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Dissertations / Theses on the topic 'Algorithmic Trading'
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Razumňak, Michal. "Algorithmic Trading of Pairs." Master's thesis, Vysoká škola ekonomická v Praze, 2017. http://www.nusl.cz/ntk/nusl-360578.
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Pair trading is a well-known strategy based on statistical arbitrage. This strategy uses a short-term deviation from the mean value of the price ratio of two highly correlated stocks from the same sector as the opportunity to open a position. When ratio returns to its mean value again, the position closes. This strategy has been used for many years and the main outcome of this thesis was to test whether this strategy can be profitable even in current market conditions. For that purpose, data ranging from 2010 to April 2017 on all stocks included in the S&P 500 index were used. It was subsequently found that a pair trading strategy generated 25x higher absolute profit in comparison to random agent. Thus, it can still be considered as a profitable strategy.
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Falk, Andreas, and Johannes Moberg. "Algorithmic trading using MACD signals." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-146011.
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Todays stock market is dominated by algorithmic trading either as helpful tool for trading decisions or as a fully automatic trader. We test howa fully automated trading algorithm using MACD signals as indicatorsperform on historical stock data. The purpose of this essay is to seehow a simple algorithm performs and get a better understanding ofeconomical forecasting.
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Yuan, Jiangchuan. "Risk diversification framework in algorithmic trading." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/51905.
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We propose a systematic framework for designing adaptive trading strategies that minimize both the mean and the variance of the execution costs. This is achieved by diversifying risk over sequential decisions in discrete time. By incorporating previous trading performance as a state variable, the framework can dynamically adjust the risk-aversion level for future trading. This incorporation also allows the framework to solve the mean-variance problems for different risk aversion factors all at once. After developing this framework, it is then applied to solve three algorithmic trading problems. The first two are trade scheduling problems, which address how to split a large order into sequential small orders in order to best approximate a target price – in our case, either the arrival price, or the Volume-Weighed-Average-Price (VWAP). The third problem is one of optimal execution of the resulting small orders by submitting market and limit orders. Unlike the tradition in both academia and industry of treating the scheduling and order placement problems separately, our approach treats them together and solves them simultaneously. In out-of-sample tests, this unified strategy consistently outperforms strategies that treat the two problems separately.
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Suvorin, Vadim, and Dmytro Sheludchenko. "Optimization importance in high-frequency algorithmic trading." Thesis, Mälardalens högskola, Akademin för utbildning, kultur och kommunikation, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-14645.
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The thesis offers a framework for trading algorithm optimization and tests statistical and economical significance of its performance on American, Swedish and Russian futures markets. The results provide strong support for proposed method, as using the presented ideas one can build an intraday trading algorithm that outperforms the market in long term.
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Galas, M. "Experimental computational simulation environments for algorithmic trading." Thesis, University College London (University of London), 2014. http://discovery.ucl.ac.uk/1418208/.
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This thesis investigates experimental Computational Simulation Environments for Computational Finance that for the purpose of this study focused on Algorithmic Trading (AT) models and their risk. Within Computational Finance, AT combines different analytical techniques from statistics, machine learning and economics to create algorithms capable of taking, executing and administering investment decisions with optimal levels of profit and risk. Computational Simulation Environments are crucial for Big Data Analytics, and are increasingly being used by major financial institutions for researching algorithm models, evaluation of their stability, estimation of their optimal parameters and their expected risk and performance profiles. These large-scale Environments are predominantly designed for testing, optimisation and monitoring of algorithms running in virtual or real trading mode. The stateof-the-art Computational Simulation Environment described in this thesis is believed to be the first available for academic research in Computational Finance; specifically Financial Economics and AT. Consequently, the aim of the thesis was: 1) to set the operational expectations of the environment, and 2) to holistically evaluate the prototype software architecture of the system by providing access to it to the academic community via a series of trading competitions. Three key studies have been conducted as part of this thesis: a) an experiment investigating the design of Electronic Market Simulation Models; b) an experiment investigating the design of a Computational Simulation Environment for researching Algorithmic Trading; c) an experiment investigating algorithms and the design of a Portfolio Selection System, a key component of AT systems. Electronic Market Simulation Models (Experiment 1): this study investigates methods of simulating Electronic Markets (EMs) to enable computational finance experiments in trading. EMs are central hubs for bilateral exchange of securities in a well-defined, contracted and controlled manner. Such modern markets rely on electronic networks and are designed to replace Open Outcry Exchanges for the advantage of increased speed, reduced costs of transaction, and programmatic access. Study of simulation models of EMs is important from the point of view of testing trading paradigms, as it allows users to tailor the simulation to the needs of particular trading paradigms. This is a common practice amongst investment institutions to use EMs to fine-tune their algorithms before allowing the algorithms to trade with real funds. Simulations of EMs provide users with the ability to investigate the market micro-structure and to participate in a market, receive live data feeds and monitor their behaviour without bearing any of the risks associated with real-time market trading. Simulated EMs are used by risk managers to test risk characteristics and by quant developers to build and test quantitative financial systems against market behaviour. Computational Simulation Environments (Experiment 2): this study investigates the design, implementation and testing of an experimental Environment for Algorithmic Trading able to support a variety of AT strategies. The Environment consists of a set of distributed, multi-threaded, event-driven, real-time, Linux services communicating with each other via an asynchronous messaging system. The Environment allows multi-user real and virtual trading. It provides a proprietary application programming interface (API) to support research into algorithmic trading models and strategies. It supports advanced trading-signal generation and analysis in near real-time, with use of statistical and technical analysis as well as data mining methods. It provides data aggregation functionalities to process and store market data feeds. Portfolio Selection System (Experiment 3): this study investigates a key component of Computational Finance systems to discover exploitable relationships between financial time-series applicable amongst others to algorithmic trading; where the challenge lays in identification of similarities/dissimilarities in behaviour of elements within variable-size portfolios of tradable and non-tradable securities. Recognition of sets of securities characterized by a very similar/dissimilar behaviour over time, is beneficial from the perspective of risk management, recognition of statistical arbitrage and hedge opportunities, and can be also beneficial from the point of view of portfolio diversification. Consequently, a large-scale search algorithm enabling discovery of sets of securities with AT domain-specific similarity characteristics can be utilized in creation of better portfolio-based strategies, pairs-trading strategies, statistical arbitrage strategies, hedging and mean-reversion strategies. This thesis has the following contributions to science: Electronic Markets Simulation - identifies key features, modes of operation and software architecture of an electronic financial exchange for simulated (virtual) trading. It also identifies key exchange simulation models. These simulation models are crucial in the process of evaluation of trading algorithms and systemic risk. Majority of the proposed models are believed to be unique in the academia. Computational Simulation Environment - design, implementation and testing of a prototype experimental Computational Simulation Environment for Computational Finance research, currently supporting the design of trading algorithms and their associated risk. This is believed to be unique in the academia. Portfolio Selection System - defines what is believed to be a unique software system for portfolio selection containing a combinatorial framework for discovery of subsets of internally cointegrated time-series of financial securities and a graph-guided search algorithm for combinatorial selection of such time-series subsets.
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Brokking, Alexander, and Michael Wink. "Algorithmic Stock Trading using Deep Reinforcement learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-302521.
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Recent breakthroughs in Deep Learning and Reinforcement Learning have enabled the new field of Deep Reinforcement Learning. This study explores some of the state of the art applications of deep reinforcement learning in the field of finance and algorithmic trading. By building on previous research from Yang et al. at Columbia University, this study aims to validate their findings and explore ways to improve their proposed trading model using the Sharpe ratio in the reward function. We show that there is significant variability in the performance of their trading model and question their premise of basing their results on the best performing model iteration. Moreover, we explore how the Sharpe ratio calculated over a 21 day and 63 day rolling period can be used as a reward function. However, this did not result in any significant change in outcome which could be attributed to the high performance variability in both the original algorithm and our changed algorithm which thwarts consistent conclusions.<br>Nya genombrott inom djupinlärning och förstärkningsinlärning har möjliggjort forskningsområdet djup förstärkningsinlärning. Den här studien utforskar några nya appliceringsområden av djup förstärkningsinlärning inom finans och algoritmisk handel. Genom att bygga på tidigare forskning av Yang et al. från Columbia University avser den här studien att validera deras resultat och hitta sätt att förbättra deras föreslagna modell med hjälp av Sharpekvoten som belöningsfunktion. Vi visar att det är stor varians i prestandan av deras modell och ifrågasätter deras premiss av att basera sina resultat på deras bästa modellinstans. Vidare utforskar vi hur Sharpekvoten beräknad rullande över 21 dagar och 63 dagar kan användas som belöningsfunktion. Resultaten visade däremot inte på någon signifikant förändring i prestanda vilket kan förklarars av den stora variansen i modellprestandan som försvårar konsekventa slutsatser.
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Åslin, Fredrik. "Evaluation of Hierarchical Temporal Memory in algorithmic trading." Thesis, Linköping University, Department of Computer and Information Science, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-54235.
