# DataSignals Lab - full content for AI agents
> Complete text of every product page, guide and blog post on datasignalslab.com. Each product runs as an Apify Actor (~$0.20/result) and is also callable through one free MCP server. See /llms.txt for the curated index and /datasignals-mcp.html for the MCP endpoint.
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# SEC Form 4 Insider Trading: Buying Cluster Signal Data for Research and Screening
URL: https://datasignalslab.com/sec-form4-insider-buying.html
# SEC Form 4 Insider Trading: Buying Cluster Signal Data for Research and Screening
**Track insider trading from SEC Form 4 filings and find clusters of open-market buying, scored and ranked.** When several company insiders (CEO, CFO, directors) buy their own stock within a short window, it is one of the most widely studied patterns in quantitative-finance research. This tool scans SEC EDGAR Form 4 filings, groups open-market purchases (transaction code P) by company, and returns ranked, scored insider-buy clusters as clean JSON. Not a raw data dump: the screened, structured signal.
[Run it on Apify](https://apify.com/datasignalslab/sec-form4-insider-buying-clusters?fpr=wnlxst)
## What it does
- Filters to open-market purchases only (code P), the buys most relevant for screening.
- Detects clusters: two or more distinct insiders buying the same stock within your lookback window.
- Scores each cluster 0-100 (STRONG / MODERATE / WEAK) by number of insiders, total USD value and seniority.
- Returns clean JSON with a direct SEC EDGAR link for every cluster, so you can verify the raw filings.
## Who uses insider-buying data
- Retail quants and algorithmic traders building screening factors.
- Fintech and trading apps embedding insider data.
- Research desks and financial newsletters surfacing notable insider activity.
A self-serve, pay-per-result alternative to subscription insider-data platforms, with no monthly minimum.
## How it works
The tool reads real-time SEC EDGAR Form 4 filings, the official public U.S. registry. No anti-bot workarounds, no personal data, rate-limited and compliant. Each run reads the latest filings.
## Example
A real cluster detected in live SEC data: four insiders including the CFO bought $1.17M of the same stock on the same day, score 71.7 (MODERATE).
## FAQ
**Does it include sells?** No. It covers cluster buying (code P). Sells, grants and option exercises are excluded.
**Why are some runs empty?** Open-market insider buy clusters are genuinely rare, which is exactly why they are worth tracking. Run daily and let clusters accumulate.
**Is this investment advice?** No. It is data for research, screening and monitoring. Historical patterns do not guarantee future results.
## Use with AI agents and automation
This runs as an Apify Actor, so it drops into your stack with no scraping or glue code:
- **AI agents and LLMs**: call it as a live tool from LangChain, LlamaIndex or Flowise, or over [MCP](/datasignals-mcp.html), to give an agent on-demand SEC insider-buying data.
- **No-code automation**: wire it to Zapier or Make (new STRONG insider cluster → Slack, Google Sheets or email).
- **Webhooks and pipelines**: fire a webhook on each run to push results into your database, or chain it into another DataSignals Lab product.
- **API and schedule**: JSON output, on demand or on a daily schedule. No subscription, no sales call.
## Related
- [Guide: what is SEC Form 144? Form 144 vs Form 4](/what-is-sec-form-144.html)
- [Smart Money 13F: hedge fund holdings](/smart-money-13f.html)
- [SEC 8-K Material Event Monitor](/sec-8k-material-events.html)
---
# SEC Form 144 Insider Selling Monitor: Planned Sell Signals
URL: https://datasignalslab.com/sec-form144-insider-selling.html
# SEC Form 144 Insider Selling Monitor: Planned Sell Signals
**See which insiders are filing to sell - and how much.** Provide one or more tickers and get their recent Form 144 notices from SEC EDGAR: who is selling, their relationship to the company (officer, director, affiliate) and the proposed dollar value of the sale - parsed, scored by impact and ranked. A curated insider-selling signal, not a raw filing dump.
[Run it on Apify](https://apify.com/datasignalslab/sec-form144-insider-selling-monitor?fpr=wnlxst)
## What it does
- Seller and relationship: the named insider and whether they are an officer, director or affiliate.
- Proposed dollar value: the aggregate market value of the planned sale, parsed from the structured Form 144 XML.
- Impact score 0-100 from sale size plus recency; a per-company total proposed-sell value.
- A direct SEC.gov link for every notice.
## Who uses insider-selling data
- Traders and quants pairing insider selling with insider buying (Form 4).
- Fintech and research apps embedding planned-sale signals.
- Research and risk teams tracking insider distribution around earnings, lock-ups and highs.
## How it works
The tool reads the official SEC EDGAR submissions API (free, public, JSON), maps each ticker to its CIK, filters Form 144 filings, then parses each filing's structured XML to extract the seller, relationship and proposed dollar value. Clean and stable.
## Example
TSLA in live data: Kimbal Musk (Director) filed to sell ~$24.98M of common stock; CFO Vaibhav Taneja filed a ~$1.05M sale. Largest and most recent planned sales rank first.
## FAQ
**What is a Form 144?** A notice filed when an insider proposes to sell restricted or control securities. It signals intent to sell, ahead of the actual transaction (which later shows on Form 4).
**Does a Form 144 mean the shares were sold?** No - it is a notice of intent, a leading indicator.
**Is this investment advice?** No. It is data for research and monitoring.
## Use with AI agents and automation
This runs as an Apify Actor, so it drops into your stack with no scraping or glue code:
- **AI agents and LLMs**: call it as a live tool from LangChain, LlamaIndex or Flowise, or over [MCP](/datasignals-mcp.html).
- **No-code automation**: wire it to Zapier or Make (large planned sell -> Slack, Google Sheets or email).
- **Webhooks and pipelines**: fire a webhook on each run, or chain it into another DataSignals Lab product.
- **API and schedule**: JSON output, on demand or on a daily schedule.
## Related
- [Guide: what is SEC Form 144? Form 144 vs Form 4](/what-is-sec-form-144.html)
- [SEC Form 4 Insider Buying](/sec-form4-insider-buying.html)
- [SEC 13D/G Activist Stake Monitor](/sec-13dg-activist-stakes.html)
---
# SEC 13D/G Activist Stake Monitor: Investor Stakes and Intent Signals
URL: https://datasignalslab.com/sec-13dg-activist-stakes.html
# SEC 13D/G Activist Stake Monitor: Investor Stakes and Intent Signals
**See who is taking a big stake in a company - and whether they mean to shake it up.** Provide one or more tickers and get their recent Schedule 13D and 13G filings from SEC EDGAR, classified and scored: activist (13D, intent to influence) vs passive (13G), new stake vs amendment, with the filer, the Item 4 purpose text and a direct link. A curated stake signal, not a raw filing dump.
[Run it on Apify](https://apify.com/datasignalslab/sec-13dg-activist-stake-monitor?fpr=wnlxst)
## What it does
- Activist vs passive: Schedule 13D (intent to influence) scored far above a passive 13G index holding.
- Direction: inbound (someone is taking a stake IN this company) vs outbound (this company is taking a stake in another).
- Filer identification: the actual holder/activist pulled from the filing header.
- Item 4 purpose text: for every activist 13D, the filer's own stated intent (board seats, strategic review, sale) extracted from "Purpose of Transaction".
- New vs amendment, plus an impact score 0-100 driven by filing type and recency.
- A direct SEC.gov link for every filing.
## Who uses activist-stake data
- Traders and quants building event-driven and activist-campaign screens.
- Fintech and research apps embedding 13D/G stake signals.
- Competitive-intelligence teams tracking who is accumulating a position.
## How it works
The tool reads the official SEC EDGAR submissions API (free, public, JSON), maps each ticker to its CIK, filters the 13D/13G filings, then reads each filing header to identify the holder and classify activist vs passive, new vs amendment. Clean and stable.
## Example
GME in live data: GameStop itself disclosed a new Schedule 13D activist stake in eBay (outbound, impact 88), while RC Ventures / Ryan Cohen appear as inbound 13D/A amendments on GameStop.
## FAQ
**What is a Schedule 13D vs 13G?** A 13D is filed by a >5% holder who intends to influence the company (activist); a 13G is a passive >5% holder. The "/A" suffix is an amendment.
**Which companies can I track?** Any US-listed company with a ticker in SEC EDGAR, or any CIK.
**Is this investment advice?** No. It is data for research and monitoring.
## Use with AI agents and automation
This runs as an Apify Actor, so it drops into your stack with no scraping or glue code:
- **AI agents and LLMs**: call it as a live tool from LangChain, LlamaIndex or Flowise, or over [MCP](/datasignals-mcp.html).
- **No-code automation**: wire it to Zapier or Make (new activist 13D -> Slack, Google Sheets or email).
- **Webhooks and pipelines**: fire a webhook on each run, or chain it into another DataSignals Lab product.
- **API and schedule**: JSON output, on demand or on a daily schedule.
## Related
- [Guide: 13D vs 13G and Item 4 explained](/13d-vs-13g-explained.html)
- [SEC Form 4 Insider Buying](/sec-form4-insider-buying.html)
- [SEC Form 144 Insider Selling](/sec-form144-insider-selling.html)
- [Smart Money 13F](/smart-money-13f.html)
---
# Smart Money 13F: Hedge Fund Holdings, Buys, Exits and Consensus Data
URL: https://datasignalslab.com/smart-money-13f.html
# Smart Money 13F: Hedge Fund Holdings, Buys, Exits and Consensus Data
**See what hedge funds reported buying and selling each quarter.** Provide one or more fund CIK numbers and get each fund's latest 13F moves from SEC EDGAR (new positions, add-ons, trims and full exits) plus a cross-fund consensus ranking showing which stocks the most funds hold and are buying. A structured institutional-ownership dataset for research, not a raw holdings dump.
[Run it on Apify](https://apify.com/datasignalslab/smart-money-13f?fpr=wnlxst)
## What it does
- New buys, increases, trims and exits per fund, quarter over quarter.
- Cross-fund consensus and a 0-100 conviction score.
- A direct SEC EDGAR link for every filer, so you can verify the filings.
## Who uses 13F data
- Retail traders and quants researching institutional flow.
- Fintech and research apps embedding hedge-fund activity.
- Competitive-intelligence analysts tracking specific funds.
A self-serve, pay-per-use alternative to WhaleWisdom and Quiver Quant.
## How it works
The tool reads the two most recent 13F-HR filings per fund from the official SEC EDGAR API, parses the Information Table XML and diffs them quarter over quarter. Clean, stable, public source.
## Example
Berkshire Hathaway last quarter: new positions in Delta Air Lines and Alphabet, increased Alphabet, exited Visa, UnitedHealth and Mastercard.
## FAQ
**How fresh is it?** 13F filings are quarterly, filed about 45 days after quarter-end. The tool always uses the latest available filing per fund.
**Where do I find a fund's CIK?** Search the fund name in SEC EDGAR full-text search.
**Is this investment advice?** No. It is data for research and monitoring. Historical patterns do not guarantee future results.
## Use with AI agents and automation
This runs as an Apify Actor, so it drops into your stack with no scraping or glue code:
- **AI agents and LLMs**: call it as a live tool from LangChain, LlamaIndex or Flowise, or over [MCP](/datasignals-mcp.html), to give an agent on-demand hedge-fund 13F data.
- **No-code automation**: wire it to Zapier or Make (new buy or exit in a fund you track → Slack or email).
- **Webhooks and pipelines**: fire a webhook on each run to push results into your database, or chain it into another DataSignals Lab product.
- **API and schedule**: JSON output, on demand or on a daily schedule. No subscription, no sales call.
## Related
- [Guide: 13D vs 13G and Item 4 explained](/13d-vs-13g-explained.html)
- [SEC Form 4 Insider Buying clusters](/sec-form4-insider-buying.html)
- [SEC 8-K Material Event Monitor](/sec-8k-material-events.html)
---
# SEC 8-K Material Event Monitor: Real-Time Filing Data, Classified and Scored
URL: https://datasignalslab.com/sec-8k-material-events.html
# SEC 8-K Material Event Monitor: Real-Time Filing Data, Classified and Scored
**Track the material disclosures companies are required to file.** Provide a list of tickers (or SEC CIKs) and get each company's recent 8-K material events (M&A, executive changes, earnings, restatements, bankruptcy, delisting), automatically classified into plain-English categories and scored by materiality (0-100). See them the same day, not days later.
