Relevant bibliographies by topics / Algorithmic Trading

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Algorithmic Trading'

Author: Grafiati
Published: 4 June 2021
Last updated: 23 February 2023

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Journal articles on the topic "Algorithmic Trading"

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Nuti, Giuseppe, Mahnoosh Mirghaemi, Philip Treleaven, and Chaiyakorn Yingsaeree. "Algorithmic Trading." Computer 44, no. 11 (November 2011): 61–69. http://dx.doi.org/10.1109/mc.2011.31.

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Wang, Yongfeng, and Guofeng Yan. "Survey on the application of deep learning in algorithmic trading." Data Science in Finance and Economics 1, no. 4 (2021): 345–61. http://dx.doi.org/10.3934/dsfe.2021019.

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<abstract> <p>Algorithmic trading is one of the most concerned directions in financial applications. Compared with traditional trading strategies, algorithmic trading applications perform forecasting and arbitrage with higher efficiency and more stable performance. Numerous studies on algorithmic trading models using deep learning have been conducted to perform trading forecasting and analysis. In this article, we firstly summarize several deep learning methods that have shown good performance in algorithmic trading applications, and briefly introduce some applications of deep learning in algorithmic trading. We then try to provide the latest snapshot application for algorithmic trading based on deep learning technology, and show the different implementations of the developed algorithmic trading model. Finally, some possible research issues are suggested in the future. The prime objectives of this paper are to provide a comprehensive research progress of deep learning applications in algorithmic trading, and benefit for subsequent research of computer program trading systems.</p> </abstract>
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Mathur, Medha, Satyam Mhadalekar, Sahil Mhatre, and Vanita Mane. "Algorithmic Trading Bot." ITM Web of Conferences 40 (2021): 03041. http://dx.doi.org/10.1051/itmconf/20214003041.

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Algorithmic trading uses algorithms that follow a trend and defined set of instructions to perform a trade. The trade can generate revenue at an inhuman and enhanced speed and frequency. The characterized sets of trading guidelines that are passed on to the program are reliant upon timing, value, amount, or any mathematical model. Aside from profitable openings for the trader, algo-trading renders the market more liquid and trading more precise by precluding the effect of human feelings on trading. Our project aims to further this revolution in the markets of tomorrow by providing an effective and efficient solution to overcome the drawbacks faced due to manual trading by building an Algorithmic Trading Bot which will automatically trade user strategies alongside its own algorithms for day-to-day trading based on different market conditions and user approach ,and throughout the course of the day invest and trade with continuous modifications to ensure the best trade turnover for the day while reducing the transaction cost, hence enabling huge profits for concerned users be it Organizations or individuals.
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V’yugin, Vladimir V., and Vladimir G. Trunov. "Universal algorithmic trading." Journal of Investment Strategies 2, no. 1 (December 2012): 63–88. http://dx.doi.org/10.21314/jois.2012.014.

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Treleaven, Philip, Michal Galas, and Vidhi Lalchand. "Algorithmic trading review." Communications of the ACM 56, no. 11 (November 2013): 76–85. http://dx.doi.org/10.1145/2500117.

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Martins Pereira, Clara. "Unregulated Algorithmic Trading: Testing the Boundaries of the European Union Algorithmic Trading Regime." Journal of Financial Regulation 6, no. 2 (August 5, 2020): 270–305. http://dx.doi.org/10.1093/jfr/fjaa008.

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Abstract Trading in modern equity markets has come to be dominated by machines and algorithms. However, there is significant concern over the impact of algorithmic trading on market quality and a number of jurisdictions have moved to address the risks associated with this new type of trading. The European Union has been no exception to this trend. This article argues that while the European Union algorithmic trading regime is often perceived as a tough response to the challenges inherent in machine trading, it has one crucial shortcoming: it does not regulate the simpler, basic execution algorithms used in automated order routers. Yet the same risk generally associated with algorithmic trading activity also arises, in particular, from the use of these basic execution algorithms—as was made evident by the trading glitch that led to the fall of United States securities trader Knight Capital in 2012. Indeed, such risk could even be amplified by the lack of sophistication of these simpler execution algorithms. It is thus proposed that the European Union should amend the objective scope of its algorithmic trading regime by expanding the definition of algorithmic trading under the Markets in Financial Instruments Directive (MiFID II) to include all execution algorithms, regardless of their complexity.
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Tucci, Gabriel, and M. Vega. "Optimal trading trajectories for algorithmic trading." Journal of Investment Strategies 5, no. 2 (March 2016): 57–74. http://dx.doi.org/10.21314/jois.2016.065.

