Foreign exchange market is the largest financial market in the world and thus prediction of foreign exchange rate values is of interest to millions of people. In this research, I evaluated the performance of Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), Autoregressive Integrated Moving Average (ARIMA) and Moving Average (MA) on the USD/CAD and USD/AUD exchange pairs for 1-day, 1-week and 2-weeks predictions. For LSTM and GRU, twelve macroeconomic indicators along with past exchange rate values were used as features using data from January 2001 to December 2019. Predictions from each model were then integrated with uncertainty modeling to find out the chance of a model’s prediction being greater than or less than a user-defined target value using the error distribution from the test dataset, Monte-Carlo simulation trials and ChancCalc excel add-in. Results showed that ARIMA performs slightly better than LSTM and GRU for 1-day predictions for both USD/CAD and USD/AUD exchange pairs. However, when the period is increased to 1-week and 2-weeks, LSTM and GRU outperform both ARIMA and moving average for both USD/CAD and USD/AUD exchange pair.

The master thesis deals with proposal of automated trading system and its implementation in the Foreign exchange market environment. This system will be developed as investment model based on the analyzes performed and then tested on real data to achieve maximum stability and profit.

This diploma thesis deals with the design of a trading strategy and subsequent implementation of an automated trading system for the forex currency market. In this thesis, a "breakout" strategy with trade filtering based on moving average is created. Consequently, an automated trading system for the MetaTrader 4 platform is developed in MQL4 language. This thesis also deals with the back-testing and optimization of the system in order to maximize the stability and profit.

p.p1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 11.0px Helvetica} Automatization is higly used in stock traiding. The thesis try to exploid optimalization principles and machine learning. Developed and tested stock traiding system proces financial time series and generate optimal strategy

First chapter is about basic information pattern learning. Second chapter is about solutions of pattern recognition and about using artificial inteligence and there are basic informations about statistics and theory of chaos. Third chapter is focused on time series, types of time series and preprocessing. There are informations about time series in financial sector. Fourth charter discuss about pattern recognition problems and about prediction. Last charter is about software, which I did and there are informations about part sof program.

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.

This thesis deals with the automatization and comparison of two different strategies for the forex markets, based on the indicator, one from the tools of technical analysis, called Bollinger Bands. Both strategies are first optimized and then compared. Automatization of strategies will be implemented by? using the Meta Quotes Language for MetaTrader broker and its testing will be done on historical data. The goal with this thesis is the operational objective application of the better strategy in the environment of real market.

The aim of this work is to create an automated trading system for trading currency pairs using technical indicators and technical analysis. The proposed trading system is tested and optimized on historical price data. To verify the robustness of the proposed system was used walk-forward analysis. Automatic trading system also uses rules for position sizing and risk management of open positions. Created system is profitabel on historical price data and also in the walk-forward analysis.

The goal of this Master's thesis is to analyze financial data by focusing primarily on the search of market inefficiencies that may lead to capitalization of found anomalies. The data comes from various sources and they need to be preprocessed. The analysis is based on high frequency time series statistical methods. The resultant characteristics are visualized.

The subject of this diploma thesis is a design and implementation of an automated trading system for the forex market. It includes an analysis of the main concepts and methods of technical analysis and money management, which constitute an essential theoretical basis for the subsequent practical design of an automatic system. The objective of this work is a development of an automated trading system whose robustness and stability is tested by a walk forward analysis.