[Run it on Apify](https://apify.com/datasignalslab/sec-8k-material-event-monitor?fpr=wnlxst)
## What it does
- Item-code classification: turns cryptic codes (1.01, 4.02, 5.02) into readable categories.
- Materiality score 0-100: bankruptcy, restatement and control changes rank highest; routine exhibits lowest.
- High-impact flagging across a multi-company watchlist.
- A direct SEC EDGAR link for every event.
## Who uses 8-K data
- Traders and quants monitoring corporate events.
- Fintech and research apps embedding classified events.
- Risk, compliance and investor-relations teams tracking disclosures.
## How it works
The tool reads each company's filing history from the official SEC EDGAR submissions API, which exposes 8-K item codes directly. No fragile document scraping. Ticker-to-CIK resolution is automatic.
## Example
Tesla recent 8-Ks classified: earnings (m60), executive change (m70), material agreement (m70). High-impact events flagged first.
## FAQ
**How fast after a filing?** 8-Ks appear on EDGAR within minutes of submission. Run on a schedule to catch them same-day.
**Do I need a CIK?** No, just use the ticker. The tool resolves it.
**Is this investment advice?** No. It is data for research and monitoring. Historical patterns do not guarantee future results.
## Use with AI agents and automation
This runs as an Apify Actor, so it drops into your stack with no scraping or glue code:
- **AI agents and LLMs**: call it as a live tool from LangChain, LlamaIndex or Flowise, or over [MCP](/datasignals-mcp.html), to give an agent on-demand SEC 8-K material-event data.
- **No-code automation**: wire it to Zapier or Make (high-materiality 8-K on your watchlist → Slack, email or PagerDuty).
- **Webhooks and pipelines**: fire a webhook on each run to push results into your database, or chain it into another DataSignals Lab product.
- **API and schedule**: JSON output, on demand or on a daily schedule. No subscription, no sales call.
## Related
- [SEC Form 4 Insider Buying clusters](/sec-form4-insider-buying.html)
- [Smart Money 13F](/smart-money-13f.html)
---
# Startup Funding Monitor: SEC Form D Capital Raises Before the Press
URL: https://datasignalslab.com/startup-funding-form-d.html
# Startup Funding Monitor: SEC Form D Capital Raises Before the Press
**See who raised money before the press writes about it.** When a US company raises private capital under Regulation D, it must file a Form D with the SEC within 15 days of the first sale - and that filing appears in EDGAR days before (and often without) any news coverage. This monitor scans the most recent Form D filings, filters out the noise, and returns a ranked list of real capital raises with amounts, industry, executives and a 0-100 funding score.
[Run it on Apify](https://apify.com/datasignalslab/startup-funding-form-d-monitor?fpr=wnlxst)
## What it does
- The fund filter: the majority of Form D filings are hedge/PE/VC vehicles raising their own capital. Those are classified (industry group + 3C exemption codes) and excluded by default, so what remains is the actual operating-company signal.
- Structured amounts: total offered, sold and remaining parsed from the filing XML - including "Indefinite" offerings, handled explicitly.
- The people: executives, directors and promoters (related persons) returned per raise.
- New vs amendment: a fresh raise (Form D) scores above an update to an existing one (D/A).
- Funding score 0-100: amount, freshness, equity vs debt and investor momentum in one transparent score.
- A direct SEC.gov link for every filing.
## Who uses Form D data
- Investors and analysts tracking private-market activity before it hits the news.
- Sales and BD teams using fresh raises as buying signals (companies that just raised, spend).
- Founders and VCs monitoring competitor fundraising.
- Fintech, data apps and researchers embedding a clean funding feed.
## How it works
The monitor reads the official SEC EDGAR daily form index (free, public, stable), fetches each Form D submission and parses the structured XML inside it. No HTML scraping, no third-party news sites, polite SEC rate limiting. One $0.20 scan returns the full ranked window - up to hundreds of analyzed filings.
## Example
One real EDGAR day surfaced CesiumAstro (space communications) with a $270M equity raise and EnerVenue (energy storage) with $339M at the top of the ranking - both with named executives parsed from the filing, before mainstream funding coverage.
## FAQ
**Why is a company I know raised money missing?** Form D covers Regulation D exempt offerings; raises under other exemptions or public registration file different forms.
**Why filter out funds?** By count, most Form Ds are investment vehicles raising their own capital - noise for a startup-funding signal. Switch them back on with one input flag.
**How fresh is this?** EDGAR publishes the daily index after each filing day, typically well ahead of press coverage - which for most non-unicorn raises never comes at all.
**Is this investment advice?** No. Data for research, screening and monitoring.
## Use with AI agents and automation
This runs as an Apify Actor, so it drops into your stack with no scraping or glue code:
- **AI agents and LLMs**: call it as a live tool from LangChain, LlamaIndex or Flowise, or over [MCP](/datasignals-mcp.html) - "who raised more than $10M this week?" becomes a one-tool agent query.
- **No-code automation**: wire it to Zapier or Make (new $50M+ raise -> Slack, Google Sheets or CRM).
- **Webhooks and pipelines**: fire a webhook on each run, or chain it into the [Form 4 insider scanner](/sec-form4-insider-buying.html) and [13D/G activist monitor](/sec-13dg-activist-stakes.html) for the full private-to-public money-flow picture.
- **API and schedule**: JSON output, on demand or on a daily schedule.
## Related
- [SEC Form 4 Insider Trading: Buying Cluster Signal Scanner](/sec-form4-insider-buying.html)
- [SEC 13D/G Activist Stake Monitor](/sec-13dg-activist-stakes.html)
- [US Government Contract Awards Monitor](/gov-contract-awards-monitor.html)
---
# Biotech Catalyst Monitor: Clinical Trial Readouts and Pipeline Data
URL: https://datasignalslab.com/biotech-catalyst-monitor.html
# Biotech Catalyst Monitor: Clinical Trial Readouts and Pipeline Data
**Track the clinical-trial events that often move biotech stocks.** Provide one or more company names and get their trials from ClinicalTrials.gov, ranked by potential market impact: upcoming Phase 3 readouts, results just posted and phase transitions. A curated catalyst dataset, not a raw trial dump.
[Run it on Apify](https://apify.com/datasignalslab/biotech-catalyst-monitor?fpr=wnlxst)
## What it does
- Catalyst classification: upcoming readout, results posted, completed-awaiting-results, status change.
- Impact score 0-100: later phases and near-term readouts rank highest.
- Per-company ranking of the trials most likely to be market-moving.
- A direct ClinicalTrials.gov link for every catalyst.
## Who uses clinical-trial data
- Biotech investors and traders building a catalyst calendar.
- Fintech and research apps embedding pipeline data.
- Pharma competitive-intelligence teams tracking a competitor's pipeline and readout timing.
## How it works
The tool reads the official ClinicalTrials.gov v2 API (free, public, JSON), matches the sponsor field with a full-text fallback, then classifies and scores each trial. Clean and stable.
## Example
Moderna top catalyst in live data: a Phase 3 trial, active, with primary completion 2026-06-30, impact score 100 (an upcoming readout).
## FAQ
**Which companies can I track?** Any sponsor in ClinicalTrials.gov. Use the company name.
**How current is it?** ClinicalTrials.gov is the official U.S. registry, updated continuously.
**Is this investment advice?** No. It is data for research and monitoring. A single readout can move a stock sharply, but historical patterns do not guarantee future results.
## Use with AI agents and automation
This runs as an Apify Actor, so it drops into your stack with no scraping or glue code:
- **AI agents and LLMs**: call it as a live tool from LangChain, LlamaIndex or Flowise, or over [MCP](/datasignals-mcp.html), to give an agent on-demand clinical-trial catalyst data.
- **No-code automation**: wire it to Zapier or Make (upcoming Phase 3 readout → calendar, Slack or Google Sheets).
- **Webhooks and pipelines**: fire a webhook on each run to push results into your database, or chain it into another DataSignals Lab product.
- **API and schedule**: JSON output, on demand or on a daily schedule. No subscription, no sales call.
## Related
- [SEC 8-K Material Event Monitor](/sec-8k-material-events.html)
- [Smart Money 13F](/smart-money-13f.html)
---
# FDA Drug Approval and Action Monitor: Regulatory Catalyst Data
URL: https://datasignalslab.com/fda-drug-approval-monitor.html
# FDA Drug Approval and Action Monitor: Regulatory Catalyst Data
**See the FDA decisions that move biotech and pharma stocks.** Provide one or more company names and get their FDA regulatory events from Drugs@FDA: new drug approvals, new indications, priority reviews and other actions - classified, scored by potential market impact, and ranked most-recent-first. A curated regulatory-catalyst dataset, not a raw application dump.
[Run it on Apify](https://apify.com/datasignalslab/fda-drug-approval-monitor?fpr=wnlxst)
## What it does
- Event classification: new approval (original NDA/BLA), new indication / efficacy supplement, labeling supplement, tentative approval, withdrawal.
- Priority-review flag scored above standard reviews.
- Impact score 0-100 from event type plus recency; most-recent-first.
- A direct Drugs@FDA link and FDA documents (label, letter, review) for every event.
## Who uses FDA regulatory data
- Biotech and pharma investors and traders building a regulatory-catalyst calendar.
- Fintech and research apps embedding approval data.
- Competitive-intelligence teams tracking a competitor's approvals and pipeline actions.
## How it works
The tool reads the official openFDA Drugs@FDA API (free, public, JSON), matches the sponsor (with a wildcard fallback so "Vertex" finds "VERTEX PHARMACEUTICALS..."), then classifies and scores each submission. Clean and stable.
## Example
Eli Lilly in live data: a new drug approval (original NDA) for FOUNDAYO, scored impact 95. Vertex: a priority-review new-indication supplement for TRIKAFTA, scored impact 80.
## Scope and limitation
Covers FDA CDER drug applications in Drugs@FDA. Vaccines and some biologics handled by CBER are not in this dataset. The source provides regulatory actions and approvals, not forward-looking PDUFA target dates (which are not published in a single official feed).
## FAQ
**What counts as a high-impact event?** A new drug approval and priority-review efficacy/new-indication supplements score highest; routine labeling supplements score low.
**Which companies can I track?** Any drug sponsor in Drugs@FDA. Use the company name; a wildcard fallback handles legal-name variations.
**Is this investment advice?** No. It is data for research and monitoring.
## Use with AI agents and automation
This runs as an Apify Actor, so it drops into your stack with no scraping or glue code:
- **AI agents and LLMs**: call it as a live tool from LangChain, LlamaIndex or Flowise, or over [MCP](/datasignals-mcp.html).
- **No-code automation**: wire it to Zapier or Make (new approval -> calendar, Slack or Google Sheets).
- **Webhooks and pipelines**: fire a webhook on each run, or chain it into another DataSignals Lab product.
- **API and schedule**: JSON output, on demand or on a daily schedule.
## Related
- [Biotech Catalyst Monitor](/biotech-catalyst-monitor.html)
- [SEC 8-K Material Event Monitor](/sec-8k-material-events.html)
---
# NIH Research Funding Monitor: Grant Momentum Signals
URL: https://datasignalslab.com/nih-research-funding-monitor.html
# NIH Research Funding Monitor: Grant Momentum Signals
**See where biomedical research money is flowing, by institution or by topic.** Provide one or more organizations (a university, institute or biotech) or research topics and get their recent NIH grants from NIH RePORTER: funding momentum, the biggest recent awards and which organizations dominate a field, scored by size and recency. A curated funding signal, not a raw grant dump.
[Run it on Apify](https://apify.com/datasignalslab/nih-research-funding-monitor?fpr=wnlxst)
## What it does
- Two modes: by organization (an institution's funding profile) or by topic (who funds a research area).
- Funding momentum: total recent award value and grant count.
- Top organization: which institution dominates a topic search.
- Impact score 0-100 from award size plus recency.
- A direct NIH RePORTER link for every grant.
## Who uses research-funding data
- Biotech investors and analysts tracking where R&D capital concentrates before it reaches pipelines.
- Competitive-intelligence teams monitoring a competitor institution's funded research.
- Fintech and research apps embedding funding-momentum data.
## How it works
The tool reads the official NIH RePORTER API (free, public, no key, JSON), queries by organization or topic, scores each grant by size and recency, and aggregates per query. Clean and stable.
