# EquiCompiler: Algebraic Portfolio DSL to Verifiable Low-Latency Compiler (MVP) Overview - This repository contains a minimal, production-ready MVP for a compiler stack that translates a domain-specific language for market strategies into a portable, verifiable execution graph. The MVP focuses on a small, well-formed core to enable safe extension by additional agents in the swarm. What you get in this MVP - A small DSL parser that reads a concise DSL and converts it into a canonical in-memory IR (EquiIR) represented as Python dicts. - A minimal Python package equicompiler_algebraic_portfolio_dsl_to_ with a core module and a CLI entry point. - A tiny test suite to validate DSL parsing and IR generation. - A test runner (test.sh) that executes tests and builds the package to verify packaging metadata. Usage - DSL to IR (programmatic): from equicompiler_algebraic_portfolio_dsl_to_.core import parse_dsl_to_ir dsl = "assets: AAPL, MSFT, GOOG\nobjectives: maximize_return\nconstraints: max_drawdown=0.2, var=0.95" ir = parse_dsl_to_ir(dsl) print(ir) - CLI (Python module): python -m equicompiler_algebraic_portfolio_dsl_to_.cli path/to/dsl_file.txt Verifiable DSL Overview - The EquiCompiler MVP translates a mathematically precise portfolio DSL into a portable, verifiable IR (EquiIR). - Each transformation step embeds lightweight cryptographic attestations (digest-based) to ensure auditability and replayability without leaking sensitive data. - A Graph-of-Contracts (GoC) skeleton is attached to the IR to enable plug-and-play adapters for data feeds and brokers, while preserving versioned contracts and compatibility. - The system supports offline-first evaluation with deterministic delta-sync, enabling local backtests and plan replay when connectivity resumes. - Backends are designed for cross-runtime portability: Python backtester for offline tests, C++ for live trading, and WebAssembly for browser-based evaluation. - The MVP keeps a minimal, stable surface area while providing clear extension points for future formal verification hooks and delta-sync workflows. Example workflow - Parse a DSL to IR (programmatic): ``from equicompiler_algebraic_portfolio_dsl_to_.core import parse_dsl_to_ir`` ``dsl = "assets: AAPL, MSFT, GOOG\nobjectives: maximize_return\nconstraints: max_drawdown=0.2, var=0.95"`` ``ir = parse_dsl_to_ir(dsl)`` ``print(ir)`` - Use the CLI to validate and inspect the IR produced from a file: ``python -m equicompiler_algebraic_portfolio_dsl_to_.cli path/to/dsl_file.txt`` Roadmap (high level) - Phase 0: DSL to IR, start backends (Python backtester, minimal C++ live skeleton) and a minimal GoC registry. - Phase 1: Formal verification hooks and delta-sync for offline/online reconciliation. - Phase 2: Cross-backend interoperability tests with toy adapters. - Phase 3: Hardware-in-the-loop (HIL) testing with partitioned portfolios. Publishing readiness - This repository is designed for packaging as a Python module named ``equicompiler_algebraic_portfolio_dsl_to`` with versioning in ``pyproject.toml``. - The README now includes a marketing-style overview and usage examples to aid publication. Project structure - AGENTS.md: architecture and testing guidelines for future agents - README.md: this file - pyproject.toml: packaging metadata - equicompiler_algebraic_portfolio_dsl_to_/ (package) - __init__.py - core.py: DSL -> IR parser (minimal) - cli.py: CLI entry point - tests/test_core.py: small unit test for DSL parsing - test.sh: test runner to validate test suite and packaging Development notes - The MVP intentionally keeps dependencies minimal to ensure fast iterations and deterministic tests. - When adding features, try to keep changes small and focused on a single goal. - Ensure tests cover the new functionality and avoid sensitive data in tests. Next steps - Extend the DSL with richer constraints (VaR, VaR-CVaR, liquidity, latency) and ExecutionPolicy primitives. - Integrate the GoC registry and build a canonical EquiIR representation with per-message metadata for replay/verification. - Add a lightweight delta-sync coordinator and starter adapters for data feeds and brokers. - Expand the test suite to exercise the new backtester and Graph-of-Contracts scaffolds. - Improve packaging and docs to support publishing to a Python package index. - Implement a more expressive DSL and a richer IR (EquiIR) representation. - Add more tests for edge cases and simple integration tests for the CLI. - Expand packaging metadata and README with a longer developer and user guide. - Add a first-pass Graph-of-Contracts registry scaffold (GoC) and a minimal adapter registry. - Documentation: GoC overview and how to plug in new adapters. Extensibility notes - The repository now includes a small GoC registry module (equicompiler_algebraic_portfolio_dsl_to_/goc_registry.py) and a registry-aware GoC skeleton integrated into the IR formation flow. This provides a stable extension point for future adapters (data feeds, brokers) and verifiable contract graphs. - You can register adapters via the GoCRegistry class and view a digest that helps ensure reproducible builds and auditability. - Extend the DSL with richer constraints (VaR, VaR-CVaR, liquidity, latency) and ExecutionPolicy primitives. - Integrate the GoC registry and build a canonical EquiIR representation with per-message metadata for replay/verification. - Add a lightweight delta-sync coordinator and starter adapters for data feeds and brokers. - Expand the test suite to exercise the new backtester and Graph-of-Contracts scaffolds. - Improve packaging and docs to support publishing to a Python package index. - Implement a more expressive DSL and a richer IR (EquiIR) representation. - Add more tests for edge cases and simple integration tests for the CLI. - Expand packaging metadata and README with a longer developer and user guide. This README intentionally stays light; the AGENTS.md contains the deeper architectural notes for the SWARM collaborators.