2896870061
New skill that collects real financial data for any US publicly traded company via yfinance. Outputs structured JSON with market data, historical financials, WACC inputs, and analyst estimates. Includes 9-check validation script and reference docs for yfinance pitfalls (NaN years, field aliases, FCF mismatch). Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
100 lines
3.1 KiB
Markdown
100 lines
3.1 KiB
Markdown
# yfinance Pitfalls & Field Mapping
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## NaN Year Patterns
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yfinance frequently returns NaN for older fiscal years. Observed patterns:
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| Ticker | NaN Years | Notes |
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|--------|-----------|-------|
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| META | 2020, 2021 | All fields NaN; must supplement from 10-K |
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| General | Varies | Older years (>3 years back) are less reliable |
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**Workaround**: Check every field with `pd.notna()`. Report NaN years to user. Never fill with estimates.
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## Field Name Variants
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yfinance row index names are not fully stable across versions. Use fallback chains:
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```python
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FIELD_ALIASES = {
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"revenue": ["Total Revenue", "Revenue", "Operating Revenue"],
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"ebit": ["Operating Income", "EBIT"],
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"ebitda": ["EBITDA", "Normalized EBITDA"],
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"tax": ["Tax Provision", "Income Tax Expense", "Tax Effect Of Unusual Items"],
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"net_income": ["Net Income", "Net Income Common Stockholders"],
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"capex": ["Capital Expenditure", "Capital Expenditures"],
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"ocf": ["Operating Cash Flow", "Cash Flow From Continuing Operating Activities"],
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"da": ["Depreciation And Amortization", "Depreciation Amortization Depletion"],
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"fcf": ["Free Cash Flow"],
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"nwc": ["Change In Working Capital", "Changes In Working Capital"],
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"total_debt": ["Total Debt"],
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"cash": ["Cash And Cash Equivalents"],
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"short_investments": ["Other Short Term Investments", "Short Term Investments"],
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"sbc": ["Stock Based Compensation"],
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}
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def safe_get(df, aliases, col):
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for alias in aliases:
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if alias in df.index:
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val = df.loc[alias, col]
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return float(val) if pd.notna(val) else None
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return None
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```
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## Datetime Column Index
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yfinance returns DataFrame columns as `pandas.Timestamp`, not integer years:
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```python
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# ❌ WRONG
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financials[2024] # KeyError
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# ✅ RIGHT
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year_col = [c for c in financials.columns if c.year == 2024][0]
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financials.loc["Total Revenue", year_col]
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```
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## Shares Outstanding Variants
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```python
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# Preferred: diluted
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shares = info.get("sharesOutstanding") # Basic shares
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# Alternative
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shares = info.get("impliedSharesOutstanding") # May be more accurate
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```
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## Risk-Free Rate via ^TNX
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```python
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tnx = yf.Ticker("^TNX")
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hist = tnx.history(period="1d")
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risk_free_rate = hist["Close"].iloc[-1] / 100 # Convert from percentage
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```
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**Pitfall**: ^TNX returns yield as percentage (e.g., 4.3), not decimal (0.043). Divide by 100.
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## Analyst Estimates
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```python
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ticker = yf.Ticker("META")
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# Revenue estimates
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rev_est = ticker.revenue_estimate # DataFrame with columns: avg, low, high, ...
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# Rows: "0q" (current quarter), "+1q", "0y" (current year), "+1y"
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# EPS estimates
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eps_est = ticker.eps_trend # Similar structure
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```
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**Pitfall**: These APIs change between yfinance versions. Always wrap in try/except.
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## FCF Definition Mismatch
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| Source | FCF Definition | META 2024 |
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|--------|---------------|-----------|
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| yfinance | Operating CF + CapEx | ~$54.1B |
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| Morgan Stanley DCF | EBITDA - Taxes - CapEx - NWC - SBC | ~$37.9B |
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| Difference | SBC (~$22B) + other adjustments | ~30% gap |
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**Always flag this in output metadata.** Downstream DCF skills need to decide whether to use yfinance FCF or reconstruct from components.
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