Thesis is focused on trading information systems in capital markets. The aim of this thesis is to analyze trading information systems and create model of hypothetical online trading platform. The model should consolidate the knowledge of the existing solutions for trading in the capital market. The thesis is divided into three parts. The theoretical part provides a basic description of trading information systems in the capital market. Second, analytic part of the thesis is focused on structure of brokerage trading information system. The first analysis is focused on analyze portfolio of the current trading platforms offered by brokerage. The second analysis is focused on analyze internal functionality structure of the selected group of trading platform. The aim of this analysis is to find and describe the portfolio functionalities of trading platforms and identified the basis or standard functionalities of trading platforms. Resulting data from second analysis are important for third part for this thesis. Third, main part of this thesis is devoted on creating model of hypothetical online trading platform. This part consist several sets of models and together created one complex model of hypothetical online trading platform.

Computational Intelligence has found a challenging testbed for various paradigms in the financial sector. Extensive research has resulted in numerous financial applications using neural networks and evolutionary computation, mainly genetic algorithms and genetic programming. More recent advances in the field of computational intelligence have not yet been applied as extensively or have not become available in the public domain, due to the confidentiality requirements of financial institutions. This study investigates how co-evolution together with the combination of par- ticle swarm optimisation and neural networks could be used to discover competitive security trading agents that could enable the timing of buying and selling securities to maximise net profit and minimise risk over time. The investigated model attempts to identify security trend reversals with the help of technical analysis methodologies. Technical market indicators provide the necessary market data to the agents and reflect information such as supply, demand, momentum, volatility, trend, sentiment and retracement. All this is derived from the security price alone, which is one of the strengths of technical analysis and the reason for its use in this study. The model proposed in this thesis evolves trading strategies within a single pop- ulation of competing agents, where each agent is represented by a neural network. The population is governed by a competitive co-evolutionary particle swarm optimi- sation algorithm, with the objective of optimising the weights of the neural networks. A standard feed forward neural network architecture is used, which functions as a market trend reversal confidence. Ultimately, the neural network becomes an amal- gamation of the technical market indicators used as inputs, and hence is capable of detecting trend reversals. Timely trading actions are derived from the confidence output, by buying and short selling securities when the price is expected to rise or fall respectively. No expert trading knowledge is presented to the model, only the technical market indicator data. The co-evolutionary particle swarm optimisation model facilitates the discovery of favourable technical market indicator interpretations, starting with zero knowledge. A competitive fitness function is defined that allows the evaluation of each solution relative to other solutions, based on predefined performance metric objectives. The relative fitness function in this study considers net profit and the Sharpe ratio as a risk measure. For the purposes of this study, the stock prices of eight large market capitalisation companies were chosen. Two benchmarks were used to evaluate the discovered trading agents, consisting of a Bollinger Bands/Relative Strength Index rule-based strategy and the popular buy-and-hold strategy. The agents that were discovered from the proposed hybrid computational intelligence model outperformed both benchmarks by producing higher returns for in-sample and out-sample data at a low risk. This indicates that the introduced model is effective in finding favourable strategies, based on observed historical security price data. Transaction costs were considered in the evaluation of the computational intelligent agents, making this a feasible model for a real-world application. Copyright / Dissertation (MSc)--University of Pretoria, 2010. / Computer Science / unrestricted

This thesis deals with the development of application for fundamental analysis of stocks. Main goal of the thesis is to make application, which will be helpful for individual investors in performing fundamental analysis of stocks. It is desktop application, which performs calculations with high precision and it uses free on-line sources of financial data. The application was developed in Java programming language. It will be available as a~freeware alternative to proprietary fundamental analysis software on the market. The first part describes capital market and stock exchange, makes characteristics of stock and explains principle of fundamental analysis of stocks. In the second part, the market research of fundamental analysis software was realized, design and implementation of the application for fundamental analyiss was described and the application was evaluated.