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
