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Real SEC financial data from 9 S&P500 companies across 7 sectors — no signup required.
S&P500 Sample
9 companies · 7 sectors · 5-year history
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| Company | Sector | FY | Revenue | Net Income | EPS (Diluted) | Operating CF |
|---|---|---|---|---|---|---|
Apple Inc.AAPL | Technology | 2024 | $391.0B | $93.7B | $6.08 | $118.3B |
| 2023 | $383.3B | $97.0B | $6.13 | $110.5B | ||
| 2022 | $394.3B | $99.8B | $6.11 | $122.2B | ||
Microsoft Corp.MSFT | Technology | 2024 | $245.1B | $88.1B | $11.80 | $118.5B |
| 2023 | $211.9B | $72.4B | $9.68 | $87.6B | ||
| 2022 | $198.3B | $72.7B | $9.65 | $89.0B | ||
Johnson & JohnsonJNJ | Healthcare | 2024 | $89.0B | $14.3B | $5.93 | $22.3B |
| 2023 | $85.2B | $35.2B | $14.44 | $23.5B | ||
JPMorgan ChaseJPM | Financials | 2024 | $180.5B | $58.5B | $19.75 | — |
| 2023 | $162.4B | $49.6B | $16.23 | — | ||
Exxon Mobil Corp.XOM | Energy | 2024 | $339.3B | $33.7B | $7.84 | $55.3B |
| 2023 | $344.6B | $36.0B | $8.89 | $55.4B | ||
Amazon.com Inc.AMZN | Consumer Discretionary | 2024 | $620.1B | $59.2B | $5.53 | $115.9B |
| 2023 | $574.8B | $30.4B | $2.90 | $84.9B | ||
Alphabet Inc.GOOGL | Communication | 2024 | $350.0B | $100.7B | $8.04 | $125.3B |
| 2023 | $307.4B | $73.8B | $5.80 | $101.7B | ||
NVIDIA Corp.NVDA | Technology | 2025 | $130.5B | $72.9B | $2.94 | $64.1B |
| 2024 | $60.9B | $29.8B | $1.19 | $28.1B | ||
Procter & GamblePG | Consumer Staples | 2024 | $84.0B | $15.0B | $6.02 | $19.8B |
| 2023 | $82.0B | $14.7B | $5.90 | $16.8B |
Source: SEC EDGAR via Valuein MCP Server. Annual data (10-K). All figures in USD. Showing cached sample data.
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Get this data via Python SDK
Install the SDK and query financial data with SQL. Under 60 seconds to first result.
pip install valuein-sdkoruv pip install valuein-sdk— no API key required to start.example.py
from valuein_sdk import ValueinClient
client = ValueinClient()
# See all available tables
print(client.tables())
# Query Apple fundamentals
sql = """
SELECT fiscal_year, revenue, net_income, eps_diluted
FROM fact
WHERE ticker = 'AAPL' AND fiscal_period = 'FY'
ORDER BY fiscal_year DESC
LIMIT 5
"""
df = client.run_query(sql)
print("results: ", df.head())