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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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High frequency trading and its impact on the financial market stability
Haushalterová, Gabriela ; Brůna, Karel (advisor) ; Pour, Jiří (referee)
The thesis analyses high frequency trading, specifically its main characteristics, which make it different from algorithmic trading. Furthermore, the thesis looks closer into major risks, which are new to market, and their impact on market quality and other investors. The next chapter is dedicated to trading strategies, which are typical for high frequency trading. In conclusion, there is discussed the impact on the market quality caused by high frequency trading, namely in terms of liquidity, volatility and price discovery.
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Statistická arbitráž při algoritmickém obchodování amerických dluhopisů
Juhászová, Jana ; Stádník, Bohumil (advisor) ; Janda, Karel (referee)
This thesis deals with statistical arbitrage as a strategy applied in algorithmic trading of US Treasury bonds in the selected timeframe from 1980 until 2017. Our aim is to prove that a specific event on the treasury market, namely reopening of the bonds, constitutes an arbitrage opportunity that enables the investor to systematically yield extraordinary profits on the market. This thesis includes a theoretical introduction to algorithmic trading and statistical arbitrage. Based on this introduction we formulate hypotheses, which are then tested in the application part by constructing an algorithm that simulates a trading strategy on historical data. Comparing three strategies we determined that this strategy is meaningful, or performs better than a random walk and that it is profitable.
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Applications for algorithmic trading
Šalovský, Vojtěch ; Pecinovský, Rudolf (advisor) ; Suchan, Vladimír (referee)
The presented work deals with analysis and implementation of algorithmic trading applications based on client requirements. Applications developed in this work are supposed to be used to collect and manage data from the stock exchange, to view information about active trading orders, and to send trading orders to the exchange via the API from Interactive Brokers. The first chapter gives an overview of selected books focused on developing applications for C # and analysis. Then the concepts of UML, OOAD, and UP are introduced. In the next chapter, requirements of the customer are defined. In the following chapter, based on the results of literature research and defined client requirements, the initial architectural design is created and cases of use with subsequent specifications are presented. This section is followed by finding analytical classes, creating a domain model, implementation of some use cases using sequence diagrams. The last two chapters describe the implementation details - the language used, the libraries, database schema, and user manual.
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Parallel Evaluation of Numerical Models for Algorithmic Trading
Ligr, David ; Kruliš, Martin (advisor) ; Zavoral, Filip (referee)
This thesis will address the problem of the parallel evaluation of algorithmic trading models based on multiple kernel support vector regression. Various approaches to parallelization of the evaluation of these models will be proposed and their suitability for highly parallel architectures, namely the Intel Xeon Phi coprocessor, will be analysed considering specifics of this coprocessor and also specifics of its programming. Based on this analysis a prototype will be implemented, and its performance will be compared to a serial and multi-core baseline pursuant to executed experiments. Powered by TCPDF (www.tcpdf.org)
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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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Comparison of double auction bidding strategies for automated trading agents
Vach, Daniel ; Maršál, Aleš (advisor) ; Burda, Martin (referee)
Comparison of double auction bidding strategies for automated trading agents Bc. Daniel Vach Absctract In this work, ZIP, GDX, and AA automated bidding strategies are compared in symmetric agent-agent experiments with a variable composition of agent population. ZIPOJA, a novel strategy based on ZIP with Oja's rule extension for updating its optimal price, is introduced. Then it is showed that ZIPOJA underperforms in competition against other strategies and that it underperforms even against the original ZIP. Dominance of AA over GDX and ZIP is questioned and it is showed that it is not robust to composition changes of agent population and that the experimental setup strongly affects the results. GDX is a dominant strategy over AA in many experiments in this work in contrast to the previous literature. Some mixed strategy Nash equilibria are found and their basins of attraction are shown by dynamic analysis.
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AI techniques in algorhitmic trading
Šmejkal, Oldřich ; Pavlíčková, Jarmila (advisor) ; Berka, Petr (referee)
Diploma thesis is focused on research and description of current state of machine learning field, focusing on methods that can be used for prediction and classification of time series, which could be then applied in the algorithmic trading field. Reading of theoretical section should explain basic principles of financial markets, algorithmic trading and machine learning also to reader, which was previously familiar with the subject only very thoroughly. Main objective of application part is to choose appropriate methods and procedures, which match current state of art techniques in machine learning field. Next step is to apply it to historical price data. Result of application of selected methods is determination of their success at out of sample data that was not used during model calibration. Success of prediction was evaluated by accuracy metric along with Sharpe ratio of basic trading strategy that is based on model predictions. Secondary outcome of this work is to explore possibilities and test usability of technologies used in application part. Specifically is tested and used SciPy environment, that combines Python with packages and tools designed for data analysis, statistics and machine learning.
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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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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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