# 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

```bash
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`.