Algorithmic Trading Certificate (ATC): A Practitioner’s Guide - Self-Paced

Algorithmic Trading Certificate (ATC): A Practitioner’s Guide - Self-Paced

About the programme 

Throughout our unique programme, we provide a strong foundation in the tools and techniques used in algorithmic trading. Studying at your own pace you'll learn the basic programming concepts, moving through advanced trading strategies and discovering methods for research into new alpha sources. Applying everything in hands-on projects throughout the course. Unlike the other courses, the Algorithmic Trading: Practitioners Guide course takes a hands-on approach to building trading pipelines, from data to features to modelling to allocation to execution to performance measurement, guiding the student through common practice as well as areas of innovation.

Level up your career: Understanding advanced trading strategies, Impact of Machine Learning and methods for research into new alpha sources. The ATC is a career-enhancing professional certificate, that can be taken worldwide.

Self-paced online learning

  • Progress through the course independently at your own pace.
  • Enjoy maximum flexibility to fit your own schedule, with no set deadlines to follow.
  • Access the real-world final project when you are ready to implement the knowledge and skills you have acquired during the course of the programme.
  • Once purchased you will receive instant access to the whole ATC.

FAQ: What is the difference between the live and self-paced courses?

The self-paced ATC offers instant access to the full cohort, and you take the course independently in your own time. The live course is taken weekly with structured lectures arriving on Monday and a follow up webinar the week after, the live course has a supporting faculty forum. Both scenarios have the same final projects and on completion you will receive the ATC certificate.

Goals of Class 

  • Provide a strong foundation in the tools and techniques used in algorithmic trading.
  • Cover everything from basic programming concepts to advanced trading strategies and
    methods for research into new alpha sources.
  • Apply everything in hands-on projects throughout the course

Duration:
📅 Self-Paced: 22+ Lecture Hours

Format:
💻 Recorded Lectures Online. Instant access to all ATC lectures and supporting material.

Evaluation:
✔ Real-World Final Project + Certificate

📆 Time Commitment:
Recorded lectures accessible any time. Take the ATC at your own pace. Up to 100 hours to complete.

 Self-paced Online:
Students will have the opportunity to apply what they learn in hands-on projects throughout the course.

📊 Certificate:
“Students are awarded the prestigious ATC Certificate from WBS Training.”

💳 Cost: £1695.00


Testimonials: ⬇️

"The comprehensive coverage of various topics provided me with a solid understanding of the intricacies of algorithmic trading. Now I have a clear direction on where to begin and how to approach each strategy effectively. I now feel equipped with the knowledge and confidence to navigate the complexities of algorithmic trading with ease. I highly recommend this training to anyone looking to dive into the world of algorithmic trading.”

Dr Abdallah Rahal

”ATC covers a vast panel of topics structured around building trading strategies and leveraging modern infrastructures. From time series theory to risk management, the authors of the course tried to give a wide overview on the field, and provided the students with valuable references/ books/ resources. I have enjoyed this course and would recommend it.”

Imen al Samarai

“I had been looking for an online course on Algorithmic Trading by a reputable organization for years but none had the in-depth syllabus I was looking for. When last year ATC was announced by WBS, I did not hesitate to sign up. Professors Nick and Brian have extensive experience on the subject and combine theoretical classes and practical sessions with valuable insights of the current industry trends. The course is well structured and paced, covering all aspects needed to build a profitable trading strategy. A well-curated  list of bibliography and paper references is provided during the course (although one could not probably be able to digest all material in a lifetime). The live sessions give students a unique chance to discuss the material and ideas with professors weekly. I strongly recommend this course if you want a thorough introduction to the field of algorithmic trading.”

Victor Montiel Argaiz


Who should enroll

Discretionary Traders / Risk Managers – Understand the mechanics of the market and develop the tools to devise and manage new and improved algorithmic strategies of different types including multi-asset strategies. Learn the importance of allocation frameworks, execution models and performance testing. Recognise pros and cons of various approaches to designing strategies and the common pitfalls encountered by algorithmic traders.

