from __future__ import annotations from typing import List from .dsl import MarketSignal, PlanDelta, StrategyDelta class ADMMCoordinator: """ Lightweight ADMM-like coordinator. It collects MarketSignals from multiple venues and emits a PlanDelta representing a synchronized hedging action. This is a minimal, deterministic stub suitable for MVP testing. """ def __init__(self, contract_id: str = "default-contract"): self.contract_id = contract_id self.last_plan: PlanDelta | None = None def reconcile(self, plan: PlanDelta) -> PlanDelta: # Minimal reconciliation: produce a single StrategyDelta placeholder to indicate coherence. merged = StrategyDelta(notes="reconciled") # Augment plan with lightweight dual/consensus metadata to simulate an ADMM-like # cross-venue coherence step. This is intentionally a small stub for MVP testing. dual_vars = {"rho": 1.0, "alpha": 0.5} new_plan = PlanDelta( deltas=[merged], timestamp=getattr(plan, "timestamp", 0.0), author=getattr(plan, "author", None), contract_id=self.contract_id, actions=getattr(plan, "actions", []), dual_vars=dual_vars, ) self.last_plan = new_plan return new_plan # Compatibility shim for existing runtime that imports Coordinator class Coordinator(ADMMCoordinator): def __init__(self, contract_id: str = "default-contract"): super().__init__(contract_id=contract_id) def coordinate(self, signals, author: str = "coordinator") -> PlanDelta: # Lightweight, deterministic plan synthesis for MVP merged = StrategyDelta(notes="coordinated") # Propagate a minimal coherence envelope along with a pointer to the contract id. dual_vars = {"rho": 1.0, "alpha": 0.9} return PlanDelta( deltas=[merged], timestamp=0.0, author=author, contract_id=self.contract_id, actions=[], dual_vars=dual_vars, )