
Automated Trading with R
Kurzinformation



inkl. MwSt. Versandinformationen
Artikel zZt. nicht lieferbar
Artikel zZt. nicht lieferbar

Beschreibung
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 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 von Conlan, Chris
Produktdetails

So garantieren wir Dir zu jeder Zeit Premiumqualität.
Über den 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.
- Hardcover
- 480 Seiten
- Erschienen 2014
- John Wiley & Sons Inc
- hardcover
- 398 Seiten
- Erschienen 2020
- Chapman and Hall/CRC
- Hardcover
- 224 Seiten
- Erschienen 2017
- John Wiley & Sons Inc
- Gebunden
- 456 Seiten
- Erschienen 2017
- Springer
- Gebunden
- 343 Seiten
- Erschienen 2016
- Wiley-VCH
- Kartoniert
- 276 Seiten
- Erschienen 2020
- Routledge
- Kartoniert
- 579 Seiten
- Erschienen 2019
- O'Reilly Media
- hardcover
- 200 Seiten
- Erschienen 2014
- CRC Press Inc
- Hardcover
- 388 Seiten
- Erschienen 1993
- Springer
- Kartoniert
- 360 Seiten
- Erschienen 2018
- Manning
- Kartoniert
- 576 Seiten
- Erschienen 2017
- Springer
- hardcover
- 208 Seiten
- Erschienen 2011
- Wiley
- paperback
- 248 Seiten
- Erschienen 2016
- Wiley-VCH