Trading Orders Algorithm Development

The chapter puts into the business of financial markets area in greater detail at FOREX currency market. It describes the main methods used for in currencies trade. The main goal of this paper is to explain the principle of creating an Automated Trading System (ATS) with the MQL4 language. The chapter shows concrete architectural elements of the program on the demonstration examples and it is a guide for the development of an ATS. The main benefit is creation of the original trading system, which optimizes an ATS usage on the base of historical data in practice. Optimization of the trading parameters is based on the equity performance in the historical periods.

Modeling and Language Support for the Pattern Management

Patterns are mentioned usually in the extraction context. Little stress is posed in their representation and management. This chapter is focused on the representation of the patterns, manipulation with patterns and query patterns. Crucial issue can be seen in systematic approach to pattern management and specific pattern query language which takes into consideration semantics of patterns. In the background we discuss two different approaches to the pattern store and manipulation (based on inductive database and PANDA project). General pattern model is illustrated using abstract data type implemented in Oracle. In the following chapters the introduction to querying patterns and simple scheme of the architecture PBMS is shown.

Distribution Signals between the Transmitter and Antenna – Event B Model

What we have solved: the possibility to receive DVB-T (Digital Video Broadcasting Terrestrial) with respect to local conditions for signal. We have deduced: variables that represent a set of so-called useful signal, i.e. the signal suitable for further processing – amplification and distribution. As a case study we have choosed few examples using Event B Method to show possibilty of solving komplex projects by this method. The resulting program can be proven to be correct as for its theoretical backgrounds. It is based on Zermelo-Fraenkel set theory with axion of choice, the concept of generalized substitution and structuring mechanismus (machine, refinement, implementation). B methods are accompanied by mathematical proofs that justify them. Abstract machine in this example connected with mathematical modelling solves an ability to receive DVB-T signal from the plurality of signals, both useful and useless for further processing.

Recognition of Patterns with Fractal Structure in Time Series

The chapter is focused on an analysis and pattern recognition in time series, which are fractal in nature. Our goal is to find and recognize important Elliott wave patterns which repeatedly appear in the market history for the purpose of prediction of subsequent trader's action. The pattern recognition approach is based on neural networks. Artificial neural networks are suitable for pattern recognition in time series mainly because of learning only from examples. This chapter introduces a methodology that allows analysis of Elliot wave's patterns in time series for the purpose of a trend prediction. The functionality of the proposed methodology was validated in experimental simulations, for whose implementation was designed and created an application environment. In conclusion, all results were evaluated and compared with each other. This chapter is composed only from our published works that present our proposed methodology. We see the main contribution of this chapter in its range, which allows us to present all our published works concerning our proposed methodology together.

Research on Processing the Brain Activity in BCI System

The basic idea of BCI (Brain Computer Interface) is to connect brain waves with an output device through some interface. Human brain activity can be measured by many technologies. In our research, we use EEG (Electroencephalography) technology. This chapter will deal with processing of EEG signal and its utilization in practical applications using BCI technology mentioned above. This chapter is organized as follows. Firstly, the basic knowledge about EEG technology, brain and biometry is briefly summarized. Secondly, research of authors is presented. Finally, the future research direction is mentioned.

Artificial Intelligence Algorithms for Classification and Pattern Recognition

Classification tasks can be solved using so-called classifiers. A classifier is a computer based agent which can perform a classification task. There are many computational algorithms that can be utilized for classification purposes. Classifiers can be broadly divided into two categories: rule-based classifiers and computational intelligence based classifiers usually called soft computing. Rule-based classifiers are generally constructed by the designer, where the designer defines rules for the interpretation of detected inputs. This is in contrast to soft-computing based classifiers, where the designer only creates a basic framework for the interpretation of data. The learning or training algorithms within such systems are responsible for the generation of rules for the correct interpretation of data.

Analysis and Classification Tools for Automatic Process of Punches and Kicks Recognition

This chapter deals with the pattern recognition in the time series. The data was obtained from the measurement of the force profiles via strain gauge sensor. This pattern recognition should help to classify different techniques of the professional defence (direct punch, direct and round kicks) and gender of the attacker. The aim is to find a suitable feature sets from the measured raw data which has to be transferred in appropriate way; in the case of this research spectral analysis or discrete cosine transformation were used. Based on the previous experience of authors, artificial neural networks with Levenberg-Marquardt training algorithm were selected as a classifier. In these experimentations, students from the Faculty of Applied Informatics, Tomas Bata University in Zlin participated. The results were successful and higher level than expected accuracy of 85% was achieved. The future plans include involving more participants and repeating the simulations to confirm the proposed technique.

Introduction to Time Series

The goal of this chapter is a description of the time series. This chapter will review techniques that are useful for analyzing time series data, that is, sequences of measurements that follow non-random orders. Unlike the analyses of random samples of observations that are discussed in the context of most other statistics, the analysis of time series is based on the assumption that successive values in the data file represent consecutive measurements taken at equally spaced time intervals. There are two main goals of time series analysis: (a) identifying the nature of the phenomenon represented by the sequence of observations, and (b) forecasting (predicting future values of the time series variable). Both of these goals require that the pattern of observed time series data is identified and more or less formally described. Once the pattern is established, we can interpret and integrate it with other data.