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Artículo: AMZ-B0G5D7T9PX
Topological Data Analysis for Quant Finance: Persistent Homology, Shape Theory, and Market Regime Classification: How Market Shape Reveals Trends, and Volatility Shifts
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1.02 kg
1.02 kg
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Sí
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Producto de
Amazon
Amazon
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USA
USA
Sobre este producto
- Reactive PublishingA breakthrough blueprint for using Topological Data Analysis (TDA) to model, classify, and predict financial markets. This book shows analysts, quants, and systematic traders how to extract hidden structure from price data using persistent homology, geometric features, and topological invariants. The result is a powerful new edge in regime detection, trend identification, and risk forecasting.TDA treats markets not as noisy time series but as high-dimensional shapes. By measuring those shapes—and tracking how they change, you can uncover signals that standard indicators and machine-learning features consistently miss.Designed as a complete, end-to-end guide, this book provides intuition, formal theory, and full Python implementations for every method.What You Will Learn• Build point-cloud embeddings of market data and construct filtrations that reveal structural patterns. • Compute persistent homology (0D, 1D, 2D) and interpret barcodes, diagrams, and summaries for real trading decisions. • Use shape theory to classify market states, identify breaks, and distinguish trending vs. mean-reverting regimes. • Engineer robust topological features that are stable, noise-resistant, and compatible with ML models. • Develop TDA-powered trading systems including: – Topological regime classifiers – Persistent volatility signatures – Shape-similarity predictive models – Hybrid ML + TDA forecasting pipelinesAll examples include clear Python code, workflows, and reusable templates.Why This Approach WorksTDA captures the geometry of market evolution, information that remains invisible to classical statistics, econometrics, and even deep learning. These methods are invariant to transformations, resilient to noise, and sensitive to early structural changes. In fast, fragmented markets, that makes TDA a new and durable source of alpha.Who This Book Is ForQuantitative traders, financial engineers, data scientists, risk professionals, and researchers looking for next-generation tools that go beyond traditional indicators and conventional machine-learning features.If you want to elevate your quant models with the mathematics of shape and structure, this is the most practical, comprehensive TDA guide available for modern finance.
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