Name: Applied Quantitative Macro: Building Predictive Regime Models in Python for Institutional Trading
SKU: 68922544
Price: 1287.00 TWD
Availability: InStock
Author: Van Der Post, Hayden
ISBN: 9798194899012

portada Applied Quantitative Macro: Building Predictive Regime Models in Python for Institutional Trading

Applied Quantitative Macro: Building Predictive Regime Models in Python for Institutional Trading

Type

Physical Book

Publisher

Independently published

Author

Van Der Post, Hayden

Format

Paperback

ISBN13

9798194899012

Applied Quantitative Macro: Building Predictive Regime Models in Python for Institutional Trading

Van Der Post, Hayden (Author) · Independently published · Paperback

New Book Imported to Taiwan

Delivery: 02 Nov - 10 Nov

NT$ 1,287

NT$ 1,287

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Synopsis "Applied Quantitative Macro: Building Predictive Regime Models in Python for Institutional Trading"

Reactive Publishing Applied Quantitative Macro delivers a rigorous, code-first framework for building production-grade regime-switching models used by modern quantitative trading desks. Designed specifically for institutional practitioners, portfolio managers, and quantitative researchers, this comprehensive guide bridges the gap between theoretical macroeconomics and actionable algorithmic execution. By shifting away from legacy tools and single-factor assumptions, this book demonstrates how to harness modern data architectures and advanced machine learning techniques to detect structural market shifts before they manifest in price action. Readers will learn how to construct high-performance data pipelines, implement dynamic factor systems, and build resilient predictive models tailored for volatile, regime-dependent markets. Inside, you will find practical implementations covering: High-Throughput Financial Data Processing: Utilizing Polars and high-performance vectorization to process macro datasets, yield curves, and cross-asset signals with maximum efficiency. Dynamic Factor & State-Space Models: Extracting latent macroeconomic drivers, isolating signal from noise, and modeling unobserved regime transitions using advanced econometric techniques. Machine Learning for Regime Classification: Deploying supervised and unsupervised learning algorithms to identify hidden market states, volatility regimes, and structural pivots. Institutional Backtesting & Risk Controls: Accounting for transaction costs, non-stationary macro data, look-ahead bias, and regime-dependent risk parameters. Systematic Strategy Architecture: Integrating regime predictions directly into portfolio optimization, dynamic asset allocation, and risk management frameworks. Whether you are upgrading an existing quantitative framework or constructing a standalone macro trading system from scratch, Applied Quantitative Macro provides the complete mathematical foundations and production-ready Python implementations required to trade today's complex financial markets.