Our syllabus

Learn exactly what you need to know to succeed in today’s challenging market

We revise and update our syllabus on a regular basis to cover the fundamentals and keep ahead of the current industry trends

Module 1: Python for Finance

The lingua franca of data science, machine learning, and finance
  • Introduction to Python
  • Data Analysis in Python
  • Analysis of financial data using Python
  • Financial market case studies using Python

Module 2: Databases in finance – Kdb+/q

The unrivaled tool for big and high-frequency data
  • Overview of kdb+/q
  • Foundations of the q programming langguage
  • Working with tables
  • Kdb+/q for big data and machine learning
  • Kdb+/q in practice

Module 3: C++ fundamentals with use cases from finance

High performance, production stability and portability
  • C++ introduction
  • Introduction to OOP in C++
  • Defining your own structures in C++
  • Introduction to the Standard Library

Module 4: Data Structures and Algorithms in C++

The core of computing
  • Analysis Tools, Recursion, and Sorting
  • Arrays, Linked Lists, Stacks, and Queues
  • Trees and Graphs
  • Git and GitHub Trees and Graphs

Module 5: Designing algorithmic trading applications

State-of-the-art content
  • The hardware of electronic trading
  • The networking of electronic trading
  • Low-latency programming and high-frequency trading (HFT)
  • Event-driven architectures
  • The workflow of a trading platform

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