OpenImpact

OpenImpact is a privacy-preserving market-impact modeling stack for multi-venue execution research.

It provides:

• deterministic synthetic venue generation for safe stress testing
• a temporary-impact regression model
• federated training across venues with masked aggregation
• a SQLite governance ledger for model-update audit trails
• deterministic replay with venue-specific latency
• an evaluation path for RMSE, latency, and a simple leakage bound

What This Repo Ships

• TemporaryImpactModel: fits a linear temporary-impact model from local order requests.
• FederatedTrainer: trains per-venue models and aggregates coefficients without exposing raw requests.
• GovernanceLedger: stores update metadata in SQLite.
• DeterministicReplayEngine: replays the same request stream reproducibly.
• SyntheticMarketConfig / generate_synthetic_market: builds repeatable venue datasets.
• evaluate_federated_setup: returns benchmark metrics against a pooled baseline.

Quick Start

python3 -m pip install -e ".[dev]"
pytest
python3 -m openimpact

Package Metadata

This project is published as idea141-openimpact-privacy-preserving and uses this README.md as its long description.

Repository Rules

• Keep the synthetic market deterministic by seed.
• Preserve the public API exported from openimpact/__init__.py.
• Update tests for every behavior change.
• Keep test.sh working; it must run tests and python3 -m build.