Academic literature on the topic 'Algorithmic Trading'

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.

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.

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.

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.

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.

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.

<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>

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.

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.

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.