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Algorithmic Trading Via AI/Machine Learning with R

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Τίτλος: Algorithmic Trading Via AI/Machine Learning with R
Περιγραφή: 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.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.Key Features: Follows a clearly defined, pedagogical structure that builds from foundational R tools to full automation and integration with APIs. Argues that while retail traders cannot match Wall Street's scale, they can use algorithms to level the playing field—building consistency, resilience, and an edge in a market designed to favor the powerful. All the book's scripts can be accessed on the book's GitHub branch. The QuantRoom YouTube channel (@quantroom) provides video tutorials and scripts that complement the book's content showcasing real-time problem-solving. Delivers a more engaging and accessible way to master algorithmic trading using R and the Schwab Trader API. The Appendix expands the book's scope beyond R by presenting a side-by-side comparison between the C++ TWS API and the IBrokers R interface, illustrating how high-level R commands map directly to their low-level C++ counterparts.
Συγγραφείς: Jason Guevara, Ričards Bulavs, Oskars Linares
Resource Type: eBook.
Θέματα: Stocks--Mathematical models--Data processing, Algorithms, Artificial intelligence--Financial applications, R (Computer program language)
Categories: BUSINESS & ECONOMICS / Statistics, BUSINESS & ECONOMICS / Finance / General, MATHEMATICS / Probability & Statistics / General
Βάση Δεδομένων: eBook Index
FullText Text:
  Availability: 0
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DbLabel: eBook Index
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AccessLevel: 6
PubType: eBook
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  Data: Algorithmic Trading Via AI/Machine Learning with R
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  Data: 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.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.Key Features: Follows a clearly defined, pedagogical structure that builds from foundational R tools to full automation and integration with APIs. Argues that while retail traders cannot match Wall Street's scale, they can use algorithms to level the playing field—building consistency, resilience, and an edge in a market designed to favor the powerful. All the book's scripts can be accessed on the book's GitHub branch. The QuantRoom YouTube channel (@quantroom) provides video tutorials and scripts that complement the book's content showcasing real-time problem-solving. Delivers a more engaging and accessible way to master algorithmic trading using R and the Schwab Trader API. The Appendix expands the book's scope beyond R by presenting a side-by-side comparison between the C++ TWS API and the IBrokers R interface, illustrating how high-level R commands map directly to their low-level C++ counterparts.
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PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=4419047
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      – Code: 332.6322
        Scheme: ddc
        Type: prePub
    Languages:
      – Code: eng
        Text: English
    Subjects:
      – SubjectFull: Stocks--Mathematical models--Data processing
        Type: general
      – SubjectFull: Algorithms
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      – SubjectFull: Artificial intelligence--Financial applications
        Type: general
      – SubjectFull: R (Computer program language)
        Type: general
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      – TitleFull: Algorithmic Trading Via AI/Machine Learning with R
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          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2026
            – D: 07
              M: 05
              Type: profile
              Y: 2026
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