{ "item_title" : "Hunting Market Anomalies", "item_author" : [" Victor Trex "], "item_description" : "Hunting Market Anomalies: Machine Learning Techniques for Contrarian TradingIn a marketplace obsessed with momentum and trend-following, this book turns deliberately in the opposite direction. Hunting Market Anomalies: Machine Learning Techniques for Contrarian Trading is written for quantitatively inclined traders, researchers, portfolio managers, and advanced students who want to systematically exploit mean reversion and behavioral overreaction. It bridges the gap between academic anomaly research and industrial-strength trading systems, focusing on practical, implementable ideas rather than toy examples.Readers will learn how to build a leak-free, point-in-time research stack; design robust statistical tests that survive multiple-hypothesis scrutiny; and engineer features that capture price, microstructure, event-driven, and cross-asset reversal signals. The book walks through a complete machine learning pipeline-time-aware validation, regularization, model selection, and hyperparameter tuning-culminating in realistic backtests, cost modeling, and portfolio construction. By the end, you will know how to translate noisy market data into contrarian signals, deploy them in live portfolios, and monitor performance under changing regimes.The text assumes familiarity with basic probability, linear algebra, and Python, but no prior experience in quantitative finance is required. Emphasis is placed on reproducible research, point-in-time simulation, and model explainability, making this a rigorous yet accessible guide for practitioners seeking durable, evidence-based t", "item_img_path" : "https://covers2.booksamillion.com/covers/bam/9/79/889/665/9798896652397_b.jpg", "price_data" : { "retail_price" : "39.99", "online_price" : "39.99", "our_price" : "39.99", "club_price" : "39.99", "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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