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A Comparison of Classical and Automatic Procedure of Property Valuation
Dobiášová, Jana ; Klika, Pavel (referee) ; Cupal, Martin (advisor)
The advancement of technology and new knowledge in the field of computing provide opportunities for innovation in the valuation environment. Traditional approaches offer established valuation procedures, which, however, can be supplemented, supported, and accelerated by automated valuation. The goal of the thesis is to create a comparative platform of traditional valuation and automated valuation procedures in the market valuation regime, defining procedural models, categorization and selection of real valuations, and comparison using case studies at the market valuation level, initially based on theoretical definitions and conditions, and then practically with illustrative comparisons of valuation procedures.
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Forex automated trading system based on neural networks
Kačer, Petr ; Honzík, Petr (referee) ; Jirsík, Václav (advisor)
Main goal of this thesis is to create forex automated trading system with possibility to add trading strategies as modules and implementation of trading strategy module based on neural networks. Created trading system is composed of client part for MetaTrader 4 trading platform and server GUI application. Trading strategy modules are implemented as dynamic libraries. Proposed trading strategy uses multilayer neural networks for prediction of direction of 45 minute moving average of close prices in one hour time horizon. Neural networks were able to find relationship between inputs and output and predict drop or growth with success rate higher than 50%. In live demo trading, strategy displayed itself as profitable for currency pair EUR/USD, but it was losing for currency pair GBP/USD. In tests with historical data from year 2014, strategy was profitable for currency pair EUR/USD in case of trading in direction of long-term trend. In case of trading against direction of trend for pair EUR/USD and in case of trading in direction and against direction of trend for pair GBP/USD, strategy was losing.
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Forex automated trading system based on neural networks
Kačer, Petr ; Honzík, Petr (referee) ; Jirsík, Václav (advisor)
Main goal of this thesis is to create forex automated trading system with possibility to add trading strategies as modules and implementation of trading strategy module based on neural networks. Created trading system is composed of client part for MetaTrader 4 trading platform and server GUI application. Trading strategy modules are implemented as dynamic libraries. Proposed trading strategy uses multilayer neural networks for prediction of direction of 45 minute moving average of close prices in one hour time horizon. Neural networks were able to find relationship between inputs and output and predict drop or growth with success rate higher than 50%. In live demo trading, strategy displayed itself as profitable for currency pair EUR/USD, but it was losing for currency pair GBP/USD. In tests with historical data from year 2014, strategy was profitable for currency pair EUR/USD in case of trading in direction of long-term trend. In case of trading against direction of trend for pair EUR/USD and in case of trading in direction and against direction of trend for pair GBP/USD, strategy was losing.
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