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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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Algorithmization for decision support
Strečková, Nikola ; Budík, Jan (referee) ; Dostál, Petr (advisor)
This thesis is focused on understanding investment strategies on cryptocurrency markets and thanks to the own algorithm create an automated program to support the decision making. To deploy and develop the algorithm is used MetaTrader5 platform, which uses the MQL5 programming language. The strategy was backtested on historical data of BTCUSD and BTCEUR to validate the efficiency of the strategy.
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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 fundamental trading
Pižl, Vojtěch ; Krištoufek, Ladislav (advisor) ; Bubák, Vít (referee)
This thesis aims to apply methods of value investing into developing field of algorithmic trading. Firstly, we investigate the effect of several fundamental variables on stock returns using the fixed effects model and portfolio approach. The results confirm that size and book- to-market ratio explain some variation in stock returns that market alone do not capture. Moreover, we observe a significant positive effect of book-to-market ratio and negative effect of size on future stock returns. Secondly, we try to utilize those variables in a trading algorithm. Using the common performance evaluation tools we test several fundamentally based strategies and discover that investing into small stocks with high book-to-market ratio beats the market in the tested period between 2009 and 2015. Although we have to be careful with conclusions as our dataset has some limitations, we believe that there is a market anomaly in the testing period which may be caused by preference of technical strategies over value investing by market participants.
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