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Patil, Mr Mihir Rajan. "Algorithmic Trading & High Frequency Trading." International Journal for Research in Applied Science and Engineering Technology 7, no. 6 (June 30, 2019): 1640–42. http://dx.doi.org/10.22214/ijraset.2019.6275.

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Lee, Joseph, and Lukas Schu. "Regulation of Algorithmic Trading: Frameworks or Human Supervision and Direct Market Interventions." European Business Law Review 33, Issue 2 (April 1, 2022): 193–226. http://dx.doi.org/10.54648/eulr2022006.

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This paper identifies the regulatory gaps that currently exist in algorithmic trading and provides a framework for machine learning regulation in finance. It compares the regulation of algorithmic trading in the capital markets by both human supervision and direct market intervention in the UK, the EU and the US to identify techniques they have in common, as well as local differences. Section II sets out what algorithmic trading is, how it is defined, which of its functions have a positive effect and which are negative for risk and impact. Section III examines how trading risks can be managed by human supervision. Section IV looks at how direct market intervention can mitigate the risks of algorithmic trading, focusing on the circuit breaker requirement. Finally, the liability of the parties involved (traders, firms, and trading venues) are examined and the possible enforcement actions that regulators may take are set out. Algorithms, high frequency trading, machine learning, financial regulation, MIFIDII, ESMA, FCA, SEC, circuit breaker, systemic risk
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Palmer, Max. "Algorithmic Trading: A Primer." Journal of Trading 4, no. 3 (June 30, 2009): 30–35. http://dx.doi.org/10.3905/jot.2009.4.3.030.

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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.
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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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.

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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&gt. "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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Books on the topic "Algorithmic Trading"

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Chan, Ernest P. Algorithmic Trading. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118676998.

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Leshik, Edward A., and Jane Cralle, eds. An Introduction to Algorithmic Trading. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781119206033.

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Davey, Kevin J. Building Winning Algorithmic Trading Systems. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118778944.

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Chan, Ernest P. Quantitative trading: How to build your own algorithmic trading business. Hoboken, N.J: John Wiley & Sons, 2009.

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Algorithmic trading & DMA: An introduction to direct access trading strategies. London: 4Myeloma Press, 2010.

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High-frequency trading: A practical guide to algorithmic strategies and trading system. Hoboken, N.J: Wiley, 2010.

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Electronic and algorithmic trading technology: The complete guide. Boston, Mass: Academic Press, an imprint of Elsevier, 2007.

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Jane, Cralle, ed. An introduction to algorithmic trading: Basic to advanced strategies. Chichester, West Sussex, UK: Wiley, 2011.

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Gomber, Peter, and Kai Zimmermann. Algorithmic Trading in Practice. Edited by Shu-Heng Chen, Mak Kaboudan, and Ye-Rong Du. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199844371.013.12.

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The use of computer algorithms in securities trading, or algorithmic trading, has become a central factor in modern financial markets. The desire for cost and time savings within the trading industry spurred buy side as well as sell side institutions to implement algorithmic services along the entire securities trading value chain. This chapter encompasses this algorithmic evolution, highlighting key cornerstones in it development discussing main trading strategies, and summarizing implications for overall securities markets quality. In addition, it touches on the contribution of algorithmic trading to the recent market turmoil, the U.S. Flash Crash, including the discussions of potential solutions for assuring market reliability and integrity.
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Algorithmic Trading Methods. Elsevier, 2021. http://dx.doi.org/10.1016/c2017-0-03456-0.

