build(agent): new-agents-4#58ba63 iteration
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@ -10,6 +10,12 @@ This repository provides a minimal, production-ready MVP skeleton for DeltaForge
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- A deterministic Backtester for end-to-end validation
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- A test harness that verifies the end-to-end flow
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Cross-Venue MVP Demo
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- A lightweight, end-to-end demonstration of two assets across two venues coordinating via a delta hedge.
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- Uses the built-in DSL primitives (Asset, PlanDelta, StrategyDelta, LocalArbProblem, SharedSignals) and the ADMM-inspired coordinator to produce a synchronized plan.
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- See deltaforge/mvp_cross_venue.py for the demo entry point. Run it with:
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python3 -m deltaforge.mvp_cross_venue # or python3 deltaforge/mvp_cross_venue.py if installed as a module
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How to run tests
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- Ensure Python 3.8+
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- Install dependencies via pip if needed (not required for the MVP as dependencies are self-contained here)
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@ -0,0 +1,64 @@
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from __future__ import annotations
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"""
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DeltaForge MVP Cross-Venue Demo
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---------------------------------
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A tiny, self-contained module that demonstrates how two venues can coordinate
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to synthesize a delta-hedge plan across assets. This is intentionally small but
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production-friendly: it uses the existing DSL primitives (Asset, PlanDelta,
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StrategyDelta, LocalArbProblem, SharedSignals) and the lightweight
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ADMM-like coordinator already present in deltaforge.coordinator.
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Usage (run as script):
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- This will construct a minimal two-venue delta hedge and print the resulting plan.
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"""
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from typing import List
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from .dsl import Asset, MarketSignal, SharedSignals, LocalArbProblem, PlanDelta, StrategyDelta
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from .coordinator import Coordinator
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import time
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def build_demo_assets() -> List[Asset]:
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# Two assets representing two venues/assets we want to coordinate across
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a1 = Asset(id="venueA-apple", type="equity", symbol="AAPL", venue="venueA")
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a2 = Asset(id="venueB-apple", type="equity", symbol="AAPL", venue="venueB")
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return [a1, a2]
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def build_demo_signals(assets: List[Asset]) -> SharedSignals:
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# A tiny set of market signals; in a real system this would come from feeds
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signals = [
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MarketSignal(asset=assets[0], timestamp=time.time(), price=150.0, source="demo"),
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MarketSignal(asset=assets[1], timestamp=time.time(), price=149.5, source="demo"),
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]
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return SharedSignals(version=1, signals=signals, privacy_tag="public")
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def synthesize_cross_venue_plan() -> PlanDelta:
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# Build a minimal per-venue delta plan: venueA wants +10 delta, venueB wants -7 delta
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d1 = StrategyDelta(id="dA", assets=[Asset(id="venueA-AAPL", type="equity", symbol="AAPL", venue="venueA")],
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delta_positions={"AAPL": 10}, notes="venueA hedge")
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d2 = StrategyDelta(id="dB", assets=[Asset(id="venueB-AAPL", type="equity", symbol="AAPL", venue="venueB")],
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delta_positions={"AAPL": -7}, notes="venueB hedge")
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base_plan = PlanDelta(deltas=[d1, d2], timestamp=time.time(), author="demo", contract_id="deltaforge-demo")
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# Run the lightweight coordinator to reconcile deltas across venues
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coord = Coordinator(contract_id="deltaforge-demo")
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reconciled = coord.reconcile(base_plan)
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return reconciled
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def main():
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assets = build_demo_assets()
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_signals = build_demo_signals(assets)
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plan = synthesize_cross_venue_plan()
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print("Demo Cross-Venue Plan:")
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print(plan)
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print("Signals:", _signals)
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if __name__ == "__main__":
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main()
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@ -1,6 +1,23 @@
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"""DeltaForge MVP package init.
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Expose lightweight APIs for a tiny cross-venue hedging engine scaffold.
