Automated trading with R : quantitative research and platform development
著者
書誌事項
Automated trading with R : quantitative research and platform development
（Books for professionals by professionals）
Apress, c2016
- : pbk
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注記
Includes index
内容説明・目次
内容説明
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
目次
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
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