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Book chapters on the topic "Algorithmic Trading"

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Yuen, William, Paul Syverson, Zhenming Liu, and Christopher Thorpe. "Intention-Disguised Algorithmic Trading." In Financial Cryptography and Data Security, 408–15. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14577-3_36.

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Koutsoupias, Elias, and Philip Lazos. "Online Trading as a Secretary Problem." In Algorithmic Game Theory, 201–12. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99660-8_18.

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Bhatia, Randeep, Julia Chuzhoy, Ari Freund, and Joseph Seffi Naor. "Algorithmic Aspects of Bandwidth Trading." In Automata, Languages and Programming, 751–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-45061-0_59.

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Kissell, Robert. "Algorithmic Trading." In The Science of Algorithmic Trading and Portfolio Management, 1–45. Elsevier, 2014. http://dx.doi.org/10.1016/b978-0-12-401689-7.00001-5.

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Kissell, Robert L. "Algorithmic Trading." In Algorithmic Trading Methods, 23–56. Elsevier, 2021. http://dx.doi.org/10.1016/b978-0-12-815630-8.00002-8.

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"About the Author." In Algorithmic Trading, 197. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118676998.about.

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"Bibliography." In Algorithmic Trading, 191–95. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118676998.biblio.

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"Backtesting and Automated Execution." In Algorithmic Trading, 1–38. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118676998.ch1.

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"The Basics of Mean Reversion." In Algorithmic Trading, 39–62. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118676998.ch2.

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"Implementing Mean Reversion Strategies." In Algorithmic Trading, 63–85. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118676998.ch3.

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Conference papers on the topic "Algorithmic Trading"

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Gruver, William A. "Algorithmic trading systems." In 2015 IEEE 13th International Symposium on Intelligent Systems and Informatics (SISY). IEEE, 2015. http://dx.doi.org/10.1109/sisy.2015.7325393.

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Othalasseril, Dheeraj, and Sana Shaikh. "TradeZilla Using Algorithmic Trading." In 2021 IEEE India Council International Subsections Conference (INDISCON). IEEE, 2021. http://dx.doi.org/10.1109/indiscon53343.2021.9582206.

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Lei, Ying, Qinke Peng, and Yiqing Shen. "Deep Learning for Algorithmic Trading." In ICCAI '20: 2020 6th International Conference on Computing and Artificial Intelligence. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3404555.3404604.

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Salkar, Tanishq, Aditya Shinde, Neelaya Tamhankar, and Narendra Bhagat. "Algorithmic Trading using Technical Indicators." In 2021 International Conference on Communication information and Computing Technology (ICCICT). IEEE, 2021. http://dx.doi.org/10.1109/iccict50803.2021.9510135.

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Xu, Jingxia, and Yun Xiong. "Algorithmic Trading Strategies for Informed Traders." In ICBDT 2022: 2022 5th International Conference on Big Data Technologies. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3565291.3565317.

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Wray, Stephen, Wayne Luk, and Peter Pietzuch. "Exploring algorithmic trading in reconfigurable hardware." In 2010 21st IEEE International Conference on Application-specific Systems, Architectures and Processors (ASAP). IEEE, 2010. http://dx.doi.org/10.1109/asap.2010.5540966.

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Gómez Martínez, Raúl, Camilo Prado Román, and María del Carmen De la Orden de la Cruz. "Algorithmic Trading Systems Based on Google Trends." In CARMA 2018 - 2nd International Conference on Advanced Research Methods and Analytics. Valencia: Universitat Politècnica València, 2018. http://dx.doi.org/10.4995/carma2018.2018.8295.

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In this paper we analyze five big data algorithmic trading systems based on artificial intelligence models that uses as predictors stats from Google Trends of dozens of financial terms. The systems were trained using monthly data from 2004 to 2017 and have been tested in a prospective way from January 2017 to February 2018. The performance of this systems shows that Google Trends is a good metric for global Investors’ Mood. Systems for Ibex and Eurostoxx are not profitable but Dow Jones, S&amp;P 500 and Nasdaq systems has been profitable using long and short positions during the period studied. This evidence opens a new field for the investigation of trading systems based on big data instead of Chartism.
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Chen, Chaoteng Jordan, Xiaotao Liu, and Kin Keung Lai. "Comparisons of Strategies on Gold Algorithmic Trading." In 2013 Sixth International Conference on Business Intelligence and Financial Engineering (BIFE). IEEE, 2013. http://dx.doi.org/10.1109/bife.2013.61.

