An investment strategy is basically a list of exact rules (algorithm), which defines investment decisions (buy and sell orders), can analyze and manage real-time risks based on current market situations. Investment decisions can be divided into two groups – entry rules for opening investment positions and exit rules for closing investment positions. Our research department uses a method which takes these two rule categories and applies different development attitude on each of them.

Standard old-fashioned approach

Standard old-fashioned development approach is based on using financial experts’ rules what depends on experience, knowledge and personal attitude. These entry and exit rules are usually very subjective and may lack a deeper analysis of various datasets (news, fundamentals, technical, psychological). However, it doesn’t mean that an investment algorithm containing these rules is wrong and can’t generate profit in a long-term period. It can be very robust and stable.

New era approach

Our research department loves new technologies which can help to deliver higher and more stable performance, lower drawdowns with a shorter recovery period, better Sharpe ratio and other key performance metrics. Current research is focused on implementing machine learning and genetic programming to investment strategies – specifically to improve the effectiveness of the exit rules. We use a few simple entry rules defined by a financial market expert (old-fashioned approach), but exit rules are generated by modern technologies for deep analysis of different datasets. Exit rules contain simple or sophisticated patterns founded by genetic programming and machine learning, but this whole approach is still under expert control and each rule is validated with strict conditions to avoid over-fitting.

Customers will be able to benefit from these algorithms by using them directly on their own accounts through specialized trading platforms or by investing in the big funds which will use these algorithms.

Vision

Our vision is based on improving human skills with data analysis technologies and deliver higher and more stable performance. We are not trying to take-over human decisions, but we are trying to make them better.

If you are interested in more information stay tuned to our website or get in touch! It would be a pleasure to invite you for an meeting at our office.

Michal Dufek

We will inform you about the milestones we have achieved in analyzing text reviews. Let’s take a look at our research.

Motivation

Our team is currently working on a project to help make decisions about buying different products. A huge amount of opinions and reviews of individual products can be found on the Internet.
These user reviews are distributed across a variety of discussion forums, product rating sites, or specific portals. For a regular user, it’s difficult to find the information needed, get a look at them, and make its own opinion.

Methodology

In order to analyze large amounts of unstructured data, we have decided to use machine learning methods. We want to use these data to identify topics that are important to users and to determine their positive or negative attitudes towards individual product features.

Current status

We are currently working on creating crawlers for downloading user reviews and articles about the selected product group. These crawlers are tailored to the structure of specific sites. Crawlers from these sites get relevant data that can help in analyzing themes and attitudes. So far, we have created eight crawlers, which have helped us to download about half a million user reviews and expert articles about two thousand products in two languages ​​(Czech and English).

Problems solved

We had to deal with several issues when acquiring the data. One of the main ones is the different way of labeling products on different sites. Although it is an identical product, there are differences in names that complicate product pairing. Another problem is limiting the number of accesses to some sites in the form of code captcha. The last issue that needs to be solved is the changing web structure that causes crawlers to fail.

Conclusion

We have a practically closed first phase of the project in which we have defined the task of creating data acquisition tools for subsequent analysis. In the next phase, using machine learning methods, we will work to uncover the topics discussed and attitudes of users.

Jan Přichystal

In the introductory article of the Reviews section, we present the Multicriterial Text Analysis Software (MTA) project, which deals with the removal of information asymmetries in news and reviews.

The MTA team of scientists from CYRRUS ADVISORY, a.s. and Mendel University in Brno uses machine learning methods to analyze text in the field of current news and product reviews.

In the following posts, you can look forward to describing the research issues and the results we have already achieved in this area.

Jiří Fuchs