This diploma thesis delas with algoritmic trading using neural networks. In the first part, some basic information about stock trading, algorithmic trading and neural networks are given. In the second part, data sets of historical market data are used in trading simulation and also as training input of neural networks. Neural networks are used by automated strategy for predicting future stock price. Couple of automated strategies with different variants of neural networks are evaluated in the last part of this work.

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

L’objectif principal de cette thèse est d’apporter de nouveaux résultats théoriques concernant la performance d’investissements basés sur des modèles stochastiques. Pour ce faire, nous considérons la stratégie optimale d’investissement dans le cadre d’un modèle d’actif risqué à volatilité constante et dont la tendance est un processus caché d’Ornstein Uhlenbeck. Dans le premier chapitre,nous présentons le contexte et les objectifs de cette étude. Nous présentons, également, les différentes méthodes utilisées, ainsi que les principaux résultats obtenus. Dans le second chapitre, nous nous intéressons à la faisabilité de la calibration de la tendance. Nous répondons à cette question avec des résultats analytiques et des simulations numériques. Nous clôturons ce chapitre en quantifiant également l’impact d’une erreur de calibration sur l’estimation de la tendance et nous exploitons les résultats pour détecter son signe. Dans le troisième chapitre, nous supposons que l’agent est capable de bien calibrer la tendance et nous étudions l’impact qu’a la non-observabilité de la tendance sur la performance de la stratégie optimale. Pour cela, nous considérons le cas d’une utilité logarithmique et d’une tendance observée ou non. Dans chacun des deux cas, nous explicitons la limite asymptotique de l’espérance et la variance du rendement logarithmique en fonction du ratio signal-sur-bruit et de la vitesse de retour à la moyenne de la tendance. Nous concluons cette étude en montrant que le ratio de Sharpe asymptotique de la stratégie optimale avec observations partielles ne peut dépasser 2/(3^1.5)∗100% du ratio de Sharpe asymptotique de la stratégie optimale avec informations complètes. Le quatrième chapitre étudie la robustesse de la stratégie optimale avec une erreur de calibration et compare sa performance à une stratégie d’analyse technique. Pour y parvenir, nous caractérisons, de façon analytique,l’espérance asymptotique du rendement logarithmique de chacune de ces deux stratégies. Nous montrons, grâce à nos résultats théoriques et à des simulations numériques, qu’une stratégie d’analyse technique est plus robuste que la stratégie optimale mal calibrée. / The aim of this thesis is to study the robustness of the optimal trading strategy. The setting we consider is that of a stochastic asset price model where the trend follows an unobservable Ornstein-Uhlenbeck process. In the first chapter, the background and the objectives of this study are presented along with the different methods used and the main results obtained. The question addressed in the second chapter is the estimation of the trend of a financial asset, and the impact of misspecification. Motivated by the use of Kalman filtering as a forecasting tool, we study the problem of parameters estimation, and measure the effect of parameters misspecification. Numerical examples illustrate the difficulty of trend forecasting in financial time series. The question addressed in the third chapter is the performance of the optimal strategy,and the impact of partial information. We focus on the optimal strategy with a logarithmic utility function under full or partial information. For both cases, we provide the asymptotic expectation and variance of the logarithmic return as functions of the signal-to-noise ratio and of the trend mean reversion speed. Finally, we compare the asymptotic Sharpe ratios of these strategies in order to quantify the loss of performance due to partial information. The aim of the fourth chapter is to compare the performances of the optimal strategy under parameters mis-specification and of a technical analysis trading strategy. For both strategies, we provide the asymptotic expectation of the logarithmic return as functions of the model parameters. Finally, numerical examples find that an investment strategy using the cross moving averages rule is more robust than the optimal strategy under parameters misspecification.

Aim of this thesis is to introduce questions about trading stocks on global stock exchange. It shows up basics ideas, which are necessary to understand the system of trading stocks, building a bussines strategy and its automatization by simple information technology techniques. In the following, there is a description of concept and implementation of business system for testing a trading strategy, which is based on historical market data analysis. The next part of this work is focused on the demonstration system and its expansion possibilities. Whole aplication is created by means of scripting language PHP and Javascript, markup language HTML, using the MySQL database system.

