dcf-model

## Environment

This skill assumes **headless openpyxl** — you are producing an .xlsx file on disk.
Follow the `excel-author` skill's conventions for cell coloring, formulas, named ranges, and sensitivity tables.
Recalculate before delivery: `python /path/to/excel-author/scripts/recalc.py ./out/model.xlsx`.

# DCF Model Builder

## Overview

This skill creates institutional-quality DCF models for equity valuation following investment banking standards. Each analysis produces a detailed Excel model (with sensitivity analysis included at the bottom of the DCF sheet).

## Tools

- Default to using all of the information provided by the user and MCP servers available for data sourcing.

## Critical Constraints - Read These First

These constraints apply throughout all DCF model building. Review before starting:

**Formulas Over Hardcodes (NON-NEGOTIABLE):**
- Every projection, margin, discount factor, PV, and sensitivity cell MUST be a live Excel formula — never a value computed in Python and written as a number
- When using openpyxl: `ws["D20"] = "=D19*(1+$B$8)"` is correct; `ws["D20"] = calculated_revenue` is WRONG
- The only hardcoded numbers permitted are: (1) raw historical inputs, (2) assumption drivers (growth rates, WACC inputs, terminal g), (3) current market data (share price, debt balance)
- If you catch yourself computing something in Python and writing the result — STOP. The model must flex when the user changes an assumption.

**Verify Step-by-Step With the User (DO NOT build end-to-end):**
- After data retrieval → show the user the raw inputs block (revenue, margins, shares, net debt) and confirm before projecting
- After revenue projections → show the projected top line and growth rates, confirm before building margin build
- After FCF build → show the full FCF schedule, confirm logic before computing WACC
- After WACC → show the calculation and inputs, confirm before discounting
- After terminal value + PV → show the equity bridge (EV → equity value → per share), confirm before sensitivity tables
- Catch errors at each stage — a wrong margin assumption discovered after sensitivity tables are built means rebuilding everything downstream

**Sensitivity Tables:**
- **Use an ODD number of rows and columns** (standard: 5×5, sometimes 7×7) — this guarantees a true center cell
- **Center cell = base case.** Build the axis values so the middle row header and middle column header exactly equal the model's actual assumptions (e.g., if base WACC = 9.0%, the middle row is 9.0%; if terminal g = 3.0%, the middle column is 3.0%). The center cell's output must therefore equal the model's actual implied share price — this is the sanity check that the table is built correctly.
- **Highlight the center cell** with the medium-blue fill (`#BDD7EE`) + bold font so it's immediately visible which cell is the base case.
- Populate ALL cells (typically 3 tables × 25 cells = 75) with full DCF recalculation formulas
- Use openpyxl loops to write formulas programmatically
- NO placeholder text, NO linear approximations, NO manual steps required
- Each cell must recalculate full DCF for that assumption combination

**Cell Comments:**
- Add cell comments AS each hardcoded value is created
- Format: "Source: [System/Document], [Date], [Reference], [URL if applicable]"
- Every blue input must have a comment before moving to next section
- Do not defer to end or write "TODO: add source"

**Model Layout Planning:**
- Define ALL section row positions BEFORE writing any formulas
- Write ALL headers and labels first
- Write ALL section dividers and blank rows second
- THEN write formulas using the locked row positions
- Test formulas immediately after creation

**Formula Recalculation:**
- Run `python recalc.py model.xlsx 30` before delivery
- Fix ALL errors until status is "success"
- Zero formula errors required (#REF!, #DIV/0!, #VALUE!, etc.)

**Scenario Blocks:**
- Create separate blocks for Bear/Base/Bull cases
- Show assumptions horizontally across projection years within each block
- Use IF formulas: `=IF($B$6=1,[Bear cell],IF($B$6=2,[Base cell],[Bull cell]))`
- Verify formulas reference correct scenario block cells

## DCF Process Workflow

### Step 1: Data Retrieval and Validation

Fetch data from MCP servers, user provided data, and the web.

**Data Sources Priority:**
1. **MCP Servers** (if configured) - Structured financial data from providers like Daloopa
2. **User-Provided Data** - Historical financials from their research
3. **Web Search/Fetch** - Current prices, beta, debt and cash when needed

**Validation Checklist:**
- Verify net debt vs net cash (critical for valuation)
- Confirm diluted shares outstanding (check for recent buybacks/issuances)
- Validate historical margins are consistent with business model
- Cross-check revenue growth rates with industry benchmarks
- Verify tax rate is reasonable (typically 21-28%)

### Step 2: Historical Analysis (3-5 years)

Analyze and document:
- **Revenue growth trends**: Calculate CAGR, identify drivers
- **Margin progression**: Track gross margin, EBIT margin, FCF margin
- **Capital intensity**: D&A and CapEx as % of revenue
- **Working capital efficiency**: NWC changes as % of revenue growth
- **Return metrics**: ROIC, ROE trends

Create summary tables showing:
```
Historical Metrics (LTM):
Revenue: $X million
Revenue growth: X% CAGR
Gross margin: X%
EBIT margin: X%
D&A % of revenue: X%
CapEx % of revenue: X%
FCF margin: X%
```

### Step 3: Build Revenue Projections

**Methodology:**
1. Start with latest actual revenue (LTM or most recent fiscal year)
2. Apply growth rates for each projection year
3. Show both dollar amounts AND calculated growth %

**Growth Rate Framework:**
- Year 1-2: Higher growth reflecting near-term visibility
- Year 3-4: Gradual moderation toward industry average
- Year 5+: Approaching terminal growth rate

**Formula structure:**
- Revenue(Year N) = Revenue(Year N-1) × (1 + Growth Rate)
- Growth %(Year N) = Revenue(Year N) /