The thesis analyses high frequency trading, specifically its main characteristics, which make it different from algorithmic trading. Furthermore, the thesis looks closer into major risks, which are new to market, and their impact on market quality and other investors. The next chapter is dedicated to trading strategies, which are typical for high frequency trading. In conclusion, there is discussed the impact on the market quality caused by high frequency trading, namely in terms of liquidity, volatility and price discovery.

In Finance, the use of Automated Trading Systems (ATS) on markets is growing every year and the trades generated by an algorithm now account for most of orders that arrive at stock exchanges (Kissell, 2020). Historically, these systems were based on advanced statistical methods and signal processing designed to extract trading signals from financial data. The recent success of Machine Learning has attracted the interest of the financial community. Reinforcement Learning is a subcategory of machine learning and has been broadly applied by investors and researchers in building trading systems (Kissell, 2020). In this thesis, we address the issue that deep reinforcement learning may be susceptible to sampling errors and over-fitting and propose a robust deep reinforcement learning method that integrates techniques from reinforcement learning and robust optimization. We back-test and compare the performance of the developed algorithm, Robust DDPG, with UBAH (Uniform Buy and Hold) benchmark and other RL algorithms and show that the robust algorithm of this research can reduce the downside risk of an investment strategy significantly and can ensure a safer path for the investor’s portfolio value.

Em nosso trabalho analisamos os dados provenientes da BM&F Bovespa, a bolsa de valores de São Paulo, no período de janeiro de 2011, referentes aos índices: BOVESPA (IND), o mini índice BOVESPA (WIN) e a taxa de câmbio (DOL). Estes dados são de alta frequência e representam vários aspectos da dinâmica das negociações. No conjunto de valores encontram-se horários e datas dos negócios, preços, volumes oferecidos e outras características da negociação. A primeira etapa da tese foi extrair as informações necessárias para análises a partir de um arquivo em protocolo FIX, foi desenvolvido um programa em R com essa finalidade. Em seguida, estudamos o carácter da dependência temporal nos dados, testando as propriedades de Markov de um comprimento de memória fixa e variável. Os resultados da aplicação mostram uma grande variabilidade no caráter de dependência, o que requer uma análise mais aprofundada. Acreditamos que esse trabalho seja de muita importância em futuros estudos acadêmicos. Em particular, a parte do carácter específico do protocolo FIX utilizado pela Bovespa. Este era um obstáculo em uma série de estudos acadêmicos, o que era, obviamente, indesejável, pois a Bovespa é um dos maiores mercados comerciais do mundo financeiro moderno. / In our work we analyzed data from BM&F Bovespa, the stock exchange in São Paulo. The dataset refers to the month January 2011 and is related to BOVESPA index (IND), mini BOVESPA index (WIN) and the exchange tax (DOL). These, are high frequency data representing various aspects of the dynamic of negotiations. The array of values includes the dates/times of trades, prices, volumes offered for trade and others trades characteristics. The first stage of the thesis was to extract information to the analysis from an archive in FIX protocol, it was developed a program in R with this aim. Afterwards, we studied the character of temporal dependence in the data, testing Markov properties of a fixed and variable memory length. The results of this application show a great variability in the character of dependence, which requires further analysis. We believe that our work is of great importance in future academic studies. In particular, the specific character of the FIX protocol used by Bovespa. This was an obstacle in a number of academic studies, which was, obviously, undesirable since Bovespa is one of the largest trading markets in the modern financial world.

One group of information systems that have attracted a lot of attention during the past decade are financial information systems, especially systems pertaining to financial markets and electronic trading. Delivering accurate and timely information to traders substantially increases their chances of making better trading decisions.Since the dawn of electronic exchanges the trading community has seen a proliferation of computer-based intelligence within the field, enabled by an exponential growth of processing power and storage capacity due to advancements in computer technology. The financial benefits associated with outperforming the market and gaining leverage over the competition has fueled the research of computational intelligence in financial information systems. This has resulted in a plethora of different techniques.The most prevalent techniques used within algorithmic trading today consist of various machine learning technologies, borrowed from the field of data mining. Neural networks have shown exceptional predictive capabilities time and time again.One recent machine learning technology that has shown great potential is Hierarchical Temporal Memory (HTM). It borrows concepts from neural networks, Bayesian networks and makes use of spatiotemporal clustering techniques to handle noisy inputs and to create invariant representations of patterns discovered in its input stream. In a previous paper [1], an initial study was carried-out where the predictive performance of the HTM technology was investigated within algorithmic trading of financial markets. The study showed promising results, in which the HTM-based algorithm was profitable across bullish-, bearish and horizontal market trends, yielding comparable results to its neural network benchmark. Although, the previous work lacked any attempt to produce near optimal trading models.Evolutionary optimization methods are commonly regarded as superior to alternative methods. The simplest evolutionary optimization technique is the genetic algorithm, which is based on Charles Darwin's evolutionary theory of natural selection and survival of the fittest. The genetic algorithm combines exploration and exploitation in the search for optimal models in the solution space.This paper extends the HTM-based trading algorithm, developed in the previous work, by employing the genetic algorithm as an optimization method. Once again, neural networks are used as the benchmark technology since they are by far the most prevalent modeling technique used for predicting financial markets. Predictive models were trained, validated and tested using feature vectors consisting of technical indicators, derived from the E-mini S&P 500 index futures market.The results show that the genetic algorithm succeeded in finding predictive models with good performance and generalization ability. The HTM models outperformed the neural network models, but both technologies yielded profitable results with above average accuracy. / Program: Magisterutbildning i informatik