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"""DeltaForge MVP core package
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Minimal production-ready skeleton to support end-to-end flow:
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- DSL seed data structures for assets, signals, plans
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- Lightweight cross-venue coordinator placeholder
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- Starter adapters for equity and options feeds
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- Toy execution engine and deterministic backtester
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"""
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from .core import StrategyDelta, Asset, MarketSignal, PlanDelta # re-export for convenience
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from .execution import ExecutionRouter # lightweight router for multi-venue dispatch (experimental)
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from . import dsl as dsl # re-export for convenience
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from . import core as core
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from . import backtester as backtester
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from . import execution as execution
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from . import adapters as adapters
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__all__ = [
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"dsl",
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"adapters",
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"core",
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"backtester",
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"execution",
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]
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@ -0,0 +1,4 @@
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from .equity_feed import EquityFeedAdapter
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from .options_feed import OptionsFeedAdapter
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__all__ = ["EquityFeedAdapter", "OptionsFeedAdapter"]
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"""Starter equity feed adapter: emits simple price signals for an equity."""
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from __future__ import annotations
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import time
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from deltaforge_mvp.core import Asset, MarketSignal
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def generate_signal(symbol: str, price: float) -> MarketSignal:
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asset = Asset(type="equity", symbol=symbol)
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return MarketSignal(asset=asset, price=price, volatility=0.2, liquidity=1.0, timestamp=time.time())
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def generate_signal(symbol: str, price: float, timestamp: float | None = None) -> MarketSignal:
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"""Tiny equity feed adapter: returns a MarketSignal for an equity asset."""
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a = Asset(type="equity", symbol=symbol)
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ts = timestamp if timestamp is not None else time.time()
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return MarketSignal(asset=a, price=price, volatility=0.2, liquidity=1.0, timestamp=ts)
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class EquityFeedAdapter:
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"""Lightweight adapter wrapper to expose a stable interface used by tests."""
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def __init__(self, name: str = "equity"):
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self.name = name
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def get_signal(self, symbol: str, price: float, timestamp: float | None = None) -> MarketSignal:
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return generate_signal(symbol, price, timestamp)
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"""Starter options feed adapter: emits option market signals."""
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from __future__ import annotations
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import time
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from deltaforge_mvp.core import Asset, MarketSignal
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def create_option_symbol(underlying: str, strike: float, expiry: str) -> Asset:
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return Asset(type="option", underlying=underlying, strike=strike, expires=expiry)
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def generate_signal(underlying: str, strike: float, expiry: str, price: float, timestamp: float | None = None) -> MarketSignal:
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"""Tiny options feed adapter: returns a MarketSignal for an option asset."""
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a = Asset(type="option", underlying=underlying, strike=strike, expires=expiry, symbol=None)
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ts = timestamp if timestamp is not None else time.time()
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return MarketSignal(asset=a, price=price, volatility=0.25, liquidity=1.0, timestamp=ts)
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def generate_signal(underlying: str, strike: float, expiry: str, price: float) -> MarketSignal:
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asset = Asset(type="option", underlying=underlying, strike=strike, expires=expiry)
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return MarketSignal(asset=asset, price=price, volatility=0.25, liquidity=0.8, timestamp=time.time())
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class OptionsFeedAdapter:
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"""Lightweight adapter wrapper to expose a stable interface used by tests."""
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def __init__(self, name: str = "options"):
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self.name = name
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def get_signal(self, underlying: str, strike: float, expiry: str, price: float, timestamp: float | None = None) -> MarketSignal:
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return generate_signal(underlying, strike, expiry, price, timestamp)
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"""Deterministic replay backtester (toy) for MVP."""
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from __future__ import annotations
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from typing import List
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from typing import Any
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import time
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from deltaforge_mvp.core import PlanDelta
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class Backtester:
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def __init__(self, seed: int = 0):
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def __init__(self, seed: int | None = None):
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self.seed = seed
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def replay(self, plan: PlanDelta) -> dict:
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# Very small deterministic stub: compute a fake PnL based on number of deltas and a seed
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pnl = 0.0
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for d in plan.deltas:
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pnl += (d.delta * 0.5) # arbitrary scaling for demo
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return {"pnl": pnl, "delta_count": len(plan.deltas), "seed": self.seed}
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def replay(self, plan: PlanDelta) -> Any:
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# Minimal deterministic replay: just echo basic info
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return {
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"status": "replayed",
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"venue": plan.venue,
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"delta_count": len(plan.deltas),
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"timestamp": time.time(),
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}
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"""ADMM-lite style coordination skeleton for cross-venue coherence."""
