Overview
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 trading
Includes lengthy tables and descriptions of performance metrics, indicators, rule sets, and brokerage plans, helping users get to production quicker
Includes performance assessments of popular strategies implemented on multi-asset portfolios, allowing users to swap components to customize, research, and deploy automated strategies
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Table of contents (13 chapters)
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Front Matter
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Problem Scope
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Front Matter
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Building the Platform
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Front Matter
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Production Trading
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Front Matter
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Back Matter
Keywords
About this book
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
Authors and Affiliations
About the author
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.
Bibliographic Information
Book Title: Automated Trading with R
Book Subtitle: Quantitative Research and Platform Development
Authors: Chris Conlan
DOI: https://doi.org/10.1007/978-1-4842-2178-5
Publisher: Apress Berkeley, CA
eBook Packages: Professional and Applied Computing, Professional and Applied Computing (R0), Apress Access Books
Copyright Information: Chris Conlan 2016
Softcover ISBN: 978-1-4842-2177-8Published: 29 September 2016
eBook ISBN: 978-1-4842-2178-5Published: 28 September 2016
Edition Number: 1
Number of Pages: XXV, 205
Number of Illustrations: 19 b/w illustrations, 16 illustrations in colour
Topics: Programming Languages, Compilers, Interpreters, Programming Techniques