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Automated Trading with R: Quantitative Research and Platform Development [Paperback]

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  • Category: Books (Computers)
  • Author:  Conlan, Chris
  • Author:  Conlan, Chris
  • ISBN-10:  148422177X
  • ISBN-10:  148422177X
  • ISBN-13:  9781484221778
  • ISBN-13:  9781484221778
  • Publisher:  Apress
  • Publisher:  Apress
  • Binding:  Paperback
  • Binding:  Paperback
  • Pub Date:  01-Oct-2016
  • Pub Date:  01-Oct-2016
  • SKU:  148422177X-11-SPRI
  • SKU:  148422177X-11-SPRI
  • Item ID: 100162220
  • List Price: $79.99
  • Seller: ShopSpell
  • Ships in: 5 business days
  • Transit time: Up to 5 business days
  • Delivery by: Nov 30 to Dec 02
  • Notes: Brand New Book. Order Now.

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 brokerages 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 plĂ›

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