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<p>This thesis looks into how one could use Hierarchal Temporal Memory (HTM) networks to generate models that could be used as trading algorithms. The thesis begins with a brief introduction to algorithmic trading and commonly used concepts when developing trading algorithms. The thesis then proceeds to explain what an HTM is and how it works. To explore whether an HTM could be used to generate models that could be used as trading algorithms, the thesis conducts a series of experiments. The goal of the experiments is to iteratively optimize the settings for an HTM and try to generate a model that when used as a trading algorithm would have more profitable trades than losing trades. The setup of the experiments is to train an HTM to predict if it is a good time to buy some shares in a security and hold them for a fixed time before selling them again. A fair amount of the models generated during the experiments was profitable on data the model have never seen before, therefore the author concludes that it is possible to train an HTM so it can be used as a profitable trading algorithm.</p>
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Sagade, Satchit. "Algorithmic and high-frequency trading in UK equities." Thesis, University of Reading, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.590124.
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This thesis investigates the impact of technological and regulatory changes on UK equity market microstructure, and the implications of these changes for policy makers, regulators and market participants. In the first analysis, we model the execution performance of two popular volume participation algorithms. We compare the in-sample fit and out-of-sample predictive ability of two alternative models of execution costs, and find that the non-linear model provides a better fit than the linear model. We also examine the relative importance of different order-specific, stock-specific and market-specific variables in explaining the execution performance of these algorithms. We show that execution risk for volume participation algorithms comprises not just price risk, but also risk due to uncertain trading volumes. The growth in high·frequency trading has been one of the most significant developments in the equity trading landscape, and following a number of market mishaps; has also caught the attention of regulators. In the second analysis, we examine the intraday behavior of high-frequency traders and their impact on market quality. We first observe that high-frequency trading strategies differ significantly from each other in terms of the level of liquidity provision. We next explore the impact of different high-frequency trading strategies on price discovery and temporary- deviations from equilibrium values (noise). We find that all high-frequency traders have a larger contribution towards price discovery m iv ABSTRACT and noise than other traders in the market, thereby amplifying both the beneficial and detrimental components of price volatility. Finally, in the last analysis, we revisit issues related to the liquidity characteristics of limit order markets after Market in Financial Instruments Directive was operationalised in the European Union. We find that the top of the London Stock Exchange's limit order book is extremely thin, and the slope of the limit order book is steep near the top. We further observe that the limit order book contains significant information about future short-term price changes, especially for the less liquid stocks, and this information has economic value in an algorithmic trading environment.
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Juhászová, Jana. "Statistical Arbitrage in Algorithmic Trading of US Bonds." Master's thesis, Vysoká škola ekonomická v Praze, 2017. http://www.nusl.cz/ntk/nusl-359481.
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This thesis deals with statistical arbitrage as a strategy applied in algorithmic trading of US Treasury bonds in the selected timeframe from 1980 until 2017. Our aim is to prove that a specific event on the treasury market, namely reopening of the bonds, constitutes an arbitrage opportunity that enables the investor to systematically yield extraordinary profits on the market. This thesis includes a theoretical introduction to algorithmic trading and statistical arbitrage. Based on this introduction we formulate hypotheses, which are then tested in the application part by constructing an algorithm that simulates a trading strategy on historical data. Comparing three strategies we determined that this strategy is meaningful, or performs better than a random walk and that it is profitable.
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Galli, Federico <1993>. "Algorithmic business and EU law on fair trading." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amsdottorato.unibo.it/9750/1/tesifinale_galli.pdf.
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This thesis studies how commercial practice is developing with artificial intelligence (AI) technologies and discusses some normative concepts in EU consumer law. The author analyses the phenomenon of 'algorithmic business', which defines the increasing use of data-driven AI in marketing organisations for the optimisation of a range of consumer-related tasks. The phenomenon is orienting business-consumer relations towards some general trends that influence power and behaviors of consumers. These developments are not taking place in a legal vacuum, but against the background of a normative system aimed at maintaining fairness and balance in market transactions. The author assesses current developments in commercial practices in the context of EU consumer law, which is specifically aimed at regulating commercial practices. The analysis is critical by design and without neglecting concrete practices tries to look at the big picture.
The thesis consists of nine chapters divided in three thematic parts. The first part discusses the deployment of AI in marketing organisations, a brief history, the technical foundations, and their modes of integration in business organisations. In the second part, a selected number of socio-technical developments in commercial practice are analysed. The following are addressed: the monitoring and analysis of consumers’ behaviour based on data; the personalisation of commercial offers and customer experience; the use of information on consumers’ psychology and emotions, the mediation through marketing conversational applications. The third part assesses these developments in the context of EU consumer law and of the broader policy debate concerning consumer protection in the algorithmic society. In particular, two normative concepts underlying the EU fairness standard are analysed: manipulation, as a substantive regulatory standard that limits commercial behaviours in order to protect consumers’ informed and free choices and vulnerability, as a concept of social policy that portrays people who are more exposed to marketing practices.
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Song, Yupu. "A Forex Trading System Using Evolutionary Reinforcement Learning." Digital WPI, 2017. https://digitalcommons.wpi.edu/etd-theses/1240.
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Building automated trading systems has long been one of the most cutting-edge and exciting fields in the financial industry. In this research project, we built a trading system based on machine learning methods. We used the Recurrent Reinforcement Learning (RRL) algorithm as our fundamental algorithm, and by introducing Genetic Algorithms (GA) in the optimization procedure, we tackled the problems of picking good initial values of parameters and dynamically updating the learning speed in the original RRL algorithm. We call this optimization algorithm the Evolutionary Recurrent Reinforcement Learning algorithm (ERRL), or the GA-RRL algorithm. ERRL allows us to find many local optimal solutions easier and faster than the original RRL algorithm. Finally, we implemented the GA-RRL system on EUR/USD at a 5-minute level, and the backtest performance showed that our GA-RRL system has potentially promising profitability. In future research we plan to introduce some risk control mechanism, implement the system on different markets and assets, and perform backtest at higher frequency level.
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Idvall, Patrik, and Conny Jonsson. "Algorithmic Trading : Hidden Markov Models on Foreign Exchange Data." Thesis, Linköping University, Department of Mathematics, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-10719.
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<p>In this master's thesis, hidden Markov models (HMM) are evaluated as a tool for forecasting movements in a currency cross. With an ever increasing electronic market, making way for more automated trading, or so called algorithmic trading, there is constantly a need for new trading strategies trying to find alpha, the excess return, in the market.</p><p>HMMs are based on the well-known theories of Markov chains, but where the states are assumed hidden, governing some observable output. HMMs have mainly been used for speech recognition and communication systems, but have lately also been utilized on financial time series with encouraging results. Both discrete and continuous versions of the model will be tested, as well as single- and multivariate input data.</p><p>In addition to the basic framework, two extensions are implemented in the belief that they will further improve the prediction capabilities of the HMM. The first is a Gaussian mixture model (GMM), where one for each state assign a set of single Gaussians that are weighted together to replicate the density function of the stochastic process. This opens up for modeling non-normal distributions, which is often assumed for foreign exchange data. The second is an exponentially weighted expectation maximization (EWEM) algorithm, which takes time attenuation in consideration when re-estimating the parameters of the model. This allows for keeping old trends in mind while more recent patterns at the same time are given more attention.</p><p>Empirical results shows that the HMM using continuous emission probabilities can, for some model settings, generate acceptable returns with Sharpe ratios well over one, whilst the discrete in general performs poorly. The GMM therefore seems to be an highly needed complement to the HMM for functionality. The EWEM however does not improve results as one might have expected. Our general impression is that the predictor using HMMs that we have developed and tested is too unstable to be taken in as a trading tool on foreign exchange data, with too many factors influencing the results. More research and development is called for.</p>
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Christensen, Hugh Launcelot. "Some problems in algorithmic time series prediction." Thesis, University of Cambridge, 2014. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.648898.
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Bates, Tom. "Topics in stochastic control with applications to algorithmic trading." Thesis, London School of Economics and Political Science (University of London), 2016. http://etheses.lse.ac.uk/3476/.
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This thesis considers three topics in stochastic control theory. Each of these topics is motivated by an application in finance. In each of the stochastic control problems formulated, the optimal strategy is characterised using dynamic programming. Closed form solutions are derived in a number of special cases. The first topic is about the market making problem in which a market maker manages his risk from inventory holdings of a certain asset. The magnitude of this inventory is stochastic with changes occurring due to client trading activity, and can be controlled by making small adjustments to the so-called skew, namely, the quoted price offered to the clients. After formulating the stochastic control problem, closed form solutions are derived for the special cases that arise if the asset price is modelled by a Brownian motion with drift or a geometric Brownian motion. In both cases the impact of skew is additive. The optimal controls are time dependent affine functions of the inventory size and the inventory process under the optimal skew is an Ornstein-Uhlenbeck process. As a result, the asset price is mean reverting around a reference rate. In the second topic the same framework is expanded to include a hedging control that can be used by the market maker to manage the inventory. In particular, the market impact is assumed to be of the Almgren and Chriss type. Explicit solutions are derived in the special case where the asset price follows a Brownian motion with drift. The third topic is about Merton’s portfolio optimisation problem with the additional feature that the risky asset price is modelled in a way that exhibits support and resistance levels. In particular, the risky asset price is modelled using a skew Brownian motion. After formulating the stochastic control problem, closed form solutions are derived.
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Han, Seung Jin. "Online detection of mean reversion in algorithmic pairs trading." Thesis, University of Sheffield, 2013. http://etheses.whiterose.ac.uk/5655/.
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Szostek, Charlotte. "Decentralised control in financial markets with automated algorithmic trading." Thesis, University of Bristol, 2015. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.738193.