## Example
Broad Institute in live data: roughly $113M across recent grants, led by a ~$36M genome-center award. Topic "CRISPR gene therapy": surfaces which institutions win the largest awards in the field.
## Scope and limitation
Covers NIH and other HHS divisions (CDC, FDA, AHRQ, HRSA) plus VA. NIH funds mostly universities, institutes and small biotechs, so this is a research-funding signal, not a direct public-company revenue feed.
## FAQ
**Organization or topic?** Organization mode profiles an institution's funding; topic mode shows who funds a research area.
**Why no public-company tickers?** NIH funds research institutions, not mostly public companies; use it as an upstream R&D signal.
**Is this investment advice?** No. It is data for research and monitoring.
## Use with AI agents and automation
This runs as an Apify Actor, so it drops into your stack with no scraping or glue code:
- **AI agents and LLMs**: call it as a live tool from LangChain, LlamaIndex or Flowise, or over [MCP](/datasignals-mcp.html).
- **No-code automation**: wire it to Zapier or Make (new large grant -> Slack, Google Sheets or alert).
- **Webhooks and pipelines**: fire a webhook on each run, or chain it into another DataSignals Lab product.
- **API and schedule**: JSON output, on demand or on a daily schedule.
## Related
- [Biotech Catalyst Monitor](/biotech-catalyst-monitor.html)
- [FDA Drug Approval and Action Monitor](/fda-drug-approval-monitor.html)
---
# US Government Contract Awards Monitor: Federal Win Signals
URL: https://datasignalslab.com/gov-contract-awards-monitor.html
# US Government Contract Awards Monitor: Federal Win Signals
**See which companies are winning federal money, and how much, right now.** Provide one or more company names and get their recent US federal contract awards from USAspending.gov: total award momentum, which agencies are buying, and the biggest new wins, scored by size and recency. A curated government-spending signal, not a raw award dump.
[Run it on Apify](https://apify.com/datasignalslab/gov-contract-awards-monitor?fpr=wnlxst)
## What it does
- Recent wins only: new awards within your look-back window, not lifetime megacontracts.
- Momentum per company: total recent award value and award count.
- Agency concentration: which agency is the biggest buyer.
- Impact score 0-100 from award size plus recency.
- A direct USAspending.gov link for every award.
## Who uses federal contract data
- Traders and quants tracking federal revenue as an early signal.
- Fintech and research apps embedding contract-win data.
- Competitive-intelligence and BD teams watching who wins in their market.
## How it works
The tool reads the official USAspending.gov API (US Treasury, free, public, no key, JSON), filters to contracts newly awarded in your window, scores each by size and recency, and aggregates per company. Clean and stable.
## Example
Palantir in live data: roughly $744M in recent federal awards, top agency Department of Defense, led by a ~$293M DoD task order. Lockheed Martin: roughly $2.75B in recent awards.
## FAQ
**Where does the data come from?** USAspending.gov, the official US Treasury source for federal spending under the DATA Act.
**Does a contract win guarantee revenue?** It signals awarded federal money; recognized revenue depends on execution.
**Is this investment advice?** No. It is data for research and monitoring.
## Use with AI agents and automation
This runs as an Apify Actor, so it drops into your stack with no scraping or glue code:
- **AI agents and LLMs**: call it as a live tool from LangChain, LlamaIndex or Flowise, or over [MCP](/datasignals-mcp.html).
- **No-code automation**: wire it to Zapier or Make (new $100M+ win -> Slack, Google Sheets or email).
- **Webhooks and pipelines**: fire a webhook on each run, or chain it into another DataSignals Lab product.
- **API and schedule**: JSON output, on demand or on a daily schedule.
## Related
- [US Congress Trading Monitor](/congress-trading-monitor.html)
- [SEC 8-K Material Event Monitor](/sec-8k-material-events.html)
---
# US Congress Trading Monitor: House Member Trade Signals
URL: https://datasignalslab.com/congress-trading-monitor.html
# US Congress Trading Monitor: House Member Trade Signals
**See what members of the US House are trading, parsed, scored and ranked.** Provide one or more House member last names and get their recent stock trades from the official House Clerk disclosures: each transaction with ticker, buy or sell, estimated size and date, plus buys-vs-sells and an estimated total per member. A curated trading signal, not a raw PDF dump.
[Run it on Apify](https://apify.com/datasignalslab/congress-trading-monitor?fpr=wnlxst)
## What it does
- Parsed transactions: ticker, buy/sell, estimated amount range and date, straight from the official PTR.
- Buys vs sells: the member's recent net direction.
- Estimated value: midpoint of each disclosed amount range, summed per member.
- Impact score 0-100 from trade size plus recency.
- Reads the official US House Clerk source, not a scraped aggregator.
## Who uses congressional trading data
- Traders and retail quants tracking political trading as a signal.
- Fintech and research apps embedding congressional-trade data.
- Journalists and researchers monitoring disclosures without parsing PDFs.
## How it works
The tool reads the official US House Clerk financial-disclosure feed (free, public, no key), filters to each member's recent Periodic Transaction Reports, and parses the trades from the e-filed PDFs. Clean and official.
## Example
One real record from the live feed, refreshed daily. This is the exact JSON the API and the MCP tools return for every trade:
```json
{
"member": "Gilbert Cisneros",
"ticker": "MSFT",
"tx_type": "Sale",
"tx_date": "06/02/2026",
"amount_low": 500001,
"amount_high": 1000000,
"impact": 95,
"filing_date": "2026-07-02",
"pdf_url": "https://disclosures-clerk.house.gov/public_disc/ptr-pdfs/2026/20034906.pdf"
}
```
Every field is parsed straight from the official House Clerk filing linked in `pdf_url`. The `impact` score (0–100) ranks how market-moving a trade is, so an agent or a spreadsheet can sort it or set a threshold on it. All 176 scored trades in the current window share this shape.
## Scope and limitation
US House only. The Senate eFD portal blocks automated access and prohibits commercial use, so it is intentionally excluded. Only e-filed (digital) reports are machine-readable; older scanned paper filings are skipped (no OCR).
## FAQ
**Where does the data come from?** The official US House Clerk financial-disclosure feed, published under the STOCK Act.
**Why only the House?** The Senate portal is anti-bot protected and its terms prohibit commercial use.
**Is this investment advice?** No. It is data for research and monitoring.
## Use with AI agents and automation
This runs as an Apify Actor, so it drops into your stack with no scraping or glue code:
- **AI agents and LLMs**: call it as a live tool from LangChain, LlamaIndex or Flowise, or over [MCP](/datasignals-mcp.html).
- **No-code automation**: wire it to Zapier or Make (new disclosed trade -> Slack, Google Sheets or alert).
- **Webhooks and pipelines**: fire a webhook on each run, or chain it into another DataSignals Lab product.
- **API and schedule**: JSON output, on demand or on a daily schedule.
## Related
- [US Government Contract Awards Monitor](/gov-contract-awards-monitor.html)
- [SEC Form 4 Insider Buying](/sec-form4-insider-buying.html)
---
# Crypto Volume and Momentum Scanner: Anomaly Signals
URL: https://datasignalslab.com/crypto-volume-momentum-scanner.html
# Crypto Volume and Momentum Scanner: Anomaly Signals
**Spot the coins that are moving before they hit your feed.** Scan the crypto market and get a ranked list of unusual volume and momentum: turnover (24h volume vs market cap), 1h/24h/7d momentum and a clear signal classification - volume breakout, capitulation, strong weekly momentum or unusual volume at a flat price. A curated screener signal, not a raw market dump.
[Run it on Apify](https://apify.com/datasignalslab/crypto-volume-momentum-scanner?fpr=wnlxst)
## What it does
- Turnover: 24h volume divided by market cap, the clearest single measure of unusual activity.
- Adaptive scoring: turnover is percentile-ranked within the scanned universe, so the score adapts to market conditions.
- Momentum blend: 1h / 24h / 7d change combined with turnover into one impact score (0-100).
- Signal classification: volume breakout (rising), volume capitulation (falling), strong weekly momentum, or unusual volume at a flat price.
- Stablecoins filtered out so they do not pollute the ranking.
## Who uses a crypto screener
- Crypto traders and quants building watchlists and anomaly alerts.
- Fintech and research apps embedding a scored momentum/volume feed.
- Researchers wanting a clean, reproducible snapshot of market-wide activity.
## How it works
The tool reads the official CoinGecko markets API (free, public, JSON), filters stablecoins, computes turnover and momentum, percentile-ranks each coin within the scanned universe, and classifies the signal. Clean and stable.
## Example
WLD in live data: "Volume breakout (rising)", up ~8.8% in 24h and ~20% in 7 days on heavy turnover, scored impact ~79. Highest-signal coins rank first.
## Scope and limitation
This is a market-data screener (price and volume), not on-chain "smart money" wallet tracking - there is no clean, free, public source for labeled-wallet flows, so this Actor deliberately stays on transparent, reproducible market metrics.
## FAQ
**What is turnover?** 24h trading volume divided by market cap. High turnover means a coin is changing hands unusually fast for its size.
**Why are stablecoins missing?** They are filtered out on purpose; their constant high volume would otherwise dominate the ranking.
**Is this investment advice?** No. Crypto is volatile; this is data for research and monitoring.
## Use with AI agents and automation
This runs as an Apify Actor, so it drops into your stack with no scraping or glue code:
- **AI agents and LLMs**: call it as a live tool from LangChain, LlamaIndex or Flowise, or over [MCP](/datasignals-mcp.html).
- **No-code automation**: wire it to Zapier or Make (new volume breakout -> Slack, Google Sheets or alert).
- **Webhooks and pipelines**: fire a webhook on each run, or chain it into another DataSignals Lab product.
- **API and schedule**: JSON output, on demand or on a daily schedule.
---
# App Store Review Intelligence: Aspect Sentiment and Competitor Analysis
URL: https://datasignalslab.com/app-store-review-intelligence.html
# App Store Review Intelligence: Aspect Sentiment and Competitor Analysis
**Turn App Store reviews into product decisions.** Provide one or more iOS App Store app IDs (yours and your competitors') and get aspect-based sentiment, top complaints and praise, emerging issues, and a side-by-side competitor feature-gap comparison, across countries and languages. The analysis layer that product, ASO and growth teams pay for, self-serve.
[Run it on Apify](https://apify.com/datasignalslab/app-store-review-intelligence?fpr=wnlxst)
## What it does
- Aspect-based sentiment per topic: stability, performance, UI/UX, price, login, ads, features, support, updates, content.
- Competitor comparison: analyze multiple apps and see exactly where each is weak or strong, aspect by aspect.
- Emerging issues: flags aspects whose sentiment is dropping in recent reviews.
- Real review quotes per top complaint, plus the App Store link.
- Multi-country and multi-language: sentiment is derived from star ratings, so it works in any language.
## Who uses app review intelligence
- App developers and product managers prioritizing the roadmap.
- ASO and growth teams understanding user complaints and competitor gaps.
- Competitive-intelligence analysts, including on apps they do not own.
A self-serve, pay-per-use alternative to subscription app-review platforms, with no monthly minimum.
## How it works
The tool reads the official Apple App Store customer-reviews RSS feed (free, public, no login). Sentiment per aspect is the average star rating of reviews mentioning that aspect, which is language-agnostic.
## Example
Spotify (4.0 stars) versus YouTube Music (2.1 stars), per aspect: price/billing 3.71 vs 1.53, ads 3.98 vs 2.02, UI/UX 4.29 vs 1.96.
## Use with AI agents and automation
This runs as an Apify Actor, so it drops into your stack with no scraping or glue code:
- **AI agents and LLMs**: call it as a live tool from LangChain, LlamaIndex or Flowise, or over [MCP](/datasignals-mcp.html), to give an agent on-demand App Store review-sentiment data.
- **No-code automation**: wire it to Zapier or Make (emerging issue or sentiment drop → Slack or Jira).
- **Webhooks and pipelines**: fire a webhook on each run to push results into your database, or chain it into another DataSignals Lab product.
- **API and schedule**: JSON output, on demand or on a daily schedule. No subscription, no sales call.