This master thesis deals with the price prediction on financial markets. It describes automated trading systems based on technical analysis and discusses a soft computing approach to construction of such systems. Also, this thesis combines conventional trading strategies with the fuzzy logic. The practical part of this thesis contains also a framework for composing, simulation and analysis of the automated trading strategies. The simulator contained in this framework is implemented in the Java language and based on DEVS formalism. Because of this, there is a possibility to embed real-time components into the trading model. This work contains also a database of historical financial data and tools for their automatic actualization.

[pt] Esta dissertação tem por objetivo investigar a possibilidade de obtenção de retornos anormais – utilizando-se o período entre jan/2005 e dez/2014 como espaço amostral – no mercado acionário brasileiro. Investigou-se, então, a hipótese de reversão à média móvel de 21 dias para os ativos integrantes do Índice Brasil 100 – IBrX-100. Estratégias contrárias com carteiras compradas em ações cujos preços estivessem abaixo da média móvel e vendidas em ações cujos preços estivessem acima da média móvel foram montadas e testadas para os referidos períodos. Por fim, não foram encontradas evidências em favor da reversão à média móvel de 21 dias para o período estudado. / [en] The goal of this study is to investigate the possibility of obtaining abnormal returns – using the period between January/2005 and December/2014 –in the Brazilian stock market. The main hypothesis in focus is the moving average of 21 days reversion of the securities of the Index Brasil 100 – IBrX 100. Contrarian strategies were used with portfolios built by buying stocks whose prices were below the moving average and selling stocks whose prices are above the moving average. There is no evidence in favor of the reversion and in favor of the possibility of abnormal returns in the study period.

This paper uses a back-propagating neural network (BPN) to predict the price movements of major crypto currencies, leveraging technical factors as well as measurements of collective sentiment derived from the micro-blogging network Twitter. Our dataset consists of daily, hourly and minutely price levels for Bitcoin, Ether and Litecoin along with 8 popular technical indicators, as well as all tweets with the currencies' cash tags during respective time periods. Insprired by previous research which suggest that artificial neural networks are superior forecasting models in this setting, we were able to create a system generating automated investment decisions on a daily, hourly and minutely time basis. The study concluded that price trends are indeed predictable, with a correct prediction rate above 50% for all models, and corrensponding profitable trading strategies for all currencies on an hourly basis when neglecting trading fees, buy-sell spreads and order delays. The overall highest predictability is obtained on the hourly trading interval for Bitcoin, yielding an accuracy of 55.74% and a cumulative return of 175.1% between October 16, 2021 and December 31, 2021. / I denna studie används ett bakåtpropagerande neoronnät (BPN) för att förutsäga prisrörelser i större kryptovalutor med hjälp av tekniska faktorer och kvantifiering av kollektivt sentimentet från mikrobloggnätverket Twitter. Vårt dataset består av dagliga, timvisa och minutvisa prisnivåer för Bitcoin, Ether och Litecoin tillsammans med 8 populära tekniska indikatorer, samt alla tweets med valutornas "cash tags" under respektive tidsperiod. Med inspiration från tidigare forskning som hävdar att artificiella nauronnät är överlägsna prognosmodeller i denna typ av analys kunde vi skapa ett system som genererar automatiska investeringsbeslut på daglig, timvis och minutvis basis. Vi hävdar med denna studie att pristrender är förutsägbara för dessa kryptovalutor, med en korrekt förutsägelsefrekvens på över 50% för alla modeller, och med lönsamma handelsstrategier för alla valutor på timbasis när man bortser från handelsavgifter, köp- och säljspreadar och orderfördröjningar. Den högsta förutsägbarheten erhålls på timhandelsintervallet för Bitcoin, vilket ger en nogrannhet på 55,74% och en ackumulerad avkastning på 175,1% mellan den 16 oktober 2021 och den 31 december 2021.

We investigate the novel idea of using computer vision to predict future stock price movement, which is performed by training a convolutional neural network (CNN) to detect patterns in images of stock graphs. Subsequently, we create a portfolio strategy based on the CNN stock price predictions to see if these predictions can generate alpha for investors. We apply this method in the Swedish stock market and evaluate the performance of CNN portfolios across two different exchanges and various stock indices segmented by market capitalisation. Our findings show that trading based on CNN predictions can outperform our benchmarks and generate positive alpha. Most of our portfolios generate positive alpha before transaction costs, while one also generates positive alpha after deducting transaction costs. Further, our results demonstrate that CNN models are capable of successfully generalising their trained knowledge, being able to detect information in stock graphs it has never seen before. This suggests that CNN models are not limited to the characteristics present in their training data, indicating that models trained under one set of market conditions can also be effective in a different market scenario. Our resultsfurther strengthen the overall findings of other researchers utilising similar methods as ours.

