Generating Trading Agent Strategies PDF Download
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Author: Daniel M. Reeves
Publisher:
ISBN:
Category :
Languages : en
Pages : 452
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Book Description
Author: Daniel M. Reeves
Publisher:
ISBN:
Category :
Languages : en
Pages : 452
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Book Description
Author: Stefan Jansen
Publisher: Packt Publishing Ltd
ISBN: 1839216786
Category : Business & Economics
Languages : en
Pages : 822
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Book Description
Leverage machine learning to design and back-test automated trading strategies for real-world markets using pandas, TA-Lib, scikit-learn, LightGBM, SpaCy, Gensim, TensorFlow 2, Zipline, backtrader, Alphalens, and pyfolio. Purchase of the print or Kindle book includes a free eBook in the PDF format. Key FeaturesDesign, train, and evaluate machine learning algorithms that underpin automated trading strategiesCreate a research and strategy development process to apply predictive modeling to trading decisionsLeverage NLP and deep learning to extract tradeable signals from market and alternative dataBook Description The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This revised and expanded second edition enables you to build and evaluate sophisticated supervised, unsupervised, and reinforcement learning models. This book introduces end-to-end machine learning for the trading workflow, from the idea and feature engineering to model optimization, strategy design, and backtesting. It illustrates this by using examples ranging from linear models and tree-based ensembles to deep-learning techniques from cutting edge research. This edition shows how to work with market, fundamental, and alternative data, such as tick data, minute and daily bars, SEC filings, earnings call transcripts, financial news, or satellite images to generate tradeable signals. It illustrates how to engineer financial features or alpha factors that enable an ML model to predict returns from price data for US and international stocks and ETFs. It also shows how to assess the signal content of new features using Alphalens and SHAP values and includes a new appendix with over one hundred alpha factor examples. By the end, you will be proficient in translating ML model predictions into a trading strategy that operates at daily or intraday horizons, and in evaluating its performance. What you will learnLeverage market, fundamental, and alternative text and image dataResearch and evaluate alpha factors using statistics, Alphalens, and SHAP valuesImplement machine learning techniques to solve investment and trading problemsBacktest and evaluate trading strategies based on machine learning using Zipline and BacktraderOptimize portfolio risk and performance analysis using pandas, NumPy, and pyfolioCreate a pairs trading strategy based on cointegration for US equities and ETFsTrain a gradient boosting model to predict intraday returns using AlgoSeek's high-quality trades and quotes dataWho this book is for If you are a data analyst, data scientist, Python developer, investment analyst, or portfolio manager interested in getting hands-on machine learning knowledge for trading, this book is for you. This book is for you if you want to learn how to extract value from a diverse set of data sources using machine learning to design your own systematic trading strategies. Some understanding of Python and machine learning techniques is required.
Author: Esther David
Publisher: Springer Science & Business Media
ISBN: 3642151167
Category : Business & Economics
Languages : en
Pages : 285
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Book Description
This volume contains 18 thoroughly refereed and revised papers detailing recent advances in research on designing trading agents and mechanisms for agent-mediated e-commerce. They were originally presented at the 11th International Workshop on Agent-Mediated Electronic Commerce (AMEC 2009) collocated with AAMAS 2009 in Budapest, Hungary, or the 2009 Workshop on Trading Agent Design and Analysis (TADA 2009) collocated with IJCAI 2009 in Pasadena, CA, USA. The papers focus on topics such as individual agent behavior and agent interaction, collective behavior, mechanism design, and computational aspects, all in the context of e-commerce applications like trading, auctions, or negotiations. They combine approaches from different fields of mathematics, computer science, and economics such as artificial intelligence, distributed systems, operations research, and game theory.
