CENG785 ALGORITHMIC TRADING AND QUANTITATIVE STRATEGIES
| Course Code: | 5710785 |
| METU Credit (Theoretical-Laboratory hours/week): | 3 (3.00 - 0.00) |
| ECTS Credit: | 8.0 |
| Department: | Computer Engineering |
| Language of Instruction: | English |
| Level of Study: | Graduate |
| Course Coordinator: | Assist.Prof.Dr SELİM TEMİZER |
| Offered Semester: | Fall Semesters. |
Course Objectives
Successful alumni of this course will have acquired a strong theoretical background and a large set of practical computational skills that are necessary for building up a competitive edge in utilizing modern trading strategies and technologies to capitalize on today’s sophisticated financial markets and trading instruments.
Course Content
Coverage of market structure, trading instruments, processes and trading in general, including necessary background and terminology. Presentation and illustration of electronic trading infrastructure, software tools, networking protocols, data structures and order execution. Theoretical and practical analysis of financial time series data. Building the necessary statistical framework for algorithmic decision making. Detailed coverage of basic and advanced trading strategies including high-frequency techniques and related requirements. Illustration of portfolio management schemes. Informative discussion of global and local markets and associated risks, compliances and regulations.
Course Learning Outcomes
Student that pass the course satisfactorily will be able to:
- Define, describe, illustrate, explain, articulate and elaborate on the fundamental terminology, concepts, principles and functionality of financial instruments, financial markets and market participants.
- Design computer-based algorithms and systems for electronic trading of financial instruments using industry-standard communication protocols.
- Understand the principles of and operate in a sound and solid algorithmic decision making framework.
- Build, analyze, compare and differentiate between computational models of financial engineering and trading by identifying, assessing and reasoning about their advantages and disadvantages.
Program Outcomes Matrix
| Contribution | |||||
| # | Program Outcomes | No | Yes | ||
| 1 | Competence in fundamental and advanced knowledge of hardware and software Proficiency in problem solving. | ✔ | |||
| 2 | The ability to follow the contemporary technical development, and Initiative and aptitude for self-directed learning. | ✔ | |||
| 3 | They are capable of designing, and conducting experiments at advanced level. | ✔ | |||
| 4 | The ability to design and implement systems involving hardware, software, and the interaction between the two through challenging projects. | ✔ | |||
| 5 | Analyze and compare relative merits of alternative software design, algorithmic approaches and computer system organization, with respect to a variety of criteria relevant to the task (e. g. efficiency, scalability, security). | ✔ | |||
| 6 | Strong interpersonal skills needed for working effectively in small, diverse groups on medium to large scale technical projects. | ✔ | |||
| 7 | Strong oral communication skills essential for effectively presenting technical material to an audience and strong written communication skills and the ability to write technical documents that include specification, design, and implementation of a major project. | ✔ | |||