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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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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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Hadoop and Business Intelligence
Kerner, Josef ; Šperková, Lucie (advisor) ; Augustín, Jakub (referee)
The main purpose of this thesis is to describe how an integration of a Hadoop platform into currently existing Business Intelligence technologies and processes can augment its data processing and analysis capabilities while encountering Big Data. Furthermore, it describes reasons why the whole Hadoop application ecosystem was founded and informs the reader about the functionality of its primary components. It continues with provision of overview about Hadoop higher-level components architecture and their use in existing Business Intelligence processes such as data ingestion, transformation and analysis. In the last theoretical chapter it focuses itself on describing specific areas of utilization of the Hadoop platform and Big Data in data warehousing, text mining and predictive analytics. From the practical point of view, a particular use case is provided, an implementation of Big Data ETL process in the field of financial markets and trading with a detailed explanation of the corresponding necessities such as data model, ETL code and proposed metrics, which can be further implemented for achieving increased return on investments.
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