Flash summer sale ends 31st August, $100 Discount!
A High-Calibre Programme – Without the High Price Tag
This introductory course is designed for professionals working in the quantitative and technology areas of the financial services industry as well as students interested in pursuing algorithmic trading roles. It offers an overview of potential opportunities and the necessary skills needed to succeed in this field.
Participants will develop a broad understanding of the framework necessary for formulating effective trading strategies, including the accompanying approaches, methodologies and processes. The course will introduce fundamental concepts, essential guidelines, rules and common pitfalls through the analysis of a case study, thereby enabling participants to assess its effectiveness in a practical context.
The new 4-module Fundamentals of Algorithmic Trading course is the perfect starting point for anyone looking to break into the world of systematic trading. As the official introductory course for the full ATC Certificate, it provides a clear, structured pathway into key concepts such as market microstructure, trading strategy design, backtesting, and execution. Whether you're a finance professional, developer, or aspiring quant, this course equips you with the foundational skills and practical insights needed to build and understand algorithmic trading systems. Delivered by industry experts and supported by hands-on examples, it's an ideal way to test the waters before committing to the full ATC program offering immediate value and a strong head start in one of finance’s most dynamic field
Duration:
📅 Self-Paced: 4 Modules
Course Structure:
📝 Access to code and learning resources.
Format:
💻 Online
ATC Github, Wiki, Forum and Community Resource:
🧰 The ATC GitHub repo details all code examples with Python, Python notebook and Jupyter notebooks.
Case Study:
📊 A Crypto Trading System
Self-paced Online:
🕛 Recorded lectures accessible any time.
💳 Cost: $149.00 (includes $100 discount - ends 31st August)
What you'll learn
- An overview of the algorithmic trading sector, together with the opportunities and roles typically available in the sector
- An examination of the essential skills required to pursue a role in algorithmic trading, along with options for skill development and learning.
- A framework outlining successful strategies, including their fundamentals, rules and potential pitfalls.
- A Case study: Review the design, methodology, and processes associated with an algorithmic trading strategy, along with an evaluation of its effectiveness.
- An opportunity to gain knowledge and insights from an experienced professional in algorithmic trading.
Skills you'll gain
Fundamentals of Algorithmic trading/introduction to Python programming applied to trading/introduction to statistics and Machine Learning relevant for algorithmic trading.
Community Resource
ATC Github, Wiki, Forum and Community Resource
The ATC GitHub repo details all code examples with Python, Python notebook and Jupyter notebooks. We also offer dedicated discussion forums for live chats with instructors and the ATC community.
An extensive algo trading wiki for references and definitions.
Extensive code is available for a crypto trading pipeline and extendible platform:
- data downloading and storing
- cleaning, filling, and correcting
- expandable feature creation
- forecasting with adaptive models
- allocation and scaling
- performance reporting
- and more
Case Study: A Crypto Trading System
Case Study Part 1: The code structure for a daily and intraday crypto trading of a single asset involving an algorithmic trading model. This entails evaluating the effectiveness of the trading model within a real-world context through an intraday trading process, which includes backtesting with historical data, leveraging technical indicators and incorporating fundamental forecasting methodologies.
Working with an Algorithmic Trading model: Assessing the efficacy of a trading model in a real-world environment: a trading process, backtesting using stored bars, technical features, and fundamental forecasting models.
Case Study Part 2: An examination of alternative and more advanced forecasting methodologies, the challenges of overfitting and execution speed, single-asset allocation strategies, the intricacies of execution including spreads and market impact, as well as a thorough performance analysis and evaluation of strategies along with potential enhancements
Dr. Nick Firoozye is a mathematician and finance professional with over 20 years of experience in research, structuring, and trading
across buy and sell-side firms, including Lehman Brothers, Deutsche Bank, Nomura, Goldman Sachs, and Citadel. He specialises in areas ranging from Quant Strategy, RV Trading, to Asset Allocation. Currently, Nick works at a mid-frequency trading prop firm based in Chicago.
As an Honorary Professor at University College London, Nick developed the Algorithmic Trading Strategies course, which he has taught PhD and MSc students since 2016. He has also created and taught several successful online versions of the class. He has supervised eight PhD students researching machine learning for algorithmic trading and finance, with several now working in AI, systematic trading, and quant research. Over 600 students have successfully completed the Master's and online courses.
Nick co-authored the book Managing Uncertainty, Mitigating Risk, which addresses uncertainty in modelling financial crises. He holds a PhD from the Courant Institute, NYU, with postdoctoral positions at the University of Minnesota, Heriot-Watt University, the University of Bonn, and NYU. Before moving to Wall Street, Nick held a tenure-track Assistant Professorship at the University of Illinois, Urbana-Champaign.