Monthly analysis of congressional trading patterns, smart money movements, and earnings integrity. Free. Always.
We track every House and Senate financial disclosure. This report quantifies the alpha generated by trading alongside — or against — congressional positions. Which sectors did lawmakers favor? Which committees bought before legislation? The data is public. The edge is not.
Insider purchase transactions, unusual options activity, and signal convergence events from the past 30 days. Which signals fired? What happened next? We track the hit rates, the false positives, and the single best trade from each month.
We score every major company's management honesty across 4 quarters of earnings calls. Who's telling the truth? Who's spinning? Divergence scores, red flag counts, and the quarterly rankings of America's most and least trustworthy CEOs.
43 new purchase transactions filed. Defense and energy sectors dominated. One senator bought $2M in defense before a budget vote.
We scored 340 S&P 500 earnings calls. 62% showed narrative-reality divergence. Top 10 most honest and most dishonest management teams.
Insider purchases hit 18-month high. Options anomalies fired 47 times — 31 were profitable within 5 trading days.
Senate tech purchases up 34% ahead of AI regulation hearings. Average 30-day return on flagged trades: +9.2%.
Full-year honesty rankings across all S&P 500 companies. Most consistent truth-tellers vs. chronic spin artists.
January effect plays, institutional rebalancing flows, and 5 insider conviction buys that outperformed by double digits.
Free, monthly. No spam. Unsubscribe any time.
All data comes from public sources: SEC EDGAR filings, House and Senate financial disclosure portals, CBOE options data, Finnhub market feeds, and government press releases. We do not use non-public information.
Raw data is processed by Gemini 2.0 Flash trained on our proprietary analytical frameworks. The AI scores divergence, calculates statistical edge, and generates plain-language summaries of the key findings.
Every report is reviewed by our editorial team before publication. We flag model uncertainty, verify statistics independently, and add context that AI analysis misses. We correct errors promptly and transparently.