Algorithmic Traders / Quants – Appreciate when commonly-used strategies work and when they don’t. Understand the statistical properties of strategies and discern the mathematically proven from the empirical. Expand your technology toolkit to incorporate the latest techniques including open-source tools and models from other areas of the quant industry.

Academics / Students / Data Scientists – Gain familiarity with the broad area of algorithmic trading strategies. Master the underlying theory and mechanics behind the most common strategies. Acquire a solid understanding of the principals and context necessary for new academic research into the large number of open questions in the area.

Final Project 

The ATC concludes with a practical final project that gives you the opportunity to implement the knowledge and skills you have acquired during the course of the programme. Self Paced students get an option three times per year to join the live cohort (when ready) to discuss the Practical Applications and Final Real World Project with the faculty. 

The purpose of this project is to enable students to practically apply the techniques and concepts learned throughout the course to a real-world financial use case. The objective is to create and backtest and justify a trading strategy. You can use any market you wish. You should explain clearly all steps in the model building process. 

Marking will be based on:

  • Clarity of presentation and explanations
  • Justification of the methodology
  • Validity of results
  • Consistency of language and mathematical notation
  • Critical interpretation of results.

Assessments

One written assessment at the end (PDF + Python Notebook), describing a strategy in detail: its behaviour, its rationale (with quoted references if applicable), implementation and performance and limitations and room for improvements. Marks for sensibility of coverage and exposition, for following the methodology, etc. (i.e., good performance only is not sufficient – you have to display it and explain it).


Interested in learning as part of the live cohort?

You can also study the ATC certificate as part of a live cohort, accessing the 12-week schedule with fixed start dates starting three times per year. Our structured schedule gives you a clear timeline to learn alongside other algorithmic traders.

The next cohort starts on 3 March, 2025

Modules: ⬇️
  • Module 1: Intro and Industry Overview
  • Module 2: Data and Features
  • Module 3: Statistics and Time Series
  • Module 4: Machine Learning
  • Module 5: Trend Following
  • Module 6: Carry and Volatility Strategies
  • Module 7: Mean Reversion
  • Module 8: Forecasting Models and Factor Investing
  • Module 9: Order Execution and Market Making
  • Module 10: Portfolio Theory and Allocation
  • Module 11: Backtesting and Performance
  • Module 12: Risk Management


Module 1: Intro and Industry Overview - 1 hour ⬇️

•    Video 1: Introduction
•    Video 2: Quant Finance in the Financial Services Sector
•    Video 3: Tracking Quant Performance
•    Video 4: The Quant Finance Landscape
•    Introduction & The Industry - Slides


Module 2: Data and Features - 1 hour 15 minutes ⬇️ 

•    Video 1: Data Sources
•    Video 2: Features
•    Video 3: Signals - Overview
•    Data & Features - Slides

 

 


Module 3: Statistics and Time Series - 3 hours ⬇️ 

•    Video 1: Introduction to Statistics
•    Video 2: Motivation - Asset Prices
•    Video 3: Types of Distribution
•    Video 4: Maximum Likelihood Estimation
•    Video 5: Multivariate Distributions
•    Video 6: Statistical Inference
•    Video 7: What Have We Learned?
•    Video 8: Introduction to Time Series
•    Video 9: Time Series
•    Video 10: General Framework
•    Video 11: Autoregressive Policies
•    Video 12: Moving Average Processes
•    Video 13: Identifying p & q
•    Video 14: ARMA(p, q) Process
•    Video 15: Maximum Likelihood Estimation
•    Video 16: What Have We Learned
•    Statistics and Time Series - Slides

 


Module 4: Machine Learning - 2 hours ⬇️

•    Video 1: Introduction to Machine Learning
•    Video 2: Introduction to Classification
•    Video 3: Regression
•    Video 4: Support Vector Machines
•    Video 5: Kernels
•    Video 6: Decision Trees
•    Video 7: Random Forests
•    Video 8: Neural Networks
•    Video 9: Reinforcement Learning
•    Machine Learning - Slides


Module 5: Trend Following – 2 hours ⬇️

•    Video 1: Trading Strategies
•    Video 2: Trend Following
•    Video 3: Momentum and Skewness
•    Video 4: Momentum and Responsiveness
•    Video 5: Cross-Sectional Momentum
•    Video 6: Other Topics In Momentum
•    Video 7: Trading Futures
•    Video 8: An Exercise
•    Video 9: References
•    Trend Following - Slides

