Fundamentals of Algorithmic Trading

Fundamentals of Algorithmic Trading (ATC)

A High-Calibre Programme – Without the High Price Tag (coming soon)

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 fields.

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 Mid-Frequency Trading System

Self-paced Online:
🕛 Recorded lectures accessible any time.

💳 Cost: $149.00

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.

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 Mid-Frequency Trading System

Case Study Part 1: The code structure for intraday (mid-frequency) 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: an intraday 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.

Modules: ⬇️

Opportunities. An overview of the algorithmic trading industry
Opportunities. Examine the various roles in algorithmic trading.
Framework: An Overview of Algorithmic Trading Workflow, Design and Models.
Framework: An overview of Forecasting Methods and Trade Scaling.
Framework: Allocation and Performance. T
Case Study Part 1: Working with an Algorithmic Trading model
Case Study Part 2 : Working with an Algo Trading Model
Wrap-up & Recap: Algo Industry and Roles, Trading System Structure

Module 1: ⬇️

Opportunities. An overview of the algorithmic trading industry. A look at the major players and the various differing approaches to algorithmic trading used by different sectors in this industry
Opportunities. Examine the various roles in algorithmic trading and the requisite skills and knowledge associated with each position. Additionally, explore avenues for skill development and educational opportunities in this field.

Module 2: ⬇️

Framework: An Overview of Algorithmic Trading Workflow, Design and Models. Covering aspects of Data processing, cleaning, and feature extraction, highlighting the significance of various data sources, alpha sources (including momentum strategies, reversion/cointegration, and others), features and feature engineering.
Framework: An overview of Forecasting Methods and Trade Scaling: This module will provide a quick overview of various time-series forecasting methods from OLS to ARMA to Adaptive Filtering techniques such as RLS and Kalman Filters, touching on Modern ML methods. It also addresses overfitting, model selection and regularisation strategies.

Module 3: ⬇️

Framework: Allocation and Performance. This module outlines the essential components of the trading process, including trade scaling and allocation, execution, and performance measures. It provides a final view of how implmentations will be evaluated.
Case Study Part 1: Working with an Algorithmic Trading model: The code structure for trading of a single asset involving an algorithmic trading model. Crypto Data acquisition for various frequencies, storage, cleaning, feature creation and daily forecasts.

Module 4: ⬇️

Case Study Part 2 : Working with an Algo Trading Model: Examination of alternative forecasting methods, combining models for alpha, risk and impact, into allocation and execution. Measuring performance and evaluating strategies. Directions for improvement.
Wrap-up & Recap: Algo Industry and Roles, Trading System Structure: Data, Features, Forecasts, Allocation, Execution, Performance. Implementing your algorithmic trading strategies. Learning Resources. Further Study and Next Steps.

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.