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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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Algorithmic Trading Using Artificial Neural Networks
Poláček, Samuel ; Beneš, Karel (referee) ; Szőke, Igor (advisor)
Algorithmic trading of many kinds of assets is not a new field at all. Domain of neural networks provides many tools, which are usefull in this field. This bachelor thesis discusses cryptocurrency trading algorithms using artificial neural network. In theoretical section of this thesis the basic theory and terms the stock market trading is based on is discussed. After the basic idea of cryptocurrencies is defined and used technical tools are introduced, the practical section starts. Sufficient configuration of neural network topology and hyperparameters values are obtained by many experiments. Subsequently after many experiments with indicators of technical analysis, acceptable neural network input configuration is obtained. Created neural network model combined with defined trading strategy generates profit.
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Algorithmic Trading Using Artificial Neural Networks
Radoš, Daniel ; Plchot, Oldřich (referee) ; Szőke, Igor (advisor)
This master's thesis is focused on algorithmic trading on the forex market using artificial neural networks. In the introduction, there are generally described terms concerning the trading. Subsequently, in the following chapters, the thesis describes the theory of neural networks and their possible use. The practical part contains designed business strategies with neural networks. Inputs used in the network are indicators of technical analysis or directly price level. Business strategies have been implemented and tested. In the conclusion, there are summarized findings of individual business models.
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Predicting stock price movements from financial news using deep neural networks
Kramoliš, Richard ; Baruník, Jozef (advisor) ; Vácha, Lukáš (referee)
Financial media are an important source of information and many articles about companies and stocks are released every day. This thesis assesses the informa- tion value of the articles and utilizes these articles for the stock price move- ment prediction task. For this purpose, models with transformer architecture are used, specifically Bidirectional Encoder Representations from Transform- ers. These models are able to process the text data and create the contextual representation of the text sequence. After adding the classification layer, the models are applied for the stock price movement predictions. The thesis evalu- ates multiple models including different techniques and parameters to find the best performing model. It focuses on two data filters that are expected to de- crease the noise in the data. Moreover, it introduces a new method to recognize the company of interest. As a result of the hyperparameter optimization, the final model is constructed. JEL Classification C45, C51, C52, C53, G11, G14, G17 Keywords BERT, Transformer, Financial Articles, Stock Trading Title Predicting stock price movements from financial news using deep neural networks
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Stock Trading Using a Deep Reinforcement Learning and Text Analysis
Benk, Dominik ; Baruník, Jozef (advisor) ; Vácha, Lukáš (referee)
The thesis focuses on exploiting imperfections on the stock market by utilizing state-of-the-art learning methods and applying them to algorithmic trading. The automated decisions are expected to have the capability of outperforming professional traders by considering much more information, reacting almost instantly and being unaffected by emotions. As an alternative to traditional supervised learning, the proposed model of reinforcement learning employs a principle of trial-and-error, which is essential for learning behaviours of all organisms. In the context of stocks, this allows to consider the involved uncer- tainty and therefore more precisely estimate the long-run returns. To collect the most relevant information for each trading decision, additionally to tech- nical indicators the models build on investor's opinion - financial sentiment. This is derived from two textual sources, news and social media, and the main goal is to compare their relative contribution to trading. Models are applied to 11 different stocks and later combined into portfolio for greater robustness of results. The textual analysis proves to be important for the learning process, especially in case of stocks with good media coverage. The Twitter is found to provide more valuable information compared to news, but their...
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Algorithmic Trading Using Artificial Neural Networks
Poláček, Samuel ; Beneš, Karel (referee) ; Szőke, Igor (advisor)
Algorithmic trading of many kinds of assets is not a new field at all. Domain of neural networks provides many tools, which are usefull in this field. This bachelor thesis discusses cryptocurrency trading algorithms using artificial neural network. In theoretical section of this thesis the basic theory and terms the stock market trading is based on is discussed. After the basic idea of cryptocurrencies is defined and used technical tools are introduced, the practical section starts. Sufficient configuration of neural network topology and hyperparameters values are obtained by many experiments. Subsequently after many experiments with indicators of technical analysis, acceptable neural network input configuration is obtained. Created neural network model combined with defined trading strategy generates profit.
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Algorithmic Trading Using Artificial Neural Networks
Radoš, Daniel ; Plchot, Oldřich (referee) ; Szőke, Igor (advisor)
This master's thesis is focused on algorithmic trading on the forex market using artificial neural networks. In the introduction, there are generally described terms concerning the trading. Subsequently, in the following chapters, the thesis describes the theory of neural networks and their possible use. The practical part contains designed business strategies with neural networks. Inputs used in the network are indicators of technical analysis or directly price level. Business strategies have been implemented and tested. In the conclusion, there are summarized findings of individual business models.
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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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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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Stock markets comparison in Central and Eastern Europe
Michalovský, Michal ; Žilák, Pavel (advisor) ; Veselá, Jitka (referee)
This thesis compares stock exchanges in Central and Eastern Europe. It covers exchanges of Prague, Budapest, Warsaw, Bucharest, Ljubljana, Zagreb, Vienna, and Istanbul. At first, all the exchanges are briefly introduced including naming five most liquid stocks. Selected market specifics are then compared including supported order types, tick sizes, fees policy, trading hours, safety breaks, taxes, market capitalization, and weights in global stock indices. Lastly, a comparison of trading activity is provided and analysis of important feature of trading -- liquidity is performed calculating selected liquidity measure for each market.
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