{ "item_title" : "Vector Databases for Quant Finance", "item_author" : [" Alice Schwartz", "Vincent Bisette "], "item_description" : "Reactive PublishingIn the new era of financial AI, speed and intelligence define the edge. Vector Databases for Quant Finance reveals how cutting-edge data architectures, once reserved for large-scale tech, are now transforming quantitative trading and portfolio management.This book bridges the gap between data engineering and quantitative strategy, teaching you how to build real-time pipelines that connect streaming market data to AI-driven trading models. You'll learn to design intelligent feature stores, build embedding-based similarity search systems, and integrate vector databases such as Pinecone, FAISS, and Chroma into live trading environments.Inside, you'll discover how to: Construct scalable real-time data ingestion pipelines for market features and order flow signalsUse vector embeddings to model relationships between securities, news, and alternative datasetsImplement retrieval-augmented generation (RAG) to power adaptive research and trading agentsCombine Python, LangChain, and LLMs to build financial knowledge graphs and autonomous analystsOptimize query latency, memory footprint, and storage for production-grade financial AI systemsBlending data science, software architecture, and algorithmic trading, this guide helps you master the emerging layer that fuels next-generation quant intelligence. Whether you're a quant researcher, data engineer, or algo developer, this book delivers the playbook for building AI-native financial systems that think, learn, and react in real time.Perfect for: Quant developers, financial data engineers, AI researchers, and systematic traders exploring the frontier of vectorized market intelligence.", "item_img_path" : "https://covers2.booksamillion.com/covers/bam/9/79/827/287/9798272874849_b.jpg", "price_data" : { "retail_price" : "41.99", "online_price" : "41.99", "our_price" : "41.99", "club_price" : "41.99", "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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