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- Academic unit or major
- Graduate major in Mathematics
- Instructor(s)
- Ninomiya Syoiti
- Class Format
- Lecture
- Media-enhanced courses
- Day/Period(Room No.)
- Intensive ()
- Group
- -
- Course number
- MTH.E440
- Credits
- 2
- Academic year
- 2018
- Offered quarter
- 2Q
- Syllabus updated
- 2018/6/26
- Lecture notes updated
- -
- Language used
- Japanese
- Access Index
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Course description and aims
Algorithmic trading is now widely utilized in the worldwide financial market. This lecture is about that. I will narrate the microstructure of investments and trading from rather a practitioner's point of view, and then examine the mathematical techniques used there one by one.
Students are expected to learn what sort of mathematical techniques are used through examples about alpha identification, strategy building and system integration in the real financial markets.
Student learning outcomes
・Understand algorithmic trading in a practical way
・Understand mathematical techniques used in alpha integration and strategy building
・Understand techniques required when building automatic trading systems
Keywords
algorithmic trading, high-frequency trading, alpha
Competencies that will be developed
| ✔ Specialist skills | Intercultural skills | Communication skills | Critical thinking skills | Practical and/or problem-solving skills |
Class flow
This is a standard lecture course. There will be some assignments.
Course schedule/Required learning
| Course schedule | Required learning | |
|---|---|---|
| Class 1 | ・Market micro structure | Details will be provided during each class |
| Class 2 | ・Mathematics of strategies | Details will be provided during each class |
| Class 3 | ・Mathematics of alpha | Details will be provided during each class |
| Class 4 | ・Mathematics behind some financial events | Details will be provided during each class |
| Class 5 | ・ Related topics | Details will be provided during each class |
Textbook(s)
Adachi, T., "Algorithmic Trading", Asakura, ISBN978-4-254-27584-1 (in Japanese)
Assessment criteria and methods
Assignments (100%).
Related courses
- MTH.C361 ： Probability Theory
- MTH.C507 ： Advanced topics in Analysis G1
- MTH.C508 ： Advanced topics in Analysis H1
Prerequisites (i.e., required knowledge, skills, courses, etc.)
None in particular
