04-800-AF   Advanced Quantitative Financial Analytics and Algorithmic Trading

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 course is designed to build on the principles and concepts covered in the introductory course, Quantitative Financial Analytics and Algorithmic Trading, and take students to the next level in algorithmic trading. The course will focus on more advanced concepts, such as data mining, automated strategy discovery, advanced signal analysis, signal validation, execution mechanisms, meta-strategies, advanced risk management, and machine learning in algorithmic trading.

Students will work on real-world projects, using Python, and gain hands-on experience in applying these concepts to develop and implement their trading strategies while emphasizing universally applicable engineering concepts and data-driven methodologies.

Learning objectives

Students will develop a strong foundation in universally applicable data engineering principles through the lens of algorithmic trading. The objective of this course is to provide students with a comprehensive understanding of the advanced concepts of quantitative financial research and algorithmic trading, and give them hands-on experience with the areas of expertise involved.

Through this course, students will learn about the dangers and caveats of data mining and automated strategy discovery, and will gain an understanding of the different types of data mining used in algorithmic trading. Students will also gain an understanding of different types of optimization and signal analysis methods, and gain hands-on experience in trading strategy validation using different statistical approaches. Trading strategy execution, meta-strategies, and advanced risk management concepts will also be covered. Throughout the course, students will utilize Python programming and various libraries, emphasizing the importance of universally applicable engineering and research principles in creating, testing, and optimizing algorithmic trading strategies, while gaining hands-on experience in addressing real-world challenges.

Outcomes

After completing this course, students should be able to:

• Understand the risks associated with data mining in algorithmic trading,
• Develop and apply their own trading strategy evaluation methods in Python,
• Create and evaluate trading strategies using their own automated or semi-automated research platform,
• Mitigate and optimize execution costs by internalizing and aggregating orders,
• Understand the concept of layered execution and meta-strategies,
• Apply advanced risk management methods, such as turnoff mechanisms, decorrelation techniques and portfolio generation using Mean-Variance Optimization
• Apply machine Learning in signal generation, validation, and execution systems
• Develop and maintain high-quality code and documentation, adhering to best practices in unit testing, code style, exception handling, and user-friendly error handling across different engineering projects.

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)
• Machine Learning in Algorithmic Trading
  • ML-based signal generation
  • ML-based validation systems
  • ML-based execution systems

Prerequisites

Background or hands-on experience in quantitative financial research and algorithmic trading, or successful completion of 04-800-H Quantitative Financial Analytics and Algorithmic Trading, with delivering the requirements specified in a passing repository.

Faculty