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from __future__ import annotations
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import time
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from typing import List
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from typing import List, Optional
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from deltaforge_mvp.core import PlanDelta, StrategyDelta
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from deltaforge_mvp.core import StrategyDelta, PlanDelta
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class LocalRiskSolver:
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def __init__(self, venue_name: str):
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self.venue_name = venue_name
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def __init__(self, venue: str):
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self.venue = venue
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def propose(self, signals: List[StrategyDelta]) -> PlanDelta:
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# Minimal heuristic: aggregate deltas and propose a single PlanDelta per venue
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# In real system this would solve a convex program; here we pass through the deltas.
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pd = PlanDelta(deltas=signals, venue=self.venue_name, author="local-solver")
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return pd
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def propose(self, deltas: List[StrategyDelta]) -> PlanDelta:
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return PlanDelta(deltas=deltas, venue=self.venue, author="LocalRiskSolver", timestamp=time.time())
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class CentralCurator:
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def __init__(self):
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pass
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def enforce(self, plans: List[PlanDelta]) -> PlanDelta:
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# Naive: merge all deltas into a single PlanDelta with combined deltas
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merged = []
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for p in plans:
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merged.extend(p.deltas)
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# ADMM-lite balancing: attempt to enforce cross-venue coherence by
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# driving the net delta toward zero. This is a lightweight, deterministic
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# adjustment suitable for a toy MVP and deterministic replay.
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if len(merged) > 0:
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total = sum(d.delta for d in merged)
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mean_adjust = total / len(merged)
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# Create adjusted copies to avoid mutating existing deltas
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adjusted = []
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for d in merged:
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adj = StrategyDelta(
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asset=d.asset,
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delta=d.delta - mean_adjust,
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vega=d.vega,
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gamma=d.gamma,
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target_pnl=d.target_pnl,
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max_order_size=d.max_order_size,
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timestamp=d.timestamp,
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)
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adjusted.append(adj)
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merged = adjusted
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return PlanDelta(deltas=merged, venue=None, author="curator")
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def enforce_with_fallback(self, plans: List[PlanDelta], fallback: Optional["ShadowFallback"] = None) -> PlanDelta:
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"""Enforce cross-venue constraints with optional shadow/fallback strategy.
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If there are no plans to enforce, and a fallback is provided, use the
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fallback to produce a safe delta plan. Otherwise, fall back to the standard
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enforcement path.
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"""
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if not plans and fallback is not None:
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return fallback.propose_safe(plans)
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return self.enforce(plans)
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class ShadowFallback:
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"""Lightweight shadow/fallback solver to propose safe deltas when disconnected."""
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def propose_safe(self, signals) -> PlanDelta:
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# If signals is a list of StrategyDelta, create a corresponding zero-delta plan
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deltas: List[StrategyDelta] = []
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# Normalize: extract StrategyDelta items whether the input contains StrategyDelta or PlanDelta
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items: List[StrategyDelta] = []
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for s in signals:
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if isinstance(s, PlanDelta):
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items.extend(s.deltas)
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elif isinstance(s, StrategyDelta):
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items.append(s)
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for st in items:
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deltas.append(StrategyDelta(asset=st.asset, delta=0.0, timestamp=0.0))
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return PlanDelta(deltas=deltas, venue=None, author="shadow-fallback")
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# Very simple merge: just return the first plan, preserving its deltas
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if not plans:
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return PlanDelta(deltas=[], venue=None, author="CentralCurator", timestamp=time.time())
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return PlanDelta(deltas=plans[0].deltas, venue=plans[0].venue, author="CentralCurator", timestamp=plans[0].timestamp)
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from __future__ import annotations
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from typing import Dict, List, Optional
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from dataclasses import dataclass
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from typing import List, Optional
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import time
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"""Core domain primitives for DeltaForge MVP.
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- Asset: canonical representation of a tradable instrument (equity/option/etc).
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- MarketSignal: market data snapshot for an asset.
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- StrategyDelta: local hedge decision/delta for an asset.
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- PlanDelta: a collection of StrategyDelta objects, annotated with venue/author info.
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"""
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@dataclass
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class Asset:
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"""Canonical asset representation.
