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Instructor
Nick Firoozye, Instructor - Fundamentals of Algorithmic Trading

Nick Firoozye

Dr. Nick Firoozye is a data scientist & statistician with over 20 years of experience in the finance industry, in both buy and sell-side firms, largely in research. He started his career in Lehman Brothers doing MBS/ABS modeling, heading teams in portfolio strategy and EM quant research, later taking a variety of senior roles at Goldman Sachs, and DeutscheBank, and at the asset managers, Sanford Bernstein, and Citadel. He is currently Managing Director and Head of Global Derivative Strategy, part of the Quantitative Strategy Group, at Nomura. He is currently an Honorary Senior Lecturer in Computer Science at University College London, focusing on Robust Machine Learning in finance. He recently co-authored a book, entitled Managing Uncertainty, Mitigating Risk, about the role of uncertainty and imprecise probability in finance, in light of the many recent financial crises, and he is writing a book on Algorithmic Trading Strategies based on his recent Ph.D. course on the same topic offered at UCL and current online course at Experfy.
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Instructor: Nick Firoozye

Develop the foundational skills and practical insights needed to build and understand algorithmic trading systems.

This course gives you 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.

Course Description

The Course
This introductory course, The Fundamentals of Algorithmic Trading,  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 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 (see bio for links), 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.

About the Instructor

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. 

Linktree for more online presence, including blog and courses. 

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What am I going to get from this course?

At the end of the course, students 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 
  • The analysis of a case study
  • enabling participants to assess its effectiveness in a practical context.
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 include
  • Fundamentals of Algorithmic Trading
  • A run-through of Python programming /Pandas applied to trading
  • Introduction to Statistics and Machine Learning relevant for algorithmic trading.
  • Basic Backtesting framework
  • Crypto basic algo trading structure
  • The framework for building further trading strategies. 

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

The 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 Fundamentals and AlgoTradingCert community. (Contact Instructor/Admins for Access)
 
An extensive algo trading wiki for references and definitions.
 
Extensive code is available for a crypto trading pipeline and an extendible platform:
  • data downloading and storing
  • cleaning, filling, and correcting
  • expandable feature creation
  • ⁠forecasting with adaptive models
  • allocation and scaling
  • performance reporting
  • and more
     

Prerequisites and Target Audience

What will students need to know or do before starting this course?

Required  - Basic familiarity with
  • Statistics / Regressions
  • Some intro Linear Algebra
  • Some basic Python (just to understand the repo)
For the required familiarity with Stats and Linear Algebra, we expect nothing more than what students would learn in A-levels Maths and Further Maths (in the UK, in 6th Form, i.e., final 2 years of Senior School), or Intro to Probability/Stats and Intro to Linear Algebra in the US (either 1st or 2nd year University Courses, in many STEM areas).  
 

Who should take this course? Who should not?

This course is meant for people with STEM backgrounds, either in school, considering going into Quantitative Mathematics (i.e., Financial Mathematics, Computational Finance, or Financial Engineering, etc) and pursuing a career in finance, or those who have completed a master's in the area and want to know more about the Algorithmic Trading sector. It is also for those in the finance industry who want to know the fundamentals of algorithmic trading as an area. 
 

Curriculum

Module 1: Module 1 - Overview of the Algo Trading Industry

Lecture 1 Module1/1. Opportunities - Introduction to the Course

Module 1/ 1  Introduction to the Course - Description of the Course Content and Format (3:43)

Please Note that Lecture Notes are in Module 9, together with reference materials and instructions on access to github repos.

Week 1: ⬇️

  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
  2. Opportunities: Roles. 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.



Lecture 2 Module 1/2. Opportunities. An overview of the industry & skills required

 Module 1/2 - a Look at the major players and their differing approaches to Algo Trading (14:00)

  •  Structure of Financial Industry
    • Asset Owners
    • Asset Managers,etc
  •  Hedge Funds and Prop Shops
  •  Algo Industry Players
  •  Classification by Speed


Week  1: ⬇️

  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
  2. Opportunities: Roles. 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.