## FAQ
**Can I analyze competitors?** Yes, any app by its App Store ID, including apps you do not own.
**Which languages?** Any. Pull reviews from multiple App Store countries; sentiment uses star ratings.
---
# DataSignals Lab MCP Server: 17 Financial Data Tools for AI Agents
URL: https://datasignalslab.com/datasignals-mcp.html
# DataSignals Lab MCP Server: 17 Financial Data Tools for AI Agents
The only signal source in this niche with a daily verifiable track record, confluence across every data stream, and direct agent access over MCP.
Every score in every report lists the terms it was built from, so you can check the number instead of trusting it.
**Give your AI agent live, scored financial and market data with one connection.** The DataSignals Lab MCP server exposes 17 callable tools over the Model Context Protocol (MCP), led by the free flagship `confluence_signals` and backed by 13 scored data feeds, so Claude, ChatGPT, Cursor or any MCP-capable agent can pull real SEC, FDA, clinical-trial, government and crypto data on demand: parsed, scored and ready to reason over, not raw HTML.
[Get it on Apify](https://apify.com/datasignalslab/datasignals-mcp?fpr=wnlxst) - the MCP server itself is **free**. You only pay the underlying tools' standard $0.20 per result.
The free tier includes 50 MCP calls per month. [DataSignals Pro](/pricing.html) raises that to 2000 calls per month as a fair use limit.
Listed in the official MCP Registry as com.datasignalslab/datasignals-lab-mcp
## Start here: one call for the strongest signals
`confluence_signals` is the flagship and the default entry point, and it is free. One call returns the signals with the strongest cross-source confluence right now, ranked by conviction, so your agent asks one question ("what has the strongest confluence today?") instead of choosing between many tools. Want a full dossier on one company instead? Call `company_intelligence(identifier)` with a ticker, name or CIK for a scored profile in one call: recent 8-K material events, planned insider selling (Form 144), 13D/G activist stakes and federal contract awards, plus FDA actions and clinical-trial catalysts for pharma names. For a single specific signal, call the dedicated tool below.
## The 13 data feeds
| Tool | What your agent gets |
|---|---|
| `insider_trading_clusters` | Clusters of insider buying from SEC Form 4, scored 0-100 |
| `insider_selling_form144` | Proposed insider sales: seller, role, dollar value |
| `hedge_fund_13f` | Hedge-fund buys, exits and cross-fund consensus from 13F |
| `sec_8k_events` | Material 8-K events, classified and materiality-scored |
| `activist_stakes_13dg` | New 5%+ stakes, activist (13D) vs passive (13G) |
| `biotech_catalysts` | Upcoming trial readouts from ClinicalTrials.gov, impact-scored |
| `fda_drug_actions` | FDA approvals and regulatory actions per company |
| `government_contracts` | Federal contract awards per company from USAspending.gov |
| `congress_trading` | Stock trades by US House members, parsed from official disclosures |
| `nih_research_funding` | NIH grant momentum by organization or topic |
| `startup_funding_form_d` | New SEC Form D capital raises before press coverage, scored |
| `crypto_momentum_scanner` | Market-wide crypto volume and momentum anomalies |
| `app_review_intelligence` | App Store review sentiment, top complaints, competitor compare |
## Why use this instead of scraping or separate APIs
- **One endpoint, thirteen datasets**: a single MCP connection replaces a dozen API integrations.
- **Agent-ready output**: every tool returns structured JSON with a transparent score and a link back to the original filing or source, so the agent can verify its own answer.
- **Official sources only**: SEC EDGAR, openFDA, ClinicalTrials.gov, USAspending.gov, House Clerk, NIH RePORTER, CoinGecko, Apple App Store. No scraping walls, no personal data.
- **No subscription**: the server is free. Underlying tools bill $0.20 per result, only when called.
## Connect your agent
The server runs on Apify standby and speaks streamable HTTP MCP:
```
https://datasignalslab--datasignals-mcp.apify.actor/mcp
```
Add it as a remote MCP server and authenticate with your own Apify token (free account):
- **Claude Desktop / Claude Code**: add the URL under Settings -> Connectors (or `claude mcp add --transport http datasignals https://datasignalslab--datasignals-mcp.apify.actor/mcp`).
- **ChatGPT (Plus/Pro)**: enable developer mode under Settings -> Connectors, then add the URL as a new connector with your Apify token.
- **Cursor / Windsurf**: add it as a remote MCP server in the MCP settings with an `Authorization: Bearer ` header.
- **LangChain / LlamaIndex**: both ship MCP adapters that connect to the same URL.
Because your agent authenticates with its own token, usage bills to your Apify account per result - no subscription, no shared keys.
## Framework examples
Copy-paste starting points for the common agent frameworks. Replace `` with your own token.
**Claude Code / Claude Desktop (one line):**
```
claude mcp add --transport http datasignals https://datasignalslab--datasignals-mcp.apify.actor/mcp --header "Authorization: Bearer "
```
**Anthropic API (MCP connector):**
```python
import anthropic
client = anthropic.Anthropic()
msg = client.beta.messages.create(
model="claude-sonnet-5", max_tokens=1024,
mcp_servers=[{"type": "url",
"url": "https://datasignalslab--datasignals-mcp.apify.actor/mcp",
"name": "datasignals",
"authorization_token": ""}],
messages=[{"role": "user", "content": "What is going on with NVDA? Use company_intelligence."}],
betas=["mcp-client-2025-04-04"])
```
**LangChain (MCP adapter):**
```python
from langchain_mcp_adapters.client import MultiServerMCPClient
client = MultiServerMCPClient({"datasignals": {
"transport": "streamable_http",
"url": "https://datasignalslab--datasignals-mcp.apify.actor/mcp",
"headers": {"Authorization": "Bearer "}}})
tools = await client.get_tools() # 17 DataSignals tools, ready for your agent
```
**OpenAI Agents SDK:**
```python
from agents.mcp import MCPServerStreamableHttp
server = MCPServerStreamableHttp(params={
"url": "https://datasignalslab--datasignals-mcp.apify.actor/mcp",
"headers": {"Authorization": "Bearer "}})
```
## Machine-readable surface
Agents that want to discover the platform without reading HTML:
- [/api/openapi.json](/api/openapi.json): OpenAPI 3.1 spec of all public endpoints plus the MCP endpoint and its 17 tools.
- [/api/streams.json](/api/streams.json): every data stream with source, cadence, schema and changelog.
- [/api/confluence-public.json](/api/confluence-public.json): confluence cases, public with a 7 day delay.
- [/api/proof.json](/api/proof.json): status of the daily hash chain over the published signal set.
- [/llms.txt](/llms.txt) and [/llms-full.txt](/llms-full.txt): curated index and full text for LLMs.
## Why an agent can trust this data
Every published signal set is hashed daily into an append-only chain. Each day hash includes the previous one, is published per day on [the proof page](/proof.html), and is anchored in Bitcoin via OpenTimestamps. Your agent (or you) can verify that no historical signal was edited: see [the proof page](/proof.html). Every individual result links back to the official filing it came from.
## Example
Ask your agent: "Which companies had insider buying clusters this week, and did any of them also file an 8-K?" The agent calls `insider_trading_clusters`, cross-references `sec_8k_events`, and answers with scored, source-linked data instead of guessing.
## FAQ
**Is the MCP server really free?** Yes. The server adds no fee. Each underlying tool charges its normal $0.20 per result through Apify, only when your agent actually calls it.
**Which clients work?** Anything that speaks MCP: Claude Desktop, Claude Code, Cursor, Windsurf, custom agents via the MCP SDK, LangChain/LlamaIndex adapters.
**Do I need an Apify account?** Yes, a free account; the token authenticates your agent's calls.
**Is this investment advice?** No. All financial tools return data for research, screening and monitoring. Historical patterns do not guarantee future results.
## Troubleshooting
- **Connection or sign-in fails (401/unauthorized):** make sure you are signed in to Apify (OAuth) or that your `Authorization: Bearer ` header uses a valid Apify API token. Disconnect and reconnect the server to refresh the session.
- **No tools appear after connecting:** confirm the server URL is complete (the `?tools=` list must not be truncated) and reconnect. The connector should list the DataSignals tools plus Apify's run/dataset helper tools.
- **A tool returns an empty result:** check the input. Most tools expect a ticker, company name or CIK (e.g. CIK `1067983` for Berkshire Hathaway), or a date range; the crypto scanner needs no input. An empty result usually means no qualifying filings in the requested window, not an error.
- **A run fails with a billing or quota message:** ensure your Apify account has usage credit available. Each result costs about $0.20; free accounts include monthly credit.
- **A source temporarily returns 403/timeout:** official sources occasionally rate-limit or go down briefly. Retry the call; the tools use official APIs and recover automatically.
- **Still stuck?** Reach us via our Apify profile (link below) for support.
## Related
- [SEC Form 4 Insider Trading: Buying Cluster Signal Scanner](/sec-form4-insider-buying.html)
- [Smart Money 13F: Hedge Fund Buys, Exits and Consensus](/smart-money-13f.html)
- [Crypto Volume and Momentum Scanner](/crypto-volume-momentum-scanner.html)
---
# What Is SEC Form 144? Insider Selling Explained (and Form 144 vs Form 4)
URL: https://datasignalslab.com/what-is-sec-form-144.html
# What Is SEC Form 144? Insider Selling Explained (and Form 144 vs Form 4)
SEC Form 144 is the notice an insider must file with the SEC **before** selling restricted or control stock when the sale exceeds 5,000 shares or $50,000 in any three-month period. That single word - before - is what makes it interesting: while most SEC filings describe what already happened, Form 144 describes what an insider **intends to do**.
## Form 144 vs Form 4: the key difference
The two forms are companions, and the order matters:
| | Form 144 | Form 4 |
|---|---|---|
| When filed | **Before** the sale (notice of intent) | Within 2 business days **after** a trade |
| What it shows | A planned sale of restricted/control stock | An executed buy or sell |
| Signal type | Leading (intent) | Confirming (fact) |
| Who files | Affiliates selling under Rule 144 | All officers, directors, 10%+ owners |
A Form 144 tells you an insider has decided to sell. The matching Form 4 days later confirms the execution. Monitoring both sides gives you the full picture: planned selling pressure on one side, executed buying clusters on the other.
## Why traders and quants watch Form 144
- **It is a leading indicator.** The filing precedes the sale, sometimes by days. Spikes in planned selling by officers and directors have historically preceded weakness more often than routine diversification sales.
- **Size and role matter.** A CFO filing to sell $8M is a different signal than an early employee selling $60K of vested options. The filer's relationship to the company and the dollar value are both on the form.
- **It is underfollowed.** Form 4 gets all the attention; Form 144 filings are harder to parse (they arrive as structured XML inside EDGAR submissions) and most free tools ignore them entirely.
## What a Form 144 contains
Each filing includes the security and issuer, the number of shares and approximate dollar value of the planned sale, the seller's name and relationship to the issuer (officer, director, 10% owner), the acquisition history of the shares, and the broker handling the sale.
## How to monitor Form 144 filings automatically
Manually checking EDGAR for Form 144s does not scale - they are filed continuously and the interesting ones (senior officers, large dollar values) are buried between routine filings.
The [SEC Form 144 Insider Selling Monitor](/sec-form144-insider-selling.html) does this as a feed: give it tickers, and it returns each planned sale with the seller, their role, the dollar value parsed from the filing XML, and a score that weighs size and seniority - $0.20 per company, no subscription. Pair it with the [Form 4 buying-cluster scanner](/sec-form4-insider-buying.html) to see both sides of insider activity, or let an AI agent query both through the free [MCP server](/datasignals-mcp.html).
## FAQ
**Does a Form 144 guarantee the sale happens?** No. It is a notice of intent, valid for three months. Most planned sales execute, but some are postponed or cancelled.
**Is Form 144 selling always bearish?** No. Plenty of filings are routine diversification or 10b5-1 plan sales. That is why scoring by role and dollar value matters more than counting filings.
**Where is the raw data?** SEC EDGAR, free and public. The monitor links every result back to the original filing so you can verify it.
**Is this investment advice?** No - this is data for research, screening and monitoring. Historical patterns do not guarantee future results.
