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Using artificial intelligence to automate trading
Čermák, František ; Hůlka, Tomáš (referee) ; Matoušek, Radomil (advisor)
This thesis deals with the use of artificial intelligence for automating stock trading. The main objective was to investigate current technologies applied in algorithmic trading and then to design and develop an automated trading system using artificial intelligence. The work focuses on various aspects of algorithmic trading, including high frequency trading, cloud solutions, machine learning, blockchain and smart contracts. It also explores the applications of AI in trading, such as predictive analytics and natural language processing, and discusses the ethical and regulatory challenges associated with this technology. The design and development of an automated trading system is described in detail, including system architecture, choice of programming languages and tools, and implementation of trading algorithms. The results show that the use of artificial intelligence can significantly increase the efficiency and accuracy of stock trading, but technological and ethical risks must be considered. This thesis makes a significant contribution to research in the field of algorithmic trading and provides a foundation for further research in optimizing trading algorithms and integrating new technologies.
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Automated trading systems
Šafář, Vítězslav ; Hříbek, David (referee) ; Rozman, Jaroslav (advisor)
Trading in the financial market is something almost everyone has heard of these days, but automated trading is still a novelty for most. The aim of this bachelor's thesis is to design and create several automatic trading systems using the application programming interface provided by XTB, and subsequently evaluate these automated trading systems using historical data. The thesis presents four differently complex automated trading systems, achieving various profits at certain risk levels. Furthermore, the thesis demonstrates the usability of the mentioned XTB application programming interface. The best-designed system evaluated was the one utilizing the MACD indicator,which achieved an average annual return of around 13.5 % with a level of risk of loss, approximately 39 %.
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Mathematical Methods in Economics
Florescu, Chiril ; Budík, Jan (referee) ; Novotná, Veronika (advisor)
The bachelor’s thesis deals with the problem of option trading and its advanced strategies applied to financial markets using algorithmic trading. The theoretical part includes the basic concept of the financial market, a detailed characterization of the investment instrument with its boundary properties, and an overview of algo-trading. In the following section, the implementation and analysis of combined option positions on underlying assets such as equities and exchange-traded funds using beta-weighted deltas are discussed. The result of the work is the design of a trading strategy, backtesting on historical data and optimization of individual parameters for higher efficiency.
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Automated Investment Strategy for Trading Selected Cryptocurrency
Melzrová, Anežka ; Budík, Jan (referee) ; Luhan, Jan (advisor)
This master's thesis deals with an automated investment strategy designed for the cryptocurrency market. The selected cryptocurrency is characterized and analyzed. Existing automated investment strategies are evaluated and then a custom automated investment strategy is proposed. All the strategies are tested on historical data of the selected cryptocurrency and their contribution is evaluated.
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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
Červíček, Karel ; Glembek, Ondřej (referee) ; Szőke, Igor (advisor)
Forex is the biggest foreign exchange market. Thanks to high liquidity it is a good candidate for intraday trading with certain trading strategies based on technical and fundamental analysis.Trading strategies can be proposed for automatic algorithmic trading.Strategy in this article is designed with a neural network that holds positions as approximator of time series data based on the exchange rate, which can predict the future.
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Low-Latency Architecture for Order Book Building
Závodník, Tomáš ; Kořenek, Jan (referee) ; Dvořák, Milan (advisor)
Information technology forms an important part of the world and algorithmic trading has already become a common concept among traders. The High Frequency Trading (HFT) requires use of special hardware accelerators which are able to provide input response with sufficiently low latency. This master's thesis is focused on design and implementation of an architecture for order book building, which represents an essential part of HFT solutions targeted on financial exchanges. The goal is to use the FPGA technology to process information about an exchange's state with latency so low that the resulting solution is effectively usable in practice. The resulting architecture combines hardware and software in conjunction with fast lookup algorithms to achieve maximum performance without affecting the function or integrity of the order book.
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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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