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Sinopsis
Learn to trade algorithmically with your existing brokerage, from data management, to strategy optimization, to order execution, using free and publicly available data. Connect to your brokerage’s API, and the source code is plug-and-play.
Automated Trading with R explains automated trading, starting with its mathematics and moving to its computation and execution. You will gain a unique insight into the mechanics and computational considerations taken in building a back-tester, strategy optimizer, and fully functional trading platform.
The platform built in this book can serve as a complete replacement for commercially available platforms used by retail traders and small funds. Software components are strictly decoupled and easily scalable, providing opportunity to substitute any data source, trading algorithm, or brokerage. This book will:
- Provide a flexible alternative to common strategy automation frameworks, like Tradestation, Metatrader, and CQG, to small funds and retail traders
- Offer an understanding of the internal mechanisms of an automated trading system
- Standardize discussion and notation of real-world strategy optimization problems
What You Will Learn
- Understand machine-learning criteria for statistical validity in the context of time-series
- Optimize strategies, generate real-time trading decisions, and minimize computation time while programming an automated strategy in R and using its package library
- Best simulate strategy performance in its specific use case to derive accurate performance estimates
- Understand critical real-world variables pertaining to portfolio management and performance assessment, including latency, drawdowns, varying trade size, portfolio growth, and penalization of unused capital
Who This Book Is For
Traders/practitioners at the retail or small fund level with at least an undergraduate background in finance or computer science; graduate level finance or data science students
"Sinopsis" puede pertenecer a otra edición de este libro.
Acerca del autor
Chris Conlan began his career as an independent data scientist specializing in trading algorithms. He attended the University of Virginia where he completed his undergraduate statistics coursework in three semesters. During his time at UVA, he secured initial fundraising for a privately held high-frequency forex group as president and chief trading strategist. He is currently managing the development of private technology companies in high-frequency forex, machine vision, and dynamic reporting.
De la contraportada
All the tools you need are provided in this book to trade algorithmically with your existing brokerage, from data management, to strategy optimization, to order execution, using free and publicly available data. Connect to your brokerage s API, and the source code is plug-and-play.
Automated Trading with R explains the broad topic of automated trading, starting with its mathematics and moving to its computation and execution. Readers will gain a unique insight into the mechanics and computational considerations taken in building a back-tester, strategy optimizer, and fully functional trading platform.
The platform built in this book can serve as a complete replacement for commercially available platforms used by retail traders and small funds. Software components are strictly decoupled and easily scalable, providing opportunity to substitute any data source, trading algorithm, or brokerage. This book will:
- Provide a flexible alternative to common strategy automation frameworks, like Tradestation, Metatrader, and CQG, to small funds and retail traders Offer an understanding of the internal mechanisms of an automated trading system
- Standardize discussion and notation of real-world strategy optimization problems
What You ll Learn:
- To optimize strategies, generate real-time trading decisions, and minimize computation time while programming an automated strategy in R and using its package library
- How to best simulate strategy performance in its specific use case to derive accurate performance estimates
- Important optimization criteria for statistical validity in the context of a time series An understanding of critical real-world variables pertaining to portfolio management and performance assessment, including latency, drawdowns, varying trade size, portfolio growth, and penalization of unused capital
"Sobre este título" puede pertenecer a otra edición de este libro.
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Automated Trading with R: Quantitative Research and Platform Development
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Automated Trading With R : Quantitative Research and Platform Development
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Automated Trading with R: Quantitative Research and Platform Development
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Automated Trading With R : Quantitative Research and Platform Development
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Automated Trading with R: Quantitative Research and Platform Development
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Automated Trading With R : Quantitative Research and Platform Development
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Automated Trading With R : Quantitative Research and Platform Development
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Kartoniert / Broschiert. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Full source code and step-by-step explanation for a plug-and-play trading platform the platform can be used in independent simulation, brokerage-assisted simulation, or end-to-end production tradingIncludes lengthy tab. Nº de ref. del artículo: 123613073
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Automated Trading With R: Quantitative Research and Platform Development
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Automated Trading with R
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Learn to trade algorithmically with your existing brokerage, from data management, to strategy optimization, to order execution, using free and publicly available data. Connect to your brokerage's API, and the source code is plug-and-play.Automated Trading with Rexplains automated trading, starting with its mathematics and moving to its computation and execution. You will gain a unique insight into the mechanics and computational considerations taken in building a back-tester, strategy optimizer, and fully functional trading platform.The platform built in this book can serve as a complete replacement for commercially available platforms used by retail traders and small funds. Software components are strictly decoupled and easily scalable, providing opportunity to substitute any data source, trading algorithm, or brokerage. This book will:Provide a flexible alternative to common strategy automation frameworks, like Tradestation, Metatrader, and CQG, to small funds and retail tradersOffer an understanding of the internal mechanisms of an automated trading systemStandardize discussion and notation of real-world strategy optimization problemsWhat You Will LearnUnderstand machine-learning criteria for statistical validity in the context of time-seriesOptimize strategies, generate real-time trading decisions, and minimize computation time while programming an automated strategy in R and using its package libraryBest simulate strategy performance in its specific use case to derive accurate performance estimatesUnderstand critical real-world variables pertaining to portfolio management and performance assessment, including latency, drawdowns, varying trade size, portfolio growth, and penalization of unused capitalWho This Book Is ForTraders/practitioners at the retail or small fund level with at least an undergraduate background in finance or computer science; graduate level finance or data science students 236 pp. Englisch. Nº de ref. del artículo: 9781484221778
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