Course description

Algorithmic trading serves as a practical application of software engineering and data science methodologies and quantitative analysis techniques within the context of financial markets. This project-based course offers an introduction to algorithmic trading and the principles behind it, while emphasizing universally applicable engineering concepts and data-driven methodologies.

Students will gain an understanding of the fundamentals of financial markets and trading systems, learn how to manage data, generate signals, backtest strategies, and use APIs to execute trades. Additionally, they will apply risk management principles, position sizing, and software development best practices such as unit testing in Python. Most importantly, the course will teach students specific thinking patterns and data science methodologies that can be applied across various engineering and data analysis fields. Students will be equipped with a toolbox needed to continue researching trading strategies, predictive analytics, or other data science-related topics independently.

Following condensed lecture videos, the course will emulate a professional environment through a series of individual assignments culminating in a functional project. Delivery of the project will be guided by direct instruction, Q&A calls, and an online chat group with the lecturers, similar to a real workplace. Students will deliver a functional project in Python, according to a specification, while also taking exams on the theoretical materials covered in the lectures.

Student progress is assessed through the delivery of practical projects according to a specification and evaluation criteria. While there are no prerequisites for this course, an understanding of statistics, probabilities, hypothesis testing, measures of spread, confidence intervals, and related topics is assumed.

Content details

• Advanced data mining & automated strategy discovery
  • Evidence-based technical analysis in detail
    • Universe size and its consequences
    • Parameter-based searches
    • Rule-based searches
    • Genetic optimization based searches
• Advanced optimization 
  • Adaptive optimization
  • Multi-objective optimization
  • Constrained optimization
  • Black box optimization
  • Grid-based optimization
  • Parameter sensitivity analysis
• Advanced signal analysis
  • MAE, MFE, Edge-related metrics
  • Stop losses, profit targets
  • Signal value identification
  • Exit optimization vs. value identification
• Signal validation
  • Statistical tests
  • Walk-forward optimization
  • Robustness testing, noise testing
  • Variance testing
  • Cross-market simulations
  • Monte Carlo simulations
  • Other methods
• Execution mechanisms
  • Mitigating execution costs
  • Spread analysis & prediction
  • Order aggregation, internalization, A & B books
• Meta-strategies
  • Execution optimization
  • Low latency operations & HFT Layering
  • Advanced turnoff mechanisms
• Advanced risk management
  • Kelly Criterion
  • Advanced decorrelation (Pearson based)
  • Advanced portfolio generation (Mean-Variance optimization vs. Black-Litterman)

Prerequisites

04-800-H Quantitative Financial Analytics and Algorithmic Trading
Interested students who have not taken the prerequisite can still register in the course; they will be assessed before being fully registered.

Faculty

Patrick McSharry and Ben Racz