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Shen, Yun, Ruihong Huang, Chang Yan, and Klaus Obermayer. "Risk-averse reinforcement learning for algorithmic trading." In 2014 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr). IEEE, 2014. http://dx.doi.org/10.1109/cifer.2014.6924100.

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Singh, Japjeet, Ruppa Thulasiram, and Aerambamoorthy Thavaneswaran. "LSTM based Algorithmic Trading model for Bitcoin." In 2022 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2022. http://dx.doi.org/10.1109/ssci51031.2022.10022021.

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Reports on the topic "Algorithmic Trading"

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Casella, Alessandra, and Thomas Palfrey. Trading Votes for Votes. A Decentralized Matching Algorithm. Cambridge, MA: National Bureau of Economic Research, October 2015. http://dx.doi.org/10.3386/w21645.

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Russo, Margherita, Fabrizio Alboni, Jorge Carreto Sanginés, Manlio De Domenico, Giuseppe Mangioni, Simone Righi, and Annamaria Simonazzi. The Changing Shape of the World Automobile Industry: A Multilayer Network Analysis of International Trade in Components and Parts. Institute for New Economic Thinking Working Paper Series, January 2022. http://dx.doi.org/10.36687/inetwp173.

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In 2018, after 25 years of the North America Trade Agreement (NAFTA), the United States requested new rules which, among other requirements, increased the regional con-tent in the production of automotive components and parts traded between the three part-ner countries, United States, Canada and Mexico. Signed by all three countries, the new trade agreement, USMCA, is to go into force in 2022. Nonetheless, after the 2020 Presi-dential election, the new treaty's future is under discussion, and its impact on the automo-tive industry is not entirely defined. Another significant shift in this industry – the acceler-ated rise of electric vehicles – also occurred in 2020: while the COVID-19 pandemic largely halted most plants in the automotive value chain all over the world, at the reopen-ing, the tide is now running against internal combustion engine vehicles, at least in the an-nouncements and in some large investments planned in Europe, Asia and the US. The definition of the pre-pandemic situation is a very helpful starting point for the analysis of the possible repercussions of the technological and geo-political transition, which has been accelerated by the epidemic, on geographical clusters and sectorial special-isations of the main regions and countries. This paper analyses the trade networks emerg-ing in the past 25 years in a new analytical framework. In the economic literature on inter-national trade, the study of the automotive global value chains has been addressed by us-ing network analysis, focusing on the centrality of geographical regions and countries while largely overlooking the contribution of countries' bilateral trading in components and parts as structuring forces of the subnetwork of countries and their specific position in the overall trade network. The paper focuses on such subnetworks as meso-level structures emerging in trade network over the last 25 years. Using the Infomap multilayer clustering algorithm, we are able to identify clusters of countries and their specific trades in the automotive internation-al trade network and to highlight the relative importance of each cluster, the interconnec-tions between them, and the contribution of countries and of components and parts in the clusters. We draw the data from the UN Comtrade database of directed export and import flows of 30 automotive components and parts among 42 countries (accounting for 98% of world trade flows of those items). The paper highlights the changes that occurred over 25 years in the geography of the trade relations, with particular with regard to denser and more hierarchical network gener-ated by Germany’s trade relations within EU countries and by the US preferential trade agreements with Canada and Mexico, and the upsurge of China. With a similar overall va-riety of traded components and parts within the main clusters (dominated respectively by Germany, US and Japan-China), the Infomap multilayer analysis singles out which com-ponents and parts determined the relative positions of countries in the various clusters and the changes over time in the relative positions of countries and their specialisations in mul-tilateral trades. Connections between clusters increase over time, while the relative im-portance of the main clusters and of some individual countries change significantly. The focus on US and Mexico and on Germany and Central Eastern European countries (Czech Republic, Hungary, Poland, Slovakia) will drive the comparative analysis.
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