modelforge-finance 0.9.7

Bulge-tier Excel financial model factory. Every cell live-formulated, every number traceable. MCP-native.

ModelForge

Bulge-tier Excel financial model factory for credit & structured finance. Every cell live-formulated. Every number traceable back to the source document page it came from.

Built for Italian private capital (unitranche, minibond, project finance, RE, NPL, structured credit) — extensible to any asset class.

Use it inside Claude Code, Cursor, ChatGPT Enterprise (MCP-native)

PyPI name: modelforge-finance (the unscoped modelforge was taken by source{d}'s ML library). Import name stays modelforge.

pip install "modelforge-finance[mcp,export]"

# wire into your MCP client config:
{
  "mcpServers": {
    "modelforge": { "command": "modelforge-mcp" }
  }
}

Then in your AI assistant:

"Build me a unitranche LBO model from this YAML spec, export the committee deck."

Tools available: list_templates · build_model · qc_workbook · list_sources · lineage_walk · ingest_dataroom · screen_deals · compute_tax · export_pptx · export_docx · plus 7 unified-feed tools (get_fundamentals, get_prices, lookup_lei, etc.) across an 11-provider data stack.

Where it actually stands: see SCORECARD.md — the canonical scorecard reflects a blind external IC review (ChatGPT 5.5 + Opus 4.7) that converged on 5.45 weighted vs the prior founder-graded 6.56. The killers, unlocks, and recommended seed structure are documented honestly. SCORECARD_v1.md / SCORECARD_v2.md / SCORECARD_v3.md are kept for historical transparency only.

The architectural principle

LLMs produce specs + sources + narrative. Deterministic Python produces the workbook.

The LLM never writes a number into a cell. It writes a typed YAML spec with source IDs. A deterministic builder emits the Excel via openpyxl. A QC gate validates before export. Excel is a render of a linkage graph; the graph is persisted to SQLite and is the canonical artifact.

Quality standards (bulge-tier, non-negotiable)

Formatting

• Blue = hardcoded input. Black = formula. Green = cross-sheet link. Red = warning.
• No mixed formulas (no magic numbers embedded). Named ranges for every driver.
• Costs NEGATIVE (sign convention enforced and checked).
• EN primary labels, IT secondary.
• Historical vs Projected column separator, obvious.
• Check row at top of every sheet (BS balance, CFS tie, covenant headroom — TRUE or 0).

Sourcing

• Every hardcoded cell has a comment with source ID (S-001, S-002, ...).
• Sources sheet lists each source: doc, page, publisher, date, URL, verified-flag.
• Assumptions (not sourced) tagged A-001 with rationale + confidence H/M/L.

Scenarios

• WORST / BASE / BEST toggle on Assumptions. Drives every sheet via CHOOSE.
• Every sheet respects the toggle — no orphan assumptions.

Audit

• QC sheet with 12 automated checks, all must pass.
• Revision log on Cover.
• Named ranges mandatory.
• Print areas set. Print-ready on every sheet.

Quick start

pip install "modelforge-finance[mcp,export,data]"

# Build any of 14 templates from a YAML spec
modelforge build examples/unitranche_cdmo.yaml

# QC the workbook (12 checks + Trust Layer plausibility)
modelforge qc output/unitranche_cdmo.xlsx --trust-strict

# Audit every example (CI uses the same gate)
modelforge audit-all examples/ --report AUDIT_REPORT.md

Trust Layer v1 (new in v0.9.7)

Why should a buyer trust the number in cell B42?

The Trust Layer is a semantic gate (separate from the structural QC gate). It answers the question every IC asks in the first five minutes: is this number plausible? It catches issues like a DCF EV that's 8× the company's real market cap before the model ever leaves QA.

25+ built-in rules cover all 14 templates:

• DCF: WACC band (3-25%), terminal growth ≤ GDP + 1%, EV vs market-cap deviation, terminal-value share, sensitivity-table monotonicity
• Three-statement: balance-sheet integrity, cash reconciliation, retained-earnings link
• NPL: cumulative recovery ≤ 100%, vintage staircase monotone
• Project finance: DSCR floor, wire degradation > 0, P90 < P50
• Sponsor LBO: XIRR plausibility, multiple expansion vs entry
• M&A / fairness / structured credit / unitranche / credit memo: per-template plausibility

Each violation produces a RedFlags worksheet inside the built workbook with severity (info / warn / fail), the rule that fired, expected-vs-actual, and the recommended remediation.

modelforge audit-all examples/   # 14/14 templates, 0 FAIL violations in current ship

See AUDIT_REPORT.md for the current ship's audit.

