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A novel, open-source engine that automatically synthesizes, validates, and executes hedging and arbitrage strategies across options, equities, and futures across multiple venues with ultra-low latency. It provides a concise DSL to declare strategy ob
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README.md

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.