Government Contracts Dataset

Track federal government contract awards sourced from USAspending.gov and mapped to public company tickers. See which companies win government contracts, the awarding agencies, contract values, industry classifications, and contract periods.

What’s Included

The Government Contracts dataset provides:

Award Details: Award ID, amount, type, and contract award type
Agency Information: Awarding agency and sub-agency
Recipient Data: Recipient company name mapped to stock ticker
Contract Period: Start and end dates for each award
Industry Classification: NAICS codes and descriptions
Contract Description: Plain-text description of the contract scope

Coverage

Source	Description	Update Frequency
USAspending.gov	Federal contract awards mapped to stock tickers	Daily

from finbrain import FinBrainClient

fb = FinBrainClient(api_key="YOUR_API_KEY")

df = fb.government_contracts.ticker("LMT", as_dataframe=True)
print(df)

import requests

API_KEY = "YOUR_API_KEY"
BASE_URL = "https://api.finbrain.tech/v2"

response = requests.get(
    f"{BASE_URL}/government-contracts/LMT",
    headers={"Authorization": f"Bearer {API_KEY}"}
)

data = response.json()
for contract in data["data"]["contracts"]:
    print(f"{contract['startDate']}: ${contract['awardAmount']:,.0f} "
          f"from {contract['awardingAgency']} — {contract['description']}")

For complete code examples in Python, JavaScript, C++, Rust, and cURL, see the API Reference.

Key Fields

Field	Description	Example
`awardId`	Unique contract award identifier	CONT_AWD_0001
`awardAmount`	Total award value in USD	50000000
`awardType`	Type of award	Contract
`awardingAgency`	Federal agency issuing the contract	Department of Defense
`awardingSubAgency`	Sub-agency within the awarding agency	Department of the Army
`recipientName`	Company receiving the contract	Lockheed Martin Corporation
`startDate`	Contract start date (YYYY-MM-DD)	2025-06-01
`endDate`	Contract end date (YYYY-MM-DD)	2026-06-01
`description`	Plain-text description of the contract	Aircraft maintenance services
`naicsCode`	NAICS industry classification code	336411
`naicsDescription`	Human-readable NAICS description	Aircraft Manufacturing
`contractAwardType`	Specific contract award mechanism	Definitive Contract

Use Cases

Top Contract Recipients

Scan defense tickers to find companies with the highest total contract value:

from finbrain import FinBrainClient

fb = FinBrainClient(api_key="YOUR_API_KEY")

def scan_contract_value(symbols):
    """Find companies with the highest government contract value"""
    results = []

    for symbol in symbols:
        try:
            df = fb.government_contracts.ticker(symbol, as_dataframe=True)

            total_value = df["awardAmount"].sum()
            results.append({
                "symbol": symbol,
                "contracts": len(df),
                "total_value": total_value
            })
        except Exception:
            continue

    return sorted(results, key=lambda x: x["total_value"], reverse=True)

defense_symbols = ["LMT", "RTX", "GD", "NOC", "BA"]
recipients = scan_contract_value(defense_symbols)

for r in recipients:
    print(f"{r['symbol']}: {r['contracts']} contracts, ${r['total_value']:,.0f} total value")

Agency Breakdown

Analyze which federal agencies award contracts to a given company:

from finbrain import FinBrainClient

fb = FinBrainClient(api_key="YOUR_API_KEY")

def agency_breakdown(symbol):
    """Break down contracts by awarding agency"""
    df = fb.government_contracts.ticker(symbol, as_dataframe=True)

    by_agency = df.groupby("awardingAgency").agg(
        contracts=("awardAmount", "count"),
        total_value=("awardAmount", "sum")
    ).sort_values("total_value", ascending=False)

    return by_agency

agencies = agency_breakdown("LMT")
for agency, row in agencies.iterrows():
    print(f"{agency}: {row['contracts']} contracts, ${row['total_value']:,.0f}")

Contract Size Analysis

Categorize contracts by size to understand the award distribution:

import pandas as pd
from finbrain import FinBrainClient

fb = FinBrainClient(api_key="YOUR_API_KEY")

def contract_size_distribution(symbol):
    """Categorize contracts by size buckets"""
    df = fb.government_contracts.ticker(symbol, as_dataframe=True)

    bins = [0, 1_000_000, 10_000_000, 100_000_000, float("inf")]
    labels = ["Under $1M", "$1M–$10M", "$10M–$100M", "$100M+"]

    df["size_bucket"] = pd.cut(df["awardAmount"], bins=bins, labels=labels)

    distribution = df.groupby("size_bucket", observed=True).agg(
        contracts=("awardAmount", "count"),
        total_value=("awardAmount", "sum")
    )

    return distribution

dist = contract_size_distribution("RTX")
for bucket, row in dist.iterrows():
    print(f"{bucket}: {row['contracts']} contracts, ${row['total_value']:,.0f}")

Government Contracts API Reference - Endpoint details, parameters, and response schema
Corporate Lobbying Dataset - Track corporate lobbying activity
Insider Transactions Dataset - Track executive trades
Senate Trades Dataset - Senate trading activity