Tony Lam
The first half of the course focuses on financial data analysis and strategy implementation. The second half of the course discusses practical techniques to manage and deploy algorithmic trading strategies in real financial world.
| Course Learning Outcomes | Relevant Programme Learning Outcome |
| CLO1. A solid understanding of the nuances of algorithmic trading, design and implement algorithmic trading strategies | |
| CLO2. The ability to apply quantitative methods to analyze financial data and build financial models | |
| CLO3. The ability to formulate trading strategies, carry out backtesting, optimization, risk management and interpret investment performance | |
| CLO4. The ability to apply various investment theories and trading techniques to the real financial market | |
| CLO5. Familiar with the current trends, and understand the limitations and challenges in the field | |
| CLO6. Complete a capstone project that includes a full cycle of trading strategy development | |
| View Programme Learning Outcomes | |
- Python programming
- Statistics and probability theory
- Mathematics and optimization theory
| Course Content | No. of Hours | Course Learning Outcomes |
| Algorithmic trading basics and financial markets | ||
| Data scraping and database management with Python | ||
| Building backtest framework and rule-based trading strategy | ||
| Statistical time series analysis for market classification | ||
| Statistical arbitrage and pairs trading | ||
| Capital and Risk Management | ||
| Performance measures and portfolio optimization | ||
| Order book and high frequency data modeling | ||
| Market practice in broker selection and account connection | ||
| Machine learning use cases in algorithmic trading | ||
| Description | Type | Weighting * |
Tentative Assessment Period / Examination Period ^ |
Course Learning Outcomes |
| Written assignment and project | Continuous Assessment | 50% | - | |
| Written examination covering all the taught contents in the course | Written Examination | 50% | - | |
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* The weighting of coursework and examination marks is subject to approval ^ The exact examination date uses to be released when all enrolments are confirmed after add/drop period by the Examinations Office. Students are obliged to follow the examination schedule. Students should NOT enrol in the course if they are not certain that they will be in Hong Kong during the examination period. Absent from examination may result in failure in the course. There is no supplementary examination for all MSc curriculums in the Faculty of Engineering. |
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| Date | Time | Venue | Remark | |
| Session 1 | 5 Feb 2025 (Wed) | 7:00pm - 10:00pm | LE-5 | |
| Session 2 | 12 Feb 2025 (Wed) | 7:00pm - 10:00pm | LE-5 | |
| Session 3 | 19 Feb 2025 (Wed) | 7:00pm - 10:00pm | LE-5 | |
| Session 4 | 26 Feb 2025 (Wed) | 7:00pm - 10:00pm | LE-5 | |
| Session 5 | 5 Mar 2025 (Wed) | 7:00pm - 10:00pm | LE-5 | |
| Session 6 | 19 Mar 2025 (Wed) | 7:00pm - 10:00pm | LE-5 | |
| Session 7 | 26 Mar 2025 (Wed) | 7:00pm - 10:00pm | LE-5 | |
| Session 8 | 2 Apr 2025 (Wed) | 7:00pm - 10:00pm | LE-5 | |
| Session 9 | 9 Apr 2025 (Wed) | 7:00pm - 10:00pm | LE-5 | |
| Session 10 | 16 Apr 2025 (Wed) | 7:00pm - 10:00pm | LE-5 | |
| LE - Library Extension Building | ||||
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HKU Moodle:
https://moodle.hku.hk/course/view.php?id=123732
(Login using your HKU Portal UID and PIN)
- Please note that the professor maintains and controls when to release the Moodle teaching website to students.
- Enrolled students should visit the Moodle teaching website regularly for latest announcements, course materials, assignment submission, discussion forum, etc.