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The Use of SVM in Environment of Financial Markets
Štechr, Vladislav ; Prochocká, Kristína (referee) ; Budík, Jan (advisor)
This thesis deals with use of regression or classification based on support vector machines from machine learning field. SVMs predict values that are used for decisions of automatic trading system. Regression and classification are evaluated for their usability for decision making. Strategy is being then optimized, tested and evaluated on foreign exchange market Forex historic data set. Results are promising. Strategy could be used in combination with other strategy that would confirm decisions for entering and exiting trades.
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Testing of Indicators for Technical Analysis in Stock Market Trading
Kaděra, Miroslav ; Hrubý, Martin (referee) ; Rozman, Jaroslav (advisor)
The topic of this thesis is testing of indicators for technical analysis, done from the point of view of their suitability for automatic stock market trading systems. The thesis tests behaviour of simple moving average, exponential moving average and the RSI indicator. A simple automatic trading system was made for each indicator. Profitability of the system was tested for various parameters of the used indicator. The testing was realized using real historical data of more than ten years historical period. The results show that profitability of the system can be increased by tens of percent. Even though for stable profitable trading the trader should work out a lot of other rules than just indicator parameters.
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Algorithmic Trading Using Twitter Data
Kříž, Jakub ; Plchot, Oldřich (referee) ; Szőke, Igor (advisor)
This master's thesis describes creation of prediction system. This system predicts future market development based on stock exchange data and twitter messages analysis. Tweets from two different sources are analysed by mood dictionaries or via recurrent neural networks. This analysis results and technical analysis of stock exchange data results are used in multilayer neural network for prediction. A business strategy is created and tested based on results of this prediction. Design and implementation of prediction system is described in this thesis. This system achieved revenue increase more than 25 % of some business strategies by tweets analysis. However this improvement applies for certain data and timeframe.
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Design and Optimalization of Automatic Trading System
Boček, František ; Plaček, Marek (referee) ; Budík, Jan (advisor)
The goal of this work is to describe approaches to financial market analysis and implement chosen approaches in automatic trading system in the MetaQuote Language environment for Metatrader platform. Another objective is to optimise the designed trading system and test additional rules to achieve maximum profit during minimalization risks.
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Design of Automatic Trading Systém Based on Fractal Geometry
Malý, Petr ; Dostál, Petr (referee) ; Budík, Jan (advisor)
This thesis deals with an analysis and prediction of foreign exchange markets. The thesis is based on the fractal market hypothesis and it uses tools based on fractal geometry for prediction of markets. The thesis also describes ways of using advanced methods of artificial intelligence for analyzing markets. The outcome is designed and implemented automatic trading system. The thesis also deals with testing of designed system on historical data and on the latest data as well.
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Design and Use of Automatic Trading System for Increasing Company's Capital
Kněžínek, Michal ; Suchomel, Michal (referee) ; Budík, Jan (advisor)
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.
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Algorithmic Trading Using Twitter Data
Kříž, Jakub ; Plchot, Oldřich (referee) ; Szőke, Igor (advisor)
This master's thesis describes creation of prediction system. This system predicts future market development based on stock exchange data and twitter messages analysis. Tweets from two different sources are analysed by mood dictionaries or via recurrent neural networks. This analysis results and technical analysis of stock exchange data results are used in multilayer neural network for prediction. A business strategy is created and tested based on results of this prediction. Design and implementation of prediction system is described in this thesis. This system achieved revenue increase more than 25 % of some business strategies by tweets analysis. However this improvement applies for certain data and timeframe.
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