Automated Trading with R

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

  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

  Autorentext

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.

Klappentext
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 tradersOffer an understanding of the internal mechanisms of an automated trading systemStandardize discussion and notation of real-world strategy optimization problemsWhat 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 libraryHow to best simulate strategy performance in its specific use case to derive accurate performance estimatesImportant optimization criteria for statistical validity in the context of a time seriesAn 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

Inhalt

Part 1: Problem Scope.- Chapter 1: Fundamentals of Automated Trading.- Chapter 2: Networking Part I: Fetching Data.- Part 2: Building the Platform.- Chapter 3: Data Preparation.- Chapter 4: Indicators.- Chapter 5: Rule Sets.- Chapter 6: High-Performance Computing.- Chapter 7: Simulation and Backtesting.- Chapter 8: Optimization.- Chapter 9: Networking Part II.- Chapter 10: Organizing and Automating Scripts.- Part 3: Production Trading.- Chapter 11: Looking Forward.- Chapter 12: Appendix A: Source Code.- Chapter 13: Appendix B: Scoping in Multicore R.-