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Algorithmic Trading Using Artificial Neural Networks
Chlud, Michal ; Pešán, Jan (referee) ; Szőke, Igor (advisor)
This diploma thesis delas with algoritmic trading using neural networks. In the first part, some basic information about stock trading, algorithmic trading and neural networks are given. In the second part, data sets of historical market data are used in trading simulation and also as training input of neural networks. Neural networks are used by automated strategy for predicting future stock price. Couple of automated strategies with different variants of neural networks are evaluated in the last part of this work.
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Testing of Indicators for Technical Analysis in Stock Market Trading
Melichar, Josef ; Žák, Jakub (referee) ; Rozman, Jaroslav (advisor)
This thesis deals with testing indicators of technical analysis and their behavior with different kinds of market. In this thesis we tested simple moving averages, exponential moving averages, relative strength index indicator, MACD indicator, and stochastic indicator. At the end we tested a combined indicator, and that was stochastic with MACD. We created an automatic trading system for each of these indicators. From the results we can find out, that by optimizing the technical indicators we can get satisfying results by increasing the profitability. Indicators that seemed non-profitable were in fact profitable after the optimization. However testing parameters of indicators of technical analysis alone is not enough to make a stable profit.
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Anomaly detection for stock market trading data
Fusková, Martina ; Kofroň, Jan (advisor) ; Kliber, Filip (referee)
Stock trading is a very complex topic that involves a lot of challenging problems. One of these problems is anomaly detection in trading flow. Real-time anomaly detection in time series is a very complicated task and thus this issue is still open. The aim of this thesis is to research various models and algorithms that can be used for this task and try to find the most fitting ones. We develop models that detect anomalies based on the density properties of the data as well as statistical models and neural networks that detect anomalies based on the comparison of predicted data and actual data. As a result we propose models that can be further researched and used in real-time environment.
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Algorithmic Trading Using Artificial Neural Networks
Chlud, Michal ; Pešán, Jan (referee) ; Szőke, Igor (advisor)
This diploma thesis delas with algoritmic trading using neural networks. In the first part, some basic information about stock trading, algorithmic trading and neural networks are given. In the second part, data sets of historical market data are used in trading simulation and also as training input of neural networks. Neural networks are used by automated strategy for predicting future stock price. Couple of automated strategies with different variants of neural networks are evaluated in the last part of this work.
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Testing of Indicators for Technical Analysis in Stock Market Trading
Melichar, Josef ; Žák, Jakub (referee) ; Rozman, Jaroslav (advisor)
This thesis deals with testing indicators of technical analysis and their behavior with different kinds of market. In this thesis we tested simple moving averages, exponential moving averages, relative strength index indicator, MACD indicator, and stochastic indicator. At the end we tested a combined indicator, and that was stochastic with MACD. We created an automatic trading system for each of these indicators. From the results we can find out, that by optimizing the technical indicators we can get satisfying results by increasing the profitability. Indicators that seemed non-profitable were in fact profitable after the optimization. However testing parameters of indicators of technical analysis alone is not enough to make a stable profit.
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Stock Prediction Using Artificial Neural Networks
Putna, Lukáš ; Grézl, František (referee) ; Szőke, Igor (advisor)
This work deals with the usage of neural network for the purpose of stock market prediction. A basic stock market theory and trading approaches are mentioned at the beginning of this work. Then neural networks and their application are discussed with their deeper description. Similar approaches are referred and finally two new prediction systems are designed. These systems are utilized by proposed trading model and tested on selected data. The results are compared to human and random trading models and new development steps are devised at the end of this work.
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Application for Fundamental Analysis
Žižka, Ladislav ; Pavlíčková, Jarmila (advisor) ; Maryška, Miloš (referee)
This thesis deals with the development of application for fundamental analysis of stocks. Main goal of the thesis is to make application, which will be helpful for individual investors in performing fundamental analysis of stocks. It is desktop application, which performs calculations with high precision and it uses free on-line sources of financial data. The application was developed in Java programming language. It will be available as a~freeware alternative to proprietary fundamental analysis software on the market. The first part describes capital market and stock exchange, makes characteristics of stock and explains principle of fundamental analysis of stocks. In the second part, the market research of fundamental analysis software was realized, design and implementation of the application for fundamental analyiss was described and the application was evaluated.
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Futures Spreads Trading
Hrečka, Marek ; Smrčka, Luboš (advisor) ; Zámečník, Petr (referee)
The purpose of the thesis is to identify factors that affect the profitability and risk of trading futures calendar spreads. The basic characteristics of futures and trading calendar spreads with seasonal time frames are described in the first part of the thesis. The selected factors such as the correlation of short-term and long-term seasonal patterns, the trading in the extreme, the trading single or multiple crops, the width of the seasonal window, the win probability, the length of backtested period and intermarket vs. intramarket spreads are analyzed from the perspective of profitability and risk in the second part. A summary of the results is contained in the conclusion.
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