QFRG and DSLab seminar: Robust optimisation in algorithmic investment strategies

12 October 2021

We invite you to the 13th seminar of the monthly series of meetings conducted jointly by QFRG (Quantitative Finance Research Group) and DSLab (Data Science Lab). The meeting will be devoted to the topic: Robust optimisation in algorithmic investment strategies, in which Sergio Castellano Gómez (QFRG) and Robert Ślepaczuk (QFRG, WNE UW) will share results of their study on algorithmic investment strategies based on personalized Walk-Forward optimisation.


QFRG is a place where research is conducted and experiences are exchanged between people engaged in examining occurrences in the world of investment from the perspective of both theory and practice, on the verge of science and business.
The activity of DSLab is focused mainly on academic projects devoted to deepening the knowledge of DSLab team, sharing it with other people interested in Data Science issues, and preparing scientific and didactic publications.

The meeting will take place on October 18th, 2021 (Monday) at 3:00 pm online on Google Meet platform. The meeting will be conducted in English. The presentation is scheduled for about 45 minutes, and after that, we invite you to a discussion.

Link to the meeting: meet.google.com/gnr-gfbh-fxc

Please log in at the latest at 2:50 PM. The presentation will start at 3:00 PM.
Joining a meeting implies consent to the recording. Please turn off cameras and microphones during the presentation and send the questions to the speaker in the chat.

Presentation abstract:
This research develops a portfolio of four algorithmic strategies that produce Long/Short signals based on t+1 close price predictions of the underlying instrument. The main instrument used is S&P 500 index, and the data covers the period from 1990-01-01 to 2021-04-23. Each strategy is based on a different theory and aims to perform well in different market regimes. The objective is to have a set of uncorrelated investment strategies based on different logics such as trend-following, contrarian approach, statistical methods, and macro-economic news. Each strategy was individually generated following a personalized Walk-Forward optimisation, in which the model seeks to choose the most robust combination of parameters rather than the best one, in terms of risk-adjusted returns. Finally, the robustness of all strategies was tested by changing all parameters selected at the beginning of the optimisation. Additionally, the robustness of the portfolio of strategies is tested by applying it to another American index, Nasdaq Composite. Results show that the portfolio obtains returns four (eight) times larger than the Buy & Hold strategy on S&P 500 (Nasdaq Composite) with a similar level of risk in the last 31 years.
The next meeting is planned for the middle of November 2021.

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