Automated Trading with R : Quantitative Research and Platform Development

This book 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 backtester, strategy optimizer, and fully functional trading platform. __Automated Trading with R__ provides automated traders with all the tools they need to trade algorithmically with their existing brokerage, from data management, to strategy optimization, to order execution, using free and publically available data. If your brokerage’s API is supported, the source code is plug-and-play. 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. The book’s three objectives are:* To provide a flexible alternative to common strategy automation frameworks, like Tradestation, Metatrader, and CQG, to small funds and retail traders. * To offer an understanding the internal mechanisms of an automated trading system. * To standardize discussion and notation of real-world strategy optimization problems. **What you’ll learn** * Programming an automated strategy in R gives the trader access to R and its package library for optimizing strategies, generating real-time trading decisions, and minimizing computation time. * How to best simulate strategy performance in their specific use case to derive accurate performance estimates. * Important machine-learning criteria for statistical validity in the context of 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. **Who This Book Is For** 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. 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 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 Front Matter....Pages i-xxv Front Matter....Pages 1-1 Fundamentals of Automated Trading....Pages 3-20 Front Matter....Pages 21-21 Networking Part I....Pages 23-35 Data Preparation....Pages 37-49 Indicators....Pages 51-58 Rule Sets....Pages 59-63 High-Performance Computing....Pages 65-81 Simulation and Backtesting....Pages 83-99 Optimization....Pages 101-130 Networking Part II....Pages 131-152 Front Matter....Pages 153-153 Organizing and Automating Scripts....Pages 155-160 Looking Forward....Pages 161-165 Source Code....Pages 167-194 Appendix B: Scoping in Multicore R....Pages 195-201 Back Matter....Pages 203-205 Chris Conlan. Place of publication from publisher's website (viewed February 1, 2017). Includes index.