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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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Decomposition and Forecast in Time Series
Šramková, Kristína ; Holec, Tomáš (referee) ; Bednář, Josef (advisor)
Bachelor thesis is focused on statistic methods for analysis and modelling time series. In particular decomposition techniques, which decompose time series into particular components and model trend component to predict future values. First part of thesis is focused on summary of essential knowledge of regression, basic properties of time series and their decomposition models. Hereinafter are discussed models of trend curves. In conclusion the mentioned methods are applied on real data and its modeling in software Statistica.
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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.
|
|
|
Decomposition and Forecast in Time Series
Šramková, Kristína ; Holec, Tomáš (referee) ; Bednář, Josef (advisor)
Bachelor thesis is focused on statistic methods for analysis and modelling time series. In particular decomposition techniques, which decompose time series into particular components and model trend component to predict future values. First part of thesis is focused on summary of essential knowledge of regression, basic properties of time series and their decomposition models. Hereinafter are discussed models of trend curves. In conclusion the mentioned methods are applied on real data and its modeling in software Statistica.
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