build(agent): molt-z#db0ec5 iteration

.gitignore

@@ -0,0 +1,21 @@
+node_modules/
+.npmrc
+.env
+.env.*
+__tests__/
+coverage/
+.nyc_output/
+dist/
+build/
+.cache/
+*.log
+.DS_Store
+tmp/
+.tmp/
+__pycache__/
+*.pyc
+.venv/
+venv/
+*.egg-info/
+.pytest_cache/
+READY_TO_PUBLISH

AGENTS.md

@@ -0,0 +1,13 @@
+# SignalVault SWARM Agent Guidance
+
+- Architecture: Python MVP with Graph primitives (SignalNode, Edge, Scenario, HedgePlan), Graph-of-Contracts registry, deterministic replay engine, and privacy primitives stubs.
+- Tech stack: Python 3.11+, dataclasses, in-memory registry, minimal cryptographic placeholders (no real crypto implemented here), toy adapters for price feed and venue.
+- Testing: pytest-based unit tests; test.sh runs pytest and builds the package.
+- Running tests: ./test.sh
+- Building: python3 -m build
+- Contribution rules: keep changes minimal and focused; add tests for new functionality; update AGENTS.md with new modules and tests.
+
+Guiding principles:
+- Start with a small, solid MVP; iterate via 1 feature per sprint.
+- Ensure deterministic replay semantics for offline reproducibility.
+- Provide adapters to demonstrate interoperability with external feeds and venues.

README.md

@@ -1,3 +1,24 @@
-# signalvault-verifiable-privacy-preservin
+# SignalVault Verifiable Privacy-Preserving Signal Repository (MVP)
 
-A portable, graph-backed signal store for finance that focuses on verifiable provenance, offline replay, and privacy-preserving sharing of market signals across venues. SignalVault stores signals as canonical graph primitives (SignalNode, Edge, Scena
+This project implements a portable, graph-backed signal store focused on verifiable provenance,
+offline replay, and privacy-preserving sharing of market signals across venues. The MVP defines
+canonical graph primitives (SignalNode, Edge, Scenario, HedgePlan) and a Graph-of-Contracts
+registry to map adapters to data feeds, risk models, and execution engines.
+
+What you get in this MVP:
+- Core graph primitives and a tiny in-memory registry
+- Deterministic replay engine for applying deltas and reconstructing signal state
+- Privacy primitives placeholders (secure aggregation, DP budgets)
+- Toy adapters for a price feed and a simulated venue
+- Lightweight tests validating schema and replay behavior
+
+How to run locally:
+- Ensure Python 3.11+ is installed
+- Run tests via: ./test.sh
+- Build package via: python3 -m build (as part of test.sh)
+
+Publishing notes:
+- Python package name: signalvault_verifiable_privacy_preservin
+- Exposes a small public API surface for MVP exploration and integration tests
+
+This repo intentionally keeps scope minimal and testable to facilitate 1-feature-per-sprint iteration.

pyproject.toml

@@ -0,0 +1,15 @@
+[build-system]
+requires = ["setuptools>=42", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "signalvault_verifiable_privacy_preservin"
+version = "0.1.0"
+description = "MVP: verifiable, privacy-preserving market signal repository with offline replay"
+readme = "README.md"
+requires-python = ">=3.11"
+
+[tool.setuptools.packages.find]
+where = ["." ]
+
+# Version is defined in [project]. No dynamic tooling required for MVP.

signalvault_verifiable_privacy_preservin/__init__.py

@@ -0,0 +1,28 @@
+"""SignalVault Verifiable, Privacy-Preserving Market Signals (MVP).
+
+A minimal Python package scaffolding for SignalVault MVP focusing on
+canonical graph primitives, a Graph-of-Contracts registry, deterministic
+replay, and privacy primitives placeholders.
+"""
+
+from .schema import SignalNode, Edge, Scenario, HedgePlan, AuditLog, PrivacyBudget
+from .registry import GraphRegistry
+from .replay import DeterministicReplayEngine
+from .privacy import SecureAggregator, DPBudget
+from .adapters.price_feed import PriceFeedAdapter
+from .adapters.simulated_venue import SimulatedVenueAdapter
+
+__all__ = [
+    "SignalNode",
+    "Edge",
+    "Scenario",
+    "HedgePlan",
+    "AuditLog",
+    "PrivacyBudget",
+    "GraphRegistry",
+    "DeterministicReplayEngine",
+    "SecureAggregator",
+    "DPBudget",
+    "PriceFeedAdapter",
+    "SimulatedVenueAdapter",
+]

signalvault_verifiable_privacy_preservin/adapters/__init__.py

@@ -0,0 +1 @@
+"""Adapter package for SignalVault MVP."""

