ANALYTICAL PLATFORM

In today’s article, we will take a look at the possibilities of starting cooperation with our team. The point is to use a specific example to show the potential strength that cooperation offers. You might be saying, what could such cooperation possibly bring me? Read the following paragraphs.

Our activities can help active traders with demonstrable trading results (it does not mean hundreds of per cent in profit but the ability to prove they understand what they are doing) and with evident experience.

What we are offering is the development and enhancement of trading strategies – we have developers, analysts, traders, statistics. A trader who administers their own strategy focuses rather on topics of robustness, trading conditions, risks and strategy workflow more than on yields. And that is exactly where we see the greatest potential for cooperation: every trading strategy can be reinforced, alternatively enhanced by one of the following points:

One of the real-life examples: during a recent opportunity, we met an options trader who successfully trades several of his (relatively well-known and standard) strategies. Knowing that his strategy “suffers” from certain weaknesses, he decided to start cooperation with our group in order to remove these weaknesses. Sharing experience and knowledge, both parties profited from the mutual cooperation – our team gained innovative incentives for our development which consist of a certain (different) view on a generally known trading strategy, and, at the same time, the trader received professional support in the area of automation and statistics/likelihood, which leads to increasing the performance of trading strategies. There can be a multitude of results – from automatic screener and notificator of new trading opportunities, to fully automatised trading strategies including execution, position-sizing and position management.

If you are interested in this article, be it from the point of view of a potential cooperator or client, do not hesitate to contact us with your questions, we are happy to meet you and discuss all possibilities.

Michal Dufek

Data for text analysis is often available only in web presentations in an unstructured form. How to get the data as easily as possible?

For the purpose of downloading text from websites, there are specialised tools called scrapers or crawlers. For some programming languages, there are frameworks which considerably simplify the creation of the scraper tool for individual websites. We use one of the most popular frameworks called Scrapy written in Python.

As a practical example, we can mention a tool for collecting subsidy incentives as supporting documents for Dotační manager (Grant Manager), the largest portal about grants in the Czech Republic, which unites “calls” from various public sources. The tool automatically looks through the structure of a portal, such as Agency for business and innovations, finds a website of s subsidy call and mechanically processes it into a structured format. The tool can be run repeatedly so that it can also catch newly published calls. The whole tool including the source code is available at http://git.pef.mendelu.cz/MTA/oppik-scraper/.

The above example is fairly simple, however, the practice tends to be more complicated. Website structure of every portal varies, often, it is not unified even within one portal, it changes in time etc. In order not to write similar tools for individual sources again and again, we are developing our own robust crawler which is able to automatically get text data from various sources.

Vladimír Vacula

The article from Technologická platforma komunikačních nástrojů a IoT

This article is only available in Czech version.

The article from Události EXTRA

This article is only available in Czech version.

The article from TECHNICKÝ TÝDENÍK

This article is only available in Czech version.

Development of investment strategies is most of the time based on an analysis of the behavior of the financial instruments on historical data. The main axiom is that we are able to quantify certain behavior patterns that will occur in the future and will be similar.

We can identify behavior patterns using three types of analysis

The most commonly used patterns are based on technical analysis. Historical pricing data is very easy to access even in one-minute granularity for all market, and basically it is an analysis of the relative positions of open, high, low and close prices over a certain time period or the value of technical indicators. To search for patterns using technical analysis, you can use the Ta-lib library (https://www.ta-lib.org/ ), which is open-source solutions and contains the well-known technical indicators (Bollinger Bands, Average True Range, Moving Average, RSI, Commodity Channel Index, …) and also includes predefined price patterns (Three Black Crows, Doji, Hanging Man, Marubozu, Shooting Star, …). The use of the price patterns themselves for the analysis of future price developments is a very powerful tool because of the possibility of automated testing on historical data and a direct expression of the price behavior of the financial instruments.

Innovation

As part of the internal development of PatternLab, the goal is to take advantage of standard, affordable and well-known price patterns and to enrich them with additional input parameters. We do enrichment in two ways. The first method is based on the position of the price pattern against the historical price. In practice, this means that in the case of monitoring the price pattern DOJI we are still interested in whether this pattern occurred, for example, by the maximum of the current week or at the maximum of the last month.

The second way to enrich pricing patterns is to deliver fundamental information such as the date of the dividend, earnings, changes in the company, and so on. This information can be obtained from Quandl (https://www.quandl.com ), which aggregates various sources of data. In practice, this means that the DOJI pattern is monitored only a few days after the dividend or only with surprising earnings results etc.

It is also possible to implement alternative data sources such as social media analysis (Instagram, Facebook, Youtube, Twitter – https://www.quandl.com/databases/SMA1 ) , satellite images (https://www.quandl.com/databases/RSMMS ), railroad traffic for individual commodities (https://www.quandl.com/data/RR1-Railroad-Traffic ).

Benefit for PatternLab users

All of these features mentioned above, you can see in PatternLab application which serve to survey and find demande price patterns. PatternLab users will be able to easily use enriched price patterns and get a tool to more accurately analyze future developments in the price of the monitored financial instruments. Users will be able to get information about enriched price patterns historical performance and will know more information about risks and profit potential.

For more information about the analytics tools, please contact us at brno@cyrrusadvisory.cz or +420 538 705 775.

Jan Budík