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.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
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.まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
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.
日本語の概要は準備中です。原文の説明を表示しています。
Execute a single deterministic Python script to harmonize units, create/update metadata, move files to output_data/, and clean up input_data/.
日本語の概要は準備中です。原文の説明を表示しています。
Execute a single deterministic Python script to compute all derived financial metrics (EBITA, Invested Capital, Tax Rates, Summary Table) from extracted financial data.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。