signalvault_verifiable_privacy_preservin/adapters/price_feed.py

@@ -0,0 +1,21 @@
+from __future__ import annotations
+
+from typing import Dict, List
+from ..schema import SignalNode
+
+
+class PriceFeedAdapter:
+    """Toy price feed adapter that exposes SignalNode objects."""
+
+    def __init__(self, feed_name: str = "toy-price-feed") -> None:
+        self.feed_name = feed_name
+
+    def to_signal(self, asset: str, venue: str, price: float, ts: int) -> SignalNode:
+        return SignalNode(
+            asset=asset,
+            venue=venue,
+            signal_type="price",
+            timestamp=ts,
+            quality=1.0,
+            metadata={"feed": self.feed_name, "price": price},
+        )

signalvault_verifiable_privacy_preservin/adapters/simulated_venue.py

@@ -0,0 +1,21 @@
+from __future__ import annotations
+
+from typing import Dict, List
+from ..schema import HedgePlan
+
+
+class SimulatedVenueAdapter:
+    """Toy venue adapter that turns HedgePlan deltas into orders."""
+
+    def __init__(self, venue_name: str = "toy-venue") -> None:
+        self.venue_name = venue_name
+
+    def delta_to_order(self, hedge_plan: HedgePlan) -> Dict[str, object]:
+        # very simple mapping: delta keys -> order dict
+        order = {
+            "venue": self.venue_name,
+            "hedge_id": hedge_plan.id,
+            "action": hedge_plan.delta.get("action", "adjust"),
+            "params": hedge_plan.delta,
+        }
+        return order

signalvault_verifiable_privacy_preservin/privacy.py

@@ -0,0 +1,40 @@
+from __future__ import annotations
+from typing import List, Dict
+
+
+class SecureAggregator:
+    """Placeholder secure-aggregation primitive.
+    In a real system this would perform cryptographic aggregation (e.g., via MPC).
+    Here we simply sum numeric signals and average others defensively.
+    """
+
+    @staticmethod
+    def aggregate(signals: List[Dict[str, object]]) -> Dict[str, object]:
+        if not signals:
+            return {}
+        # naive aggregation by key
+        keys = signals[0].keys()
+        result: Dict[str, object] = {}
+        for k in keys:
+            try:
+                vals = [float(s.get(k, 0)) for s in signals]
+                result[k] = sum(vals) / len(vals)
+            except Exception:
+                # fallback to last value
+                result[k] = signals[-1].get(k)
+        return result
+
+
+class DPBudget:
+    """Per-signal differential privacy budget placeholder."""
+
+    def __init__(self, total_budget: float = 1.0) -> None:
+        self.total_budget = float(total_budget)
+        self.used = 0.0
+
+    def allocate(self, amount: float) -> float:
+        amount = float(amount)
+        remaining = max(0.0, self.total_budget - self.used)
+        take = min(remaining, amount)
+        self.used += take
+        return take

signalvault_verifiable_privacy_preservin/registry.py

@@ -0,0 +1,26 @@
+from __future__ import annotations
+
+from typing import Dict, Optional
+from .schema import RegistryEntry
+
+
+class GraphRegistry:
+    """A tiny in-memory Graph-of-Contracts registry mapping adapters to contracts."""
+
+    def __init__(self) -> None:
+        self._registry: Dict[str, RegistryEntry] = {}
+
+    def register_adapter(self, adapter_name: str, contract: Dict[str, object], conformance: bool = False) -> None:
+        self._registry[adapter_name] = RegistryEntry(
+            adapter_name=adapter_name,
+            contract=contract,
+            conformance=conformance,
+        )
+
+    def get_contract(self, adapter_name: str) -> Optional[Dict[str, object]]:
+        entry = self._registry.get(adapter_name)
+        return entry.contract if entry else None
+
+    def is_conformant(self, adapter_name: str) -> bool:
+        entry = self._registry.get(adapter_name)
+        return bool(entry and entry.conformance)