 


Module 6: Carry and Volatility – 2 hours ⬇️

•    Video 1: Foreign Exchange
•    Video 2: The Carry Trade
•    Video 3: Physical and Risk-Neutral Measures
•    Video 4: Margin
•    Video 5: Volatility Strategies
•    Carry & Volatility - Slides


Module 7: Mean Reversion – 2 hours 15 minutes ⬇️

•    Video 1: Mean Reversion
•    Video 2: Cointegration
•    Video 3: Implementing Mean Reverting Strategies
•    Video 4: Pairs Trading
•    Video 5: Statistical Arbitrage
•    Video 6: Factor Models and PCA
•    Video 7: Mean Reversion As Liquidity Provision
•    Video 8: Change Point Detection and Regime Switching - Part 1
•    Video 9: Change Point Detection and Regime Switching - Part 2
•    Mean Reversion - Slides


Module 8: Forecasting Models and Factor Investing – 1 hour 45 minutes: ⬇️

•    Video 1: Intro
•    Video 2: Signals
•    Video 3: Factor Trading Part 1 - CAPM
•    Video 4: Factor Trading Part 2 - APT
•    Video 5: Factor Trading Part 3 - Factor Portfolios
•    Video 6: Factor Trading Part 4 - MHT
•    Video 7: Combining Signals/Forecasting
•    Video 8: Regularization
•    Video 9: Dimension Reduction
•    Video 10: Adaptive Models
•    Video 11: WHWL
•    Forecasting Models and Factor Investing - Slides


Module 9: Order Execution and Market Making – 2 hours 15 minutes ⬇️ 

•    Video 0: Intro
•    Video 1: Market Microstructure
•    Video 2: Market Structure
•    Video 3: Price Formation and Price Discovery
•    Video 4: Liquidity
•    Video 5: Algorithmic Trading
•    Video 6: Order Types
•    Video 7: Market Impact
•    Video 8: Minimising Market Impact
•    Video 9: Market Making
•    Video 10: Order Book Dynamics
•    Video 11: Markov Decision Process
•    Order Execution and Market Making - Slides

 


Module 10: Portfolio Theory and Allocation – 1 hour 30 minutes ⬇️

•    Video 0: Introduction
•    Video 1: Asset Pricing Models
•    Video 2: Portfolio Theory
•    Video 3: Two Asset Portfolios
•    Video 4: N Asset Portfolios
•    Video 5: Adding Transaction Costs
•    Video 6: Quadratic Problems
•    Video 7: Tactical Asset Allocation
•    Video 8: Optimal Scaling for Strategies
•    Portfolio Theory and Allocation - Slides


Module 11: Back-testing and Performance – 1 hour 45 minutes ⬇️

•    0: Introduction
•    1: Performance Indicators
•    2: Illustrating Drawdowns
•    3: Python for Analysis
•    4: Performance Indicator Comparisons
•    5: A Realistic Backtest
•    6: Optimizing Parameters
•    7: Multi-Objective Optimization
•    8: What Have We Learned?
•    Performance Repot Class - Python
•    Backtesting and Performance Measurement - Slides

 


Module 12: Risk Management – 1 hour 45 minutes ⬇️

•    Video 1: Risk Management
•    Video 2: Linear Market-Risk
•    Video 3: Non-Linear Market-Risk
•    Video 4: The Impact of Time
•    Video 5: Operational Risk
•    Video 6: Optimal Scaling for Strategies
•    Video 7: Value at Risk and Related Approaches
•    Video 8: Factor Models
•    Risk Management and Portfolio Theory - Slides


Real-World Final Project: ⬇️

  • ATC Final Assignment
  • Final project submission

Final Project

The Self-Paced Algorithmic Trading Certificate (ATC): A Practitioner’s Guide concludes with a practical final project that gives you the opportunity to implement the knowledge and skills you have acquired during the course of the programme. Self-Paced students get an option three times per year to join the live cohort (when ready) to discuss the final project with the faculty. 
 

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