Em nosso trabalho analisamos os dados provenientes da BM&F Bovespa, a bolsa de valores de São Paulo, no período de janeiro de 2011, referentes aos índices: BOVESPA (IND), o mini índice BOVESPA (WIN) e a taxa de câmbio (DOL). Estes dados são de alta frequência e representam vários aspectos da dinâmica das negociações. No conjunto de valores encontram-se horários e datas dos negócios, preços, volumes oferecidos e outras características da negociação. A primeira etapa da tese foi extrair as informações necessárias para análises a partir de um arquivo em protocolo FIX, foi desenvolvido um programa em R com essa finalidade. Em seguida, estudamos o carácter da dependência temporal nos dados, testando as propriedades de Markov de um comprimento de memória fixa e variável. Os resultados da aplicação mostram uma grande variabilidade no caráter de dependência, o que requer uma análise mais aprofundada. Acreditamos que esse trabalho seja de muita importância em futuros estudos acadêmicos. Em particular, a parte do carácter específico do protocolo FIX utilizado pela Bovespa. Este era um obstáculo em uma série de estudos acadêmicos, o que era, obviamente, indesejável, pois a Bovespa é um dos maiores mercados comerciais do mundo financeiro moderno. / In our work we analyzed data from BM&F Bovespa, the stock exchange in São Paulo. The dataset refers to the month January 2011 and is related to BOVESPA index (IND), mini BOVESPA index (WIN) and the exchange tax (DOL). These, are high frequency data representing various aspects of the dynamic of negotiations. The array of values includes the dates/times of trades, prices, volumes offered for trade and others trades characteristics. The first stage of the thesis was to extract information to the analysis from an archive in FIX protocol, it was developed a program in R with this aim. Afterwards, we studied the character of temporal dependence in the data, testing Markov properties of a fixed and variable memory length. The results of this application show a great variability in the character of dependence, which requires further analysis. We believe that our work is of great importance in future academic studies. In particular, the specific character of the FIX protocol used by Bovespa. This was an obstacle in a number of academic studies, which was, obviously, undesirable since Bovespa is one of the largest trading markets in the modern financial world.

This thesis covers three topics in Market Microstructure. Chapter 1 demonstrates that market access frictions may play a significant role in the competition between trading platforms. Analyzing a recent dataset of the trading activity in French and German stocks, we provide evidence that the incumbent markets dominate because the sole market entrant exposes liquidity providers to an excessive adverse selection risk due to a lack of noise traders. Chapter 2 presents a theoretical model of price formation in a dynamic limit order market with slow human traders and fast algorithmic traders. We show that in most cases, algorithmic trading has a detrimental effect on human traders’ welfare. Finally, Chapter 3 empirically analyzes the role of pre-trade transparency in call auctions. Comparing the trading mechanisms in place on the French and German stock exchanges, we find that transparency is associated with higher trading volume, greater liquidity, and better price discovery. / Esta tesis estudia tres temas diferentes de la microestructura de los mercados financieros. El capítulo 1 demuestra que fricciones en el acceso al mercado pueden desempeñar un papel significativo en la competencia entre plataformas de negociación de activos. El análisis de un conjunto de datos recientes de la actividad en acciones francesas y alemanas demuestra que los mercados primarios dominan debido a que el único mercado satélite expone los proveedores de liquidez a un riesgo excesivo de selección adversa, causado por una falta de noise traders. El capítulo 2 presenta un modelo teórico de formación de precios en un mercado dinámico con limit order book poblado por agentes humanos lentos y agentes algorítmicos rápidos. Se demuestra que, en la mayoría de los casos, la negociación algorítmica tiene un efecto negativo sobre el bienestar de agentes humanos. Por último, el capítulo 3 analiza empíricamente el papel de la transparencia pre-negociación en las subastas de apertura y de cierre. Comparando los mecanismos en las bolsas francesas y alemanas, encontramos que la transparencia está asociada con un volumen mayor, una liquidez mayor y un mejor price discovery.

The presented work deals with analysis and implementation of algorithmic trading applications based on client requirements. Applications developed in this work are supposed to be used to collect and manage data from the stock exchange, to view information about active trading orders, and to send trading orders to the exchange via the API from Interactive Brokers. The first chapter gives an overview of selected books focused on developing applications for C # and analysis. Then the concepts of UML, OOAD, and UP are introduced. In the next chapter, requirements of the customer are defined. In the following chapter, based on the results of literature research and defined client requirements, the initial architectural design is created and cases of use with subsequent specifications are presented. This section is followed by finding analytical classes, creating a domain model, implementation of some use cases using sequence diagrams. The last two chapters describe the implementation details - the language used, the libraries, database schema, and user manual.

Diploma thesis is focused on research and description of current state of machine learning field, focusing on methods that can be used for prediction and classification of time series, which could be then applied in the algorithmic trading field. Reading of theoretical section should explain basic principles of financial markets, algorithmic trading and machine learning also to reader, which was previously familiar with the subject only very thoroughly. Main objective of application part is to choose appropriate methods and procedures, which match current state of art techniques in machine learning field. Next step is to apply it to historical price data. Result of application of selected methods is determination of their success at out of sample data that was not used during model calibration. Success of prediction was evaluated by accuracy metric along with Sharpe ratio of basic trading strategy that is based on model predictions. Secondary outcome of this work is to explore possibilities and test usability of technologies used in application part. Specifically is tested and used SciPy environment, that combines Python with packages and tools designed for data analysis, statistics and machine learning.

This master's thesis is focused on algorithmic trading on the forex market using artificial neural networks. In the introduction, there are generally described terms concerning the trading. Subsequently, in the following chapters, the thesis describes the theory of neural networks and their possible use. The practical part contains designed business strategies with neural networks. Inputs used in the network are indicators of technical analysis or directly price level. Business strategies have been implemented and tested. In the conclusion, there are summarized findings of individual business models.

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.