This master thesis is focused on technical analysis, which is used for forecasting the future trends of stocks. In the first part of master’s thesis are described theoretical basis, which are a base of practical part of this thesis. Next part of this thesis describes the design of application designed to support the technical analysis. Main part of this master thesis deals with building own trading rules and trading strategy.

Abstract The impenetrable movement and crash of the stock market is always the most intriguing research task of any financial researcher. Nowadays, it has been proved that the movements of financial asset have the property of non-linearity or near-chaos and shows some tendency within a given period. We used the R/S analysis as the tool to indicate the tendency, and those stocks as our researching objects. We then combined purely price technical analysis indicators and genetic algorithms to form a predicting model. Then we compared our genetic predicting model with the traditional ARIMA analysis and hope to find out the invisible pattern under price volatility. And we hope our model could assist investors in assessing the stock markets more objectively and reduce the risk of stock investment. The researching target is TSMC(2330). We covered the period from 5 September 1994 to 28 December 1999, resulting in 1490 trading days. Historical data are available from Taiwan Economic Journal (TEJ). We execute the researching comparison by bear-market, bull-market, and bull-then-bear market and concluded as follows. 1. After the R/S analysis, we got the Hurst exponent of TSMC to be 0.849855 and the trending cycle was 940. It has proved that the market has tendency and indirectly showed that the Taiwan stock market was not efficient. 2. According to directional precision, our predicting model apparently outpaced the ARIMA model in these three periods. The reason was that our model grabbed more information than the ARIMA model. 3. If we only think about the inputs and outputs, our model seems to be a proper framework for explaining the relationships among variables in comparison with the neural network model having the same input and output variables. 4. We can deduce the invisible relationships of price technical indicators and the closing price. 5. Genetic predicting model can detect the prevailing trend of the learning periods. 6. The shorter the learning period, the better the predicting effects. As a whole and conservatively speaking, we have 70% confidence in directional precision. 7. If we combine proper trading strategy with genetic predicting model and deduct the transaction cost, we still get a better profit than buy-and-hold strategy and have some maneuvering flexibility. 8. After hypothesis testing, our predicting model seems to have some potential of ex ante prediction, but the stability and usability still need further study. In short, we proposed the ex post stock price movement learning model and the viable direction of ex ante prediction. Investors can take advantage of the flexibility of the predicting model and avoid using the over-complex and rigid trading strategies.

The diploma thesis deals with the design of an automatic trading system for trading on the market of selected commodities, constructed with the help of technical indicators. It also includes system optimization using genetic algorithms to maximize profit and stability. Finally, an economic evaluation of the achieved results is prepared.

The thesis deals with technical analysis and introduction to the possibilities of its use in deciding to invest in the capital market. The theoretical part describes the theoretical background needed for the initial introduction to technical analysis and its indicators. In the practical part are gradually elaborated various indicators of technical analysis applied to the development of the shares of Intel Corporation and in the end there is the comparison of their profitability.

This diploma thesis focuses on the general characteristic of the technical analysis and its use in deciding to invest in the stock market. Thesis includes theoretical data, which deal with problems concerning an investment in the capital market. In the practical part are some indicators of technical analysis applied to the selected shares, by means of them is formed investing strategy for the beginning investor. Furthermore, in practical part is described the design of application for technical analysis.

This thesis focused on general characteristics of technical analysis and the use of its instruments to support making decisions when trading in stock market. Within the theoretic part are processed theoretic solutions which are primarily relevant to technical analysis. At the specific stock item is by the help of proposed application consequently implemented the analysis of individual indicators of technical analysis. At the conclusion is compared the profitability and reliability of used indicators.

This master’s thesis deals with the problems of a technical analysis and its use. The first part of thesis describes theoretical background of the technical analysis and basic concepts and principles of the currency market Forex. The second part is devoted to analyzing the current situation in the environment of currency market. The output of the thesis is a desktop application for the support of technical analysis. The design and development of the application is described in the last part of this thesis.

The foreign exchange (Forex) market has over 5 trillion USD turnover per day. In addition, it is one of the most volatile and dynamic markets in the world. Market conditions continue to change every second. Algorithmic trading in Financial markets have received a lot of attention in recent years. However, only few literature have explored the applicability and performance of various dynamic multi-objective algorithms (DMOAs) in the Forex market. This dissertation proposes a dynamic multi-swarm multi-objective particle swarm optimization (DMS-MOPSO) to solve dynamic MOPs (DMOPs). In order to explore the performance and applicability of DMS-MOPSO, the algorithm is adapted for the Forex market. This dissertation also explores the performance of di erent variants of dynamic particle swarm optimization (PSO), namely the charge PSO (cPSO) and quantum PSO (qPSO), for the Forex market. However, since the Forex market is not only dynamic but have di erent con icting objectives, a single-objective optimization algorithm (SOA) might not yield pro t over time. For this reason, the Forex market was de ned as a multi-objective optimization problem (MOP). Moreover, maximizing pro t in a nancial time series, like Forex, with computational intelligence (CI) techniques is very challenging. It is even more challenging to make a decision from the solutions of a MOP, like automated Forex trading. This dissertation also explores the e ects of ve decision models (DMs) on DMS-MOPSO and other three state-of-the-art DMOAs, namely the dynamic vector-evaluated particle swarm optimization (DVEPSO) algorithm, the multi-objective particle swarm optimization algorithm with crowded distance (MOPSOCD) and dynamic non-dominated sorting genetic algorithm II (DNSGA-II). The e ects of constraints handling and the, knowledge sharing approach amongst sub-swarms were explored for DMS-MOPSO. DMS-MOPSO is compared against other state-of-the-art multi-objective algorithms (MOAs) and dynamic SOAs. A sliding window mechanism is employed over di erent types of currency pairs. The focus of this dissertation is to optimized technical indicators to maximized the pro t and minimize the transaction cost. The obtained results showed that both dynamic single-objective optimization (SOO) algorithms and dynamic multi-objective optimization (MOO) algorithms performed better than static algorithms on dynamic poroblems. Moreover, the results also showed that a multi-swarm approach for MOO can solve dynamic MOPs. / Dissertation (MEng)--University of Pretoria, 2019. / Computer Science / MSc / Unrestricted

本研究定義運用技術指標建構投資決策之步驟，明確描述各步驟細節，投資人根據此流程定義，可利用技術指標逐步運算出投資標的之投資價值，作為最終投資決策之依據。同時，本研究建立技術指標、偵測機制等分類架構，讓投資人主觀的投資需求對應（map）到技術指標，建立個人化的投資決策。 / This paper defines the stages that how to build an investment decision with technical indicators and describes the details of each stage definitely. According to the process definition, investors can calculate the investment value of the investment target with technical indicators step by step. The investment value can be the foundation of the final investment decision. This paper also establishs both classificaton models of technical indicators and detect mechanisms. It makes investors map their subjective demand for investment information to technical indicators, personlize their investment descions.

This thesis has two main objectives. It attempts to describe the process of developing an intraday discretionary trading strategy based on technical analysis and to create through the process an intraday discretionary strategy for speculative trading of contracts for difference on the OTC market. The theoretical part of the thesis is divided into three chapters providing the necessary knowledge for creation of an intraday discretionary trading strategy in the practical part of the thesis. The emphasis is put on the description of different tools and methods technical traders usually use in their strategies. The development process of the strategy is divided into three parts which are Strategy Creation, Backtest and Optimization. These parts are further divided into smaller sections dealing with different issues of the strategy development process. The final outcome of the work is structured development process of discretionary trading strategies and also highly profitable intraday discretionary strategy for trading of Gold based contracts for difference.