Obsah Použité zkratky ...................................................................................................................... 9 Úvod .................................................................................................................................... 10 Výzkumný problém a cíle práce...................................................................................... 11 Využité metody práce...................................................................................................... 12 1 Teoretická východiska................................................................................................. 13 1.1 Informatické myšlení ............................................................................................ 14 1.2 Algoritmické myšlení ........................................................................................... 18 1.2.1 Algoritmus..................................................................................................... 19 1.2.2 Vlastnosti algoritmů ...................................................................................... 20 1.2.3 Způsoby zápisu algoritmů ............................................................................. 22 1.2.4 Základní algoritmické konstrukce...

The presented diploma thesis examines the possibilities of developing algorithmic thinking in primary school pupils. Algorithmic thinking is a necessary tool for effective analysis of the problem and the creation of a procedure for its subsequent repeatable solution. The main research topic of this work is effective and efficient ways of developing algorithmic thinking, especially how to formulate a problem and how to develop algorithmic thinking with the greatest effect in the educational process. The work summarizes various ways of developing algorithmic thinking and the approach to its teaching. The concepts of computer thinking, algorithmic thinking, algorithm and algorithmization are defined and analyzed. Furthermore, contemporary means of developing algorithmic thinking described in the literature are specified. They are then critically evaluated and at the end of the theoretical part the most suitable set of means is selected and the way of their use is proposed. Action research was carried out in the circle of informatics at the primary school in Prague. The verification of the effectiveness of the selected instruments took place during fifteen sixty-minute lessons. The focus of this research lies in increasing the quality of pedagogical practice of the teacher, in the development of his didactic...

In this thesis we study sparse classes of graphs and their properties usable for design of algorithms and data structures. Our specific focus is on the con- cepts of bounded expansion and tree-depth, developed in recent years mainly by J. Nešetřil and P. Ossona de Mendez. We first give a brief introduction to the theory as whole and survey tools and results from related areas of parametrised complexity and algorithmic model theory. The main part of the thesis, application of the theory, presents two new dynamic data structures. The first is for keeping a tree-depth decomposition of a graph, the second counts appearances of fixed subgraphs in a given graph. The time and space complexity of operations of both structures is guaranteed to be low when used for sparse graphs. 1

This thesis aims to apply methods of value investing into developing field of algorithmic trading. Firstly, we investigate the effect of several fundamental variables on stock returns using the fixed effects model and portfolio approach. The results confirm that size and book- to-market ratio explain some variation in stock returns that market alone do not capture. Moreover, we observe a significant positive effect of book-to-market ratio and negative effect of size on future stock returns. Secondly, we try to utilize those variables in a trading algorithm. Using the common performance evaluation tools we test several fundamentally based strategies and discover that investing into small stocks with high book-to-market ratio beats the market in the tested period between 2009 and 2015. Although we have to be careful with conclusions as our dataset has some limitations, we believe that there is a market anomaly in the testing period which may be caused by preference of technical strategies over value investing by market participants.

This thesis explores algorithmic thinking development on a high school student level. In the introduction algorithmic thinking as well as current practices of teaching algorithmic thinking are examined. A closer look on programming languages, programming methods and tools which can be utilised to develop algorithmic thinking (such as robotic kits, personal computers and mobile devices with touch screens) follows. After analyzing general properties of these tools one mobile device is selected and its programming possibilities are then explored in the empirical part of this paper. As part of the selected proactive action research a number of activities as well as overall process approach is first designed following by their practical verification in a high school. Final results are summarised as suggestions for improvements for further teaching.

The diploma thesis deals with the development of algorithmic thinking among the lower-secondary students as one of their digital competences. It is based on the principle of algorithmic thinking related to the concept of computational thinking and it searches for the ways how to develop it in pupils. The theoretical part is supported by Skinner's concept of programmed learning and with the help of gamification proposes the research based on the students' observation in one school year in which the competence is developed. The raised issue is solved by setting a complex set of teaching materials and learning activities (both in the digital environment and unplugged activities) and their partial transfer into the teaching to a virtual, algorithmic environment of the specially designed application called Prográmko. The goal of the thesis is the analysis of the possibilities of how to include the gamification and the programmed learning into the practice of the development of algorithmic thinking taught at the lower-secondary schools. The research also evaluates the applicability of the application Prográmko and the proposed set of materials provided with methodological guidelines in the practice.

The diploma thesis deals with problems of algorithmization and programming at elementary schools. It examines the influence of teaching the basics of programming in Scratch on the development of pupils' algorithmic thinking and their ability to develop logical thinking and problem solving. The theoretical part deals with the definition of related concepts and the relation of programming to the key competencies defined by the RVP ZV. The practical part evaluates the experience with the teaching of pupils of the 5th and 6th grades of elementary school within the pedagogical experiment, whose aim was to study the influence of teaching with the use of the visual programming language Scratch on the development of pupils' algorithmic thinking.

Pair trading is a well-known strategy based on statistical arbitrage. This strategy uses a short-term deviation from the mean value of the price ratio of two highly correlated stocks from the same sector as the opportunity to open a position. When ratio returns to its mean value again, the position closes. This strategy has been used for many years and the main outcome of this thesis was to test whether this strategy can be profitable even in current market conditions. For that purpose, data ranging from 2010 to April 2017 on all stocks included in the S&P 500 index were used. It was subsequently found that a pair trading strategy generated 25x higher absolute profit in comparison to random agent. Thus, it can still be considered as a profitable strategy.

This thesis will address the problem of the parallel evaluation of algorithmic trading models based on multiple kernel support vector regression. Various approaches to parallelization of the evaluation of these models will be proposed and their suitability for highly parallel architectures, namely the Intel Xeon Phi coprocessor, will be analysed considering specifics of this coprocessor and also specifics of its programming. Based on this analysis a prototype will be implemented, and its performance will be compared to a serial and multi-core baseline pursuant to executed experiments. Powered by TCPDF (www.tcpdf.org)

This thesis deals with statistical arbitrage as a strategy applied in algorithmic trading of US Treasury bonds in the selected timeframe from 1980 until 2017. Our aim is to prove that a specific event on the treasury market, namely reopening of the bonds, constitutes an arbitrage opportunity that enables the investor to systematically yield extraordinary profits on the market. This thesis includes a theoretical introduction to algorithmic trading and statistical arbitrage. Based on this introduction we formulate hypotheses, which are then tested in the application part by constructing an algorithm that simulates a trading strategy on historical data. Comparing three strategies we determined that this strategy is meaningful, or performs better than a random walk and that it is profitable.