Deploy a production-grade algorithmic compliance and market microstructure analysis engine built directly on the latest quantitative research from University College London & University of Zurich (Koulouris & Campajola, 2026).

This professional-grade asset utilizes a Symmetric Double Deep Q-Network (DDQN) architecture to simulate institutional duopoly market interactions. By processing historical execution data, the engine independently trains learning agents to detect structural regime shifts, competitive Nash boundaries, and memory-induced supra-competitive outcomes in the Silver futures market (SI=F).

What You Get inside the Bundle:

• Complete R-Core Production Script (tidyverse, tidyquant, keras, tensorflow configuration)

• Isolated Python Dependency Lockfile (requirements.txt locking TensorFlow & NumPy variants)

• Monolithic Multi-Language Compiler Layer (Production-Ready Dockerfile)

• Comprehensive User & Deployment Manual (README Guide)

• Production-Ready Agent Tool Node (agent_tool_gateway.py locking native LangChain & CrewAI integration)

Engineered for zero-friction deployment. Run the entire multi-agent reinforcement learning simulation suite seamlessly on any local or cloud environment with a single command via Docker.