The Science of Algorithmic Trading and Portfolio Management, with its emphasis on algorithmic trading processes and current trading models, sits apart from others of its kind. Robert Kissell, the first author to discuss algorithmic trading across the various asset classes, provides key insights into ways to develop, test, and build trading algorithms. Readers learn how to evaluate market impact models and assess performance across algorithms, traders, and brokers, and acquire the knowledge to implement electronic trading systems. This valuable book summarizes market structure, the formation of prices, and how different participants interact with one another, including bluffing, speculating, and gambling. Readers learn the underlying details and mathematics of customized trading algorithms, as well as advanced modeling techniques to improve profitability through algorithmic trading and appropriate risk management techniques. Portfolio management topics, including quant factors and black box models, are discussed, and an accompanying website includes examples, data sets supplementing exercises in the book, and large projects. It prepares readers to evaluate market impact models and assess performance across algorithms, traders, and brokers. It helps readers design systems to manage algorithmic risk and dark pool uncertainty. It summarizes an algorithmic decision making framework to ensure consistency between investment objectives and trading objectives.
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Robert Kissell is an Executive Director responsible for analytics product initiatives within UBS Direct Execution and UBS Portfolio Trading. Prior to joining UBS, he was with JP Morgan where he served as Head of Quantitative Trading Strategies.
Its emphasis on algorithmic trading processes and current trading models sets this book apart from others. As the first author to discuss algorithmic trading across the various asset classes, Robert Kissell provides key insights into ways to develop, test, and build trading algorithms. He summarizes market structure, the formation of prices, and how different participants interact with one another, including bluffing, speculating, and gambling. He shows readers the underlying details and mathematics required to develop, build, and test customized algorithms, providing them with advanced modeling techniques to improve profitability through algorithmic trading and appropriate risk management techniques. The accompanying website includes examples, data sets underlying exercises in the book, and large projects. Readers learn how to evaluate market impact models and assess performance across algorithms, traders, and brokers, as well as acquiring the ability to implement electronic trading systems.
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Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The Science of Algorithmic Trading and Portfolio Management, with its emphasis on algorithmic trading processes and current trading models, sits apart from others of its kind. Robert Kissell, the first author to discuss algorithmic trading across the various asset classes, provides key insights into ways to develop, test, and build trading algorithms. Readers learn how to evaluate market impact models and assess performance across algorithms, traders, and brokers, and acquire the knowledge to implement electronic trading systems. This valuable book summarizes market structure, the formation of prices, and how different participants interact with one another, including bluffing, speculating, and gambling. Readers learn the underlying details and mathematics of customized trading algorithms, as well as advanced modeling techniques to improve profitability through algorithmic trading and appropriate risk management techniques. Portfolio management topics, including quant factors and black box models, are discussed, and an accompanying website includes examples, data sets supplementing exercises in the book, and large projects. Prepares readers to evaluate market impact models and assess performance across algorithms, traders, and brokers. Helps readers design systems to manage algorithmic risk and dark pool uncertainty. Summarizes an algorithmic decision making framework to ensure consistency between investment objectives and trading objectives. 496 pp. Englisch. Seller Inventory # 9780124016897
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