Syllabus
- 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.
Fundamentals Course Modules & Case Studies. The Fundamentals of Algorithmic Trading is the pre-recorded primer for the ATC Suite.
Primer 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.
Primer 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.
Primer 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.
Primer 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.
Case Study: A Mid-Frequency Trading System
- 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.
Modules: ⬇️
- Fundamentals of Algorithmic Trading (Fundamentals of Algorithmic trading/introduction to Python programming applied to trading/introduction to statistics and Machine Learning relevant for algorithmic trading).
- 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 – ⬇️
- 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 – ⬇️
- Video 1: Data Sources
• Video 2: Features
• Video 3: Signals – Overview
• Data & Features – Slides
Module 3: Statistics and Time Series – ⬇️
- 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 – ⬇️
- 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 – ⬇️
- 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 – ⬇️
- 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 – ⬇️
- 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 – ⬇️
- 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 – ⬇️
- 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 – ⬇️
- 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 – ⬇️
- 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 – ⬇️
- 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 Hands-on Project with Faculty – ⬇️
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.
Duration:
📅 16 Modules: 32+ Lecture Hours
Faculty:
💡 Dedicated Faculty Support available every step of the way. Weekly seminar & student forum.
Two weekly Live webinars:
- ATC Early Office Hours – 12.00 UK Time
- ATC Late Office Hours – 19.00 UK Time
Evaluation:
✔ Real-World Final Project + Certificate
The ATC 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.
📆 8 – 10 Hours Weekly: Time Commitment
Weekly lectures accessible any time, from your educational portal.
🕛 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.”
Assessment
- 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).
Summary
- Key takeaways
- Designing your own strategies
- Doing active research
- Sourcing and cleaning data
- Algorithmic Trading Certificate (ATC): A Practitioner’s Guide
- Keeping tech stack up-to-date
- Maintenance and Improvement
- Next steps
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.
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