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Automated Trading with R - Quantitative Research and Platform Development (Paperback, 1st ed.)
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Automated Trading with R - Quantitative Research and Platform Development (Paperback, 1st ed.)
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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
General
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