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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

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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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Chris Conlan began his career as an independent data scientist specializing in trading algorithms. He attended the University of Virginia where he completed his undergraduate statistics coursework in three semesters. During his time at UVA, he secured initial fundraising for a privately held high-frequency forex group as president and chief trading strategist. He is currently managing the development of private technology companies in high-frequency forex, machine vision, and dynamic reporting.

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