{ "item_title" : "Time Series Filtering and Regime Detection for Quant Finance", "item_author" : [" Alice Schwartz", "James Preston "], "item_description" : "Reactive PublishingTime series data in quantitative finance is noisy, non-stationary, and full of regime shifts. Extracting reliable signals from this data is one of the core technical challenges in systematic trading and research.This book provides a practical treatment of time series filtering and regime detection methods tailored for quantitative finance applications. It focuses on techniques for signal extraction and denoising that practitioners can implement and evaluate directly.Topics covered include:Classical and modern filtering approaches for financial time seriesMethods for identifying market regimes and state changesPractical signal extraction and noise reduction techniquesImplementation considerations for real-world quant workflowsThe material is written for quantitative researchers, systematic traders, and developers who work with financial time series and need clear, implementable methods rather than purely theoretical treatments.No prior expertise in advanced signal processing is assumed, but readers should be comfortable with basic time series concepts and Python or a similar quantitative programming environment.", "item_img_path" : "https://covers1.booksamillion.com/covers/bam/9/79/819/274/9798192749180_b.jpg", "price_data" : { "retail_price" : "35.99", "online_price" : "35.99", "our_price" : "35.99", "club_price" : "35.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }

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