财务数据采集
financial-data-collector
by daymade
从 yfinance 抓取美国上市公司股价、财报、WACC 参数和分析师预期，校验后输出带来源标注的标准化 JSON，适合 DCF、可比估值和财务分析前置取数。
做美股基本面分析时，它能从免费公开源一键抓取财务与市场数据，直接产出适配DCF和可比分析的结构化JSON，省去手工整理。
安装
claude skill add --url github.com/daymade/claude-code-skills/tree/main/financial-data-collector文档
Financial Data Collector
Collect and validate real financial data for US public companies using free data sources. Output is a standardized JSON file ready for consumption by other financial skills.
Critical Constraints
NO FALLBACK values. If a field cannot be retrieved, set it to null with _source: "missing".
Never substitute defaults (e.g., beta or 1.0). The downstream skill decides how to handle missing data.
Data source attribution is mandatory. Every data section must have a _source field.
CapEx sign convention: yfinance returns CapEx as negative (cash outflow). Preserve the original sign. Document the convention in output metadata. Do NOT flip signs.
yfinance FCF ≠ Investment bank FCF. yfinance FCF = Operating CF + CapEx (no SBC deduction). Flag this in output metadata so downstream DCF skills don't overstate FCF.
Workflow
Step 1: Collect Data
Run the collection script:
python scripts/collect_data.py TICKER [--years 5] [--output path/to/output.json]
The script collects in this priority:
- yfinance — market data, historical financials, beta, analyst estimates
- yfinance ^TNX — 10Y Treasury yield as risk-free rate proxy
- User supplement — for years where yfinance returns NaN (report to user, do not guess)
Step 2: Validate Data
python scripts/validate_data.py path/to/output.json
Checks: field completeness, cross-field consistency (Market Cap = Price × Shares), range sanity (WACC 5-20%, beta 0.3-3.0), sign conventions.
Step 3: Deliver JSON
Single file: {TICKER}_financial_data.json. Schema in references/output-schema.md.
Do NOT create: README, CSV, summary reports, or any auxiliary files.
Output Schema (Summary)
{
"ticker": "META",
"company_name": "Meta Platforms, Inc.",
"data_date": "2026-03-02",
"currency": "USD",
"unit": "millions_usd",
"data_sources": { "market_data": "...", "2022_to_2024": "..." },
"market_data": { "current_price": 648.18, "shares_outstanding_millions": 2187, "market_cap_millions": 1639607, "beta_5y_monthly": 1.284 },
"income_statement": { "2024": { "revenue": 164501, "ebit": 69380, "tax_expense": ..., "net_income": ..., "_source": "yfinance" } },
"cash_flow": { "2024": { "operating_cash_flow": ..., "capex": -37256, "depreciation_amortization": 15498, "free_cash_flow": ..., "change_in_nwc": ..., "_source": "yfinance" } },
"balance_sheet": { "2024": { "total_debt": 30768, "cash_and_equivalents": 77815, "net_debt": -47047, "current_assets": ..., "current_liabilities": ..., "_source": "yfinance" } },
"wacc_inputs": { "risk_free_rate": 0.0396, "beta": 1.284, "credit_rating": null, "_source": "yfinance + ^TNX" },
"analyst_estimates": { "revenue_next_fy": 251113, "revenue_fy_after": 295558, "eps_next_fy": 29.59, "_source": "yfinance" },
"metadata": { "_capex_convention": "negative = cash outflow", "_fcf_note": "yfinance FCF = OperatingCF + CapEx. Does NOT deduct SBC." }
}
Full schema with all field definitions: references/output-schema.md
<correct_patterns>
Handling Missing Years
if pd.isna(revenue):
result[year] = {"revenue": None, "_source": "yfinance returned NaN — supplement from 10-K"}
# Report missing years to the user. Do NOT skip or fill with estimates.
CapEx Sign Preservation
capex = cash_flow.loc["Capital Expenditure", year_col] # -37256.0
result["capex"] = float(capex) # Preserve negative
Datetime Column Indexing
year_col = [c for c in financials.columns if c.year == target_year][0]
revenue = financials.loc["Total Revenue", year_col]
Field Name Guards
if "Total Revenue" in financials.index:
revenue = financials.loc["Total Revenue", year_col]
elif "Revenue" in financials.index:
revenue = financials.loc["Revenue", year_col]
else:
revenue = None
</correct_patterns>
<common_mistakes>
Mistake 1: Default Values for Missing Data
# ❌ WRONG
beta = info.get("beta", 1.0)
growth = data.get("growth") or 0.02
# ✅ RIGHT
beta = info.get("beta") # May be None — that's OK
Mistake 2: Assuming All Years Have Data
# ❌ WRONG — 2020-2021 may be NaN
revenue = float(financials.loc["Total Revenue", year_col])
# ✅ RIGHT
value = financials.loc["Total Revenue", year_col]
revenue = float(value) if pd.notna(value) else None
Mistake 3: Using yfinance FCF in DCF Models Directly
yfinance FCF does NOT deduct SBC. For mega-caps like META, SBC can be $20-30B/yr, making yfinance FCF ~30% higher than investment-bank FCF. Always flag this in output.
Mistake 4: Flipping CapEx Sign
# ❌ WRONG — double-negation risk downstream
capex = abs(cash_flow.loc["Capital Expenditure", year_col])
# ✅ RIGHT — preserve original, document convention
capex = float(cash_flow.loc["Capital Expenditure", year_col]) # -37256.0
</common_mistakes>
Known yfinance Pitfalls
See references/yfinance-pitfalls.md for detailed field mapping and workarounds.
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