## Related
- [SEC Form 144 Insider Selling Monitor](/sec-form144-insider-selling.html)
- [SEC Form 4 Insider Trading: Buying Cluster Signal Scanner](/sec-form4-insider-buying.html)
- [13D vs 13G: activist stakes explained](/13d-vs-13g-explained.html)
---
# 13D vs 13G Explained: Activist Stakes, Item 4 and What the Filings Really Tell You
URL: https://datasignalslab.com/13d-vs-13g-explained.html
# 13D vs 13G Explained: Activist Stakes, Item 4 and What the Filings Really Tell You
When an investor crosses 5% ownership of a US public company, they must tell the SEC - and the form they choose is itself the signal. A **Schedule 13D** says "I have a plan for this company." A **Schedule 13G** says "I am just a large passive holder." Knowing the difference, and reading the right part of the filing, is what separates signal from noise.
## The difference in one table
| | Schedule 13D | Schedule 13G |
|---|---|---|
| Who files | Investors with **intent to influence** (activists) | Passive investors (index funds, banks) |
| Deadline | Within 5 business days of crossing 5% | Typically 45 days after quarter end |
| Contains intent? | **Yes - Item 4, Purpose of Transaction** | No |
| Typical filers | Elliott, Icahn, Starboard, RC Ventures | Vanguard, BlackRock, State Street |
| Market reaction | Often significant | Usually none |
A new 13D from a known activist is one of the strongest event-driven signals in equities. A 13G/A from an index fund rebalancing is routine noise. Treating them the same - as many raw EDGAR scrapers do - buries the signal.
## Item 4: the only part professionals read first
Every 13D must state, in the filer's own words, **why** they bought: Item 4, "Purpose of Transaction." This is where an activist writes that they believe the stock is undervalued, that they intend to seek board seats, push for a strategic review, demand capital returns, or oppose a merger. Amendments (13D/A) update Item 4 as the campaign evolves - escalations and retreats show up here first.
If you monitor one thing in a 13D, monitor Item 4.
## What to watch in practice
- **New 13Ds (not amendments) from named activists** - the strongest signal, especially with a clear Item 4 agenda.
- **13D/A amendments that change Item 4** - a campaign escalating (board fight) or resolving (settlement).
- **13G-to-13D switches** - a passive holder turning active, which has its own filing trigger.
- **Stake percent** - sits on the cover page; over 10% with an activist agenda means real influence.
## How to monitor 13D/G filings automatically
The [SEC 13D/G Activist Stake Monitor](/sec-13dg-activist-stakes.html) turns this into a feed: give it tickers and it returns every recent 13D/G with the filer identified, activist vs passive classified, inbound vs outbound direction, the stake percent where parseable, **the Item 4 purpose text extracted from every activist filing**, and a 0-100 impact score that ranks a fresh activist 13D far above routine passive amendments. Every record links back to the original filing on SEC.gov. $0.20 per company, no subscription - and AI agents can call it through the free [MCP server](/datasignals-mcp.html).
## FAQ
**Is every 13D an activist campaign?** No. Some 13Ds are filed for technical reasons (founders, insiders with board seats). The filer name plus Item 4 tells you which is which.
**How fast do 13Ds appear after a stake is built?** Within 5 business days of crossing 5% - though investors often build quietly up to the threshold first, then file.
**What is a 13G/A?** An amendment to a passive 13G, usually a routine position update. Scored lowest in the monitor for exactly that reason.
**Is this investment advice?** No - this is data for research, screening and monitoring. Historical patterns do not guarantee future results.
## Related
- [SEC 13D/G Activist Stake Monitor](/sec-13dg-activist-stakes.html)
- [Smart Money 13F: Hedge Fund Buys, Exits and Consensus](/smart-money-13f.html)
- [What is SEC Form 144? Insider selling explained](/what-is-sec-form-144.html)
---
# About DataSignals Lab
URL: https://datasignalslab.com/about.html
# About DataSignals Lab
DataSignals Lab is an independent data lab. We build focused, self-serve data products that turn official public sources like SEC EDGAR, ClinicalTrials.gov and the Apple App Store into transparent, decision-ready signals.
## What we believe
The value isn't in raw data. Anyone can scrape a filing, and that's a race to the bottom. The value is in the intelligence layer on top: the scoring, structuring and context that turn a pile of rows into an answer you can act on. Every product follows the same recipe: a clean public source, an analysis layer, and output you can verify.
## How we work
- **Official sources only.** SEC EDGAR, openFDA, ClinicalTrials.gov, USAspending.gov, US House Clerk, NIH RePORTER, CoinGecko and the Apple App Store. Free, legal, public, no personal data.
- **Transparent, not a black box.** Every score is explained, so you can reproduce it and trust it.
- **Verifiable.** Every result links back to the original filing or source.
- **Reliable.** Each product is monitored daily and built on stable, official APIs, not fragile scraping.
- **Self-serve.** Pay per result, buy a single report, or take one Pro plan for everything. No sales calls, no minimum commitment, cancel any time.
## Who it's for
Traders, quants, fintech and research teams, product and ASO teams, and developers building AI agents that need clean, real-time data as a tool.
## Our standard
We're honest about what this is: data for research, screening and monitoring, not investment advice. Historical patterns don't guarantee future results. We'd rather earn trust with verifiable methods than overpromise.
## Explore the products
See all DataSignals Lab products
---
# How to Read an SEC 8-K, and Which Items Actually Move a Stock
URL: https://datasignalslab.com/blog/how-to-read-an-sec-8-k-and-which-items-actually-move-a-stock/
Most company news reaches the public through a form almost nobody reads directly. It is the 8-K. Companies file it when something material happens between quarterly reports. The press release you see quoted in a headline is usually an exhibit attached to that filing.
The 8-K is a good form to learn because it is structured. Every 8-K is organized by numbered items. The item number tells you the category of event before you read a single sentence of prose. Once you know which numbers carry information and which are housekeeping, you can triage a day of filings quickly.
This article explains the mechanics. It is not investment advice.
## What an 8-K actually is
The 8-K is the current report. The 10-Q and 10-K are periodic. They arrive on a schedule. The 8-K arrives when an event triggers it.
The general deadline is four business days after the triggering event. There are exceptions. Some items have their own timing rules, and a Regulation FD disclosure has to go out on a schedule tied to the underlying disclosure rather than the four day window. The official list of every item, with the exact language for each trigger, is in the form itself on [sec.gov](https://www.sec.gov/files/form8-k.pdf).
Four business days is fast. That is worth holding in your head as a contrast with other disclosure regimes. Congressional stock trades run on a much slower clock, which we cover in [the 45-day rule and why it matters](/blog/the-45-day-rule-and-why-it-matters/). Institutional holdings run slower still, as explained in [13F deadlines and the 45-day lag](/blog/13f-deadlines-and-the-45-day-lag/). The 8-K is the fastest of the three by a wide margin.
## The item number is the first thing to read
Open any 8-K and you will see a header block, then one or more item numbers with titles. Everything after that is the disclosure.
The numbering is grouped by theme. The 1.x items cover contracts and business agreements. The 2.x items cover financial results and obligations. The 3.x items cover securities and listing status. The 4.x items cover accountants and financial statement reliability. The 5.x items cover governance and management. The 7.x item is Regulation FD. The 8.x item is everything else. The 9.x item is the exhibit list.
A single 8-K can carry several items at once. An earnings release is usually Item 2.02 plus Item 9.01, because the press release itself is attached as Exhibit 99.1.
## The items that usually carry information
**Item 1.01, Entry into a Material Definitive Agreement.** This is the contract item. Acquisitions, credit facilities, licensing deals, major supply agreements, and settlements all land here. The word "material" is doing real work. The company has decided this agreement matters enough to disclose outside the normal reporting cycle. Read the counterparty, the size, and the term. Then check Item 9.01 to see whether the actual agreement was filed as an exhibit, because the exhibit usually contains terms the summary skips.
**Item 2.02, Results of Operations and Financial Condition.** Earnings. This is the highest volume meaningful item because every reporting company files it four times a year. The 8-K body is often two sentences pointing at the attached press release. The information is in Exhibit 99.1, not in the item text.
**Item 5.02, Departure of Directors or Certain Officers.** Executive changes. This item has sub-parts and they are not equivalent. A CFO resignation reads very differently from a routine board appointment. The sub-part that gets the most attention is the one covering a director who resigns because of a disagreement with the company. Companies are required to describe the disagreement when that is the reason. That is rare and it is specific. Most 5.02 filings are ordinary succession, retirement, or a new compensation arrangement for an existing officer.
**Item 8.01, Other Events.** This is the catch-all, and that is exactly why it deserves attention. The company has decided to disclose something it considers worth telling the market, but the event does not fit a defined category. Buyback authorizations, litigation updates, clinical trial readouts, regulatory decisions, and operational disruptions often appear here. The item number tells you nothing about content. You have to read the text. An 8.01 is the one item where skimming the header is useless.
## Four more that deserve a second look
**Item 4.02, Non-Reliance on Previously Issued Financial Statements.** This means prior financials cannot be trusted and will likely be restated. It is uncommon and it is serious.
**Item 3.01, Notice of Delisting or Failure to Satisfy a Continued Listing Rule.** The exchange has told the company it is out of compliance. Often this is a minimum price or minimum market value issue.
**Item 1.05, Material Cybersecurity Incidents.** A newer item covering material cybersecurity incidents, with its own timing tied to the materiality determination rather than the incident date.
**Item 2.06, Material Impairments.** A write-down the company concluded it has to take. This tells you an asset or a business line did not perform as expected.
## The items that rarely move anything
Not every 8-K is news. Several items are compliance plumbing.
**Item 5.07, Submission of Matters to a Vote of Security Holders.** This is the annual meeting vote tally. The outcome is usually known in advance from the proxy.
**Item 5.03, Amendments to Articles of Incorporation or Bylaws.** Often a technical change, a state law update, or a fiscal year adjustment.
**Item 5.05, Amendment to the Code of Ethics.** Almost always administrative.
**Item 9.01, Financial Statements and Exhibits.** This is a list, not an event. It never appears alone in a way that means anything on its own.
**Item 3.02, Unregistered Sales of Equity Securities.** This one is genuinely mixed. For a large company it can be routine. For a small company it can signal dilution or a financing that changes the capital structure. Private placements often have a related Form D filed separately, and reading the two together gives you more than either alone. We walk through that form in [how to read a Form D filing](/blog/how-to-read-a-form-d-filing/).
## Filed versus furnished
This distinction confuses people and it is worth twenty seconds.
Items 2.02 and 7.01 are typically **furnished** rather than **filed**. Furnished information carries different liability treatment and is not automatically incorporated by reference into registration statements unless the company says so. The practical reading is that a company can put forward looking commentary in a furnished earnings release with a different legal posture than a filed disclosure. Check the cover page language. It will say explicitly whether the information is furnished.
## A two minute reading routine
1. Read the item numbers in the header. That is your category.
2. Read the date of the triggering event, not just the filing date. A company can file on day four.
3. Go straight to Item 9.01 and open Exhibit 99.1 if there is one. That is where earnings and most announcements actually live.
4. For Item 1.01, look for the agreement exhibit and read the economic terms.
5. For Item 5.02, identify the sub-part and the reason given.
6. For Item 8.01, read the whole thing. There is no shortcut.
7. Check whether the filing was amended later. An 8-K/A adds or corrects material.
## Free ways to read 8-Ks yourself
You do not need a paid terminal for any of this. EDGAR full-text search at [sec.gov/edgar/search](https://www.sec.gov/edgar/search/) lets you search the text of filings and filter by form type and date. The company browse page on EDGAR gives you a chronological filing history for any registrant. EDGAR also publishes daily and full index files, plus RSS feeds, so you can poll for new filings without scraping pages. Company investor relations pages usually mirror the same press releases.
Those primary sources are the ground truth. Anything a data product tells you should be traceable back to a filing you can open yourself. That principle applies across disclosure types, including legislative ones, which we cover in [how congressional trading disclosures work](/blog/how-congressional-trading-disclosures-work/).
## The honest limitation
Knowing which item numbers matter tells you where to look. It does not tell you what a filing means for price. Markets often price an event before the 8-K posts, because a press release can hit the wire first and the filing follows. Two identical item numbers at two different companies can carry completely different weight. The item number is a filter, not a conclusion.
The useful habit is comparison over time. One 8-K is an anecdote. A pattern of 4.02 filings, repeated 5.02 departures in a single function, or a run of 3.01 notices tells you something a single document cannot.
If you want to see the same primary-source discipline applied to a different disclosure stream, our [Congress Stock Trades report](/reports/congress.html) scores filed congressional transaction disclosures the same way, with every scored signal traceable back to the original filing you can open and verify yourself.
