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Book synopsis

Reactive Publishing

In 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 signals

• Use vector embeddings to model relationships between securities, news, and alternative datasets

• Implement retrieval-augmented generation (RAG) to power adaptive research and trading agents

• Combine Python, LangChain, and LLMs to build financial knowledge graphs and autonomous analysts

• Optimize query latency, memory footprint, and storage for production-grade financial AI systems

Blending 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.

Book details

• Full title: Vector Databases for Quant Finance
• Subtitle: Real-Time Feature Stores and Embedding Pipelines for Trading AI: Build Intelligent Market Systems with Pinecone, FAISS, and Chroma
• Author: Vincent Bisette, Alice Schwartz
• Language: English
• Binding: Paperback
• Number of pages: 634
• EAN: 9798272874849
• ID: 50591006
• Publisher: Independently published
• Weight: 837 g
• Dimensions: 229 × 152 × 32 mm
• Date of publishing: 03. November 2025

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