LEADER 04315nam 22005295 450 001 9910254754303321 005 20220623183941.0 010 $a1-4842-2178-8 024 7 $a10.1007/978-1-4842-2178-5 035 $a(CKB)3710000000873214 035 $a(DE-He213)978-1-4842-2178-5 035 $a(MiAaPQ)EBC4699976 035 $a(CaSebORM)9781484221785 035 $a(PPN)195514319 035 $a(EXLCZ)993710000000873214 100 $a20160928d2016 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAutomated Trading with R$b[electronic resource] $eQuantitative Research and Platform Development /$fby Chris Conlan 205 $a1st ed. 2016. 210 1$aBerkeley, CA :$cApress :$cImprint: Apress,$d2016. 215 $a1 online resource (XXV, 205 p. 35 illus., 16 illus. in color.) 300 $aIncludes index. 311 $a1-4842-2177-X 327 $aPart 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 -- . 330 $aAll 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. 606 $aProgramming languages (Electronic computers) 606 $aComputer programming 606 $aR (Computer program language) 606 $aProgramming Languages, Compilers, Interpreters$3https://scigraph.springernature.com/ontologies/product-market-codes/I14037 606 $aProgramming Techniques$3https://scigraph.springernature.com/ontologies/product-market-codes/I14010 615 0$aProgramming languages (Electronic computers). 615 0$aComputer programming. 615 0$aR (Computer program language). 615 14$aProgramming Languages, Compilers, Interpreters. 615 24$aProgramming Techniques. 676 $a005.13 700 $aConlan$b Chris$4aut$4http://id.loc.gov/vocabulary/relators/aut$0859262 801 0$bUMI 801 1$bUMI 906 $aBOOK 912 $a9910254754303321 996 $aAutomated Trading with R$91917781 997 $aUNINA