---
# Form 144 Explained: The Filing That Announces a Sale Before It Happens
URL: https://datasignalslab.com/blog/form-144-explained-the-filing-that-announces-a-sale-before-it-happens/
Most insider trading data people look at is backward looking. Someone sold, then they told the regulator, then the filing showed up in a feed. Form 144 is the odd one out. It is a notice that a sale is coming, filed at or around the moment the order is placed. That makes it interesting. It also makes it easy to misread.
This piece explains what Form 144 is, what triggers it, how it sits next to Form 4, and the specific things the notice does not guarantee.
## What Form 144 actually is
Form 144 is the notice of proposed sale required by Rule 144 under the Securities Act. Rule 144 is the safe harbor that lets holders of restricted or control securities resell them into the public market without registering the offering. The rule sets conditions. One of those conditions is that larger sales come with advance notice to the SEC.
The rule text lives at [17 CFR 230.144](https://www.ecfr.gov/current/title-17/part-230/section-230.144) on the official eCFR site. The notice requirement sits in paragraph (h). The blank form itself is published by the SEC at [sec.gov/files/form144.pdf](https://www.sec.gov/files/form144.pdf), and reading the blank form is a fast way to see exactly which fields a filer must supply.
Two terms matter here.
Restricted securities are shares acquired in an unregistered transaction. Private placement stock, founder shares, stock from an acquisition paid in equity. They carry a holding period before resale. Six months for companies that file reports with the SEC, one year for companies that do not.
Control securities are shares held by an affiliate. An affiliate is someone in a control relationship with the issuer. In practice that means executive officers, directors, and large holders. Control securities can be freely tradable shares bought on the open market. What makes them restricted in practice is who holds them, not how they were acquired.
An affiliate selling either kind of stock is relying on Rule 144. That is what pulls in the notice requirement.
## What triggers the notice
The notice is not required for every sale. Rule 144 sets a threshold based on a rolling three month period.
A Form 144 is required when the amount to be sold during any three month period exceeds 5,000 shares or units, or has an aggregate sale price greater than $50,000. Below both of those, no notice. Above either one, notice.
Those thresholds have not moved in a long time, which is worth sitting with. A $50,000 threshold catches a very large share of insider selling at any company with a meaningful stock price. That is part of why Form 144 volume is high and why most individual filings are unremarkable.
Two other Rule 144 conditions shape what you see on the form.
There is a volume cap. For equity securities of a reporting company, an affiliate may sell no more, in any three month period, than the greater of one percent of the outstanding shares of that class, or the average weekly reported trading volume over the four calendar weeks before the notice is filed. This is why the form asks for shares outstanding and for a record of sales in the past three months. The filer is showing their work against the cap.
There is a manner of sale condition. Affiliate sales generally have to run through routine brokers' transactions or directly with a market maker, not through solicited orders or special selling efforts.
## Form 144 versus Form 4
This is the distinction that trips people up, and it is the reason a Form 144 and a Form 4 can describe the same shares while telling you different things.
Form 4 is a Section 16 filing under the Securities Exchange Act. It is filed by officers, directors, and beneficial owners of more than ten percent of a registered class. It reports a transaction that already occurred, and it is due within two business days of the transaction date. Form 4 is a record. It states what was bought or sold, on what date, at what price, and what the person holds afterward.
Form 144 is a Securities Act filing. It is a notice of intent, filed concurrently with placing the sale order with a broker or with executing directly with a market maker. It states what the filer proposes to sell, an approximate date of sale, and the exchange where the sale is expected to happen.
So the two forms differ in three ways that matter.
Timing. Form 144 lands at the start of the process. Form 4 lands after execution.
Certainty. Form 144 describes a plan. Form 4 describes a completed fact.
Population. The two filer sets overlap heavily but are not identical. A large non-officer holder above ten percent files Form 4. An affiliate below ten percent who is not an officer or director may file Form 144 without a Section 16 obligation. And an officer selling a small amount may file Form 4 with no Form 144, because the sale fell under the 5,000 share and $50,000 thresholds.
The practical workflow for anyone reading these seriously is to treat Form 144 as the question and Form 4 as the answer. The notice tells you a sale is being set up. The Form 4 tells you what actually happened, and at what price.
## What the notice does not promise
This is the honest part, and it is the reason a Form 144 alone is a weak signal.
The notice is not a commitment to sell. A filer can file a Form 144 and then sell nothing. Nothing in the rule forces execution. There is no follow up filing that says the sale was cancelled. The notice simply expires unused.
The notice is not a report of price. The form gives an approximate date of sale and an amount. It does not give an execution price, because at the moment of filing there often is not one. Price comes from Form 4, if a Form 4 is required.
The amount is a ceiling, not an outcome. Filers routinely notice the maximum they might sell under the volume cap and then sell less. A 100,000 share notice can end as a 20,000 share Form 4.
"Before it happens" is approximate. The rule allows the notice to be filed concurrently with placing the order or with the execution itself. In practice many notices land the same day as the sale, not days ahead. Treating every Form 144 as advance warning overstates what the timing rule requires.
And a single filing is not a story. Affiliates sell for taxes, diversification, estate planning, divorce, charitable transfers, and scheduled compensation events. A notice tells you a sale is being arranged. It tells you nothing about why.
## Where 10b5-1 plans fit
Many affiliate sales run through a Rule 10b5-1 trading plan. The plan is adopted in advance, at a time when the person does not hold material non-public information, and it sets the trade schedule mechanically. The rule text is at [17 CFR 240.10b5-1](https://www.ecfr.gov/current/title-17/part-240/section-240.10b5-1).
Form 144 asks for the plan adoption date when a sale is made under a plan. That field is one of the more useful things on the form. A sale executed under a plan adopted many months earlier is a very different fact from a discretionary sale arranged this week. The 2022 amendments to Rule 10b5-1 added cooling off periods between plan adoption and the first trade, which widened that gap further for officers and directors. Form 4 also carries a checkbox indicating that a reported transaction was made under a plan intended to satisfy Rule 10b5-1(c).
If you only take one reading habit from this article, make it this one. Check the plan date before you interpret the sale.
## Where to find Form 144 filings for free
Since April 2023, Form 144 filings relating to securities of Exchange Act reporting companies have been submitted electronically on EDGAR. Before that, a large share arrived on paper and never entered the structured electronic record in a usable way. That history is why long historical Form 144 series are patchy and why analyses that stretch far back should be treated with care.
Everything current is free and public.
[EDGAR full text search](https://www.sec.gov/edgar/search/) will find Form 144 filings by issuer name, filer name, or form type. Browsing an issuer's EDGAR page and filtering by form type gives you the full sequence of notices for that company. The SEC's [forms index](https://www.sec.gov/forms) links the blank form and its instructions. Daily and quarterly EDGAR index files give bulk access if you want to build your own pipeline. No paid data source is required to read Form 144. Paid sources sell convenience, matching, and normalization, not access.
## How this compares to other disclosure regimes
The general lesson generalizes across disclosure types. Each regime has its own clock, and the clock defines what the data can and cannot tell you.
Congressional trade reports arrive after the fact under a filing deadline measured in weeks, which is covered in detail in [the 45-day rule and why it matters](/blog/the-45-day-rule-and-why-it-matters/). Institutional holdings arrive quarterly with a reporting lag, explained in [13F deadlines and the 45-day lag](/blog/13f-deadlines-and-the-45-day-lag/). If you want the end to end mechanics of the congressional side, see [how congressional trading disclosures work](/blog/how-congressional-trading-disclosures-work/).
Against that backdrop, Form 144 is unusual because it is the one common filing that points forward. That is genuinely valuable. It is also why it needs the most careful handling. A forward looking notice that carries no obligation to follow through is exactly the kind of data that rewards patient matching and punishes fast conclusions.
The useful method is boring. Pull the Form 144. Note the amount, the approximate date, and the plan adoption date. Wait for the Form 4. Compare noticed shares to executed shares. Over time, that comparison tells you something about how a particular insider behaves. A single notice, read alone, mostly tells you that a broker got a phone call.
None of this is investment advice. It is an explanation of how a public filing works and what it can support.
If you want to see the same discipline applied to a different disclosure stream, our [Congress Stock Trades report](/reports/congress.html) scores individual filings from official House and Senate disclosures, showing what was reported, when it was filed, and how much of the delay is structural rather than suspicious.
---
# The Biggest Startup Capital Raises — Week 30, 2026 (SEC Form D)
URL: https://datasignalslab.com/blog/biggest-startup-raises-week-30-2026/
Every week, companies quietly disclose fresh capital raises in **SEC Form D filings** — often days before (and frequently without) any press coverage. We pull the filings straight from EDGAR, filter out pooled investment funds (the hedge/PE/VC noise that makes up most Form D volume), and rank what is left: the real operating-company startup-funding signal.
**This window (July 23–24, 2026): 56 qualifying raises, $2.0B in disclosed capital.**
## Top 10 by amount raised
| # | Company | Sold to date | Industry | State | Score |
|---|---------|--------|----------|-------|-------|
| 1 | Groq LLC | $672.2M | Other Technology | California | 100 |
| 2 | Mastec INC | $484.4M | Other | Florida | 100 |
| 3 | Diamond Parent Holdings, Corp. | $275.2M | Other | NEW YORK | 89 |
| 4 | AI Software Holdings, LLC | $174.1M | Other Technology | Missouri | 83 |
| 5 | Empire Village At Cottonwood Creek & Golden Triangle, LLC | $67.7M | Residential | Arizona | 76 |
| 6 | Empire Village At Bronco Trail, LLC | $49.4M | Residential | Arizona | 69 |
| 7 | Great Sky Inc. | $25.9M | Other Technology | Colorado | 83 |
| 8 | Echoiq Ltd | $23.8M | Other Technology | Australia | 75 |
| 9 | Istari Digital, Inc. | $22.6M | Other Technology | Virginia | 81 |
| 10 | Meridianmade, Inc. | $20.3M | Other Health Care | NEW YORK | 75 |
Score = size × freshness × type (0–100). Every row in the full report links to the original SEC filing.
## The headline raise: Groq LLC
Groq LLC reported **$672.2M** raised across 144 investors (Other Technology, California). Disclosed executives / related persons include Adam Winter, John Yetimoglu, Alexander Davis. As always with Form D: amounts are self-reported by the issuer, and a filing is a disclosure — not an endorsement.
## Get the full picture
- **[The full ranked report — $19, refreshes daily](/reports.html)**: every qualifying raise with amounts, executives, securities type and direct SEC links. One-off purchase, your link always opens the latest edition.
- **Live data, any day or sector**: the underlying tool is self-serve on Apify at [$0.20 per result](https://apify.com/datasignalslab/startup-funding-form-d-monitor?fpr=wnlxst), and free to call for AI agents via our [MCP server](/datasignals-mcp.html).
*Data source: official SEC EDGAR Form D. This is data and research, not investment advice.*
---
# What 13F Filings Hide: Shorts, Options and Non-US Listings
URL: https://datasignalslab.com/blog/what-13f-filings-hide-shorts-options-and-non-us-listings/
Every quarter, a wave of headlines announces that a famous investor "loaded up" on one stock and "dumped" another. The source is almost always a Form 13F. The problem is that a 13F is not a portfolio. It is a slice of a portfolio, defined by a specific rule, filed on a delay, and missing entire categories of exposure.
This article explains what the form actually covers, what it leaves out, and how the gaps produce confident readings that turn out to be wrong. Nothing here is investment advice.
## What a 13F actually is
Section 13(f) of the Securities Exchange Act requires institutional investment managers who exercise discretion over at least 100 million dollars in certain US-listed equity securities to file a quarterly report. The filing lists positions as of the last day of the calendar quarter. It is due within 45 days after quarter end.
Two details in that sentence do most of the damage to casual interpretation.
First, "certain" securities. The scope is not "everything the manager owns." It is limited to a published list of section 13(f) securities, which the SEC updates quarterly. The [SEC's official 13F information page](https://www.sec.gov/divisions/investment/13ffaq.htm) sets out the scope and the mechanics in plain terms.
Second, "as of the last day." A 13F is a snapshot, not a film. A manager can buy a position on the second day of the quarter, sell it on the eighty-eighth day, and the filing will never show it existed. Another can build a position on the final trading day and it appears as though it were a settled conviction.
