# DeltaForge MVP

Real-Time Cross-Asset Strategy Synthesis Engine for Options and Equities

This repository provides a minimal, production-ready MVP skeleton for DeltaForge as a Python package. It includes:
- A concise DSL sketch for assets, market signals, strategy deltas, and plan deltas
- A lightweight ADMM-inspired curator that enforces cross-venue coherence
- Two starter adapters: equity_feed and options_feed
- A toy ExecutionEngine for latency-aware routing across venues
- A deterministic Backtester for end-to-end validation
- A test harness that verifies the end-to-end flow

Cross-Venue MVP Demo
- A lightweight, end-to-end demonstration of two assets across two venues coordinating via a delta hedge.
- Uses the built-in DSL primitives (Asset, PlanDelta, StrategyDelta, LocalArbProblem, SharedSignals) and the ADMM-inspired coordinator to produce a synchronized plan.
- See deltaforge/mvp_cross_venue.py for the demo entry point. Run it with:
  python3 -m deltaforge.mvp_cross_venue  # or python3 deltaforge/mvp_cross_venue.py if installed as a module

How to run tests
- Ensure Python 3.8+
- Install dependencies via pip if needed (not required for the MVP as dependencies are self-contained here)
- Run tests: bash test.sh

Packaging and publishing
- This MVP is structured to be packaged as deltaforge-mvp and built with python3 -m build or pip wheel.
- A READY_TO_PUBLISH file will be created upon satisfying all requirements.

For contributors
- See AGENTS.md for architectural guidelines and testing commands.
- Open issues for API changes; keep DSL changes backward compatible where feasible.

Technical Overview (MVP scope)
- Assets and venues: The MVP models two assets across two venues as a minimal cross-venue coordination example.
- DSL surface: The DSL exposes Asset, MarketSignal, SharedSignals, PlanDelta and StrategyDelta to declare objectives and plan hedges.
- Coordination: A lightweight ADMM-inspired coordinator (and a compatibility shim) aggregates per-venue plans into a globally coherent plan.
- Adapters: Two starter adapters (equity_feed, options_feed) provide feed-like signals used in demos.
- Execution: A toy execution engine scaffolds latency-aware routing across venues.
- Backtesting: Deterministic replay engine validates end-to-end flows.
- Tests: A comprehensive test suite exercises MVP components and end-to-end flows.

How to extend (high level)
- Implement a new asset class or venue by extending the Asset model and providing a simple adapter.
- Extend the coordinator with more refined dual-variable state and additional feasibility checks for multi-venue plans.
- Add new primitives (e.g., calendar spreads, dispersion plays) in dsl.py and wiring in the planner.
- Build more realistic adapters (data feeds and brokers) and a real execution adapter.
- Expand the backtester to support Monte Carlo risk/PnL simulations and deterministic replay with more scenarios.

Publishing readiness
- The package is configured to read README.md into the distribution metadata via pyproject.toml.
- A READY_TO_PUBLISH file will be created once all requirements are satisfied and tests pass in CI.