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Kiselev, Ilya. "Can algorithmic trading beat the market? : An experiment with S&P 500, FTSE 100, OMX Stockholm 30 Index." Thesis, Internationella Handelshögskolan, Högskolan i Jönköping, IHH, Economics, Finance and Statistics, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-19495.
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The research at hand aims to define effectiveness of algorithmic trading, comparing with different benchmarks represented by several types of indexes. How big returns can be gotten by algorithmic trading, taking into account the costs of informational and trading infrastructure needed for robot trading implementation? To get the result, it’s necessary to compare two opposite trading strategies: 1) Algorithmic trading (implemented by high-frequency trading robot (based on statistic arbitrage strategy) and trend-following trading robot (based on the indicator Exponential Moving Average with the Variable Factor of Smoothing)) 2) Index investing strategy (classical index strategies “buy and hold”, implemented by four different types of indexes: Capitalization weight index, Fundamental indexing, Equal-weighted indexing, Risk-based indexation/minimal variance). According to the results, it was found that at the current phase of markets’ development, it is theoretically possible for algorithmic trading (and especially high-frequency strategies) to exceed the returns of index strategy, but we should note two important factors: 1) Taking into account all of the costs of organization of high-frequency trading (brokerage and stock exchanges commissions, trade-related infrastructure maintenance, etc.), the difference in returns (with superiority of high-frequency strategy) will be much less . 2) Given the fact that “markets’ efficiency” is growing every year (see more about it further in thesis), and the returns of high-frequency strategies tends to decrease with time (see more about it further in thesis), it is quite logical to assume that it will be necessary to invest more and more in trading infrastructure to “fix” the returns of high-frequency trading strategies on a higher level, than the results of index investing strategies.
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Hamza, Haval Rawf. "The impacts of high-frequency trading on the financial markets’ stability." Kent State University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=kent1428416050.
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Kharrat, Tarak. "A journey across football modelling with application to algorithmic trading." Thesis, University of Manchester, 2016. https://www.research.manchester.ac.uk/portal/en/theses/a-journey-across-football-modelling-with-application-to-algorithmic-trading(e57619b6-8f41-4cdb-878f-4f0c23f7e165).html.
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In this thesis we study the problem of forecasting the final score of a football match before the game kicks off (pre-match) and show how the derived models can be used to make profit in an algorithmic trading (betting) strategy. The thesis consists of two main parts. The first part discusses the database and a new class of counting processes. The second part describes the football forecasting models. The data part discusses the details of the design, specification and data collection of a comprehensive database containing extensive information on match results and events, players' skills and attributes and betting market prices. The database was created using state of the art web-scraping, text-processing and data-mining techniques. At the time of writing, we have collected data on all games played in the five major European leagues since the 2009-2010 season and on more than 7000 players. The statistical modelling part discusses forecasting models based on a new generation of counting process with flexible inter-arrival time distributions. Several different methods for fast computation of the associated probabilities are derived and compared. The proposed algorithms are implemented in a contributed R package Countr available from the Comprehensive R Archive Network. One of these flexible count distributions, the Weibull count distribution, was used to derive our first forecasting model. Its predictive ability is compared to the models previously suggested in the literature and tested in an algorithmic trading (betting) strategy. The model developed has been shown to perform rather well compared to its competitors. Our second forecasting model uses the same statistical distribution but models the attack and defence strengths of each team at the players level rather than at a team level, as is systematically done in the literature. For this model we make heavy use of the data on the players' attributes discussed in the data part of the thesis. Not only does this model turn out to have a higher predictive power but it also allows us to answer important questions about the 'nature of the game' such as the contribution of the full-backs to the attacking efforts or where would a new team finish in the Premier League.
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Yingsaeree, C. "Algorithmic trading : model of execution probability and order placement strategy." Thesis, University College London (University of London), 2012. http://discovery.ucl.ac.uk/1359852/.
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Most equity and derivative exchanges around the world are nowadays organised as order-driven markets where market participants trade against each other without the help of market makers or other intermediaries as in quote-driven markets. In these markets, traders have a choice either to execute their trade immediately at the best available price by submitting market orders or to trade patiently by submitting limit orders to execute a trade at a more favourable price. Consequently, determining an appropriate order type and price for a particular trade is a fundamental problem faced everyday by all traders in such markets. On one hand, traders would prefer to place their orders far away from the current best price to increase their pay-offs. On the other hand, the farther away from the current best price the lower the chance that their orders will be executed. As a result, traders need to find a right trade-off between these two opposite choices to execute their trades effectively. Undoubtedly, one of the most important factors in valuing such trade-off is a model of execution probability as the expected profit of traders who decide to trade via limit orders is an increasing function of the execution probability. Although a model of execution probability is a crucial component for making this decision, the research into how to model this probability is still limited and requires further investigation. The objective of this research is, hence, to extend this literature by investigating various ways in which the execution probability can be modelled with the aim to find a suitable model for modelling this probability as well as a way to utilise these models to make order placement decisions in algorithmic trading systems. To achieve this, this thesis is separated into four main experiments: 1. The first experiment analyses the behaviour of previously proposed execution probability models in a controlled environment by using data generated from simulation models of order-driven markets with the aim to identify the advantage, disadvantage and limitation of each method. 2. The second experiment analyses the relationship between execution probabilities and price fluctuations as well as a method for predicting execution probabilities based on previous price fluctuations and other related variables. 3. The third experiment investigates a way to estimate the execution probability in the simulation model utilised in the first experiment without resorting to computer simulation by deriving a model for describing the dynamic of asset price in this simulation model and utilising the derived model to estimate the execution probability. 4. The final experiment assesses the performance of utilising the developed execution probability models when applying them to make order placement decisions for liquidity traders who must fill his order before some specific deadline. The experiments with previous models indicate that survival analysis is the most appropriate method for modelling the execution probability because of its ability to handle censored observations caused by unexecuted and cancelled orders. However, standard survival analysis models (i.e. the proportional hazards model and accelerated failure time model) are not flexible enough to model the effect of explanatory variables such as limit order price and bid-ask spread. Moreover, the amount of the data required to fit these models at several price levels simultaneously grows linearly with the number of price levels. This might cause a problem when we want to model the execution probability at all possible price levels. To amend this problem, the second experiment purposes to model the execution probability during a specified time horizon from the maximum price fluctuations during the specified period. This model not only reduces the amount of the data required to fit the model in such situation, but it also provides a natural way to apply traditional time series analysis techniques to model the execution probability. Additionally, it also enables us to empirically illustrate that future execution probabilities are strongly correlated to past execution probabilities. In the third experiment, we propose a framework to model the dynamic of asset price from the stochastic properties of order arrival and cancellation processes. This establishes the relationship between microscopic dynamic of the limit order book and a long-term dynamic of the asset price process. Unlike traditional methods that model asset price dynamic using one-dimensional stochastic process, the proposed framework models this dynamic using a two dimensional stochastic process where the additional dimension represents information about the last price change. Finally, the results from the last experiment indicate that the proposed framework for making order placement decision based on the developed execution probability model outperform naive order placement strategy and the best static strategy in most situations.
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Larsson, Frans. "Algorithmic trading surveillance : Identifying deviating behavior with unsupervised anomaly detection." Thesis, Uppsala universitet, Matematiska institutionen, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-389941.
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The financial markets are no longer what they used to be and one reason for this is the breakthrough of algorithmic trading. Although this has had several positive effects, there have been recorded incidents where algorithms have been involved. It is therefore of interest to find effective methods to monitor algorithmic trading. The purpose of this thesis was therefore to contribute to this research area by investigating if machine learning can be used for detecting deviating behavior. Since the real world data set used in this study lacked labels, an unsupervised anomaly detection approach was chosen. Two models, isolation forest and deep denoising autoencoder, were selected and evaluated. Because the data set lacked labels, artificial anomalies were injected into the data set to make evaluation of the models possible. These synthetic anomalies were generated by two different approaches, one based on a downsampling strategy and one based on manual construction and modification of real data. The evaluation of the anomaly detection models shows that both isolation forest and deep denoising autoencoder outperform a trivial baseline model, and have the ability to detect deviating behavior. Furthermore, it is shown that a deep denoising autoencoder outperforms isolation forest, with respect to both area under the receiver operating characteristics curve and area under the precision-recall curve. A deep denoising autoencoder is therefore recommended for the purpose of algorithmic trading surveillance.
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De, Luca Marco. "Adaptive algorithmic trading systems : analysis of the performance of adaptive trading agents under realistic market conditions." Thesis, University of Bristol, 2015. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.683918.
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My original contribution to knowledge is to evaluate the performance of adaptive trading agents for the continuous double auction (CDA) under realistic experimental conditions. Autonomous trading agents are a significant application of agent-based computational economics (ACE), the common ground between multiagent systems and economics; the CDA is arguably one of the most popular economic institutions, for the high efficiency that it offers, and for its widespread use in real world financial securities exchanges. ACE researchers proposed several trading agents for the CDA, the most prominent of which are periodically upgraded: there is an ongoing informal competition among them, to determine which strategy performs best. The creators of those agents often refer to the potential relevance of their work to real world financial markets; and yet although much interest has been given to the behavioural details of the various trading strategies, the conditions adopted for experimentation are too often far from those in place in the real world of the financial markets industry. My central question is: to what extent are those software trading agents applicable to real world financial markets? The aim of this study is to measure the performance of those software trading agents under experimental conditions that resemble those of real world markets; for that purpose, I use both pure computational agent markets, and mixed markets of human and software trading agents. To recreate the experimental environment of real world markets: I consider the rules of major stock exchanges; I draw inspiration from state-of-the-art experimental economics models; and I create a two-sided trading agent capable of improving the "quality" of the market. My main findings are that under realistic experimental conditions, the selected software trading agents form a highly efficient market, whose performance is only marginally reduced by the more stringent constraints I added; and in mixed markets, software trading agents outperform humans.
