Könyv Advanced Market Architectures James Preston

Advanced Market Architectures

Building Multi-Agent Trading Systems with Deep RL and Real-Time Data: Design, Deploy, and Optimize Autonomous AI Trading Environments for Global Markets

Nyelv: Angol
Kötés: Puha kötésű
Elérhetőség: Beszállítói készleten
Küldés 14-21 napon belül
27 194 Ft
Reactive PublishingBuild the Next Generation of AI-Powered Trading Infrastructure.In Advanced Market...

Információk a könyvről

Nyelv
Angol
Kötés
Könyv - Puha kötésű
Kiadva
2025
oldal
482
EAN
9798291109250
Enbook ID
52821877
Súly
578
Méretek
152 x 229 x 30

Teljes leírás

Reactive Publishing

Build the Next Generation of AI-Powered Trading Infrastructure.

In Advanced Market Architectures, bestselling author James Preston takes you beyond the theory, and into the heart of modern algorithmic warfare. This isn't just about teaching agents to trade. It's about building an intelligent market ecosystem where autonomous systems think, learn, and evolve in real time.

If you mastered the foundations in Reinforcement Learning for Trading, this is your next step: scaling to multi-agent frameworks, real-time data ingestion, cloud deployment, and institutional-grade execution engines. Preston brings battle-tested insight and deep technical clarity to guide you through every stage of development, from raw data to a fully operational AI trading grid.

Whether you're a quant, data scientist, software engineer, or a solo trader building elite tools, this book hands you the keys to a new market paradigm.


Inside, You'll Learn How To:
  • Design intelligent trading environments with cooperative and competitive RL agents

  • Build real-time data pipelines using WebSockets, Kafka, and Redis

  • Deploy and scale models with Ray RLlib, Kubernetes, and AWS/GCP

  • Execute trades with low-latency precision and dynamic order flow modeling

  • Integrate portfolio optimization, risk-aware decision policies, and multi-asset strategies

  • Simulate live markets using tick-level data for training robust agents

  • Leverage meta-learning to adapt agents across multiple asset classes