This master's thesis deals with the analysis of selected rates of shares by using statistical methods including regression analysis and analysis of time series. Using moving averages as technical indicators in technical analysis of securities to predict the future development of rates of shares and finding buy and sell signals that these indicators generate. The results of this work are suggestions for stock trading based on the use of these methods.

This diploma thesis describes issues of use of means of artificial intelligence for the decision making support on stock market. It includes theoretical knowledge of technical, fundamental and psychological analysis and artificial intelligence. Based on these facts have been created specific suggestions for the use of artificial neural networks to forecast the future value of the index S&P 500 by using development environment of the MATLAB software.

This diploma thesis discusses about the possibilities of investing in the capital market with a focus on the foreign exchange market. Analysis of the company, whose output is SWOT analysis, is focused on the economic justification of investments. The essence is the proposal of automatic trading systems that will automatically trade on the basis of information from the market and add value to ivested capital. This automatic trading systems are designed in analytic platform named MetaTrader and their parameters are optimized by genetic algorithms.

This master’s thesis is focused on the development, optimization and testing automated trading systems (ATS) using technical analysis. The first part, which describes mainly theoretical background, is followed by the practical part. This section deals with designing workflow for the development of ATS. Outcome of this thesis is portfolio of strategies that can be traded on e-mini markets. Some systems have been designed in Adaptrade Builder using genetic algorithms, while all testing was performed in TradeStation platform.

In the last years, we indicated progress in use of alternative methods for drainage. Thanks the information from abroad we started design vacuum and pressure methods of drainage instead gravity ones. The main content of this script is especially point out deficiencies of nowadays used alternative methods for drainage, which we can defuse. Another important part is also design of suitable technical indicators describing these drainages.

The aim of this work is to create an automated trading system for trading currency pairs using technical indicators and technical analysis. The proposed trading system is tested and optimized on historical price data. To verify the robustness of the proposed system was used walk-forward analysis. Automatic trading system also uses rules for position sizing and risk management of open positions. Created system is profitabel on historical price data and also in the walk-forward analysis.

The subject of this diploma thesis is a design and implementation of an automated trading system for the forex market. It includes an analysis of the main concepts and methods of technical analysis and money management, which constitute an essential theoretical basis for the subsequent practical design of an automatic system. The objective of this work is a development of an automated trading system whose robustness and stability is tested by a walk forward analysis.

The master’s thesis deals with trading the currency market. The aim of thesis is the creation of an automated trading system based on technical analysis. This thesis is divided into several parts. The theoretical aspects and analysis of current situation are followed by automated trading system proposal. The system is designed on basis of technical indicators and tested on historical data and then optimized.

This diploma work initially conduct research in the area of descriptions and analysis of time series. The thesis then proceed to introduce the problems of technical analysis of price charts as well as indicators, price patterns and method of Pure Price Action. The method Pure Price Action is demonstrated in this work in two practical examples of its application to real businesses with a view to discovering and analyzing price patterns, as well as analysis and prediction of future price and financial evolution. This analysis is an introduction to the processes of successful business, following on from this we discuss the theme of Pattern Recognition and the Instance Based Learning method. The practical aspect of this work is carried out with the aid of a MATLAB applied algorithm for the analysis of the price pattern Correction for sale and purchase in dynamic time segments, specifically in trading price graphs, like those used for commodities or stock trading. For the analysis of time series we use the Pure Price Action method. The Instance Based Learning method is used by the algorithm to recognize price patterns. The created algorithm is verified on real data of a 5 minute time series of the US Dow Jones price charts for the years 2006, 2007, 2008. The achieved accuracy is evaluated with the aid of Equity Curves.