2018 Special lectures on advanced topics in Mathematics Q

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Academic unit or major
Graduate major in Mathematics
Ninomiya Syoiti 
Course component(s)
Mode of instruction
Day/Period(Room No.)
Intensive ()  
Course number
Academic year
Offered quarter
Syllabus updated
Lecture notes updated
Language used
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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


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


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

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