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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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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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Algorithmic and high-frequency trading on capital market
Kádě, Lukáš ; Kohajda, Michael (advisor) ; Kotáb, Petr (referee)
Algorithmic and high-frequency trading on capital market Abstract The subject of this diploma thesis is legal regulation and development of regulation of algorithmic and high-frequency trading on capital market within Community Law but also within several European countries, USA and Japan. The aim of this diploma thesis is to define terms of algorithmic and high-frequency trading, which were not thoroughly regulated until lately, to outline development of legal regulation, to compare different approaches to their regulation in different countries and to assess the phenomenon of algorithmic and high- frequency trading. The diploma theses uses descriptive method to define the fundamental terms and discuss positive legal framework. It also uses deduction for assessment and comparative method to examine different approaches to legal regulation in different countries. The first chapter characterizes capital market as a place in which algorithmic and high- frequency trading takes place, including its historical development, participants and supervisory authorities. The second chapter defines terms of algorithmic and high-frequency trading considering their historical development and both mutual similarities their differences and their characteristics. It also includes an analysis of their key aspects and related...
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Neural Networks for Machine Learning in Algorithmic Trading
Koubek, David ; Krištoufek, Ladislav (advisor) ; Debatz, Laure (referee)
This thesis investigates the forecasting ability of the artificial neural network (ANN) models on five major currency pairs and compares the accuracy of several ANN ar- chitectures to the difficult to outperform random walk (RW) benchmark. The ANNs mostly stand ground against the RW, yet fail to attain significantly different results for most of the currencies in out-of-sample testing. A good predictive accuracy of a few ANN models was shown only for the Japanese yen in our results. Less complex neural network architectures supported the notion of having better generalisation capabilities for most of our datasets. JEL Classification C01, C32, C45, C51, C52, C53, C87 Keywords artificial neural networks, machine learning, finan- cial markets, Forex, day trading, algorithmic trad- ing, pattern recognition, computational learning the- ory, backtesting, forecasting Author's e-mail 56374598@fsv.cuni.cz, mrkoubek@gmail.com Supervisor's e-mail ladislav.kristoufek@fsv.cuni.cz Abstrakt Tato práce zkoumá schopnost modelů na bázi neuronových sítí (ANN) předpovídat budoucí cenu pěti hlavních měnových párů a porovnává přesnost předpovědí s těžce překonatelným modelem random walk (RW), který vždy hádá následující cenu jako totožnou se současnou cenou. ANN modely převážně obstály oproti RW, ale pro většinu měn...
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Algoritmické obchodování párů
Razumňak, Michal ; Stádník, Bohumil (advisor) ; Fučík, Vojtěch (referee)
Pair trading is a well-known strategy based on statistical arbitrage. This strategy uses a short-term deviation from the mean value of the price ratio of two highly correlated stocks from the same sector as the opportunity to open a position. When ratio returns to its mean value again, the position closes. This strategy has been used for many years and the main outcome of this thesis was to test whether this strategy can be profitable even in current market conditions. For that purpose, data ranging from 2010 to April 2017 on all stocks included in the S&P 500 index were used. It was subsequently found that a pair trading strategy generated 25x higher absolute profit in comparison to random agent. Thus, it can still be considered as a profitable strategy.
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