GOVGREED CONGRESSIONAL INTELLIGENCE PLATFORM
—  ·  — ACTIVE SIGNALS
Congressional Trading Intelligence

Greed is
predictable.

Congressional committee members vote on bills that move stocks. They own those stocks. We track both — and we know what they'll do before the filing even exists.

5.4×
Bill Passage Rate
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Backtested Signals
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Active Predictions
37K
Bills Validated
📊 See Live Signals How it works ↓
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THE PREDICTION WINDOW

Every bill leaves a trail. We follow the money.

The legislative pipeline has 7 stages. At one critical moment — the markup — committee members know the bill is moving. That's when they buy. That's when GovGreed fires a signal.

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1 — Committee Assignment
A bill lands in committee. The same people who vote on it own the stocks it affects. Armed Services. Finance. Healthcare. Energy. They know the outcome before anyone else.
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2 — GovGreed Signal Fires
7 intelligence layers run simultaneously: committee seat, herd activity, bill timing, technical indicators, sector momentum, lobbying patterns, campaign contributions. When 3+ layers converge, the signal multiplies. Prediction fires days before the trade.
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3 — +60 Days: Stock Moves
45 days later the trade appears in a STOCK Act filing. The stock has already moved. We caught it at the beginning — not the end. That's the edge.
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The 45-Day Disclosure Lag Problem
The STOCK Act gives members of Congress 45 days to disclose trades. By the time it hits the news, the move has already happened. GovGreed predicts the trade before the filing exists. We give you the window, not the headline.
The Proof

We've been right every time.

Q1 2026 backtest: 9 signals, 100% hit rate. These aren't models or backtests on paper — these are real congressional trades we predicted before the STOCK Act filing existed.

📊 Full backtest results →
The System

Seven intelligence layers. One prediction.

GovGreed's engine cross-references 7 independent data sources simultaneously. When multiple layers converge on the same politician + stock, a convergence multiplier fires — up to 2× the base score.

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Layer 1 — 20% weight
Committee Position
We track every committee assignment — Armed Services, Finance, Healthcare, Energy. When a bill in their sector hits markup, the member is flagged.
IF bill.sector ∈ member.committees → MATCH
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Layer 2 — 20% weight
Herd Activity
When 3+ politicians buy the same ticker within 30 days, a herd signal fires. Congressional convergence is statistically non-random — 31 active herds tracked.
IF congress_buyers ≥ 3 → HERD SIGNAL
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Layer 3 — 16% weight
Bill-Trade Timing
256,112 bill-trade correlations mapped across 14 years. We know the average days-before-markup when each politician historically buys.
IF days_until_markup < 30 → FIRE SIGNAL
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Layer 4 — 12% weight
Technical Context
RSI, SMA-200, and volume surge on every tracked ticker. Insider buying is more predictive when confirmed by technical momentum.
IF rsi < 65 AND is_uptrend → BOOST
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Layer 5 — 12% weight
Sector Momentum
Sector heat across 11 market sectors scored daily. HOT sector status amplifies congressional insider signals that align with macro tailwinds.
IF sector_tier = 'HOT' → MULTIPLIER
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Layer 6 — 10% weight
Lobbying Activity
2,101 lobbying patterns mapped to congressional trading timelines. Lobbying spend preceding a stock buy is a documented pre-trade signal.
IF lobby_spend → recent buy → CORRELATE
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Layer 7 — 10% weight
Campaign Contributions
565 contribution patterns: donors fund politicians who then trade in the donor's industry. 14 years of FEC data confirms the pattern.
IF donor.industry = trade.sector → PATTERN
7-Layer Signal Score Formula
score = committee 20% + herd 20% + bill timing 16% + technical 12% + sector 12% + lobbying 10% + contributions 10%
S ≥ 75 A+ ≥ 60 A ≥ 50 B ≥ 40 C/D/F below Convergence: 3 layers = 1.3× · 4 = 1.5× · 5+ = 2.0×
Watchlist

12 politicians. Proven track records. 3+ years.

Identified through backtesting 189,595 congressional trades. These members buy sector stocks before every committee markup in their specialty — year after year.

PROVEN CONGRESSIONAL INSIDERS — 3-YEAR BACKTEST
VERIFIED
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Maximum Convergence — Rep. Ro Khanna: Sits on Armed Services Committee. Represents Silicon Valley. Buys tech before every Armed Services markup. Herd patterns confirm. Lobbying data aligns. Five intelligence layers firing simultaneously = Our highest-confidence signal class.
Live Intelligence

Active predictions right now.

Updated nightly. Each prediction represents a committee member with a documented pattern of trading before an upcoming markup. These are live signals — not historical.

Loading active signals…
🏛️ Bill Pipeline → 📊 Paper Trade Signals →
By The Numbers

The math doesn't lie.

Backtested Q4 2025 through February 2026. All signals generated before the trade was disclosed. All returns measured from signal date to 30-day close vs SPY benchmark.

72.7%
A+ Tier Win Rate
A+ signals: 72.7% positive 30-day returns. Loaded from backtest.
5.4×
Bill Passage Rate
Bills with committee + trade + contribution overlap pass 5.4× more often — validated on 37,143 bills.
+10.7%
Avg A+ Return (30d)
Average 30-day return on A+ BULLISH signals.
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Active Signals
Live predictions in the system today.
Best Signal · Q1 2026
ADC +15.83%
Agree Realty Corporation · Predicted Jan 9, 2026
Agriculture Committee member with a 3-year pattern of buying REIT stocks before committee markups. $1.7M trade — filed 45 days after our signal. Stock up +15.83% from signal date. Alpha vs SPY: +16.10%. We called it on January 9th. The news reported it on February 23rd.
+15.83%
30-day return from signal date
Access
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