The South African white maize market is considered to be significantly more volatile than any other agricultural product traded on the South African Futures Exchange (SAFEX). This accentuates the need to effectively manage price risk, by means of hedging, to ensure a more profitable and sustainable maize production sector (Geyser, 2013:39; Jordaan, Grové, Jooste, A. & Jooste, Z.G., 2007:320). However, hedging at lower price levels might result in significant variation margins or costly buy–outs in order to fulfil the contract obligations. This challenge is addressed in this study by making use of technical analysis, focusing on the development of a practical and applicable composite technical indicator with the purpose of improving the timing of price risk management decisions identified by individual technical indicators. This may ultimately assist a producer in achieving a higher average hedge level compared to popular individual technical indicators. The process of constructing a composite indicator was commenced by examining the prevailing tendency of the market. By making use of the Directional Movement Index (DMI), as identified in the literature study, the market was found to continually shift between trending prices (prices moving either upwards or downwards) and prices trading sideways. Consequently, implementing only a leading (statistically more suitable for trading markets) or lagging (statistically more suitable for trending markets) technical indicator may generate false sell signals, as demonstrated by the application of these technical indicators in the white maize market. This substantiated the motivation for compiling a composite indicator that takes both leading and lagging indicators into account to more accurately identify hedging opportunities. The composite indicator made use of the Relative Strength Index (RSI) and Stochastic oscillator as leading indicators, and the Exponential Moving Average (EMA) and Moving Average Convergence Divergence (MACD) as lagging indicators. The results validated the applicability of such a composite indicator, as the composite indicator outperformed the individual technical indicators in the white maize market. The composite indicator achieved the highest average hedge level, the lowest average sell signals generated over the entire period, as well as the highest average hedge level as a percentage of the maximum price over the entire period. Hence, the composite indicator recognised hedging opportunities more accurately compared to individual technical indicators, which ultimately led to higher achieved hedging levels. / MCom. (Risk management), North-West University, Potchefstroom Campus, 2014

The South African white maize market is considered to be significantly more volatile than any other agricultural product traded on the South African Futures Exchange (SAFEX). This accentuates the need to effectively manage price risk, by means of hedging, to ensure a more profitable and sustainable maize production sector (Geyser, 2013:39; Jordaan, Grové, Jooste, A. & Jooste, Z.G., 2007:320). However, hedging at lower price levels might result in significant variation margins or costly buy–outs in order to fulfil the contract obligations. This challenge is addressed in this study by making use of technical analysis, focusing on the development of a practical and applicable composite technical indicator with the purpose of improving the timing of price risk management decisions identified by individual technical indicators. This may ultimately assist a producer in achieving a higher average hedge level compared to popular individual technical indicators. The process of constructing a composite indicator was commenced by examining the prevailing tendency of the market. By making use of the Directional Movement Index (DMI), as identified in the literature study, the market was found to continually shift between trending prices (prices moving either upwards or downwards) and prices trading sideways. Consequently, implementing only a leading (statistically more suitable for trading markets) or lagging (statistically more suitable for trending markets) technical indicator may generate false sell signals, as demonstrated by the application of these technical indicators in the white maize market. This substantiated the motivation for compiling a composite indicator that takes both leading and lagging indicators into account to more accurately identify hedging opportunities. The composite indicator made use of the Relative Strength Index (RSI) and Stochastic oscillator as leading indicators, and the Exponential Moving Average (EMA) and Moving Average Convergence Divergence (MACD) as lagging indicators. The results validated the applicability of such a composite indicator, as the composite indicator outperformed the individual technical indicators in the white maize market. The composite indicator achieved the highest average hedge level, the lowest average sell signals generated over the entire period, as well as the highest average hedge level as a percentage of the maximum price over the entire period. Hence, the composite indicator recognised hedging opportunities more accurately compared to individual technical indicators, which ultimately led to higher achieved hedging levels. / MCom. (Risk management), North-West University, Potchefstroom Campus, 2014