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:

bash

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

bash

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)

json

{
  "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

python

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

python

capex = cash_flow.loc["Capital Expenditure", year_col]  # -37256.0
result["capex"] = float(capex)  # Preserve negative

Datetime Column Indexing

python

year_col = [c for c in financials.columns if c.year == target_year][0]
revenue = financials.loc["Total Revenue", year_col]

Field Name Guards

python

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

python

# ❌ 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

python

# ❌ 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

python

# ❌ 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.

financial-data-collector

NPX Install

Tags

SKILL.md Content

Financial Data Collector

Critical Constraints

Workflow

Step 1: Collect Data

Step 2: Validate Data

Step 3: Deliver JSON

Output Schema (Summary)

Handling Missing Years

CapEx Sign Preservation

Datetime Column Indexing

Field Name Guards

Mistake 1: Default Values for Missing Data

Mistake 2: Assuming All Years Have Data

Mistake 3: Using yfinance FCF in DCF Models Directly

Mistake 4: Flipping CapEx Sign

Known yfinance Pitfalls