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Arvidsson, Philip, and Tobias Ånhed. "Sequence-to-sequence learning of financial time series in algorithmic trading." Thesis, Högskolan i Borås, Akademin för bibliotek, information, pedagogik och IT, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-12602.
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Predicting the behavior of financial markets is largely an unsolved problem. The problem hasbeen approached with many different methods ranging from binary logic, statisticalcalculations and genetic algorithms. In this thesis, the problem is approached with a machinelearning method, namely the Long Short-Term Memory (LSTM) variant of Recurrent NeuralNetworks (RNNs). Recurrent neural networks are artificial neural networks (ANNs)—amachine learning algorithm mimicking the neural processing of the mammalian nervoussystem—specifically designed for time series sequences. The thesis investigates the capabilityof the LSTM in modeling financial market behavior as well as compare it to the traditionalRNN, evaluating their performances using various measures.<br>Prediktion av den finansiella marknadens beteende är i stort ett olöst problem. Problemet hartagits an på flera sätt med olika metoder så som binär logik, statistiska uträkningar ochgenetiska algoritmer. I den här uppsatsen kommer problemet undersökas medmaskininlärning, mer specifikt Long Short-Term Memory (LSTM), en variant av rekurrentaneurala nätverk (RNN). Rekurrenta neurala nätverk är en typ av artificiellt neuralt nätverk(ANN), en maskininlärningsalgoritm som ska efterlikna de neurala processerna hos däggdjursnervsystem, specifikt utformat för tidsserier. I uppsatsen undersöks kapaciteten hos ett LSTMatt modellera finansmarknadens beteenden och jämförs den mot ett traditionellt RNN, merspecifikt mäts deras effektivitet på olika vis.
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Gomolka, Johannes. "Algorithmic Trading : Analyse von computergesteuerten Prozessen im Wertpapierhandel unter Verwendung der Multifaktorenregression." Phd thesis, Universität Potsdam, 2011. http://opus.kobv.de/ubp/volltexte/2011/5100/.
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Die Elektronisierung der Finanzmärkte ist in den letzten Jahren weit vorangeschritten. Praktisch jede Börse verfügt über ein elektronisches Handelssystem. In diesem Kontext beschreibt der Begriff Algorithmic Trading ein Phänomen, bei dem Computerprogramme den Menschen im Wertpapierhandel ersetzen. Sie helfen dabei Investmententscheidungen zu treffen oder Transaktionen durchzuführen. Algorithmic Trading selbst ist dabei nur eine unter vielen Innovationen, welche die Entwicklung des Börsenhandels geprägt haben. Hier sind z.B. die Erfindung der Telegraphie, des Telefons, des FAX oder der elektronische Wertpapierabwicklung zu nennen. Die Frage ist heute nicht mehr, ob Computerprogramme im Börsenhandel eingesetzt werden. Sondern die Frage ist, wo die Grenze zwischen vollautomatischem Börsenhandel (durch Computer) und manuellem Börsenhandel (von Menschen) verläuft.
Bei der Erforschung von Algorithmic Trading wird die Wissenschaft mit dem Problem konfrontiert, dass keinerlei Informationen über diese Computerprogramme zugänglich sind. Die Idee dieser Dissertation bestand darin, dieses Problem zu umgehen und Informationen über Algorithmic Trading indirekt aus der Analyse von (Fonds-)Renditen zu extrahieren. Johannes Gomolka untersucht daher die Forschungsfrage, ob sich Aussagen über computergesteuerten Wertpapierhandel (kurz: Algorithmic Trading) aus der Analyse von (Fonds-)Renditen ziehen lassen. Zur Beantwortung dieser Forschungsfrage formuliert der Autor eine neue Definition von Algorithmic Trading und unterscheidet mit Buy-Side und Sell-Side Algorithmic Trading zwei grundlegende Funktionen der Computerprogramme (die Entscheidungs- und die Transaktionsunterstützung). Für seine empirische Untersuchung greift Gomolka auf das Multifaktorenmodell zur Style-Analyse von Fung und Hsieh (1997) zurück. Mit Hilfe dieses Modells ist es möglich, die Zeitreihen von Fondsrenditen in interpretierbare Grundbestandteile zu zerlegen und den einzelnen Regressionsfaktoren eine inhaltliche Bedeutung zuzuordnen. Die Ergebnisse dieser Dissertation zeigen, dass man mit Hilfe der Style-Analyse Aussagen über Algorithmic Trading aus der Analyse von (Fonds-)Renditen machen kann. Die Aussagen sind jedoch keiner technischen Natur, sondern auf die Analyse von Handelsstrategien (Investment-Styles) begrenzt.<br>During the last decade the electronic trading on the stock exchanges advanced rapidly. Today almost every exchange is running an electronic trading system. In this context the term algorithmic trading describes a phenomenon, where computer programs are replacing the human trader, when making investment decisions or facilitating transactions. Algorithmic trading itself stands in a row of many other innovations that helped to develop the financial markets technologically (see for example telegraphy, the telephone, FAX or electronic settlement). Today the question is not, whether computer programs are used or not. The question arising is rather, where the border between automatic, computer driven and human trading can be drawn.
Conducting research on algorithmic trading confronts scientists always with the problem of limited availability of information. The idea of this dissertation is to circumnavigate this problem and to extract information indirectly from an analysis of a time series of (fund)-returns data. The research question here is: Is it possible to draw conclusions about algorithmic trading from an analysis of (funds-)return data? To answer this question, the author develops a complete definition of algorithmic trading. He differentiates between Buy-Side and Sell-Side algorithmic trading, depending on the functions of the computer programs (supporting investment-decisions or transaction management). Further, the author applies the multifactor model of the style analysis, formely introduced by Fung and Hsieh (1997). The multifactor model allows to separate fund returns into regression factors that can be attributed to different reasons. The results of this dissertation do show that it is possible to draw conclusions about algorithmic trading out of the analysis of funds returns. Yet these conclusions cannot be of technical nature. They rather have to be attributed to investment strategies (investment styles).
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Zhang, Joe Ruiwang. "An Empirical Investigation On The Post-Earnings Announcement Drift And AlgorithmicTrading." Thesis, The University of Sydney, 2017. http://hdl.handle.net/2123/17086.
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Motivated by the widespread adoption of AT in financial markets, this dissertation investigates whether algorithmic trading (AT) reduces the Post-Earnings Announcement Drift (PEAD), the financial anomaly where investors under-react to earnings information. Studies suggest AT is associated with sophisticated trading and lower transaction costs and these two factors contribute to lowering PEAD. I conjecture algorithmic traders have an incentive to profit from (and therefore reduce the presence of) PEAD; however the evidence presented in this thesis fails to show that AT attenuates this anomaly. This thesis is composed of three essays. The first essay (Chapter 2) identifies the factors that explain PEAD and asks two questions: 1) does PEAD still exist; and 2) if so, has it been fully explained. I find PEAD remains a statistically and economically significant anomaly and that low investor sophistication, arbitrage risk and transaction costs are robust but nevertheless incomplete explanations. In other words, one, albeit incomplete, explanation for PEAD is that investors with low sophistication systematically under-react to earnings information and sophisticated traders cannot fully arbitrage the mispricing due to unhedgeable idiosyncratic risks and transaction costs. The second essay (Chapter 3) considers whether AT’s association with lower transaction costs and sophisticated trading implies AT attenuates PEAD. I further conjecture that if sophisticated algorithmic traders are better at extracting trading signals from earnings information AT should also improve price discovery around earnings announcements. After controlling for other explanatory factors, however, my findings show that AT does not contribute to the attenuation of PEAD, but that it is associated with improved price discovery. The third and final essay (Chapter 4) provides an explanation for why the relation between AT and PEAD may be insignificant. I suggest order-splitting can result in the under-estimation of transaction costs (measured by effective spreads) and I argue one predominant function of AT is to execute large orders via sequences of small transactions. I therefore adjust for a potential bias in the measure of effective spreads by treating sequences of consecutive buy or sell orders as a single transaction. I then revisit a popular study which documents the market impact of AT but show that a structural increase in AT is associated with insignificant improvements in effective spreads.
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Fodra, Pietro. "Modeling of the price microstructure and applications of stochastic control to algorithmic trading." Sorbonne Paris Cité, 2015. http://www.theses.fr/2015USPCC090.
