Description

Introduction

High-Frequency Trading (HFT) systems demand ultra-low latency, high-throughput data processing, and real-time analytics. kdb+, powered by the q language, is a leading time-series database widely adopted across global financial institutions for building high-performance trading, surveillance, and analytics platforms.

This training provides a deep, hands-on understanding of designing, developing, and optimizing HFT systems using kdb+. Participants will learn how to process tick data, manage order books, build real-time analytics pipelines, and architect scalable low-latency trading infrastructures.

The program combines theory, architecture design, and practical lab sessions to equip professionals with the skills required to build production-grade HFT systems.

Prerequisites

1. Basic understanding of financial markets (equities, derivatives, FX, etc.)
2. Familiarity with trading concepts (order types, market data, order books)
3. Basic programming knowledge (any language)
4. Prior exposure to q/kdb+ (recommended but not mandatory)
5. Understanding of Linux/Unix environments
6. Basic knowledge of networking concepts (TCP/IP, multicast – beneficial)

Table of Contents

Module 1: HFT Fundamentals & Market Microstructure

1. Overview of High-Frequency Trading
2. Market Microstructure Concepts
3. Latency, Throughput & Determinism
4. Exchange Connectivity & Market Data Feeds
5. Co-location & Infrastructure Considerations

Module 2: kdb+ Architecture for Trading Systems

1. kdb+ Core Architecture
2. Real-Time Database (RDB) vs Historical Database (HDB)
3. Tick Architecture (Tickerplant, Feed Handlers)
4. Intraday Write & End-of-Day Processing
5. Scaling kdb+ for Trading Environments

Module 3: Market Data Handling & Tick Processing

1. Parsing and Normalizing Tick Data
2. Trade & Quote Data Structures
3. Order Book Reconstruction
4. Handling Out-of-Order & Late Data
5. Data Validation & Integrity Checks

Module 4: Building Low-Latency Analytics in q

1. Vectorized Computations in q
2. Real-Time Aggregations
3. Windowed Analytics
4. VWAP, TWAP, Slippage Calculations
5. Event-Driven Calculations

Module 5: Designing HFT Data Pipelines

1. Feed Handlers & Multicast Handling
2. Tickerplant Design
3. Publishing & Subscribing Models
4. Fault Tolerance & Recovery Strategies
5. Replay Mechanisms

Module 6: Strategy Development & Backtesting

1. Historical Data Replay
2. Signal Generation
3. Feature Engineering for HFT
4. Intraday Backtesting Framework
5. Performance Metrics & Risk Measures

Module 7: Performance Optimization

1. Memory Management in kdb+
2. CPU Optimization & Parallelism
3. Partitioning & Splayed Tables
4. Query Optimization Techniques
5. Benchmarking & Latency Testing

Module 8: Production Deployment & Monitoring

1. Deployment Architectures
2. High Availability Setup
3. Monitoring & Logging
4. Capacity Planning
5. Disaster Recovery

Module 9: Risk, Compliance & Surveillance

1. Real-Time Risk Checks
2. Market Abuse Detection
3. Audit Trail Management
4. Regulatory Reporting Considerations

Module 10: Capstone Project – Building an HFT Framework

1. End-to-End System Design
2. Live Market Data Simulation
3. Order Book & Analytics Integration
4. Performance Testing
5. Final Presentation & Review