Algorithmic Trading via AI/ML with R by Jason Guevara (.PDF)
File Size: 21.1 MB
Algorithmic Trading via AI/Machine Learning with R by Jason Guevara, Ričards Bulavs, Oskars Linares
Requirements: .PDF reader, 21.1 MB | True PDF
Overview: Algorithmic Trading via AI/Machine Learning with R aims to demonstrate how algorithmic trading can empower retail traders to compete more effectively in markets long dominated by institutional giants. By translating advanced techniques into practical, systematic strategies, the book shows how automation, disciplined risk management, and data-driven decision making can help individuals filter out market noise, avoid manipulation, and exploit opportunities that once belonged exclusively to large firms. The book’s purpose is to give you a framework where R is not just a statistical environment, but a trading laboratory and execution engine. Every chapter includes reproducible examples you can extend into your own practice and research pipeline. By the end, you will not merely understand algorithmic trading―you will have built, tested, and connected live strategies to market data. At its core, it demonstrates how R―a language renowned for statistical computing―can be transformed into a complete research and execution platform for trading. Algorithmic trading requires programming languages that effectively balance research capabilities, performance, and live execution. While Python has become the industry standard, R has evolved into a fully competitive alternative. With modern libraries such as tidyverse, tidyquant, Backtest, Portfolio-Analytics and PerformanceAnalytics replacing older frameworks, R now offers traders a robust ecosystem for data analysis, strategy design, and portfolio optimization that stands alongside Python in both research and applied settings. This book is aimed at anyone who wants to learn, or use R, for AI/Machine Learning and algorithmic trading. It is also for individuals doing or interested in doing securities research and financial systems development and for retail traders who may wish to use R to gain an algorithmic trading edge.
Genre: Non-Fiction > Tech & Devices
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