Feature Comparison Congressional trading data features compared side by side
| Feature | GovGreed | QuiverQuant |
|---|---|---|
| STOCK Act congressional tradesBoth house and senate PTR disclosures | ✓ 1,000+ trades | ✓ Comprehensive |
| Bill investability ML scoreWhich bills insiders are most motivated to pass | ✓ 42K bills scored | — |
| Executive pre-vote buy signalsSEC Form 4 mapped to pending legislation | ✓ 22K+ Form 4 trades | — |
| Triple Signal detectionCommittee chair + trade + campaign contribution overlap | ✓ 752 active signals | — |
| Committee markup calendarUpcoming votes on high-signal bills | ✓ 17K meetings tracked | — |
| Committee assignmentsWhich bills each member oversees | ✓ 3,908 assignments | Partial |
| Campaign contributions (FEC)Which industries funded each politician | ✓ 22,900 contributions | — |
| Lobbying filings (LDA)Industry lobbying spend by bill/sector | ✓ 4,995 filings | — |
| Government contractsCompanies with federal contract exposure to bills | ✓ 266 contracts | — |
| Historical tradesBack to 2012 for backtesting | ✓ 189K trades, 2012– | ✓ Historical |
| REST API for trading botsMachine-readable JSON, clean endpoints | ✓ Free tier available | ✓ Paid plans only |
| Alternative data breadthETF flows, lobbying, sentiment, etc. | — Congress-focused only | ✓ Broad alt-data |
| Official government API partnershipsRegistered access vs unofficial scraping | ✓ 5 federal APIs — registered | Unconfirmed |
| Proprietary company data enrichmentUnstructured filings → structured signals | ✓ 582 companies enriched | — |
| Starting price | Waitlist — Summer 2026 | $25–$100+/mo |
The Five Signals QuiverQuant Doesn't Have Why algo traders building congressional-alpha bots switch to GovGreed
🔴 Triple Signal
Fires when a committee member has (1) oversight of a bill, (2) traded stock in an affected company, and (3) received campaign contributions from that industry. 752 active. Validated at 5.4× pass rate vs medium bills.
📋 Bill Investability Score
ML model trained on 42,143 bills across 3 congressional sessions using 25 features: insider density, sector exposure, exec buy timing, committee markup activity. Every bill gets a 0–100 score. 253 bills score ≥70.
🏢 Exec Pre-Vote Buy Signal
22,731 SEC Form 4 transactions mapped to bills via company-sector-legislation graph. When a CEO is buying their own stock within 90 days of a vote their company has exposure to — that timing has meaning.
📅 Committee Markup Calendar
17,104 committee meetings tracked. We flag upcoming markups for high-signal bills and surface the days-before-vote window for exec buys and congressional trades. The markup is the last stop before a floor vote.
🐦 Exec Flock Signal
Consortium buy detection. One CEO buying is noise. Five C-suite officers in the same sector buying simultaneously with zero sellers is a flock. GovGreed scores by distinct buyer headcount, separates PE fund exits from officer conviction, and with 10 years of Form 4 history can answer: was this CEO right the last time?
Why GovGreed goes deeper than QuiverQuant on congressional signal
QuiverQuant is a great product for what it does — it aggregates alternative data across many categories (government contracts, ETF flows, political trading, sentiment). Congressional trades are one tab of many. For a trader who wants broad alternative data in one dashboard, it's solid.
GovGreed is built exclusively around one thesis: congressional insiders have informational advantages over the market, and those advantages leave a paper trail across multiple public disclosure systems. STOCK Act trades are just the entry point. The signal doesn't really hit until you layer committee assignments, bill text analysis, lobbying dollars, FEC contributions, and SEC Form 4 exec buys on top.
The ML model is the clearest differentiation. GovGreed trained a 25-feature model on 3 congressional sessions and validated it on held-out data: bills scoring ≥70 are enacted at 9.1% vs 1.7% for medium-tier bills — 5.4× more likely to pass. QuiverQuant shows you who traded what. GovGreed tells you which trades are likely to pay off and why.
When QuiverQuant is the right choice
- You want broad alternative data: ETF flows, lobbying sentiment, Twitter activity, patent data — QuiverQuant aggregates many non-standard data sources in one place.
- You prefer a visual research interface: QuiverQuant's UI is built for exploring data manually. GovGreed is built for quantitative consumption via API.
- You don't need deep congress-specific signal: If congressional data is just one factor in a larger multi-factor model, QuiverQuant's breadth makes sense.
When GovGreed is the right choice
- You're building a congressional-alpha trading bot: Clean REST API with Triple Signal, exec buy signals, and bill ML scores — purpose-built for programmatic consumption.
- You want the correlation layer: Congressional trade + campaign contribution + committee assignment + exec buy all on the same bill is a qualitatively different signal than the trade alone.
- You want historical data for backtesting: 188,695 historical trades back to 2012, 22K+ exec trades, roll call votes, and bill outcomes for strategy validation.
- Budget matters: GovGreed is launching Summer 2026 with waitlist-first access (30 days free for everyone who signs up). QuiverQuant API access starts at higher price points with no trial.
Pricing Comparison
- ✓ Triple Signal + exec flock signals
- ✓ Bill investability ML scores (42K bills)
- ✓ Committee markup calendar
- ✓ REST API — full access
- ✓ Historical trades back to 2012
- ✓ 30 days free for waitlist members
- ✓ Congressional trades (STOCK Act)
- ✓ Broad alternative data access
- ✓ Research dashboard UI
- ✓ API access (paid plans)
- — No bill ML scores
- — No exec pre-vote signals