Automatic Transactional Systems
Informacje ogólne
Kod przedmiotu: | 2400-QFU2TSA |
Kod Erasmus / ISCED: |
14.3
|
Nazwa przedmiotu: | Automatic Transactional Systems |
Jednostka: | Wydział Nauk Ekonomicznych |
Grupy: |
Przedmioty obowiązkowe dla II roku Quantitative Finance |
Punkty ECTS i inne: |
4.00
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Język prowadzenia: | angielski |
Rodzaj przedmiotu: | obowiązkowe |
Skrócony opis: |
The general aim of this course is to help students develop trading strategies using the Python language. By the end of the course, students will: ● Understand the mechanics of quantitative trading in financial markets. ● Recognize the characteristics of financial data. ● Learn methods to evaluate trading strategies. A specific objective is to provide a practical background for preparing advanced quantitative trading strategies. |
Pełny opis: |
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 |
Literatura: |
Harris, L. Trading and Exchanges: Market Microstructure for Practitioners. 2. Johnson, B. Algorithmic Trading and DMA: An Introduction to Direct Access Trading Strategies. 3. Pole, A. Statistical Arbitrage: Algorithmic Trading Insights and Techniques. 4. Chan, E. P. Quantitative Trading: How to Build Your Own Algorithmic Trading Business. 5. Hull, J. C. Options, Futures, and Other Derivatives. 6. López de Prado, M. Advances in Financial Machine Learning. 7. Davey, K. Building Winning Algorithmic Trading Systems. 8. Boatright, J. R. Ethics in Finance. |
Efekty uczenia się: |
Knowledge ● Understands the fundamentals of Python programming. ● Knows how to use Python packages to prepare and analyze data to solve financial problems and build trading strategies. Skills ● Can set up a Python programming environment and install the required packages. ● Can implement investment strategies in Python. Social Competence ● Recognizes that expert use of Python requires continuous practice and skill development. ● Acquires the ability to seek and update knowledge in response to continually evolving Python libraries. (KW01, KW02, KU01, KU02) |
Metody i kryteria oceniania: |
1. Article Review: Unscored, but required to pass. 2. Python Script Submissions: 15 points 3. Final Test: 40 points (students must score ≥ 20 points to pass the course) 4. Project: 45 points 5. Activity: Up to 7 extra points Total Score = (Article Review + Short tests + Final Test + Project + Activity) / 100 Attendance: ● Attendance is mandatory. ● Four or more unjustified absences result in course failure. Class Attendance Registration: ● Students register their attendance by signing the attendance list. ● Mandatory short tests are also submitted at this time. 4. Grading Scale Grade Total Score % Description 5 > 90% Very good 4+ > 80% Better than good 4 > 70% Good 3+ > 60% Satisfactory 3 > 50% Sufficient 2 < 50% Fail (total < 50% or final test < 50% or missed more than 4 classes) |
Zajęcia w cyklu "Semestr letni 2024/25" (zakończony)
Okres: | 2025-02-17 - 2025-06-08 |
Przejdź do planu
PN WT KON
ŚR CZ PT |
Typ zajęć: |
Konwersatorium, 30 godzin
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Koordynatorzy: | Robert Ślepaczuk | |
Prowadzący grup: | Bartosz Bieganowski | |
Lista studentów: | (nie masz dostępu) | |
Zaliczenie: |
Przedmiot -
Zaliczenie na ocenę
Konwersatorium - Zaliczenie na ocenę |
Właścicielem praw autorskich jest Uniwersytet Warszawski, Wydział Nauk Ekonomicznych.