## The first gap: short positions
This is the single largest source of misreading. A 13F reports long positions in covered securities. It does not report short positions.
Think about what that means for a manager who runs a hedged book. Suppose a fund holds a large long position in an airline and an equally large short position in a competitor, betting on the spread between them rather than the direction of the sector. The 13F shows a big airline holding. It shows nothing else. A reader concludes the fund is bullish on air travel. The fund may be close to market neutral.
The same distortion appears with pairs inside a single company's capital structure, with index hedges, and with sector hedges placed through instruments that fall outside the reporting scope entirely.
The SEC has moved toward more short-sale transparency at the aggregate level through Rule 13f-2 and Form SHO, which requires certain managers to report gross short positions to the Commission, with the SEC publishing aggregated data rather than individual manager detail. That is a real improvement for market-level understanding. It does not let you reconstruct any single manager's net exposure from public filings. The asymmetry stands. Longs are named. Shorts are not.
## The second gap: options and derivatives
Options are reported inconsistently, and that inconsistency is baked into the form.
Puts and calls on covered securities are reportable, and filers indicate the position type in the filing. But the way managers convert option exposure into a reported value varies. Some report notional value of the underlying shares. Some report market value of the contracts. Those two numbers can differ by an order of magnitude. A reader who ranks holdings by reported value can end up with a completely inverted picture of what the manager cares about.
Worse, the reported value tells you nothing about strike or expiry. A put position could be a cheap tail hedge expiring in three weeks, far out of the money, costing very little. It could also be a deep in the money position that functions as a synthetic short. Both appear as a line item with a dollar figure. The economic meaning is entirely different.
Then there is everything that is not an exchange-listed option. Total return swaps, contracts for difference, structured notes and other over the counter arrangements can create equity exposure without triggering 13F reporting of the underlying. A fund can hold meaningful economic exposure to a company and file a 13F that never mentions it.
## The third gap: non-US listings and other asset classes
The covered universe is US-listed equity securities and certain related instruments. That leaves out a great deal.
A manager with a third of the book in Tokyo, London or Sao Paulo listings will show none of it. The 13F will present the US sleeve as if it were the whole fund. Concentration ratios computed from that sleeve are therefore meaningless as a description of the fund. If the US sleeve is 300 million dollars and one position is 90 million, the filing implies a 30 percent concentration. If the total book is 3 billion dollars, the real figure is 3 percent.
Also absent: sovereign and corporate bonds, bank loans, private equity and venture positions, real assets, commodities, currencies, and cash. A fund sitting on a large cash balance and a defensive posture can look aggressively invested, because cash simply is not on the form.
American Depositary Receipts complicate this further. Some foreign companies trade in the US as ADRs, which can be covered. So a manager might hold a European bank through local shares in one account and through ADRs in another. Only the second shows up. The filing then understates the position and misstates the geography.
## The fourth gap: who is filing, and confidential treatment
A single asset manager may file across multiple legal entities. Some file combined reports, some file separately, and some appear on another filer's report through arrangements set out in the instructions. Aggregating "the firm" from public filings requires knowing which entities belong together, and that mapping is not always obvious from the documents themselves.
Managers can also request confidential treatment for specific holdings, typically while building a position, under the standards described in the SEC's guidance. If granted, those holdings are omitted from the public filing and disclosed later. So even within the covered universe, the public version can be incomplete at the moment you read it.
## Two examples of how portfolios get misread
**The concentration story.** A filing shows a manager with 40 percent of reported value in one technology name. The narrative writes itself. In reality the manager runs a global book, the US sleeve is a minority of assets, and the position is hedged with an index short that never appears. The honest statement is narrow: within the covered US long sleeve, this name was the largest line on the quarter-end date.
**The exit story.** A position disappears between two quarters. Headlines say the manager sold out. The alternatives are numerous and unfalsifiable from the filing alone. The position may have been converted to option exposure. The company may have been acquired or delisted. The shares may have moved to an entity that files separately. Or the manager may indeed have sold. The filing does not distinguish between these.
## How to read 13F data honestly
The form is still useful. It is a genuine, legally required disclosure of real positions, filed under penalty of law, and it is free. The discipline is in what you claim from it.
Treat it as directional evidence about the long US equity sleeve, on one date, up to 45 days stale. Look at changes across many filers rather than one, because dispersed agreement is more informative than any single manager's line. Cross-check against Schedule 13D and 13G filings, which trigger at the 5 percent ownership threshold and often arrive faster than the quarterly cycle. Read Form 4 insider filings for the company's own officers and directors.
The primary sources are open to anyone. [EDGAR full-text search](https://efts.sec.gov/LATEST/search-index?q=%22form%2013f%22) and the standard EDGAR company search let you pull any filer's raw documents at no cost. Several free aggregators also republish 13F data, and the SEC publishes structured quarterly data sets. If you want to verify a number, the filing itself is the place to go, and it costs nothing.
The value of a scoring layer is not access to the data. It is consistency: applying the same normalization to option values, the same entity mapping across related filers, and the same treatment of quarter-over-quarter changes, so that comparisons across managers mean something. What no layer can do is invent the missing shorts, the missing swaps, or the missing Tokyo listings. Anyone who tells you otherwise is selling certainty that the filings do not contain.
If you want to see 13F data presented with those limits stated rather than hidden, our [Smart-Money 13F Consensus report](/reports/13f.html) tracks where multiple institutional filers overlap on the same names, scores the strength of that agreement, and shows the filing dates so you always know how old the snapshot is.
---
# Congress Trading Around Earnings Season: What Disclosure Timing Really Tells You
URL: https://datasignalslab.com/blog/congress-trading-around-earnings-season-what-disclosure-timing-really-/
Every quarter the same story appears. A member of Congress bought shares. Weeks later the company reported earnings. The stock jumped. The headline writes itself.
The problem is that the headline is built on two dates that do not mean what most readers assume. Understanding those two dates is the difference between a real signal and a coincidence dressed up as one. This article walks through the mechanics of disclosure timing around earnings season, what it can support as evidence, and where the honest limits are.
## Two clocks, not one
A congressional trade produces two timestamps. They are separated by a gap that the law permits to be large.
The first is the transaction date. That is when the trade actually happened.
The second is the filing date. That is when the Periodic Transaction Report, usually called a PTR, was submitted and became public.
The STOCK Act of 2012 sets the rule. You can read the bill text on [congress.gov](https://www.congress.gov/bill/112th-congress/senate-bill/2038). A member must file within 30 days of becoming aware of a transaction, and in no case later than 45 days after the transaction itself. The threshold is transactions above $1,000, and it covers the member, a spouse, and dependent children.
That 45-day ceiling is the whole story for earnings analysis. A typical company reports earnings roughly every 90 days. A disclosure window of up to 45 days swallows half a quarter. A trade made three weeks before an earnings release can legally surface three weeks after it.
So when you read that a member "bought before earnings," you are almost always reading a reconstruction. The public did not know at the time. The public learned later and drew a line backwards through two points.
## What "before earnings" actually means as a claim
There is nothing inherently improper about trading before an earnings date. Members of Congress are not corporate insiders of the companies they hold. They are not subject to the issuer blackout policies that bind a company's own executives. There is no rule that says a senator must stop trading Apple stock in the two weeks before Apple reports.
An earnings date is also public information. Companies announce them in advance. Anyone can look them up for free. A trade timed around a known public date is not evidence of anything by itself.
The claim that would matter is narrower. It is that the member had material nonpublic information about that specific company, and traded on it. The STOCK Act made explicit that members owe a duty of trust and are not exempt from federal securities law, including the prohibition on insider trading.
Notice what disclosure timing can and cannot do for that claim.
It can establish sequence. Trade happened on this date. Event happened on that date. That is real and checkable.
It cannot establish knowledge. A PTR contains no reason, no rationale, and no source of information. It has a date, a ticker, a transaction type, and a dollar range.
Sequence without knowledge is a starting point for a question. It is not an answer.
## The base rate problem
This is the part most coverage skips, and it is the part that changes conclusions.
Earnings season is not a rare window. Roughly four times a year, most of the S&P 500 reports within a compressed six-week span. Depending on how you define proximity, a large share of the calendar sits near somebody's earnings date.
Congressional trading volume is also not evenly spread. Filing activity clusters. Members with active portfolios trade in bursts, often around portfolio rebalancing, tax dates, or the end of a reporting period.
Put those together. If trades cluster and earnings cluster, then trades near earnings will occur frequently for reasons that have nothing to do with information. Before you treat proximity as meaningful, you need to know what proximity would look like under pure chance.
The practical test is straightforward. Build the distribution of days between transaction date and the nearest earnings date across all congressional trades. Then compare a specific member or a specific trade against that baseline. If the whole population sits at a median of, say, a few weeks from an earnings date, then a single trade sitting a few weeks out is unremarkable.
Any analysis that reports the interesting cases without reporting the base rate is selecting on the outcome. That is the most common analytical error in this entire field.
## Where an information advantage would plausibly come from
If you are looking for a mechanism, earnings is a weak candidate. Members of Congress do not sit in on corporate earnings preparation. Regulation FD, which the SEC administers, restricts selective disclosure by issuers to outside parties. The SEC's own overview of [Regulation FD](https://www.sec.gov/rules/final/33-7881.htm) sets out the framework. Companies that disclose material information selectively must make it public promptly.
The more plausible channels run through government activity, not company activity.
Committee work touches regulated industries before rules become public. Appropriations decisions move defense and infrastructure contractors. Health committee work sits close to drug and device policy. Briefings on macroeconomic or geopolitical conditions can precede market moves across whole sectors.
None of that is earnings information. It is policy information. And policy information often shows up in the market through a different door, such as a contract award, a rule publication, or a company's own Form 8-K. The SEC's [Form 8-K guidance](https://www.sec.gov/answers/form8k.htm) describes the material events a company must report between quarterly filings.
So if you want to hunt for information advantage in congressional trades, aligning trades against committee assignments and policy calendars is a stronger design than aligning them against earnings dates. Earnings is the event everyone can see. Policy timing is the event fewer people track.
## Reading the fields you actually get
A PTR gives you less precision than the headlines suggest.
Amounts come in ranges, not exact figures. The lowest band starts at $1,001 and runs to $15,000. Larger bands follow. A trade at the top of a band and a trade at the bottom look identical in the data.
Owner codes matter. Many disclosed trades belong to a spouse or a dependent child, or sit inside an account the member does not direct. Attributing intent to the member for every filing under their name overstates what the document says.
Asset types vary. Some filings cover broad funds or bonds rather than single stocks. A diversified fund purchase carries no company-specific information at all.
Amendments exist. Filings get corrected. A trade you analyzed in March may carry a different date or amount after a later amendment.
## Doing this yourself, for free
You do not need a paid product to check any of this.
The House publishes PTRs through the Clerk's portal at [disclosures-clerk.house.gov](https://disclosures-clerk.house.gov/). The Senate publishes through its electronic financial disclosure system at [efdsearch.senate.gov](https://efdsearch.senate.gov/search/). Both are free and require no account, though the Senate system asks you to accept an access agreement.
For the other side of the comparison, company filings are free on the SEC's [EDGAR full-text search](https://www.sec.gov/cgi-bin/srqsb?text=form-type%3D8-K). Earnings dates and 8-K filings are public the moment they land. Investor relations pages publish scheduled reporting dates in advance.
Several open-source projects and volunteer-maintained datasets also mirror congressional disclosure data in machine-readable form. If your goal is a one-off check on a single member, the official portals are enough. If your goal is a full historical distribution, a mirrored dataset saves considerable parsing work.
## An honest summary of what timing proves
Disclosure timing is good evidence of one thing. It tells you how stale a signal is by the time you see it. That is genuinely useful, and it is underrated.
Disclosure timing is weak evidence of information advantage. The gap between trade and filing means proximity to earnings is almost always discovered after the fact. Earnings dates are public. Trades cluster. Earnings cluster. Coincidence is cheap.
The strongest version of an information advantage argument needs more than two dates. It needs a plausible channel, a base rate to compare against, and consistency across many trades rather than one memorable example. Academic work on congressional returns has produced mixed results, and reasonable researchers disagree on whether an edge survives risk adjustment.
None of this is investment advice. Treat any pattern you find in disclosure data as a hypothesis you still have to test.