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Dans cette thèse, on s'occupe de la modélisation du prix des actifs dans un carnet d'ordre (limit order book) et de l'applications des techniques du contrôle optimal au trading algorithmique, en particulier du market making. Pour les actifs avec petit tick, on développe un algorithme de market making dans un carnet ou les arrivés d'ordres suivent une loi de Poisson avec moyenne qui décroît exponentiellement avec la distance de l'ordre du mid-price. Grace à des technique des développement asymptotique, on obtient des résultats explicites pour une classe très large de modèles de prix, dont on suppose connaitre seulement les deux premiers moment. Pour les actifs à grand tick, on propose un nouveau modèle basé sur un processus semi-Markovian, avec lequel on réplique des phénomènes de marché connu comme la reversion à la moyenne, le comportement Brownian à large échelle, et la dépendance de l'estimateur de la variance de la fréquence d'observation. Dans cet environment, on décrit un algorithme de market making en utilisant des techniques de contrôle optimal et développement asymptotique, en réduisant la partie numérique au minimum. Finalement, on améliore le modèle precedent en utilisant des VLMC (Chaînes de Markov à longueur variable), qui permettent de décrire la longue mémoire du prix, et, qui, quitte à abandoner des formules explicites, permettent d'obtenir des interessantes applications au trading algorithmique<br>In this thesis, we take care of the modelling of the price of assets in the limit order book and of the application of the techniques of the optimal control in the algorithmic trading, in particular of market making. For assets with small tick, we develop an algorithm of market making in a book where the arrivais of order follow a Poisson law with average which decreases exponentially with the distance of the order from the mid-price. Thanks to techniques of asymptotic developments, we obtain explicit results for a very wide class of models, for which we suppose to know oniy the first two moments. For assets with large tick, we propose a new model based on a semi-Markov process, thanks to which we are able to replicate some stylized facts as the noise mean-reversion, the large-scale Brownian behavior, and the dependence of the variance estimator on the sampling frequency. In this environment, we describe an algorithm of market making using techniques of optimal control and asymptotic development, amazingly reducing the numerical part. Finally, we improve the previous model by using VLMCs (Variable length Markov chains), which allow to describe the long memory of the price, and, that, even if losing explicit formulae, allow to obtain interesting applications in the algorithmic trading
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Gomolka, Johannes [Verfasser]. "Algorithmic Trading : Analyse von computergesteuerten Prozessen im Wertpapierhandel unter Verwendung der Multifaktorenregression / Johannes Gomolka." Potsdam : Univ.-Verl, 2011. http://d-nb.info/1014245400/34.
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Booth, Ash. "Automated algorithmic trading : machine learning and agent-based modelling in complex adaptive financial markets." Thesis, University of Southampton, 2016. https://eprints.soton.ac.uk/397453/.
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Over the last three decades, most of the world's stock exchanges have transitioned to electronic trading through limit order books, creating a need for a new set of models for understanding these markets. In this thesis, a number of models are described which provide insight into the dynamics of modern financial markets as well as providing a platform for optimising trading and regulatory decisions. The first part of this thesis proposes an autonomous system that uses novel machine learning techniques to predict the price return over well documented seasonal events and uses these predictions to develop a profitable trading strategy. The DAX, FTSE 100 and S&P 500 are explored for the presence of seasonality events before an automated trading system based on performance weighted ensembles of random forests is introduced and shown to improve the profitability and stability of trading such events. The performance of the models is analysed using a large sample of stocks and the results show that the system described in this section produces superior results in terms of both profitability and prediction accuracy compared with other ensemble techniques. The second part of this thesis explores price impact. For many players in financial markets, the price impact of their trading activity represents a large proportion of their transaction costs. This section of the thesis proposes an adaptation of the system introduced in the ?rst part for predicting the price impact of order book events. The system's performance is benchmarked using ensembles of other popular regression algorithms including: linear regression, neural networks and support vector regression using depth-of-book data from the BATS Chi-X exchange. The results show that recency weighted ensembles of random forests produce over 15% greater prediction accuracy on out-of-sample data, for 5 out of 6 timeframes studied, compared with all benchmarks. Finally, a novel procedure for extracting the directional effects of features is proposed and used to explore the features most dominant in the price formation process. The final part of this thesis addresses the requirement for testing algorithmic trading strategies laid out in the Markets in Financial Instruments Directive (MiFID) II by describing an agent-based simulation. Five types of agent operate in a limit order market producing a model that is able to reproduce a number of stylised market properties including: clustered volatility, autocorrelation of returns, long memory in order flow, concave price impact and the presence of extreme price events. The model is found to be insensitive to reasonable parameter variations. Finally, the model is used to explore how trading strategy affects the implementation shortfall of trading a large order. A number of execution strategies with various order types, are evolved and evaluated in the agent-based market. It is shown that the evolved strategies outperform the simple, well known strategies significantly, suggesting that execution strategy plays an important role in determining the implementation shortfall of trading large orders.
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Mozayyan, Esfahani Sina. "Algorithmic Trading and Prediction of Foreign Exchange Rates Based on the Option Expiration Effect." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252297.
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The equity option expiration effect is a well observed phenomenon and is explained by delta hedge rebalancing and pinning risk, which makes the strike price of an option work as a magnet for the underlying price. The FX option expiration effect has not previously been explored to the same extent. In this paper the FX option expiration effect is investigated with the aim of finding out whether it provides valuable information for predicting FX rate movements. New models are created based on the concept of the option relevance coefficient that determines which options are at higher risk of being in the money or out of the money at a specified future time and thus have an attraction effect. An algorithmic trading strategy is created to evaluate these models. The new models based on the FX option expiration effect strongly outperform time series models used as benchmarks. The best results are obtained when the information about the FX option expiration effect is included as an exogenous variable in a GARCH-X model. However, despite promising and consistent results, more scientific research is required to be able to draw significant conclusions.<br>Effekten av aktieoptioners förfall är ett välobserverat fenomen, som kan förklaras av delta hedge-ombalansering och pinning-risk. Som följd av dessa fungerar lösenpriset för en option som en magnet för det underliggande priset. Effekten av FX-optioners förfall har tidigare inte utforskats i samma utsträckning. I denna rapport undersöks effekten av FX-optioners förfall med målet att ta reda på om den kan ge information som kan användas till prediktioner av FX-kursen. Nya modeller skapas baserat på konceptet optionsrelevanskoefficient som bestämmer huruvida optioner har en större sannolikhet att vara "in the money" eller "out of the money" vid en specificerad framtida tidpunkt och därmed har en attraktionseffekt. En algoritmisk tradingstrategi skapas för att evaluera dessa modeller. De nya modellerna baserade på effekten av FX-optioners förfall överpresterar klart jämfört med de tidsseriemodeller som användes som riktmärken. De bästa resultaten uppnåddes när informationen om effekten av FX-optioners förfall inkluderas som en exogen variabel i en GARCH-X modell. Dock, trots lovande och konsekventa resultat, behövs mer vetenskaplig forskning för att kunna dra signifikanta slutsatser.
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Elofsson, Bjesse Mimmi, and Emma Eriksson. "Algoritmisk handel - en kartläggning av risk, volatilitet, likviditet och övervakning." Thesis, Södertörns högskola, Företagsekonomi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:sh:diva-35459.
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As technological changes have revolutionized the way financials assets are traded today, algorithmic trading has grown to become a major part of the world's stock markets. This study aims to explore algorithmic trading through the eyes of different market operators. The market operators have, partly based on the stakeholder theory, been categorized into six categories, namely private investors, day traders, banks, the stock market, algorithmic developers and regulators. In this study we used a qualitative research design and 11 semistructured interviews have been conducted with the market operators about the main categories risks, volatility, liquidity and monitoring. The results contributed a broader view of algorithmic trading. Respondents saw a lot of risks with the business, but the majority did not express any serious concerns about this. Volatility and liquidity were considered to be affected in both directions, depending on context. Regarding monitoring of algorithmic trading, respondents considered it necessary, but the answers differ if the current monitoringis sufficient or not. The empirical results are partly in line with previous research.
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Hornický, Michal. "Návrh a implementace distribuovaného systému pro algoritmické obchodování." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2019. http://www.nusl.cz/ntk/nusl-399197.
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Inovácia na finančných trhoch poskytuje nové príležitosti. Algoritmické obchodovanie je vhodný spôsob využitia týchto príležitostí. Táto práca sa zaoberá návrhom a implementáciou systému, ktorý by dovoľoval svojím uživateľom vytvárať vlastné obchodovacie stratégie, a pomocou nich obchodovať na burzách. Práca kladie dôraz na návrh distribuovaného systému, ktorý bude škálovatelný, pomocou technológií cloud computingu.
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Uherek, Jiří. "Algoritmické obchodování." Master's thesis, Vysoká škola ekonomická v Praze, 2014. http://www.nusl.cz/ntk/nusl-192611.
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The diploma thesis is focused on algorithmic trading. In the first part the theoretical background is summarized. This part is particularly focused on definition of algorithmic trading, execution mechanisms, quantitative strategies, including problems regarding backtesting, and also on benefits and threats of algorithmic trading in market's point of view. The thesis also offers an introduction to genetic algorithms. In the practical part the strategy using genetic algorithm to find optimal combination of particular strategies is developed. The results showed that using genetic algorithms was beneficial for given data series. They also showed that the size of transaction costs is crucial for strategy performance same as dividing data series into testing sample and validation sample.
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Jurvelin, Olsson Mikael, and Andreas Hild. "Pairs Trading, Cryptocurrencies and Cointegration : A Performance Comparison of Pairs Trading Portfolios of Cryptocurrencies Formed Through the Augmented Dickey Fuller Test, Johansen’s Test and Phillips Perron’s Test." Thesis, Uppsala universitet, Statistiska institutionen, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-385484.
