Automatic Transactional Systems 2400-QFU2TSA

Lecture 1: Introduction to Continuous Double Auction Microstructure &
Python Assessment
Topics Covered
● Overview of financial markets and trading mechanisms
● Continuous Double Auction (CDA) microstructure
● Order types: market, limit, stop orders
● Order book dynamics and price formation
● Liquidity, depth, and market impact
● Role of market microstructure in trading strategy development
Activities
● Python proficiency test to assess readiness
● Discussion on how microstructure influences trading opportunities
Recommended Reading
1. Harris, L. Trading and Exchanges: Market Microstructure for Practitioners. (Chapters
1-3)
Lecture 2: Market Making Strategies with Constant Midprice
Topics Covered
● Fundamentals of market making and liquidity provision
● Implementing constant midprice strategies
● Setting symmetric bid and ask quotes
● Managing inventory risk
● Analysis of bid-ask spreads and profit calculation
Activities
● Coding a basic market-making algorithm in Python
● Simulating market scenarios with a constant midprice
Recommended Reading
2. Johnson, B. Algorithmic Trading and DMA: An Introduction to Direct Access Trading
Strategies. (Chapters 4-5)
Lecture 3: Dynamic Market Making with Variable Midprice
Topics Covered
● Adapting to changing market conditions and price volatility
● Strategies for updating quotes based on market signals
● Incorporating order flow information
● Adjusting for volatility and spread dynamics
● Advanced inventory and risk management techniques
Activities
● Enhancing the market-making algorithm to adjust to variable midprices
● Testing strategies on simulated data
Recommended Reading
2. Johnson, B. Algorithmic Trading and DMA: An Introduction to Direct Access Trading
Strategies. (Chapters 6-7)
Lecture 4: Pairs Trading and Statistical Arbitrage
Topics Covered
● Introduction to statistical arbitrage and mean-reversion
● Identifying cointegrated asset pairs
● Statistical tests for cointegration
● Spread computation and z-score analysis
● Execution of pairs trading strategies
Activities
● Data analysis to find suitable trading pairs
● Implementing a pairs trading algorithm in Python
Recommended Reading
3. Pole, A. Statistical Arbitrage: Algorithmic Trading Insights and Techniques. (Chapters 2-4)
Lecture 5: Arbitrage Opportunities in Financial Markets
Topics Covered
● Different forms of arbitrage: pure, risk, and statistical
● Spotting mispricings in related assets
● Execution challenges and transaction costs
Activities
● Case studies on historical arbitrage opportunities
● Simulating arbitrage strategies under different market conditions
Recommended Reading
4. Chan, E. P. Quantitative Trading: How to Build Your Own Algorithmic Trading Business.
(Chapters 5-6)
Lecture 6: Option Arbitrage Strategies
Topics Covered
● Fundamentals of options pricing
● Black-Scholes model and its assumptions
● The Greeks and their significance
● Identifying arbitrage opportunities in options markets
● Put-call parity violations
● Volatility arbitrage techniques
Activities
● Coding option pricing models in Python
● Developing algorithms for option arbitrage
Recommended Reading
5. Hull, J. C. Options, Futures, and Other Derivatives. (Chapters 9-11)
Lecture 7: Advanced Algorithmic Trading Techniques
Topics Covered
● Integrating multiple strategies for robust performance
● Optimization of algorithm parameters
● Introduction to machine learning in trading
● Supervised vs. unsupervised learning
● Overfitting and model validation
Activities
● Parameter tuning using cross-validation
● Exploring simple ML models for predictive analytics
Recommended Reading
6. López de Prado, M. Advances in Financial Machine Learning. (Chapters 1-3)
Lecture 8: Preparing for the Trading Competition
Topics Covered
● Overview of the simulated trading environment
● Competition rules and evaluation metrics
● Profitability, Sharpe ratio, drawdowns
● Best practices for developing robust trading algorithms
Activities
● Finalizing trading strategies
● Stress-testing algorithms under simulated conditions
Recommended Reading
7. Davey, K. Building Winning Algorithmic Trading Systems. (Chapters 7-8)
Lecture 9: Trading Competition Kick-Off
Activities
● Submission of trading scripts
● Live simulation begins, algorithms trade against each other
● Real-time monitoring and logging of performance
Objectives
● Apply theoretical knowledge in a practical setting
● Experience the dynamics of a competitive trading environment
Lecture 10: Results Analysis and Student Presentations
Activities
● Students present their trading strategies and results
● Explanation of the algorithm’s logic
● Performance metrics and outcome analysis
● Challenges faced and solutions implemented
● Peer feedback and discussion
Objectives
● Reflect on practical experiences
● Learn from diverse approaches and perspectives
Lecture 11: Advanced Topics in Trading Strategies
Topics Covered
● In-depth machine learning strategies
● Neural networks and deep learning applications
● Support vector machines for classification tasks
● Alternative data sources and their utilization
Activities
● Exploratory coding of ML models
● Discussion on future trends in algorithmic trading
Recommended Reading
6. López de Prado, M. Advances in Financial Machine Learning. (Chapters 5-7)
Lecture 12: Ethical and Regulatory Considerations
Topics Covered
● Regulatory environment for algorithmic trading (MiFID II, SEC rules, etc.)
● Ethical implications of automated trading
● Market manipulation concerns
● Fairness and transparency
Activities
● Case studies on regulatory actions
● Debate on ethical dilemmas in high-frequency trading
Recommended Reading
8. Boatright, J. R. Ethics in Finance. (Chapters 5-7)
Lecture 13: Course Review and Future Directions
Activities
● Summarizing key learnings
● Feedback session on content and structure
● Guidance on pursuing careers in algorithmic trading
Objectives
● Consolidate knowledge acquired
● Provide resources for continued learning