Methodology

Data sources #

• Primary: Capitol Trades (capitoltrades.com) — aggregates House and Senate STOCK Act filings, scraped every 30 minutes.
• Secondary (currently degraded): house-clerk PTR XML feed and Senate EFD search results — both upstream of Capitol Trades, ingested for redundancy.
• Disclosure data is exactly what Congress members file under the STOCK Act. We do not collect non-disclosed trades, private investments without ticker symbols (~15,902 rows in our DB), or trades by family members not legally required to be disclosed.

How disclosure lag is computed #

• Lag = (disclosure_date - trade_date), clamped at 0 (negative values quarantined as date-parser errors).
• Median: 27 days. We display median, not mean, because disclosure lag is heavy-tailed: a small number of very-late filings (some over a year) drag the mean to 72 days, which misrepresents typical behavior.
• "% past 45-day deadline" = count(rows where lag > 45) / total. Currently 17%.
• 31 rows with corrupted dates (lag > 1825 days, almost certainly date-parser errors from source filings) are excluded from all aggregations. They retain their ticker, politician, and amount data — only time-based aggregates skip them.

How simulator returns are computed #

• Entry price: adjusted close on trade_date (or nearest prior trading day if trade_date was a market holiday/weekend).
• Exit price: adjusted close on most recent trading day.
• Adjusted close handles stock splits and dividend payments — so a 4-for-1 split doesn't show as a -75% drop.
• Hypothetical return = (exit - entry) / entry × 100.
• Trade size: STOCK Act discloses ranges ($1,001-$15,000, $15,001-$50,000, etc.) not exact amounts. Simulator uses the midpoint of each range. True trade size could differ by up to ±50% per trade.
• We compare against SPY (S&P 500 ETF) total return over the same window.
• "If you copied this politician" assumes equal dollar weight per trade, on the disclosure date. Disclosure date is the earliest a public observer could have known about the trade — using trade_date here would be lookahead bias.

How conflict scoring works #

• Each politician's committee assignments are stored in committees.json (sourced from clerk.house.gov primary).
• Each committee has a set of sector jurisdictions (e.g. House Armed Services covers Defense, Aerospace, Industrials, Technology, Cybersecurity, Communications).
• Each ticker has a GICS sector classification (sourced from Finnhub).
• A trade is "conflicted" if the politician sits on a committee whose sectors include the ticker's GICS sector.
• "High-Conflict Activity" cluster: 3+ politicians trade the same ticker within 7 days, AND at least one of them has a committee jurisdiction overlap with that ticker's sector.
• Conflict scoring is mechanical, not editorial. We don't claim individual conflicts are unethical — we surface the overlap. Readers draw their own conclusions.

Committee jurisdiction overlap (Panel 4) #

Panel 4 on the homepage — "trades within member's committee jurisdiction" — counts trades disclosed in the last 7 days where the trader sits on a committee whose jurisdiction sectors include the security's GICS sector. Same join logic as the High-Conflict Activity widget above, but applied per-trade rather than per-cluster, so the count answers "how many recent trades carry committee overlap" not "how many clusters."

• For every trade with a non-null ticker disclosed in the last 7 days, we resolve the ticker to a GICS sector (preferring trades.sector, falling back to a static map).
• We batch-pull the trader's committee assignments from politician_committees.
• The trade is counted if any of those committees has a jurisdiction sector that exactly matches the ticker's sector (case-insensitive, no adjacent-sector dilution). One trade counts once even when the trader sits on multiple matching committees.

Coverage (read this before quoting the number)

Backfill from the @unitedstates/congress-legislators committee-membership feed plus a server-side legal-name alias mirror last ran 2026-05-06. Current coverage:

• 7 of 7 (100%) distinct members who disclosed trades with a ticker in the last 7 days are covered. The remaining client-side display-vs-legal mismatches (e.g. Lizzie/Elizabeth Fletcher, Felix Barry Moore, John P Ricketts, Ladda Tammy Duckworth, Nanette Barragan, Rafael E Cruz) are now mirrored into politician_committees under both forms via a small bioguide-keyed alias map in the backfill script.
• 203 of 380 (53%) distinct trade-name strings ever seen in our trades table are covered. The remaining 177 are almost entirely historical members who left Congress in 2018–2024 and rarely disclose now — they don't affect the recent-window panel numbers.
• Coverage progression: 102 / 379 (27%) pre-backfill → 196 / 379 (52%) after the upstream backfill (CTA-17) → 203 / 380 (53%) after the legal-name alias mirror (CTA-19). Panel 4 count progression on the 7-day window: 11 → 23 → 31 (+182% from baseline) on the same query logic.

The methodology is unchanged — only coverage changes. The Panel 4 number remains a floor, not a ceiling: any uncovered member with genuine jurisdictional overlap is silently missed. We re-run the backfill whenever upstream membership data updates or new alias mismatches surface in trades.

How the benchmark works #

• 28 questions, hand-authored, with ground truth computed live from the D1 database at run time (not cached, not pre-baked).
• Two conditions: cold (no tools, model answers from training data only) and MCP-augmented (model has access to our 12-tool MCP server).
• Models tested: Claude Opus 4.6, Sonnet 4.6, GPT-5, Grok-4 (automated harness, last run 2026-04-17).
• One additional row: Claude Opus 4.7 hand-fed via chat (manual, single run, not comparable to the automated rows — labeled as such).
• Scoring: numeric tolerance (±5%) for number questions, Jaccard set overlap for list questions, LLM-as-judge (Claude Haiku) for free-text questions.
• Results are reproducible: harness, questions, and scoring scripts are in the repo.
• Disclosure lag stats computed on records with valid trade_date and disclosure_date (corrupt date entries excluded).

Full leaderboard and per-category scores at /benchmark.

Limitations #

• Trade size is range-based, not exact (STOCK Act limitation).
• Disclosure lag is bounded by what Congress files — late filings inflate the long tail.
• Capitol Trades is the primary source; if their site changes format we go blind until we adapt.
• Benchmark covers 28 questions; not exhaustive coverage of "everything an LLM could be asked about congressional trading."
• Returns calculations assume held-since-trade. Real politicians may have sold positions we don't see if the sale wasn't disclosed yet (45-day window).
• Public API endpoints are rate-limited per IP to prevent abuse. See /mcp-docs for MCP-specific limits.

Reproducibility #

• Public dataset: every trade visible on the site with date, politician, ticker, amount range, source-link to the original filing.
• Open MCP server at /mcp (no auth, JSON-RPC).
• Benchmark harness, questions, scoring scripts, and frozen result snapshots in the repo: https://github.com/freshcod3s/congress-trade-alerts.
• All copy on the site that cites a number is traceable to a query against the public dataset.

Corrections #

Found an error? Email us at congresstradealertsapp@gmail.com.