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    Pandas + AI for Finance: Machine Learning-Driven Strategy Design

    Posted By: TiranaDok
    Date: 30 Oct 2025 20:03:09
    Pandas + AI for Finance: Machine Learning-Driven Strategy Design

    Pandas + AI for Finance: Machine Learning-Driven Strategy Design: From Regression to Reinforcement, Unleash AI with Clean, Financial-Grade Data by Hayden Van Der Post, Johann Strauss, Vincent Bisette
    English | June 8, 2025 | ISBN: N/A | ASIN: B0FCG9JDLG | 684 pages | EPUB | 0.63 Mb

    Reactive Publishing

    Pandas + AI for Finance: Machine Learning-Driven Strategy Design
    By Hayden Van Der Post
    Design the Future of Finance. One Model at a Time.
    Pandas + AI for Finance is a cutting-edge guide that bridges data science and market intelligence, empowering you to build machine learning-driven trading systems with precision, clarity, and power. Whether you're a financial analyst, quant, or entrepreneur, this book gives you the tools to move beyond theory and into real-world algorithmic design.
    Written by Hayden Van Der Post, a leader in AI-assisted finance publishing, this book delivers practical, actionable techniques for transforming raw market data into deployable, adaptive strategies using Python’s most powerful tools.
    What You’ll Learn:
    • Master advanced Pandas workflows to structure, reshape, and clean financial datasets at scale
    • Implement feature engineering pipelines specific to equities, options, macroeconomics, and crypto
    • Train and evaluate machine learning models using scikit-learn, XGBoost, and LightGBM
    • Avoid common data leakage traps and overfitting errors in time-series forecasting
    • Integrate cross-validation, rolling windows, and walk-forward optimization for true out-of-sample performance
    • Explore emerging AI tools and automation frameworks to streamline research and deployment
    Who This Book Is For:
    • Financial analysts seeking to expand into data-driven strategy design
    • Traders and portfolio managers transitioning from intuition to automation
    • Engineers and data scientists entering the finance domain
    • Researchers building reproducible, scalable pipelines for institutional-grade insights

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