Author: Lakhmi C Jain
Publisher: Springer Science & Business Media
ISBN: 3540880488
Category : Mathematics
Languages : en
Pages : 325
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Book Description
Knowledge processing and decision making in agent-based systems constitute the key components of intelligent machines. The contributions included in the book are: Innovations in Knowledge Processing and Decision Making in Agent-Based Systems Towards Real-World HTN Planning Agents Mobile Agent-Based System for Distributed Software Maintenance Software Agents in New Generation Networks: Towards the Automation of Telecom Processes Multi-agent Systems and Paraconsistent Knowledge An Agent-based Negotiation Platform for Collaborative Decision-Making in Construction Supply Chain An Event-Driven Algorithm for Agents at the Web A Generic Mobile Agent Framework Toward Ambient Intelligence Developing Actionable Trading Strategies Agent Uncertainty Model and Quantum Mechanics Representation Agent Transportation Layer Adaptation System Software Agents to Enable Service Composition through Negotiation Advanced Technology Towards Developing Decentralized Autonomous Flexible Manufacturing Systems
Author: Geun Sik Jo
Publisher: Springer
ISBN: 3540785825
Category : Computers
Languages : en
Pages : 928
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Book Description
Following from the very successful First KES Symposium on Agent and Multi-Agent Systems – Technologies and Applications (KES-AMSTA 2007), held in Wroclaw, Poland, 31 May–1 June 2007, the second event in the KES-AMSTA symposium series (KES-AMSTA 2008) was held in Incheon, Korea, March 26–28, 2008. The symposium was organized by the School of Computer and Information Engineering, Inha University, KES International and the KES Focus Group on Agent and Mul- agent Systems. The KES-AMSTA Symposium Series is a sub-series of the KES Conference Series. The aim of the symposium was to provide an international forum for scientific research into the technologies and applications of agent and multi-agent systems. Agent and multi-agent systems are related to the modern software which has long been recognized as a promising technology for constructing autonomous, complex and intelligent systems. A key development in the field of agent and multi-agent systems has been the specification of agent communication languages and formalization of ontologies. Agent communication languages are intended to provide standard declarative mechanisms for agents to communicate knowledge and make requests of each other, whereas ontologies are intended for conceptualization of the knowledge domain. The symposium attracted a very large number of scientists and practitioners who submitted their papers for nine main tracks concerning the methodology and applications of agent and multi-agent systems, a doctoral track and two special sessions.
Author: Michael P. Wellman
Publisher: MIT Press
ISBN: 026223260X
Category : Agents intelligents (Logiciels)
Languages : en
Pages : 251
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Book Description
E-commerce increasingly provides opportunities for autonomous bidding agents: computer programs that bid in electronic markets without direct human intervention. Automated bidding strategies for an auction of a single good with a known valuation are fairly straightforward; designing strategies for simultaneous auctions with interdependent valuations is a more complex undertaking. This book presents algorithmic advances and strategy ideas within an integrated bidding agent architecture that have emerged from recent work in this fast-growing area of research in academia and industry. The authors analyze several novel bidding approaches that developed from the Trading Agent Competition (TAC), held annually since 2000. The benchmark challenge for competing agents--to buy and sell multiple goods with interdependent valuations in simultaneous auctions of different types--encourages competitors to apply innovative techniques to a common task. The book traces the evolution of TAC and follows selected agents from conception through several competitions, presenting and analyzing detailed algorithms developed for autonomous bidding. Autonomous Bidding Agents provides the first integrated treatment of methods in this rapidly developing domain of AI. The authors--who introduced TAC and created some of its most successful agents--offer both an overview of current research and new results. Michael P. Wellman is Professor of Computer Science and Engineering and member of the Artificial Intelligence Laboratory at the University of Michigan, Ann Arbor. Amy Greenwald is Assistant Professor of Computer Science at Brown University. Peter Stone is Assistant Professor of Computer Sciences, Alfred P. Sloan Research Fellow, and Director of the Learning Agents Group at the University of Texas, Austin. He is the recipient of the International Joint Conference on Artificial Intelligence (IJCAI) 2007 Computers and Thought Award.