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Example: {"type": "equity", "symbol": "AAPL"} or {"type": "option", "underlying": "AAPL", "strike": 150, "expires": "2026-12-17"}
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"""
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type: str # 'equity', 'option', 'future'
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type: str
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symbol: Optional[str] = None
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underlying: Optional[str] = None
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strike: Optional[float] = None
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expires: Optional[str] = None
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def canonical_id(self) -> str:
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if self.type == "equity":
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return f"EQ:{self.symbol}"
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if self.type == "option":
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return f"OP:{self.underlying}:{self.strike}:{self.expires}"
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if self.type == "future":
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return f"FU:{self.symbol or self.underlying}:{self.expires}"
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return f"UNK:{self.symbol or 'UNDEF'}"
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@dataclass
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class MarketSignal:
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"""Lightweight market signal used by adapters to convey prices, liquidity, etc."""
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asset: Asset
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price: float
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volatility: float = 0.0
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liquidity: float = 1.0
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timestamp: float = 0.0
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volatility: float
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liquidity: float
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timestamp: float
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@dataclass
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class StrategyDelta:
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"""Local decision block; describes intent to adjust hedges for an asset.
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This is a light DSL-like structure that adapters translate into venue orders.
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"""
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asset: Asset
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delta: float # directional hedge to apply (positive means buy delta exposure, etc.)
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vega: float = 0.0
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gamma: float = 0.0
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target_pnl: Optional[float] = None
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max_order_size: float = 1.0
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timestamp: float = 0.0
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delta: float
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timestamp: float
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@dataclass
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class PlanDelta:
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"""Incremental hedges/adjustments with metadata for auditability."""
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deltas: List[StrategyDelta]
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confidence: float = 1.0
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venue: Optional[str] = None
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author: str = "system"
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author: str = ""
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timestamp: float = 0.0
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signature: Optional[str] = None # placeholder for cryptographic tag
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from .dsl import LocalArbProblem, SharedSignals # kept for potential internal use
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class ADMMCoordinator:
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"""Tiny ADMM-inspired coordinator stub for cross-venue coherence.
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It returns a minimal PlanDelta reflecting the input local arb problems.
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"""
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def coordinate(self, problems: Dict[str, LocalArbProblem], signals: SharedSignals) -> PlanDelta:
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# Create a trivial delta list that represents no changes yet but records activity
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# This maintains compatibility with the PlanDelta dataclass defined above.
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deltas: List[StrategyDelta] = []
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# Build a no-op delta for each asset referenced in problems
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for p in problems.values():
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for asset in p.assets:
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# We synthesize an Asset from the string; keep minimal fields
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a = Asset(type="unknown", symbol=asset)
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deltas.append(StrategyDelta(asset=a, delta=0.0, timestamp=time.time()))
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return PlanDelta(deltas=deltas, venue="coordinator", author="ADMM", timestamp=time.time())
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__all__ = ["ADMMCoordinator"]
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from __future__ import annotations
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from dataclasses import dataclass, asdict
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from typing import Dict, List, Optional
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import json
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@dataclass
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class LocalArbProblem:
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id: str
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venue: str
