Execute a single deterministic Python script to compute WACC, DCF assumptions, projected free cash flows, enterprise value, and intrinsic value per share. Updates the metadata markdown.
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实测评分 未实测
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All modeling logic is consolidated into a single script that executes the complete sequence: WACC → Assumptions → DCF → Intrinsic Value.
python skills/financial_modeling/scripts/calculate.py {TICKER} {TICKER_metadata_path}| Step | Action | Details |
|---|---|---|
| 1 | Fetch Market Data | Calls tools/market_data.py profile {TICKER} for share price, beta, market cap |
| 2 | Read Historical Data | Parses Financial History table from metadata for L4Q averages |
| 3 | Read Qualitative Data | Parses Economic Moat, Margin Outlook, Growth Outlook from metadata |
| 4 | Calculate WACC | CAPM with Blume-adjusted beta, capital structure weights |
| 5 | Generate Assumptions | Three-stage DCF assumptions blending historical trends + qualitative outlook |
| 6 | Run DCF Projections | 10-year projections with interpolated growth/margin, terminal value via Gordon Growth |
| 7 | Compute Intrinsic Value | Equity bridge: EV + Cash - Debt → Per Share |
| 7b | FX & ADR Conversion | If reporting currency != USD, convert IVPS to USD. Apply ADR ratio to share count if applicable |
| 8 | Update Metadata | Replaces WACC, Assumptions, DCF Model, and Intrinsic Value sections in markdown |
output_data/TICKER/TICKER_metadata.md must exist with Financial History and Qualitative Assessment sectionsyfinance installed| Parameter | Source | Default |
|---|---|---|
| Risk-Free Rate | Hardcoded (TODO: fetch 10Y Treasury) | 4.20% |
| Equity Risk Premium | Hardcoded | 5.00% |
| Terminal Growth | Moat rating: Wide=4%, Narrow=3%, None=2.5% | 3.00% |
| MCT | Derived from IC, default if negative IC | 100.0x |
| Tax Rate (statutory) | Hardcoded for WACC | 25% |
| Tax Rate (NOPAT) | L4Q average adjusted tax rate | Varies |
| WACC Bounds | Floor 6%, Cap 15% | — |
| FX Rate | market_data.py if Currency != USD | 1.0 |
| ADR Ratio | Parsed from metadata header | 1.0 |
null for beta. Added explicit checks for share_price, market_cap, and shares_outstanding.还没有评价。欢迎分享你的使用感受。
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还没有评论。来写第一条吧。
Classify financial PDFs by extracting company identity, document type, and date metadata. Validates ticker via Yahoo Finance with multi-step fallback. Uses a Python orchestration script for validation, file modification, and writing markdown.
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Execute a single deterministic Python script to harmonize units, create/update metadata, move files to output_data/, and clean up input_data/.
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Execute a single deterministic Python script to compute all derived financial metrics (EBITA, Invested Capital, Tax Rates, Summary Table) from extracted financial data.
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Extract balance sheet, income statement, shares outstanding, organic growth, and GAAP reconciliation data from financial PDFs. Standardize via Tiger-Transformer and append structured output to the document markdown.
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Extracts the WACC, assumptions, and DCF projections from the markdown metadata file and converts them into a clean, structured JSON file for the frontend interactive viewer.
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Assess a company's economic moat, EBITA margin trajectory, and organic growth trajectory from analyst reports, earnings transcripts, or long-form articles. Produces structured qualitative outlook that feeds into the Financial Modeling skill's assumption generation.
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