Data-room ingestion (v0.3.1)

Turn a directory of PDFs, XLSXs and CSVs into a validated ModelForge YAML spec using Claude Opus. Every extracted number traces back to a doc page via the auto-built Sources registry.

pip install -e .[ingest]                # installs anthropic, pdfplumber, pypdf
export ANTHROPIC_API_KEY=sk-ant-...      # required

modelforge ingest path/to/dataroom/ \
    --template project_finance \
    -o output/my_deal.yaml --verbose

# Review output/my_deal.yaml + output/my_deal.ingestion.md
# (INGESTION_REPORT.md lists every extracted field, S-id, confidence)

modelforge build output/my_deal.yaml     # produces the workbook
modelforge qc output/my_deal.xlsx        # 8/8 quality gate

Supported template: project_finance (MVP). Templates 1, 3, 5-8 queued for v0.3.2. See PRD_v03_dataroom_ingestion.md for the full spec.

Package layout

modelforge/
├── graph/            # First-class linkage graph (nodes, edges, SQLite persistence)
├── spec/             # Pydantic schemas per template
│   ├── base.py       # Source, Assumption, Scenario, Target (shared types)
│   └── unitranche.py # Template 1: Unitranche LBO
├── builder/          # Deterministic openpyxl writer
│   ├── styles.py     # Bulge-tier formatting library
│   ├── formulas.py   # Formula string builders
│   ├── i18n.py       # EN/IT label dictionary
│   ├── workbook.py   # Top-level builder
│   └── sheets/       # One module per sheet (cover, sources, assumptions, ...)
├── qc/               # Quality gate (12 checks + PDF report)
├── data/             # Market data loaders (Damodaran, ECB, Borsa minibond)
└── cli.py            # modelforge build|qc|sources|inspect

Templates (14, all shipped)

1. ✅ Unitranche LBO — Italian mid-market direct lending (Cash sweep + IFRS 9 EIR)
2. ✅ Minibond — Banca Finint territory (Gross YTM + Net YTM + Italian WHT)
3. ✅ Credit Memo — Extends Unitranche with recovery waterfall + PD×LGD×EAD
4. ✅ Project Finance — Construction + operating phases, DSCR-driven
5. ✅ Real Estate — NOI build, exit cap, LP/GP promote waterfall
6. ✅ NPL Portfolio — Collection curves, servicing fees, senior/mezz capital structure
7. ✅ Structured Credit — Tranche waterfall with attachment/detachment points
8. ✅ 3-Statement — P&L + BS + CFS with BS balance integrity check
9. ✅ DCF — WACC build, fade, terminal normalization, 2D sensitivity (Trust Layer protected)
10. ✅ Merger — Accretion/dilution, breakeven, contribution, collar, PPA
11. ✅ Fairness Opinion — Selected comps, regression, premium analysis
12. ✅ Sponsor LBO — Returns waterfall, debt schedule, 14-story block
13. ✅ IPO — Float build, lock-up, stabilization, fee schedule
14. ✅ Restructuring — Going-concern recovery, plan-feasibility, creditor classes

Run modelforge list-templates to see them all. Each ships with an anonymized example YAML in examples/.

Tax jurisdictions (7)

IT  · IRES / IRAP / SIIQ / PEX
US  · Federal CIT + state + NOL + R&D credit + GILTI + BEAT + ASC 740
UK  · FRS 102 + main rate + marginal relief + RDEC + AIA + WDA + group relief
DE  · KSt + SolZ + GewSt (Hebesatz + § 8 add-backs + min-tax loss CF)
FR  · IS + small-profits + social surcharge + CVAE + CIR + 88% participation
ES  · IS + SME 23% + newly-created 15% + 95% participation + R&D + min-tax 15%
JP  · NCT + LCT + Enterprise Tax + Special Local Corp Tax + R&D credit

Data providers (11, unified Provider Protocol)

Tier-0 (free, live today): EDGAR · OpenFIGI · GLEIF Tier-1 (low-cost paid): Polygon ($29/mo) · FMP ($19/mo) · Finnhub · Tiingo Tier-2 (institutional): Bloomberg · Refinitiv · FactSet · S&P Capital IQ

Tier-1 and Tier-2 are interface-complete — paid keys activate them via env vars. Local TTL cache prevents rate-limit blow-ups.

Security & SBOM

• CycloneDX 1.5 SBOM auto-generated by CI on every push and attached to every GitHub release (scripts/generate_sbom.py)
• CI gates: pytest across Python 3.11 + 3.12, ruff lint, SBOM structure validation (.github/workflows/ci.yml)
• Audit log with append-only SQLite (modelforge/audit_log.py)
• Trust Layer semantic gates auto-injected into every built workbook
• Security policy: see SECURITY.md

Procurement-grade controls (SOC 2 Type II, ISO 27001, pen-test, multi-tenant SaaS with SSO/SCIM) are Phase-B work — see SCORECARD.md for the honest path.

The pitch

Bulge-tier Excel models, every cell live-formulated, every number traceable back to the data room page it came from.