Agentic AI for Financial Markets
Building Intelligent Multi-Agent Systems for Equity Analysis and Autonomous Trading
Created by ViplavMost software architects approaching AI-driven finance assume that scaling LLMs and adding reinforcement learning will automatically produce profitable trading systems. The reality is far more nuanced. Building production-grade agentic AI for financial markets requires mastering a complex interplay of multi-agent orchestration, domain-specific LLM fine-tuning, real-time data infrastructure, and quantitative analysis techniques that go far beyond standard machine learning practices. This book provides the architectural patterns, implementation strategies, and practical frameworks you need to design intelligent systems that analyze equities, predict market movements, and execute trades autonomously. You'll learn how to architect multi-agent systems with sophisticated communication protocols, build vector databases for semantic financial search, implement real-time market data pipelines, fine-tune LLMs on financial corpora, and deploy reinforcement learning agents in non-stationary market environments. Each pattern is grounded in production considerations: latency constraints, regulatory compliance, risk management, and operational monitoring. Whether you're building internal trading systems, financial analytics platforms, or autonomous investment tools, this book equips you with the architectural knowledge and implementation patterns to succeed.
Est. Length
253 pages (12 chapters)
Language
English
Publication date
Nov 1, 2025
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