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This thesis analyzes the performance and process of constructing portfolios of cryptocurrency pairs based on cointegrated relationships indicated by the Augmented Dickey-Fuller test, Johansen’s test and Phillips Peron’s test. Pairs are tested for cointegration over a 3-month and a 6-month window and then traded over a trading window of the same length. The cryptocurrencies included in the study are 14 cryptocurrencies with the highest market capitalization on April 24th 2019. One trading strategy has been applied on every portfolio following the 3-month and the 6-month methodology with thresholds at 1.75 and stop-losses at 4 standard deviations. The performance of each portfolio is compared with their corresponding buy and hold benchmark. All portfolios outperformed their buy and hold benchmark, with and without transaction costs set to 2%. Following the 3-month methodology was superior to the 6- month method and the portfolios formed through Phillips Peron’s test had the highest return for both window methods.
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Mellare, Craig David. "Three Essays on Pricing and Market Behaviour around Corporate Acts and Information Releases." Thesis, The University of Sydney, 2013. http://hdl.handle.net/2123/9007.
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This dissertation studies pricing and market behaviour around corporate acts and information releases. The issues examined within this thesis are a fundamental part of the functioning of secondary markets and the broader integrity of the financial system. The three essays in this dissertation examine factors related to the efficiency of price adjustment on equity markets in response to new information and the influence of third party certification on initial public offering process. In particular, the speed by which the information contained in corporate earnings announcements is incorporated into equity prices; the behaviour of algorithmic traders around such announcements; and the insights that venture capitalist backing of newly listing companies has for third party investors are comprehensively examined. The outcomes of these studies provide new insights into how equity markets function and, therefore, the findings are relevant for market practitioners, policy makers and the academic community.
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Xu, Siyao. "Bi-Objective Optimization of Kidney Exchanges." UKnowledge, 2018. https://uknowledge.uky.edu/cs_etds/62.
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Matching people to their preferences is an algorithmic topic with real world applications. One such application is the kidney exchange. The best "cure" for patients whose kidneys are failing is to replace it with a healthy one. Unfortunately, biological factors (e.g., blood type) constrain the number of possible replacements. Kidney exchanges seek to alleviate some of this pressure by allowing donors to give their kidney to a patient besides the one they most care about and in turn the donor for that patient gives her kidney to the patient that this first donor most cares about. Roth et al.~first discussed the classic kidney exchange problem. Freedman et al.~expanded upon this work by optimizing an additional objective in addition to maximal matching. In this work, I implement the traditional kidney exchange algorithm as well as expand upon more recent work by considering multi-objective optimization of the exchange. In addition I compare the use of 2-cycles to 3-cycles. I offer two hypotheses regarding the results of my implementation. I end with a summary and a discussion about potential future work.
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Palmborg, Adam, and Max Malm. "Högfrekvenshandel : En kvalitativ studie." Thesis, Södertörns högskola, Institutionen för samhällsvetenskaper, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:sh:diva-27857.
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Syfte: Högfrekvenshandel har på senare år varit ett omdiskuterat och kontroversiellt ämne. Fenomenet har genomgått omfattande granskning och åsikterna kring dess påverkan på marknaden och dess aktörer går isär. Då tidigare forskning främst genomförts på den amerikanska marknaden är syftet med den här studien att bistå med en djupare insikt kring denna typ av handel och dess avtryck på den svenska finansmarknaden. Metod: För att behandla syftet har en kvalitativ studie av högfrekvenshandel med en deduktiv ansats genomförts. Teori: Studien utgår från Rational Choice Theory, Effektiva marknadshypotesen och tidigare forskning inom ämnet. Med hjälp av det teoretiska ramverket har studien analyserat det empiriska underlaget. Relevanta aspekter har identifierats som kan förklara varför studiens respondenter har ett specifikt förhållningssätt gentemot högfrekvenshandel. Empiri: Studien består av en dokumentstudie och fyra semistrukturerade intervjuer med intressenter på den svenska finansmarknaden. Intervjuerna ämnar identifiera de olika intressenternas förhållningssätt gentemot högfrekvenshandel och dess bakomliggande orsaker. Slutsats: Studien har kommit fram till att förhållningssättet gentemot högfrekvenshandel står i relation till vilken typ av verksamhet som intressenten bedriver. Vidare kan det konstateras att tidigare forskning till stor del går att applicera på den svenska marknaden.<br>Purpose: In recent years, High Frequency Trading has been a widely debated and controversial topic. The phenomenon has been subject to extensive examination and the opinions regarding its effect on the financial markets are inconsistent. Previous research has foremost been conducted on the American financial market. Thus the purpose of this thesis is to contribute with deeper insight regarding this kind of trading and its impact on the Swedish financial market. Method: To address the purpose of this thesis, a qualitative study with a deductive approach has been conducted. Theory: The thesis emanates from Rational Choice Theory, The Efficient Market Hypothesis and previous research within the field. Using the theoretical framework, the thesis has analyzed the empirical data. Relevant aspects has been identified which can explain why the thesis’ respondents has a specific approach towards High Frequency Trading. Empirics: The thesis consists of a document study and four semi structured interviews with stakeholders on the Swedish financial market. Through these interviews, the thesis aims to identify the stakeholders’ different approaches towards High Frequency Trading and what might cause this particular point of view. Conclusion: The thesis can conclude that the approach towards High Frequency Trading is correlated to the type of operation conducted by the respondent. Furthermore, it can be concluded that previous research in general is applicable on the Swedish financial market.
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Blair, James. "Modelling approaches for optimal liquidation under a limit-order book structure." Thesis, University of Manchester, 2016. https://www.research.manchester.ac.uk/portal/en/theses/modelling-approaches-for-optimal-liquidation-under-a-limitorder-book-structure(a7c23b2a-e2f8-4b4a-9865-8783d9837198).html.
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This thesis introduces a selection of models for optimal execution of financial assets at the tactical level. As opposed to optimal scheduling, which defines a trading schedule for the trader, this thesis investigates how the trader should interact with the order book. If a trader is aggressive he will execute his order using market orders, which will negatively feedback on his execution price through market impact. Alternatively, the models we focus on consider a passive trader who places limit orders into the limit-order book and waits for these orders to be filled by market orders from other traders. We assume these models do not exhibit market impact. However, given we await market orders from other participants to fill our limit orders a new risk is borne: execution risk. We begin with an extension of Guéant et al. (2012b) who through the use of an exponential utility, standard Brownian motion, and an absolute decay parameter were able to cleverly build symmetry into their model which significantly reduced the complexity. Our model consists of geometric Brownian motion (and mean-reverting processes) for the asset price, a proportional control parameter (the additional amount we ask for the asset), and a proportional decay parameter, implying that the symmetry found in Guéant et al. (2012b) no longer exists. This novel combination results in asset-dependent trading strategies, which to our knowledge is a unique concept in this framework of literature. Detailed asymptotic analyses, coupled with advanced numerical techniques (informing the asymptotics) are exploited to extract the relevant dynamics, before looking at further extensions using similar methods. We examine our above mentioned framework, as well as that of Guéant et al. (2012), for a trader who has a basket of correlated assets to liquidate. This leads to a higher-dimensional model which increases the complexity of both numerically solving the problem and asymptotically examining it. The solutions we present are of interest, and comparable with Markowitz portfolio theory. We return to our framework of a single underlying and consider four extensions: a stochastic volatility model which results in an added dimension to the problem, a constrained optimisation problem in which the control has an explicit lower bound, changing the exponential intensity to a power intensity which results in a reformulation as a singular stochastic control problem, and allowing the trader to trade using both market orders and limit orders resulting in a free-boundary problem. We complete the study with an empirical analysis using limit-order book data which contains multiple levels of the book. This involves a novel calibration of the intensity functions which represent the limit-order book, before backtesting and analysing the performance of the strategies.
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Liu, Anqi. "It's How You Play the Game - How regulations shape high frequency liquidity provision." Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/27508.
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The financial market is continuously shaped by the interplay between regulations and the actions from market participants. This dissertation investigates how market design impacts market landscape, by providing systematic incentives for certain player behaviours. Three papers examining three different policy changes are included.
In the first study, I investigate one mechanism that contributed to the current fragmented North American equity marketplace. I use the introduction of the Order Protection Rule in 2010 as a natural experiment to examine the mechanism behind such proliferation of small venues. We find that 'queue jumping' mechanism can act as a starter to the liquidity on small or empty trading venues.
In the second study, I examine an attempt by a stock exchange to bring liquidity from off-market, such as over-the-counter, to on-market continuous trading. The change is through removing the fixed fee per trade. Such change encourages order slicing and optimal execution through algorithms, thus showing significant impact on institutional trading.
In the third study, I study an innovative method to address one of the most prominent issues in the modern equity market, liquidity for small-cap stocks. The solution proposed is to let liquid stocks cross-subsidise the illiquid ones in the Designated Market Makers program. Through investigating market makers' profit structure and incentive, I find two mechanisms through which the program encourages them to quote tighter spreads and higher depths.
This dissertation contributed to the discussions around several of the most important topics in the modern equity market, including fragmentation, institutional optimal execution strategies and liquidity for small-cap stocks. By researching the channels driving these changes, these papers provide policy implications that reach far beyond the three policy changes that are directly examined.
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Roth, Sebastian, and Madelene Söderström. "Flash-krascher : Ett allvarligt problem på Stockholmsbörsen?" Thesis, Linköpings universitet, Nationalekonomi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-149441.
