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Introduction to Systematic Investment Strategies

Seminar

taught by Dr. Sergey Perminov, Konstantin Andreyev

Content

This course is teaching students design and implement systematic investment strategies, which were long restricted to hedge funds and investment banks:

  • learn, design and program a modern systematic investment algo to capture market's inefficiencies,
  • use the secret weapon used by Wall Street to incorporate volumes of data, test strategies, conquer emotional biases, and beat the market,
  • learn to utilize Quantopian platform which provides end-to-end support for algorithmic investing, including algorithm writing, backtesting with a database of 12 years of historical & fundamental data, forward testing with live data, paper trading,
  • learn to evaluate, optimize and pick the best algo based on various backtest characteristics.

 

Using the Quantopian platform:

  • get access to the tools, capabilities and community you need to create and optimize your own trading algorithms in an open and transparent environment,
  • put those algorithms to work in the simulated historical trading.

 

You will be able to engage in a community where people can discuss concepts, processes and performance and learn from peers and experts. The result is a better way to understand design and function of systematic models.

Community: Quantopian is built around a community of more than 130.000 members. The members include finance professionals, scientists, developers, and students from more than 180 countries from around the world. These quants teach, advise, and help each other every day.

Python: Algorithms in Quantopian are written in Python. The Python language is ubiquitous in the tech and academic world today, and is not a proprietary, niche language only used for trading.

Date
weekly
Fridays, 10am-12pm, room 1023 KG I
First meetingFriday, April the 26th, 2019
LanguageEnglish
 
Prerequisite-
 
ECTS4 as a seminar, 6 as a topics course
 
ExamThe course can be taken as a standalone seminar (4 ECTS) or as a topics course (6 ECTS). In the seminar students will learn how to use quantopian platform to analyze investment problems, create algos and backtest them. The participants will move step by step and every week will be given a small task to complete. At each meeting, randomly selected students will present their work, telling fellow students how they approached the problem, what were the challenges and how it was solved. Students are encouraged to ask questions and discuss relevant points and challenges they are facing in order to improve mutual learning. Students will communicate online and offline with the project supervisors (Beneficious Investment Management) and other students on project-related issues.

Students wishing to earn extra credits, can take the course in the form of a topics course. This will require the same steps as the seminar as well as completion of an individual research based on learned techniques in the seminar. Individual research will be about exploring the real world phenomena, creating bespoke investment strategy, backtest and optimizing it.

For a correct identification, please bring your UniCard AND your ID to the exam.

ILIAS

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Credit

The course is open for students of  M.Sc. Economics,  M.Sc. VWL as well as M.Sc. Mathematik.

In M.Sc. Economics, the topics course can be credited in the profile "Finance".

In M.Sc. VWL the course can be credited in Accounting, Finance, and Taxation (new examination regulations, valid from winter term 2014/15 onwards).

Students of M.Sc. Mathematik can credit this course as an elective in economics (wirtschaftswissenschaftliches Wahlpflichtmodul) within the profile "Finanzmathematik.

Application

The number of participants for this seminar is limited. Applications for the seminar in the summer term 2019 can be send until Sunday, April 21st, 2019. Please send your application attached with:

  • Overview of credit points,
  • student ID number,
  • Course of studies/Year of study
     

at our secretariat.

                               

 

 

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