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 and total award value
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

Government contracts data has 5+ years of historical awards available for backtesting, with history extending over time.

Using the Data Point-in-Time

The period-of-performance startDate is a fixed, clean timing anchor for event studies and backtests. Award records are deduplicated by federal award ID and updated as contracts evolve, so awardAmount and endDate reflect the latest reported state of each award rather than a historical snapshot. For strictly look-ahead-free work, anchor on startDate and treat the award amount as a current-state field.

Note: awardType and contractAwardType are present in the schema but are not populated by the source award endpoint — use naicsDescription for sector and category classification.

FinBrain Terminal

Browse government contracts for any ticker directly in the FinBrain Terminal — with summary stats, filtering, and contract details at a glance.

Use the Government Contracts screener to scan recent contract awards across all tickers, with filtering by company, agency, industry, and award amount.

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 federal award identifier (dedup key)	CONT_AWD_0001
`awardAmount`	Total award value in USD (latest reported state)	50000000
`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`	Period-of-performance start (YYYY-MM-DD) — the point-in-time anchor	2025-06-01
`endDate`	Period-of-performance end (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
`awardType`, `contractAwardType`	Present in the schema but not populated by the source award endpoint — use `naicsDescription` for classification	(empty)

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
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