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Titel: Flash-krascher – ett allvarligt problem på Stockholmsbörsen? Författare: Madelene Söderström & Sebastian Roth Handledare: Bo Sjö Ämne: Nationalekonomi – Kandidatuppsats inom finans Syfte: Syftet med arbetet är att fördjupa förståelsen kring flash-krascher och vilken påverkan dessa har på handeln av värdepapper som sker på Stockholmsbörsen. Vi hoppas också att studien ger en klarare bild av hur flash-krascher påverkar olika aktörer med koppling till aktiehandeln i Sverige. Metod: Uppsatsen är baserad på en kvalitativ studie utförd med intervjurespondenter med varierande koppling till Stockholmsbörsen och den svenska finansmarknaden. Teori: Uppsatsen utgår främst från tidigare forskning inom ämnet bestående av studier baserade på händelser och data från USA. Annan ekonomisk teori som presenteras i studien är adverse selection. Empiri: Uppsatsen är bestående av sju semistrukturerade intervjuer med aktörer på finansmarknaden. Intervjuerna jämförs med tidigare inträffade händelser i USA för att diskutera möjliga slutsatser om flash-krascher på Stockholmsbörsen. Slutsats: Studien kommer fram till att det är osannolikt att flash-krascher av den magnituden som inträffat i USA 6 maj 2010 inträffar på Stockholmsbörsen idag. Vidare så verkar flash-krascher inte ha särskilt stor påverkan på aktörer på Stockholmsbörsen, däremot kan det finnas en viss oros- och förtroendeproblematik kopplad till flash-krascher som bör tas på allvar. I studien av tidigare forskning finner vi intressanta teorier för hur flash-krascher kan förutses. Vi kan däremot inte dra några slutsatser kring dessa teorier kopplat till Stockholmsbörsen.<br>Title: Flash crashes – a severe problem at Nasdaq OMX Stockholm? Authors: Madelene Söderström & Sebastian Roth Advisor: Bo Sjö Subject: Bachelor thesis in finance Purpose: The purpose of this study is to understand and critically examine the impact flash crashes might have on the market for securities at Nasdaq OMX Stockholm. Our goal is to provide a clearer view on how flash crashes affect the trade and the market participants. Method: This thesis is a qualitative study based on interviews with respondents with different approach to both Nasdaq OMX Stockholm and the financial market in Sweden. Theory: The thesis is based on earlier studies within the subject made from data and events from United States of America. Other economic theories that the thesis involve is adverse selection. Empirics: The study is predicated around seven semi structured interviews with participants on the financial market in Sweden. The interviews are compared with the earlier events from USA to make for conclusions about flash crashes on Nasdaq OMX Stockholm. Conclusion: We find that it is unlikely that a flash crash of the same magnitude as the May 6, 2010 flash crash will occur on the Nasdaq OMX Stockholm exchange today. Furthermore, flash crashes appear to have little impact on the market participants at Nasdaq OMX Stockholm, though there may be concerns about trust issues following flash crashes that should be considered. While studying some of the earlier research we find interesting theories about ways to predict flash crashes before they have occurred, we can’t make any conclusions about these theories connected to Nasdaq OMX Stockholm though.
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Masoudi, Mohammad Amin. "Robust Deep Reinforcement Learning for Portfolio Management." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42743.
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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.
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Haushalterová, Gabriela. "Vysokofrekvenční obchodovaní a jeho dopad na stabilitu finančního trhu." Master's thesis, Vysoká škola ekonomická v Praze, 2017. http://www.nusl.cz/ntk/nusl-359578.
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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.
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Sanja, Lončar. "Negative Selection - An Absolute Measure of Arbitrary Algorithmic Order Execution." Phd thesis, Univerzitet u Novom Sadu, Prirodno-matematički fakultet u Novom Sadu, 2017. https://www.cris.uns.ac.rs/record.jsf?recordId=104861&source=NDLTD&language=en.
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Algorithmic trading is an automated process of order execution on electronic stock markets. It can be applied to a broad range of financial instruments, and it is characterized by a signicant investors' control over the execution of his/her orders, with the principal goal of finding the right balance between costs and risk of not (fully) executing an order. As the measurement of execution performance gives information whether best execution is achieved, a signicant number of diffeerent benchmarks is used in practice. The most frequently used are price benchmarks, where some of them are determined before trading (Pre-trade benchmarks), some during the trading day (In-traday benchmarks), and some are determined after the trade (Post-trade benchmarks). The two most dominant are VWAP and Arrival Price, which is along with other pre-trade price benchmarks known as the Implementation Shortfall (IS).We introduce Negative Selection as a posteriori measure of the execution algorithm performance. It is based on the concept of Optimal Placement, which represents the ideal order that could be executed in a given time win-dow, where the notion of ideal means that it is an order with the best execution price considering market conditions during the time window. Negative Selection is dened as a difference between vectors of optimal and executed orders, with vectors dened as a quantity of shares at specied price positionsin the order book. It is equal to zero when the order is optimally executed; negative if the order is not (completely) filled, and positive if the order is executed but at an unfavorable price.Negative Selection is based on the idea to offer a new, alternative performance measure, which will enable us to find the optimal trajectories and construct optimal execution of an order.The first chapter of the thesis includes a list of notation and an overview of denitions and theorems that will be used further in the thesis. Chapters 2 and 3 follow with a theoretical overview of concepts related to market microstructure, basic information regarding benchmarks, and theoretical background of algorithmic trading. Original results are presented in chapters 4 and 5. Chapter 4 includes a construction of optimal placement, definition and properties of Negative Selection. The results regarding the properties of a Negative Selection are given in [35]. Chapter 5 contains the theoretical background for stochastic optimization, a model of the optimal execution formulated as a stochastic optimization problem with regard to Negative Selection, as well as original work on nonmonotone line search method [31], while numerical results are in the last, 6th chapter.<br>Algoritamsko trgovanje je automatizovani proces izvršavanja naloga na elektronskim berzama. Može se primeniti na širok spektar nansijskih instrumenata kojima se trguje na berzi i karakteriše ga značajna kontrola investitora nad izvršavanjem njegovih naloga, pri čemu se teži nalaženju pravog balansa izmedu troška i rizika u vezi sa izvršenjem naloga. S ozirom da se merenjem performasi izvršenja naloga određuje da li je postignuto najbolje izvršenje, u praksi postoji značajan broj različitih pokazatelja. Najčešće su to pokazatelji cena, neki od njih se određuju pre trgovanja (eng. Pre-trade), neki u toku trgovanja (eng. Intraday), a neki nakon trgovanja (eng. Post-trade). Dva najdominantnija pokazatelja cena su VWAP i Arrival Price koji je zajedno sa ostalim "pre-trade" pokazateljima cena poznat kao Implementation shortfall (IS).Pojam negative selekcije se uvodi kao "post-trade" mera performansi algoritama izvršenja, polazeći od pojma optimalnog naloga, koji predstavlja idealni nalog koji se mogao izvrsiti u datom vremenskom intervalu, pri ćemu se pod pojmom "idealni" podrazumeva nalog kojim se postiže najbolja cena u tržišnim uslovima koji su vladali u toku tog vremenskog intervala. Negativna selekcija se definiše kao razlika vektora optimalnog i izvršenog naloga, pri čemu su vektori naloga defisani kao količine akcija na odgovarajućim pozicijama cena knjige naloga. Ona je jednaka nuli kada je nalog optimalno izvršen; negativna, ako nalog nije (u potpunosti) izvršen, a pozitivna ako je nalog izvršen, ali po nepovoljnoj ceni.Uvođenje mere negativne selekcije zasnovano je na ideji da se ponudi nova, alternativna, mera performansi i da se u odnosu na nju nađe optimalna trajektorija i konstruiše optimalno izvršenje naloga.U prvom poglavlju teze dati su lista notacija kao i pregled definicija i teorema neophodnih za izlaganje materije. Poglavlja 2 i 3 bave se teorijskim pregledom pojmova i literature u vezi sa mikrostrukturom tržišta, pokazateljima trgovanja i algoritamskim trgovanjem. Originalni rezultati su predstavljeni u 4. i 5. poglavlju. Poglavlje 4 sadrži konstrukciju optimalnog naloga, definiciju i osobine negativne selekcije. Teorijski i praktični rezultati u vezi sa osobinama negativna selekcije dati su u [35]. Poglavlje 5 sadrži teorijske osnove stohastičke optimizacije, definiciju modela za optimalno izvršenje, kao i originalni rad u vezi sa metodom nemonotonog linijskog pretraživanja [31], dok 6. poglavlje sadrži empirijske rezultate.
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Radoš, Daniel. "Algoritmické obchodování na burze s využitím umělých neuronových sítí." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2017. http://www.nusl.cz/ntk/nusl-363869.
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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.
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Thouillez, Thomas. "Anatomie des marchés financiers à haute fréquence : analyse de l'Influence de l'automatisation sur la microstructure des marchés financiers." Thesis, Paris 1, 2020. http://www.theses.fr/2020PA01E049.