Author: Wolfgang Ketter
Publisher: Springer Science & Business Media
ISBN: 3642152368
Category : Business & Economics
Languages : en
Pages : 201
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Book Description
This volume contains 13 thoroughly refereed and revised papers detailing recent advances in research on trading agents, negotiating agents, dynamic pricing, and auctions. They were originally presented at the 10th International Workshop on Agent-Mediated Electronic Commerce (AMEC 2008) collocated with AAMAS 2008 in Estoril, Portugal, or the 6th Workshop on Trading Agent Design and Analysis (TADA 2008) collocated with AAAI 2008 in Chicago, IL, USA. The papers originating from AMEC 2008 address agent modeling and multi-agent problems in the context of e-negotiations and e-commerce. The TADA papers stem from the effort to design scenarios where trading agents and market designers can be pitched against each other in applications from supply chain management and procurement. They are all characterized by interdisciplinary research combining fields such as artificial intelligence, distributed systems, game theory, and economics.
Author: Michael Thomaz
Publisher: Springer Nature
ISBN: 3031015541
Category : Computers
Languages : en
Pages : 93
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Book Description
Automated trading in electronic markets is one of the most common and consequential applications of autonomous software agents. Design of effective trading strategies requires thorough understanding of how market mechanisms operate, and appreciation of strategic issues that commonly manifest in trading scenarios. Drawing on research in auction theory and artificial intelligence, this book presents core principles of strategic reasoning that apply to market situations. The author illustrates trading strategy choices through examples of concrete market environments, such as eBay, as well as abstract market models defined by configurations of auctions and traders. Techniques for addressing these choices constitute essential building blocks for the design of trading strategies for rich market applications. The lecture assumes no prior background in game theory or auction theory, or artificial intelligence. Table of Contents: Introduction / Example: Bidding on eBay / Auction Fundamentals / Continuous Double Auctions / Interdependent Markets / Conclusion
Author: Richard S. Sutton
Publisher: MIT Press
ISBN: 0262352702
Category : Computers
Languages : en
Pages : 549
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Book Description
The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.
Author: Fabio-Cesare Bagliano
Publisher: OUP Oxford
ISBN: 0191532932
Category : Business & Economics
Languages : en
Pages : 296
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Book Description
Dynamic Approaches to Macroeconomics provides the advanced student with key methodological tools for the dynamic analysis of a core selection of macroeconomic phenomena, including consumption and investment choices, employment and unemployment outcomes, and economic growth. The technical treatment of these tools will enable the student to handle current journal literature, while not assuming any particular familiarity with advanced analytical tools or mathematical notions. As these tools are introduced, they are related to particular applications to illustrate their use. Chapters are linked by various formal and substantive threads. Discrete-time optimization under uncertainty, introduced in Chapter 1, is motivated and discussed by applications to consumption theory, with particular attention to empirical implementation. Chapter 2 focuses on continuous-time optimization techniques, and discusses the relevant insights in the context of partial-equilibrium investment models. Chapter 3 revisits many of the previous chapters' formal derivations with applications to dynamic labour demand, in comparison to optimal investment models, and characterizes labor market equilibrium when not only individual firms' labor demand, but also individual labor supply by workers, is subject to adjustment costs. Chapter 4 proposes broader applications of methods introduced in the previous chapters and studies continuous-time equilibrium dynamics of representative agent economies, featuring both consumption and investment choices, with applications to long-run growth frameworks of analysis. Chapter 5 illustrates the role of decentralized trading in determining aggregate equilibria, and characterizes aggregate labor market dynamics in the presence of frictional unemployment. Chapters 4 and 5 pay particular attention to strategic interactions and externalities: even when each agent correctly solves his or her individual dynamic problem, modern microfounded macroeconomic models recognize that macroeconomic equilibrium need not have unambiguously desirable properties. By bridging the gap between undergraduate economics and modern microfounded macroeconomic research, this book will be of interest to graduate students in economics, and as a technical reference for economic researchers.
