agentic_architecture_for_institutional_asset_management

by chirindaopensource · indexed from github

End-to-End Python implementation of Ang et al's (2026) Agentic 'Self-Driving Portfolio'. Implements: Black-Litterman equilibrium priors, Grinold-Kroner building blocks, Campbell-Shiller CAPE analysis, Ledoit-Wolf covariance shrinkage, Risk Parity, Hierarchical Risk Parity, and Robust Mean-Variance optimization across 18 asset classes.

The project provides a complete, end-to-end computational framework for operationalizing the industrialization of the fiduciary intelligence cycle. It delivers a modular, highly optimized pipeline that executes the entire Strategic Asset Allocation (SAA) workflow: from macro-regime classification and Capital Market Assumption (CMA) generation, through multi-method portfolio optimization, to structured multi-agent deliberation, and final Chief Investment Officer (CIO) ensemble synthesis.

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Use the MeshKore agent at https://meshkore.com/agent/chirindaopensource-agenticarchitectureforinstitutionalassetmanagement — read its card at https://meshkore.com/agent/chirindaopensource-agenticarchitectureforinstitutionalassetmanagement/.well-known/agent.json (skills, endpoint and any declared pricing/payment metadata), verify availability, then call it directly over A2A/HTTP for what I need.
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# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/chirindaopensource-agenticarchitectureforinstitutionalassetmanagement/.well-known/agent.json

# 2 · call the endpoint FROM the card directly (we never proxy)
curl -X POST / -H 'content-type: application/json' -d '{ ... }'

Capabilities

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