+ (In-Memory & On-Disk)
4. Overview of q Language
5. Installing and Setting Up kdb+

Module 2: Fundamentals of q Programming

1. Data Types in q
2. Lists, Dictionaries, and Tables
3. Functional Programming Concepts in q
4. Writing and Executing q Scripts
5. Querying Data with q-SQL

Module 3: Working with Financial Time-Series Data

1. Time-Series Data Structures
2. Tick Data vs Bar Data
3. Creating and Managing Partitioned Databases
4. Handling Timestamps and Temporal Data
5. Aggregations and Windowed Calculations

Module 4: Querying and Analytics in q

1. Select, Update, Delete Queries
2. Grouped Aggregations
3. As-Of Joins (aj) for Market Data
4. Window Functions for Trading Analytics
5. VWAP, TWAP, and OHLC Calculations

Module 5: Real-Time Data Processing

1. Streaming Data Concepts
2. Publish-Subscribe Model in kdb+
3. Real-Time Tick Capture
4. Building a Simple Real-Time Analytics Engine
5. Intraday Monitoring Dashboards

Module 6: Performance Optimization

1. Memory Management in kdb+
2. Columnar Storage Benefits
3. Indexing and Partitioning Strategies
4. Query Optimization Techniques
5. Scaling kdb+ for Enterprise Environments

Module 7: Financial Use Cases & Mini Project

1. Trade & Quote Analytics
2. Order Book Analysis
3. Risk & Exposure Monitoring
4. Market Surveillance Concepts
5. Mini Project: Build a Historical Tick Analytics Engine