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Automated Trading with R: Quantitative Research and Platform Development, by Chris Conlan
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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
- Sales Rank: #163295 in Books
- Published on: 2016-09-29
- Released on: 2016-09-29
- Original language: English
- Number of items: 1
- Dimensions: 10.00" h x .54" w x 7.01" l, .0 pounds
- Binding: Paperback
- 205 pages
From the Back Cover
All 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
About the Author
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.
Most helpful customer reviews
5 of 5 people found the following review helpful.
A must for anyone interested in automated trading.
By Michael S.
The author does a great job of walking the reader through all aspects of automated trading.
The examples, code, and technical details are all highly relevant, up to date, and useful for designing a trading system.
This book is a must for anyone building out an automated trading platform, or even just someone learning the theory and code behind trading.
I learned a lot, thank you.
4 of 4 people found the following review helpful.
Highly Recommend
By Amazon Customer
If you're looking for a readable book for learning trading in R, that is also incredibly in-depth, then this is a great buy. The author has a way about explaining the development of the code as well as the making of the platform which makes it incredibly easy to grasp. This book gave great insights into the theory behind automated trading, and I highly recommend it for anyone interested in getting into this field. 10/10
1 of 1 people found the following review helpful.
A Thorough Treatment of Automated Trading
By Eric Scheier
Automated Trading with R is a thoughtful, clearly laid out, and complete description of how to build trading systems that actually work. So often books of this nature leave out crucial pieces of system construction either out of incompetence or "as an exercise for the reader," but that is not the case here. The scope is broad enough and each subject addressed with enough specificity that anybody from a relative novice to a seasoned pro can gain something from reading this book.
Conlan takes us through:
- Networking: how to get data and make trades via the internet
- Data Preparation: what to do with data once you have it
- Indicators: how to construct the signals that inform trading decisions
- Rule Sets: combining indicators at the portfolio level to decide when & how to trade
- High-Performance Computing: where to look for performance gains in one's code
- Simulation and Backtesting: putting everything together to predict historical performance
- Optimization: finding strategies that work
In addition to covering the fundamental theory and technical hurdles involved in each topic, the author focuses on providing functional example code which by the end amounts to a fully functional trading system. I look forward to implementing the lessons learned in my own trading system, including much of the example code provided.
Disclosure: I was provided with a copy of the book in exchange for my honest review.
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