
Welcome to the course!
Learn about the various participants in the global trading ecosystem and how they interact with each other.
Understand the different front office roles within an investment bank's global markets division (sales & trading) and how they work together.
Learn the various methods traders employ to manage their trading book's position.
Understand what an order book is and what type of orders traders can submit to the order book.
Acquire the fundamental financial maths theory needed to price fixed income products: interest rates, compounding and discounting.
Understand the purpose of debt markets and what instruments trade within these markets.
Learn how to price a fixed rate bond.
Students will learn the different credit spreads used in industry and their various pros and cons.
Students will learn the fundamentals of pricing interest rate swaps and bond forwards.
Introduction to Python programming basics.
Students will learn the principles of object orientated programming (OOP) in Python - covering notably functions and classes.
Explains key Python packages often used in creating trading algorithms (e.g. pandas, numpy, datetime etc.)
Students will learn core software engineering principles which all software engineers and algorithmic traders must be familiar with.
Learn the history of algorithmic trading: what it replaced, how it started, its market impact and key algorithmic trading principles.
Understand the underlying statistical theory which pairs trading is based upon - covering time series, stationarity, cointegration and ADF tests.
We introduce the general outline which our bond pairs trading algorithm will follow.
An in-depth walk-through the Python trading code.
An in-depth walk-through of the backtester built to test our trading strategy over historical market data.
Analyse the results of our trading algorithm.
There’s more information available about finance than ever before. Yet, there remains little information about the most important growing trend in financial markets - algorithmic trading. This is partly a reflection of the nature of the industry, one shrouded in technical concepts and secrecy being typically the preserve of a select few quants and software engineers with PhDs from illustrious universities. Automated trading is accelerating, the percentage of electronically traded US investment grade corporate bonds reached just under 50% in 2024 compared to 26% in 2018, and there is a need for more people to understand algorithmic trading especially if they wish to work in financial markets. This course offers an introduction into this world, with no prior experience assumed, yet covering key technical details where useful. We cover the four key skills of an algorithmic trader: financial mathematics, (python) programming, statistics and financial product knowledge, culminating in a final case-study walkthrough of how to build a profitable bond pairs trading algorithm in Python (with all the code made available).
Created by a former algorithmic trader and current derivatives trader for a major European investment bank, students will learn exactly what it is that algorithmic traders do and how they can build, backtest and evaluate their own trading algorithms to then successfully implement them in world markets.