Lecture 3 Module 1/3. Opportunities: Roles in the Algo Trading Industry

Module 1/Part 3 - The roles in Algo Trading, and the skills required. 

Week 1: ⬇️

  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
  2. Opportunities: Roles. 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: Module.2: Roles in the Algo Trading Industry

Lecture 4 Module 2: Opportunities: Different Roles in Algo Trading

The different roles in the Algo Trading Industry (16:16)

Week 1: ⬇️

  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
  2. Opportunities: Roles. 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 3: Module3: Framework: An Overview of Algorithmic Trading Workflow, Design and Models.

Lecture 5 Module 3: An Overview of the Trading Workflow, Design and Modules

Module  3/1 An Overview of the Trading Workflow, Design and Modules (6:05)


Week 2: ⬇️ 

3. 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.

4. 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.



Lecture 6 Module 3/2 An Overview of the Trading Workflow, Design and Modules - Data and Data Sources

Module 3/2 An Overview of the Trading Workflow, Design and Modules - Data  and Data Sources (8:22)


Week 2: ⬇️ 

3. 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.

4. 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.

Lecture 7 Module 3/3 An Overview of the Trading Workflow, Design and Modules - Data Processing and Cleaning

Module  3/3 An Overview of the Trading Workflow, Design and Modules - Data Processing and Cleaning (12:55)
* Scraping
* Raw Storage
* Cleaning and Transformation 
    - Outliers 
    - Imputation
    - Transformation
* Storage

Module 2: ⬇️ 

3. 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.

4. 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.

Lecture 8 Module 3/4 An Overview of the Trading Workflow, Design and Modules - Features or Alphas

Module 3/4  An Overview of the Trading Workflow, Design and Modules  - Features or Alphas (21:08)
* Standard Features
* Different Types of Features and their characteristics
* Feature Storage/Feature Managers
Recap of Section 3

Module 2: ⬇️ 

3. 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.

4. 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 4: Module 4: Framework: Forecasting and Trade Scaling

Lecture 9 4. Framework: An overview of Forecasting Methods and Trade Scaling

Module 2 / Section 4 An Overview of Forecasting Methods and Trade Scaling (40:20)

A (long) lecture on Forecasting Methods. 

Week 2: ⬇️ 

3. 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.

4. 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 5: Framework - Allocation and Performance Measurement

Lecture 10 Module 5: Framework - Allocation and Performance

5: Framework - Allocation  and Scaling (15:54)


5 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 implementations will be evaluated.
6 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.

Lecture 11 Module 5: Framework - Performance

Performance (16:31)

Module 6: Module 6 - Case Study Part 1

Lecture 12 Module6: Case Study Part 1 Structure of the System

Structure of the Algo Trading System (5:49)

Lecture 13 Module 6: Case Study Part 1: Downloading Data, Storing Data

Downloading and Storing Data (23:54)

Lecture 14 Module 6: Case Study Part 1: Cleaning the Data

Cleaning the Data (16:54)

Lecture 15 Module 6: Case Study Part 1: Exploring Good vs Not so good Features

Good vs Not so Good Features (4:47)

Module 7: Module 7- Case Study Part 2

Lecture 16 Module 7: Case Study Part 2 - Setting up Costs and Allocation

Setting up Costs and Allocation (7:15)

7. 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.
8. 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.

Lecture 17 Module 7, Case Study Part 2 - RLS/Adaptive Filters and Forecasting and Allocation

RLS, Forecast Evaluation, etc. (18:22)

Module 8: Module 8 - Recap

Lecture 18 Recap - What have we learned?

Module 8 Wrapup and Recap (9:11)

Recap: What have we learned?

Day 4: ⬇️

7. 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.
8. 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.

Module 9: Module 9 Resources - Ref Materials and Lecture Notes

Lecture 19 Description of Repo, Updates and Other external Assets

NOTE: All access to the repo is via GitHub. Users must supply their GitHub username to the instructor or Experfy for access.
Private Repo on Fundamentals of Algorithmic Trading: https://github.com/ATC-WBSTraining/FundamentalsAlgoTrading
Public Repo on Crypto Causality: https://github.com/firoozye/crypto_causality