signalvault_verifiable_privacy_preservin/replay.py

@@ -0,0 +1,26 @@
+from __future__ import annotations
+
+from typing import Dict, List
+from .schema import SignalNode, Edge, HedgePlan
+
+
+class DeterministicReplayEngine:
+    """A tiny deterministic replay engine for applying deltas to a stored graph state."""
+
+    def __init__(self) -> None:
+        pass
+
+    def apply_delta(self, state: Dict[str, object], delta: Dict[str, object]) -> Dict[str, object]:
+        """Apply a delta to a state dict in a deterministic way.
+        This is a minimal placeholder: keys in delta override state.
+        """
+        new_state = dict(state)
+        for k, v in delta.items():
+            new_state[k] = v
+        return new_state
+
+    def replay(self, base_state: Dict[str, object], deltas: List[Dict[str, object]]) -> Dict[str, object]:
+        state = dict(base_state)
+        for d in deltas:
+            state = self.apply_delta(state, d)
+        return state

signalvault_verifiable_privacy_preservin/schema.py

@@ -0,0 +1,61 @@
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import List, Dict, Optional
+
+
+@dataclass
+class SignalNode:
+    asset: str
+    venue: str
+    signal_type: str
+    timestamp: int  # epoch seconds
+    quality: float = 0.0
+    metadata: Dict[str, object] = field(default_factory=dict)
+
+
+@dataclass
+class Edge:
+    from_id: str
+    to_id: str
+    relation: str  # e.g., "causal", "temporal"
+    timestamp: int
+    metadata: Dict[str, object] = field(default_factory=dict)
+
+
+@dataclass
+class Scenario:
+    id: str
+    nodes: List[str] = field(default_factory=list)
+    edges: List[str] = field(default_factory=list)
+    description: str = ""
+    version: int = 1
+
+
+@dataclass
+class HedgePlan:
+    id: str
+    delta: Dict[str, object]  # minimal representation of hedging actions
+    version: int = 1
+    approvals: List[str] = field(default_factory=list)
+
+
+@dataclass
+class AuditLog:
+    entry: str
+    timestamp: int
+    actor: Optional[str] = None
+
+
+@dataclass
+class PrivacyBudget:
+    signal_id: str
+    budget: float  # DP budget allocated per signal
+    policy: Dict[str, object] = field(default_factory=dict)
+
+
+@dataclass
+class RegistryEntry:
+    adapter_name: str
+    contract: Dict[str, object]
+    conformance: bool = False

test.sh

@@ -0,0 +1,12 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+echo "Running tests..."
+# Ensure the repo root is on PYTHONPATH so local packages are importable
+export PYTHONPATH="${PYTHONPATH:+$PYTHONPATH:}$PWD"
+pytest -q
+
+echo "Building package..."
+python3 -m build
+
+echo "All tests passed and build completed."

tests/test_core.py

@@ -0,0 +1,30 @@
+import time
+from signalvault_verifiable_privacy_preservin.schema import SignalNode, HedgePlan
+from signalvault_verifiable_privacy_preservin.replay import DeterministicReplayEngine
+
+
+def test_delta_application_is_deterministic():
+    # base state represents a simple graph state snapshot
+    base = {
+        "signals": [],
+        "hedges": [],
+        "version": 1,
+    }
+
+    delta1 = {"signals": ["s1"], "version": 2}
+    delta2 = {"hedges": ["h1"], "version": 3}
+
+    engine = DeterministicReplayEngine()
+    st1 = engine.apply_delta(base, delta1)
+    st2 = engine.apply_delta(st1, delta2)
+
+    assert st2["version"] == 3
+    assert st2["signals"] == ["s1"]
+    assert st2["hedges"] == ["h1"]
+
+
+def test_schema_dataclasses_are_instantiable():
+    sn = SignalNode(asset="ETH", venue="VenueA", signal_type="price", timestamp=int(time.time()), quality=0.9)
+    hp = HedgePlan(id="hp1", delta={"action": "buy"})
+    assert sn.asset == "ETH"
+    assert hp.id == "hp1"