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Introduction to Systematic Investment Strategies

Seminar

gehalten von Dr. Sergey Perminov, Konstantin Andreyev

 

Inhalt                          

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.
 

Termin wöchentlichFreitags, 12-14 Uhr, 1228 KG I
 
Erster Termin25. Oktober 2019
 
SpracheEnglisch
 
ECTS4 als Seminar oder 6 als Topics Course
 
Abschlussklausur

The 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.

Um sich korrekt auszuweisen, bringen Sie bitte zur Klausur Ihre UniCard UND Ihren Personalausweis mit!

Anrechnung

Die Veranstaltung kann von Studierenden der Studiengänge M.Sc. Economics,  M.Sc. VWL sowie M.Sc. Mathematik belegt werden.

Im M.Sc. Economics kann das Seminar in der Profillinie "Finance" angerechnet werden.

Im M.Sc. VWL kann das Seminar im Spezialisierungsbereich Accounting, Finance, and Taxation (PO 2014, gültig ab WS 2014/2015) angerechnet werden.

Master-Studierende der Mathematik des Profils Finanzmathematik können den Kurs als wirtschaftswissenschaftliches Wahlpflichtmodul verbuchen.

Bewerbung

Die Teilnehmeranzahl ist begrenzt. Eine Bewerbung für das Seminar im Wintersemester 2019/20 ist bis zum 20. Oktober 2019 möglich. Bitte bewerben Sie sich hierzu mit den folgenden Informationen per Mail:

  • Kreditpunkteauszug,
  • Matrikelnummer,
  • Studiengang/Studienjahr
     

an Sekretariat.

 

 

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