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.
"For anyone looking to translate rigorous quantitative ideas into deployable trading frameworks without needing a full-time coding boot camp, this certificate delivers an exceptionally well-structured, academically grounded path to practical alpha.”
Including live real-world final project with ATC faculty
- 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.
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
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.
Duration: (Syllabus)
📅 16 weeks: 32+ Lecture Hours
Format:
💻 Recorded Lectures Online. Instant access to all ATC lectures and supporting material.
Evaluation:
✔ Real-World Final Hands-on 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
Who should enrol:
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 Algorithmic Trading Certificate (ATC): A Practitioner’s Guide culminates in a comprehensive hands-on final project designed to consolidate and apply the practical skills developed throughout the course. This capstone project challenges candidates to design, implement, and backtest a fully functional algorithmic trading strategy using real-world market data and industry-standard tools. Participants demonstrate proficiency in coding, strategy development, performance evaluation, and risk management. The project not only showcases technical and analytical capabilities but also prepares candidates for real-world trading desk environments by emphasizing robustness, scalability, and regulatory awareness.
Self-Paced students get to apply and join the next live cohort to discuss the final 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).
Fundamentals of Algorithmic Trading (now included)
📝 Four weeks entirely online/self-paced weekly modules, approximately two hours per week. Access to code and learning resources.
Course Modules & Case Studies
Week 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.
Week 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.
Week 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.
Week 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.
- Case Study: A Mid-Frequency Trading System