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Technical Analysis of Selected Stocks
Mazal, Tomáš ; Varsányi, Peter (referee) ; Sojka, Zdeněk (advisor)
This thesis deals with applying of technical analysis to selected stocks. The first part of the thesis is the theoretical basis of technical analysis and also mentions psychological and fundamental analysis. Followed by the analytic part where specific stock market and several stocks being traded on it are selected. These stocks will be analyzed by technical analysis (visual formations, simple moving average, Bollinger Bands, Relative Strength Index, MACD) outputing graphical representation of signals received and commentary. The last part of the thesis is estimation of further stock price development, recommendation of technical indicators to be followed. Furthermore a portfolio for imaginary investor is created. The work ends by expressing the conclusion.
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Technical Analysis
Němec, Ondřej ; Dvořák, Zdeněk (referee) ; Novotná, Veronika (advisor)
The subject of my thesis is technical analysis - creation of an investment strategies. The theoretical part describes the theoretical background relating to technical analysis and indicators. The practical part map the current situation in the environment of investing in forex - comparing brokers, choice of platform, etc. The solution contains a description of the investment strategies that have been programmed in Meta Quotes Language 4 and tested and optimized using genetic algorithms in platform MetaTrader 4. In my thesis is also calculated the interdependence of investment strategies.
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Use of technical analysis in the cryptocurrency market
BOROVKA, Štěpán
This thesis focuses on utilizing technical analysis in the cryptocurrency market, specifically creating a personal trading strategy using principles and procedures of technical analysis. An important aspect of creating a strategy is defining the conditions for entering and exiting trades. The created strategy was backtested over a selected period on the Bitcoin market, evaluated for its success, and compared to the market's return for the same period. The evaluation revealed that the strategy was profitable over the selected period on the Bitcoin market. However, the market's return during that time was significantly higher than the return on the trading strategy. On the other hand, the market also experienced a significantly higher maximum drawdown, which is a crucial metric for trading. It can be concluded that, with the implementation of the recommendations included at the end of the thesis, this trading strategy can be used for trading in real market conditions on the Bitcoin market or other volatile markets.
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Adaptive Trading Strategies for Cryptocurrencies
Filip, Marek ; Perešíni, Martin (referee) ; Homoliak, Ivan (advisor)
Obchodní strategie pro kryptoměny bývají založeny na padajícím nebo stoupajícím trhu. Kámen úrazu nastává, když jsou aplikovány na špatný trend v tak nestabilním trhu, jako je ten s kryptoměnami. Tato práce se zabývá možností adaptivních obchodních strategií, které se dokáží přizpůsobit na klesající a stoupající trendy v kryptoměnovém trhu. Analyzováním ceny Bitcoinu a vytvořením metriky risku, kde se díváme na extrémy vytvořené funkce, můžeme dojít k řešení návrhu adaptivních strategií. Zkoumají se jak dlouhodobé, tak krátkodobé možnosti investování. K vyhodnocování strategií a vykreslování časových řad je vytvořen rozšířitelný program pro testování historických dat. Výsledky jsou porovnány s tradičními přístupy, jako je HODL a rebalancování, přičemž bylo zjištěno, že při použití správných kritérií se mohou více než ztrojnásobit. Práce nabízí investorům nové způsoby zisků a zároveň dává čtenářům možnost nahlédnout do tvorby (adaptivních) strategií a jejich zpětného testování v kódu. Předpokládá se, že výsledky práce budou využívány automatizovanými obchodními systémy.
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Backtesting of Time Series Models
Stroukalová, Marika ; Houfková, Lucia (advisor) ; Zichová, Jitka (referee)
Title: Backtesting of Time Series Models Author: Marika Stroukalová Department: Department of Probability and Mathematical Statistics Supervisor: Mgr. Lucia Jarešová Supervisor's e-mail address: lucia.jaresova@centrum.cz Abstract: In the present work we study the basic models of financial time series (ARMA, GARCH), we focus on parameter estimation and forecasting in estimated models. We describe the means of estimating parametres and future values in the program R. In the theoretical section we also discuss the features of financial time series, define simple returns and log returns and we introduce the benefits of the log returns. We also apply the white noise model, ARMA(1,1) and GARCH(1,1) on historic time series of logarithmic returns of chosen stock exchange indices, we also backtest 1-step ahead fore- cats and 5-step ahead forecasts and we compare the results of these models. By empirical comparison of real data we also analyze how the models reac- ted on the present financial crisis and evaluate how the normal distribution assumption for the data held up. Keywords: time series, ARMA, GARCH, backtesting. 1
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Modeling Conditional Quantiles of Central European Stock Market Returns
Burdová, Diana ; Baruník, Jozef (advisor) ; Krištoufek, Ladislav (referee)
Most of the literature on Value at Risk concentrates on the unconditional nonparametric or parametric approach to VaR estimation and much less on the direct modeling of conditional quantiles. This thesis focuses on the direct conditional VaR modeling, using the flexible quantile regression and hence imposing no restrictions on the return distribution. We apply semiparamet- ric Conditional Autoregressive Value at Risk (CAViaR) models that allow time-variation of the conditional distribution of returns and also different time-variation for different quantiles on four stock price indices: Czech PX, Hungarian BUX, German DAX and U.S. S&P 500. The objective is to inves- tigate how the introduction of dynamics impacts VaR accuracy. The main contribution lies firstly in the primary application of this approach on Cen- tral European stock market and secondly in the fact that we investigate the impact on VaR accuracy during the pre-crisis period and also the period covering the global financial crisis. Our results show that CAViaR models perform very well in describing the evolution of the quantiles, both in abso- lute terms and relative to the benchmark parametric models. Not only do they provide generally a better fit, they are also able to produce accurate forecasts. CAViaR models may be therefore used as a...
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Application of technical analysis on algorithmic trading
Šíla, Jan ; Krištoufek, Ladislav (advisor) ; Křehlík, Tomáš (referee)
The thesis takes on the question of profitability of algorithmic trading based on trend and momentum indicators and examines whether or not it is possible to acquire systematic profits. It reviews the development of relevant literature over the last 100 years to determine whether the inner workings of the market can be quantified and plausibly modelled. On three major U.S. stock indices are then tested several different strategies to determine whether in the long- term, passive investment can be outperformed by active trading. Merit of the work lies in backtesting several strategies and interpreting the results according to unique characteristics of the indices.
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Algorithmization for decision support
Šišlák, Petr ; Budík, Jan (referee) ; Dostál, Petr (advisor)
The bachelor thesis is focused on the development of price indicators, which are used for effective trading on the stock exchange. Part of the work is a technical analysis of the selected currency pair at a chosen time and based on the results of create effective indicators and test them at different time intervals.
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Backtesting Value-at-Risk: Comparison of selected approaches
Šedivý, Milan ; Hendrych, Radek (advisor) ; Hurt, Jan (referee)
This thesis focuses on the evaluation of different backtesting methods that are routinely applied to one of the most commonly used risk measure Value- at-Risk. The main goal of this thesis is to present approaches used to backtest Value-at-Risk (including an introduction to common methods associated with Value-at-Risk forecasting). These statistical evaluation methods are then applied to historical data from the years 2005 to 2010, during which we experienced two major financial crises. Afterwards, the output of our analysis is thoroughly discussed. 1
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