Follow the money in the 2026 midterms.

Coverage

• 2026 cycle plus 2022 and 2018 history
• 8,812 federal candidates and 20,915 committees
• $7.42B of individual donations mapped by city, ZIP, state, Census division and region
• $2.51B outside spending (super PACs and independent expenditures) for and against every race
• $4.33B of campaign spending by vendor city across 16 categories
• Donor flows state to state (56% of House and Senate candidate money comes from out of state)
• State and city money for California, Texas, New York, Washington, Colorado and Hawaii plus Los Angeles, New York City, Austin, Oakland and Dallas
• Updated daily

Why it's clean

• Amended filings superseded
• Memo entries removed
• Junk filings excluded and counted (one fake "$776M" committee, $9B bogus expenditure reports)
• ActBlue/WinRed double counting removed ($128M)
• Every rollup sums exactly
• No individual donor names stored

Headline stats

Pricing

One-time, each with its own $19.95/month daily updates.

Free: every race's totals plus national, regional and state donor geography, no card needed.

API

curl "https://api.finzdata.com/v1/politics/races?state=TX&office=S" -H "X-API-Key: YOUR_KEY"

curl "https://api.finzdata.com/v1/politics/candidates" -H "X-API-Key: YOUR_KEY"

curl "https://api.finzdata.com/v1/politics/donor-geo" -H "X-API-Key: YOUR_KEY"

curl "https://api.finzdata.com/v1/politics/race-geo" -H "X-API-Key: YOUR_KEY"

curl "https://api.finzdata.com/v1/politics/flows" -H "X-API-Key: YOUR_KEY"

curl "https://api.finzdata.com/v1/politics/spending" -H "X-API-Key: YOUR_KEY"

curl "https://api.finzdata.com/v1/politics/vendors" -H "X-API-Key: YOUR_KEY"

curl "https://api.finzdata.com/v1/politics/state-local" -H "X-API-Key: YOUR_KEY"

curl "https://api.finzdata.com/v1/politics/state-local-geo" -H "X-API-Key: YOUR_KEY"

curl "https://api.finzdata.com/v1/politics/catalog" -H "X-API-Key: YOUR_KEY"

pip install finzdata

import requests
headers = {"X-API-Key": "YOUR_KEY"}
r = requests.get("https://api.finzdata.com/v1/politics/races?state=TX&office=S", headers=headers)
data = r.json()
print(data[0])

Available as JSON, CSV and Parquet.

Who it's for

• Quants and hedge funds (election-risk and policy-sensitive sectors)
• AI agents (MCP)
• Campaigns and consultants
• Newsrooms
• Government-affairs teams
• Researchers

Source: FEC filings, cleaned and updated daily.