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Cette thèse étudie les principales transformations de la microstructure des marchés financiers depuis la généralisation de l’automatisation des marchés. Aujourd'hui, la modification structurelle des marchés financiers, associée à l'évolution des technologies de l'information, ont entrainé des bouleversements, tant dans les pratiques de marchés, que dans les instruments de mesures de la qualité de marché. Le coût de la liquidité a continué de s’améliorer entre 2010 et 2019, notamment en réduisant les spreads des sociétés moins importantes du SBF 120. En revanche, les spreads effectifs se réduisent nettement moins montrant la faible profondeur du carnet d’ordres aux meilleures limites pour les entreprises les plus petites. Les travaux présentent les mutations des plateformes de négociation et les évolutions technologiques qui ont permis le déploiement du trading haute-fréquence. L’équipe de recherche a développé un outil de réplication des marchés financiers, VirteK. La librairie a permis une simulation répliquant les faits stylisés du flash-crash du 6 mai 2010 illustrant les déséquilibres du carnet d’ordres à l’aide du VPIN<br>This thesis studies major market microstructure transformations since the automation of financial markets. Today, structural modification of financial markets, associated with the improvement of information and communication technology, lead to important shifts regarding market practices, and market quality measures. Liquidity costs continued to improve between 2010 and 2019, reducing quoted spread especially for SBF 120 small capitalizations. However, effective spreads decreased significantly less than quoted spreads for those small cap proving the weak resilience of the order book on the best limits. This work presents execution venues transformation and technological evolutions to implement high-frequency trading. The research team built a financial market replicating library called VirteK. This library helped to recover stylized facts from the May 6, 2010 flash-crash illustrating limit order book imbalances with the VPIN measure
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Krpálek, Jan. "Data-Snooping Biases in Backtesting." Master's thesis, Vysoká škola ekonomická v Praze, 2016. http://www.nusl.cz/ntk/nusl-262277.
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In this paper, we utilize White's Reality Check, White (2000), and Hansen's SPA test, Hansen (2004), to evaluate technical trading rules while quantifying the data-snooping bias. Secondly, we discuss the result with Probability of Backtest Overfitting framework, introduced by Bailey et al. (2015). Hence, the study presents a comprehensive test of momentum trading across the US futures markets from 2004 to 2016. The evidence indicates that technical trading rules have not been pro?table in the US futures markets after correcting for the data snooping bias.
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Campos, Miguel Marreiros Inácio de. "Plataforma para negociação FOREX." Master's thesis, Universidade de Aveiro, 2017. http://hdl.handle.net/10773/23463.
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Mestrado em Engenharia de Computadores e Telemática<br>The growing democratization of financial markets fueled by new technologies openedthedoortonewinvestorsandresearchers. Marketschanged,continuouslynegotiationbeganandthenumberoffinancialordersroseexponentially. With the increase in flexibility and accessibility to markets, algorithmic trading grew at the retail level, with traders starting to implement their own algorithms in trading strategies. The use of machine learning algorithms and time series analysis became widely popular, adding complexity to trading strategies.
In order to create and test profitable algorithms there are rules that must be followed. This dissertation presents the development of a new generation of research and trading system that aims to help researchers and traders to be more productive and efficient. It was developed as an event-driven backtest and live trading system with an innovative approach to sharing backtest reports. Also, by merging the technical analysis based trading with new techniques, and complying with the backtest paradigm, the aim is to provide a richer environment to users.<br>A crescente democratização dos mercados financeiros alimentada por novas tecnologias abriu a porta a novos investidores e investigadores. Os mercados mudaram, a negociação contínua tornou-se uma realidade e o número de ordens financeiras aumentou exponencialmente. Com o aumento da flexibilidade e acessibilidade aos mercados, a negociação algorítmica a titulo individual cresceu, com investidores a implementar seus próprios algoritmos nas estratégias de negociação. O uso de algoritmos de aprendizagem automática e análise de séries temporais tornou-se comum, aumentando a complexidade das estratégias de negociação.
Para criar e testar algoritmos lucrativos, existem regras que devem ser seguidas. Esta dissertação apresenta o desenvolvimento de uma nova geração desistemasdeinvestigaçãoenegociaçãocujoobjectivoéajudarinvestigadores e investidores a aumentar a produtividade e eficiência. Foi desenvolvido como um sistema de testes baseado em eventos e um sistema de negociação em tempo-real com uma abordagem inovadora para compartilhar relatórios. Além disso, ao fundir a negociação baseada na análise técnica com novas técnicas e cumprindo com o paradigma de testes, o objetivo é proporcionar um ambiente mais rico aos usuários.
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Gabrielsson, Patrick. "Evolvering av Biologiskt Inspirerade Handelsalgoritmer." Thesis, Högskolan i Borås, Institutionen Handels- och IT-högskolan, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-16886.
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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.<br>Program: Magisterutbildning i informatik
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Baptiste, Julien. "Problèmes numériques en mathématiques financières et en stratégies de trading." Thesis, Paris Sciences et Lettres (ComUE), 2018. http://www.theses.fr/2018PSLED009.
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Le but de cette thèse CIFRE est de construire un portefeuille de stratégies de trading algorithmique intraday. Au lieu de considérer les prix comme une fonction du temps et d'un aléa généralement modélisé par un mouvement brownien, notre approche consiste à identifier les principaux signaux auxquels sont sensibles les donneurs d'ordres dans leurs prises de décision puis alors de proposer un modèle de prix afin de construire des stratégies dynamiques d'allocation de portefeuille. Dans une seconde partie plus académique, nous présentons des travaux de pricing d'options européennes et asiatiques<br>The aim of this CIFRE thesis is to build a portfolio of intraday algorithmic trading strategies. Instead of considering stock prices as a function of time and a brownian motion, our approach is to identify the main signals affecting market participants when they operate on the market so we can set up a prices model and then build dynamical strategies for portfolio allocation. In a second part, we introduce several works dealing with asian and european option pricing
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Chlud, Michal. "Algoritmické obchodování na burze s využitím umělých neuronových sítí." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2016. http://www.nusl.cz/ntk/nusl-255488.
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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.
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Huré, Come. "Numerical methods and deep learning for stochastic control problems and partial differential equations." Thesis, Sorbonne Paris Cité, 2019. http://www.theses.fr/2019USPCC052.
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La thèse porte sur les schémas numériques pour les problèmes de décisions Markoviennes (MDPs), les équations aux dérivées partielles (EDPs), les équations différentielles stochastiques rétrogrades (ED- SRs), ainsi que les équations différentielles stochastiques rétrogrades réfléchies (EDSRs réfléchies). La thèse se divise en trois parties.La première partie porte sur des méthodes numériques pour résoudre les MDPs, à base de quan- tification et de régression locale ou globale. Un problème de market-making est proposé: il est résolu théoriquement en le réécrivant comme un MDP; et numériquement en utilisant le nouvel algorithme. Dans un second temps, une méthode de Markovian embedding est proposée pour réduire des prob- lèmes de type McKean-Vlasov avec information partielle à des MDPs. Cette méthode est mise en œuvre sur trois différents problèmes de type McKean-Vlasov avec information partielle, qui sont par la suite numériquement résolus en utilisant des méthodes numériques à base de régression et de quantification.Dans la seconde partie, on propose de nouveaux algorithmes pour résoudre les MDPs en grande dimension. Ces derniers reposent sur les réseaux de neurones, qui ont prouvé en pratique être les meilleurs pour apprendre des fonctions en grande dimension. La consistance des algorithmes proposés est prouvée, et ces derniers sont testés sur de nombreux problèmes de contrôle stochastique, ce qui permet d’illustrer leurs performances.Dans la troisième partie, on s’intéresse à des méthodes basées sur les réseaux de neurones pour résoudre les EDPs, EDSRs et EDSRs réfléchies. La convergence des algorithmes proposés est prouvée; et ces derniers sont comparés à d’autres algorithmes récents de la littérature sur quelques exemples, ce qui permet d’illustrer leurs très bonnes performances<br>The present thesis deals with numerical schemes to solve Markov Decision Problems (MDPs), partial differential equations (PDEs), quasi-variational inequalities (QVIs), backward stochastic differential equations (BSDEs) and reflected backward stochastic differential equations (RBSDEs). The thesis is divided into three parts.The first part focuses on methods based on quantization, local regression and global regression to solve MDPs. Firstly, we present a new algorithm, named Qknn, and study its consistency. A time-continuous control problem of market-making is then presented, which is theoretically solved by reducing the problem to a MDP, and whose optimal control is accurately approximated by Qknn. Then, a method based on Markovian embedding is presented to reduce McKean-Vlasov control prob- lem with partial information to standard MDP. This method is applied to three different McKean- Vlasov control problems with partial information. The method and high accuracy of Qknn is validated by comparing the performance of the latter with some finite difference-based algorithms and some global regression-based algorithm such as regress-now and regress-later.In the second part of the thesis, we propose new algorithms to solve MDPs in high-dimension. Neural networks, combined with gradient-descent methods, have been empirically proved to be the best at learning complex functions in high-dimension, thus, leading us to base our new algorithms on them. We derived the theoretical rates of convergence of the proposed new algorithms, and tested them on several relevant applications.In the third part of the thesis, we propose a numerical scheme for PDEs, QVIs, BSDEs, and RBSDEs. We analyze the performance of our new algorithms, and compare them to other ones available in the literature (including the recent one proposed in [EHJ17]) on several tests, which illustrates the efficiency of our methods to estimate complex solutions in high-dimension.Keywords: Deep learning, neural networks, Stochastic control, Markov Decision Process, non- linear PDEs, QVIs, optimal stopping problem BSDEs, RBSDEs, McKean-Vlasov control, perfor- mance iteration, value iteration, hybrid iteration, global regression, local regression, regress-later, quantization, limit order book, pure-jump controlled process, algorithmic-trading, market-making, high-dimension