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assets: List[str]
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objective: Dict[str, float]
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constraints: Dict[str, float]
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solver_hint: Optional[str] = None
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def to_json(self) -> str:
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return json.dumps(asdict(self))
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@staticmethod
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def from_json(s: str) -> "LocalArbProblem":
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data = json.loads(s)
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return LocalArbProblem(**data)
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@dataclass
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class SharedSignals:
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version: int
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signals: Dict[str, float]
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privacy_tag: str = "public"
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def to_json(self) -> str:
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return json.dumps(asdict(self))
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@staticmethod
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def from_json(s: str) -> "SharedSignals":
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data = json.loads(s)
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return SharedSignals(**data)
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@dataclass
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class PlanDelta:
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delta: Dict[str, float]
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timestamp: float
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author: str
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contract_id: str
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signature: str = ""
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def to_json(self) -> str:
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return json.dumps(asdict(self))
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@staticmethod
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def from_json(s: str) -> "PlanDelta":
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data = json.loads(s)
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return PlanDelta(**data)
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@dataclass
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class DualVariables:
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multipliers: Dict[str, float]
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convergence_status: str = "not-converged"
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def to_json(self) -> str:
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return json.dumps(asdict(self))
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||||
|
||||
@staticmethod
|
||||
def from_json(s: str) -> "DualVariables":
|
||||
data = json.loads(s)
|
||||
return DualVariables(**data)
|
||||
|
||||
|
||||
@dataclass
|
||||
class PrivacyBudget:
|
||||
budget: float
|
||||
expiry: float
|
||||
leakage_bound: Optional[float] = None
|
||||
|
||||
def to_json(self) -> str:
|
||||
return json.dumps(asdict(self))
|
||||
|
||||
@staticmethod
|
||||
def from_json(s: str) -> "PrivacyBudget":
|
||||
data = json.loads(s)
|
||||
return PrivacyBudget(**data)
|
||||
|
||||
|
||||
@dataclass
|
||||
class AuditLog:
|
||||
entry: str
|
||||
signer: str
|
||||
timestamp: float
|
||||
contract_id: str
|
||||
version: str = "0.1"
|
||||
|
||||
def to_json(self) -> str:
|
||||
return json.dumps(asdict(self))
|
||||
|
||||
@staticmethod
|
||||
def from_json(s: str) -> "AuditLog":
|
||||
data = json.loads(s)
|
||||
return AuditLog(**data)
|
||||
|
||||
|
||||
@dataclass
|
||||
class PolicyBlock:
|
||||
safety: Dict[str, float]
|
||||
exposure_controls: Dict[str, float]
|
||||
|
||||
def to_json(self) -> str:
|
||||
return json.dumps(asdict(self))
|
||||
|
||||
@staticmethod
|
||||
def from_json(s: str) -> "PolicyBlock":
|
||||
data = json.loads(s)
|
||||
return PolicyBlock(**data)
|
||||
|
||||
|
||||
__all__ = [
|
||||
"LocalArbProblem",
|
||||
"SharedSignals",
|
||||
"PlanDelta",
|
||||
"DualVariables",
|
||||
"PrivacyBudget",
|
||||
"AuditLog",
|
||||
"PolicyBlock",
|
||||
]
|
||||
|
|
@ -1,43 +1,28 @@
|
|||
"""Lightweight cross-venue execution router (experimental)."""
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import List, Optional
|
||||
|
||||
from deltaforge_mvp.core import PlanDelta
|
||||
from typing import Dict, Any, List
|
||||
from .core import PlanDelta
|
||||
|
||||
|
||||
class ExecutionRouter:
|
||||
"""Simple round-robin routing of a PlanDelta to available venues.
|
||||
"""Routing layer that assigns a venue to a given PlanDelta."""
|
||||
|
||||
This is a tiny, self-contained shim that demonstrates how a real executor
|
||||
would dispatch plan deltas to venue adapters with latency/fees metadata.
|
||||
"""
|
||||
|
||||
def __init__(self, venues: Optional[List[str]] = None):
|
||||
def __init__(self, venues: List[str]):
|
||||
self.venues = venues or []
|
||||
self._idx = 0
|
||||
|
||||
def route(self, plan: PlanDelta) -> dict:
|
||||
"""Route the given plan to a venue (or no venue if unknown).
|
||||
|
||||
Returns a dict with routing metadata that downstream systems can consume.
|
||||
"""
|
||||
if not self.venues:
|
||||
return {"routed": False, "reason": "no_venues_configured"}
|
||||
|
||||
venue = self.venues[self._idx % len(self.venues)]
|
||||
self._idx += 1
|
||||
# Attach simple routing metadata to the plan (clone-like behavior)
|
||||
routed = PlanDelta(
|
||||
deltas=plan.deltas,
|
||||
confidence=plan.confidence if hasattr(plan, "confidence") else 1.0,
|
||||
venue=venue,
|
||||
author=plan.author,
|
||||
timestamp=plan.timestamp,
|
||||
signature=plan.signature,
|
||||
)
|
||||
def route(self, plan: PlanDelta) -> Dict[str, Any]:
|
||||
venue = self.venues[0] if self.venues else None
|
||||
routed_plan = PlanDelta(deltas=plan.deltas, venue=venue, author=plan.author, timestamp=plan.timestamp)
|
||||
return {
|
||||
"routed": True,
|
||||
"venue": venue,
|
||||
"plan": routed,
|
||||
"plan": routed_plan,
|
||||
}
|
||||
|
||||
|
||||
# Backwards-compatibility alias (if any downstream code imports ExecutionEngine)
|
||||
class ExecutionEngine(ExecutionRouter):
|
||||
pass
|
||||
|
||||
|
||||
__all__ = ["ExecutionRouter", "ExecutionEngine"]
|
||||
|
|
|
|||
Loading…
Reference in New Issue