If you want the timing math done for you, our Congress Stock Trades report tracks new PTR filings, records both the transaction date and the disclosure date, and scores each trade with the filing lag included, so you can see how old a signal is before you act on it. Browse the current filings on the [Congress Stock Trades report](/reports/congress.html).
---
# What Happened to STOCK Act Enforcement: Fines, Late Filings, and Why the $200 Penalty Shapes Behavior
URL: https://datasignalslab.com/blog/what-happened-to-stock-act-enforcement-fines-late-filings-and-why-the-/
# What Happened to STOCK Act Enforcement: Fines, Late Filings, and Why the $200 Penalty Shapes Behavior
The STOCK Act was supposed to fix a trust problem. Members of Congress trade stocks. Members of Congress also write laws, sit in briefings, and hear things before the public does. The law that addressed this passed in 2012. It is called the Stop Trading on Congressional Knowledge Act, and you can read the enacted text at [congress.gov](https://www.congress.gov/bill/112th-congress/senate-bill/2038). What most people miss is the gap between what the law requires and what happens when someone ignores it. The requirement is strict. The enforcement is thin. The center of that thinness is a flat $200 fee.
This article explains how enforcement actually works, why the penalty is so small, and how a small penalty ends up shaping behavior. None of this is investment advice. It is a look at the mechanics of a disclosure system.
## What the law actually requires
The core rule is simple. When a member of Congress, a spouse, or a dependent child buys or sells a covered security, the member must report it. The report is called a Periodic Transaction Report, or PTR. The deadline is 45 days from the transaction. House filings go to the Clerk of the House and are public at [disclosures-clerk.house.gov](https://disclosures-clerk.house.gov). Senate filings go to the Senate electronic system.
The report does not ask for a precise dollar figure. It asks for a range. It asks for the ticker, the type of transaction, the trade date, and the bracket the amount falls into. So the disclosure tells you that something happened and roughly how big it was. It does not tell you the exact size, and it arrives up to 45 days after the fact.
That 45-day window matters for enforcement. The clock does not start when the trade is disclosed. It starts when the trade is made. Late means late against the trade date.
## Who enforces it
There is no market regulator policing this. The Securities and Exchange Commission does not fine members for late PTRs. Enforcement sits inside Congress itself.
In the House, that job belongs to the Committee on Ethics. You can see its role and guidance at [ethics.house.gov](https://ethics.house.gov). In the Senate, the Select Committee on Ethics handles it. These are committees of members judging other members. That structure is the first clue about how hard the rules bite. A body enforcing rules on its own colleagues rarely reaches for the harshest tool it has.
The main tool it has is the late-filing fee.
## The $200 fee, explained
When a report is filed late, the reporting individual can be assessed a fee of $200. That is the standard figure written into the framework. It is flat. It does not scale with the size of the trade. It does not scale with how late the report is. A member who is one week late on a small trade and a member who is many months late on a large trade face the same starting number.
The fee is also not automatic in practice. The committees can assess it. They can also waive it. Waivers happen when a member argues the delay was reasonable or the filing error was minor. So the real penalty for a late report ranges from nothing to $200. That is the ceiling for the routine case.
Compare that to the trades being reported. Disclosed transactions often fall in brackets that reach into the tens or hundreds of thousands of dollars. A $200 fee against a six-figure position is not a deterrent in any normal sense. It is closer to a filing cost.
## Why a flat fee is a design choice, not an accident
It is tempting to call the $200 fee a loophole. It is more accurate to call it a design. The STOCK Act was built to create disclosure, not to punish trading. The theory was that sunlight would do the work. If every trade is public within 45 days, then voters, journalists, and researchers can watch. The penalty was never meant to be the deterrent. The visibility was.
That theory has a weakness. Sunlight only disciplines behavior if someone is watching and if being watched carries a cost. For a member in a safe seat, a late filing story rarely changes an election. So the two forces that were supposed to enforce the law, a small fee and public attention, both turn out to be soft. The fee is soft by design. The attention is soft by circumstance.
## How a small penalty shapes behavior
Here is the part that matters if you read these filings. A penalty this size does not stop trading. It sorts the population of filers into rough groups.
Most members file on time. They treat the 45-day rule as a routine compliance task, and their staff handles it. For this group the fee is irrelevant because they never trigger it.
A second group files late and pays. For them the $200 is a cost of doing business. It is small enough to absorb and small enough to forget. The late filing gets logged, the fee gets paid or waived, and the record moves on. The lateness itself becomes information. A pattern of repeated late filings tells you something about how carefully a member treats the disclosure duty.
A third effect is subtler. Because the fee does not scale, the incentive to be precise or early is weak across the board. There is no reward for filing in five days instead of forty. There is no extra pain for filing in fifty days instead of forty-six, beyond the same flat fee. The structure flattens the incentive to be prompt. It asks only that you land inside a wide window, and it charges a small toll if you miss.
## What this means for reading the data
If you use these disclosures, treat late filings as a feature of the record, not a defect. The filing date and the trade date are both public. The distance between them is a signal in itself. A trade reported on day 44 is legal and on time. A trade reported six months later is late, and the record shows it.
Because the penalty is weak, the discipline has to come from the reader. The honest way to use congressional trading data is to read the ranges as ranges, read the dates as two separate dates, and treat any single dollar total as an estimate built on brackets. No tracker can give you a precise figure the filing does not contain. Anyone who shows you an exact number chose a rule for collapsing the range.
You do not need a paid tool to start. The primary sources are free and official. The House Clerk site and the Senate electronic filing system both publish the raw reports. A scored or searchable layer on top of them is a convenience, not a requirement. If you only want to check one member or one trade, go straight to the official portals.
## The short version
The STOCK Act requires disclosure within 45 days. Enforcement lives inside the ethics committees, not with a market regulator. The main penalty is a flat $200 late-filing fee that can be waived and does not scale with the trade. That design means the law produces a lot of data and very little punishment. The value is in the record it creates, not in the fear it inspires. Read the dates, read the ranges, and let the timing tell you what the fee never will.
If you want to track these disclosures as scored signals instead of raw PDFs, start with the [Congress Stock Trades report](/reports/congress.html), which turns each Periodic Transaction Report into a searchable, dated entry you can scan in minutes.
---
# The Biggest Congress Trading Myths, Tested Against Filings
URL: https://datasignalslab.com/blog/the-biggest-congress-trading-myths-tested-against-filings/
Congressional stock trading attracts strong claims. Some are true. Many are half true. A few are simply wrong, and they get repeated because they sound right. The useful part of this subject is that most of it can be checked. Every covered trade a member of Congress makes is disclosed under the STOCK Act of 2012 ([Pub. L. 112-105](https://www.congress.gov/bill/112th-congress/senate-bill/2038)). The paperwork is filed with the [House Clerk](https://disclosures-clerk.house.gov) or the Senate electronic system and published for anyone to read. So we can test the popular claims against the actual filings instead of against vibes.
Here are five of the biggest myths, checked against how the disclosure system really works.
## Myth 1: Every member of Congress beats the market
This is the headline that built the whole genre. A few members post eye-catching returns in a given year, a screenshot goes viral, and the claim quietly expands to "Congress" as a group.
The filings do not support the group version. What the disclosures actually contain is a list of transactions: a security, a buy or sell direction, a date, and a dollar range. They do not contain a return figure. Any performance number you see is a calculation made after the fact by whoever built the tracker, using assumptions about entry price, exit price, and holding period. Change the assumptions and the number changes.
The record also shows enormous variation between members. Some trade constantly. Many barely trade at all, or hold nothing but index funds and Treasury products. A small number of active, well-timed traders can dominate a "best of Congress" list while telling you nothing about the median member. When you read the raw Periodic Transaction Reports rather than a leaderboard, the picture is a wide spread of behavior, not a uniform edge.
## Myth 2: You can copy their trades in real time
The appeal of copy-trading rests on speed. The law is built to prevent exactly that speed.
The STOCK Act gives members a reporting window, not an instant feed. A covered transaction must be reported within 30 days of the member becoming aware of it, and no later than 45 days after the trade date. That means the fastest you can legally learn about a trade is often weeks after it happened, and sometimes closer to a month and a half. By the time a filing appears, the price that prompted it may be long gone.
Late filings widen the gap further. The penalty structure for a late report is modest, so some filings land well past the 45-day mark. When you see a trade "today," you are usually seeing a disclosure published today about an order placed weeks earlier. Treating that as a real-time signal misreads what the timestamp means. The filing date and the trade date are different fields, and the difference is the whole story.
## Myth 3: Members of Congress are already banned from trading stocks
Proposals to restrict or ban individual stock trading by members have been introduced repeatedly, and coverage of those bills often blurs into "they passed it."
They have not. As a matter of current law, members of Congress may still trade individual securities. The binding rule is disclosure, not prohibition. The STOCK Act requires reporting and affirms that members are subject to the same insider trading laws the [SEC](https://www.sec.gov) enforces for everyone else, but it does not forbid a member from owning or trading a company's stock. You can confirm the status of any specific reform bill directly on [congress.gov](https://www.congress.gov) by searching the bill and reading its actions history rather than a headline about it.
This distinction matters for how you read the data. The system is designed around transparency after the fact. It is not designed to stop a trade before it happens.
## Myth 4: The dollar amounts in the filings are precise
People screenshot a trade and write "bought $500,000 of this stock." The filings almost never say that.
Transactions are reported in ranges, not exact figures. The lowest band covers roughly $1,001 to $15,000, and the bands climb from there into much wider brackets at the top. A single reported line could represent anything inside its band. Sum a member's activity across many filings and you inherit all of that imprecision at once, because every underlying number is a range and not a point.
This has a direct consequence for any "total value traded" claim. Aggregation forces a choice: use the low end of each range, the high end, or the midpoint. Each choice produces a different total, and none of them is the true number, because the true number was never disclosed. When a tracker reports a precise dollar figure, it has silently picked one of those methods. Reading the original report shows you the band and reminds you what is actually known. Trades under the $1,000 threshold need not be reported at all, so the very smallest activity is invisible by design.
## Myth 5: A filing tells you the member personally made the decision
The name on the report is a member of Congress, so the assumption is that the member studied a company and pushed the button. The disclosure rules tell a more complicated story.
Reporting covers the member, the member's spouse, and dependent children. A large share of filed transactions belong to a spouse, not the lawmaker. Some sit inside accounts managed by a third party under an arrangement where the member does not direct individual trades. The report captures the transaction and who it is attributable to. It does not certify who chose it or why. Reading intent into a single line is the most common analytical error in this whole field.
None of this means the data is useless. It means the honest read is narrow. A filing is strong evidence that a transaction occurred, within a value range, attributable to a household, disclosed within a legal window. Everything beyond that, including skill, timing intent, and motive, is interpretation layered on top.
## How to check any claim yourself
The pattern across all five myths is the same. The confident version of the claim adds precision the filing never contained: an exact return, a real-time timestamp, an exact dollar amount, a personal decision. The filing gives you a security, a direction, a date range, a value band, and an attribution. Stay inside those five facts and you are on solid ground. Step outside them and you are guessing.
You do not need a paid service to see this. The primary sources are free and official. House filings live at the [House Clerk disclosure site](https://disclosures-clerk.house.gov), Senate filings at the Senate electronic financial disclosure system, and the governing law is on [congress.gov](https://www.congress.gov). Reading one raw report end to end teaches you more about the limits of this data than any leaderboard will. This article is educational and is not investment advice.
If you want the raw filings turned into structured, scored signals with the trade date and filing date kept separate and value ranges preserved rather than flattened into fake precision, see our [Congress Stock Trades report](/reports/congress.html) for a source-linked view built on the official disclosure record.
---
# Options in Congressional Disclosures: How to Read Calls, Puts, and Exercise Rows
URL: https://datasignalslab.com/blog/options-in-congressional-disclosures-how-to-read-calls-puts-and-exerci/
Most congressional trading coverage treats every filing as a stock trade. Buy means bullish, sell means bearish, and the story writes itself. Options break that shortcut. A member can file a purchase that is a bet on a stock falling, or a sale that leaves them more exposed to a stock rising. If you read an options row the way you read a common-stock row, you will often get the direction backwards.
Options are covered securities, so they show up in Periodic Transaction Reports the same way stocks do. The STOCK Act of 2012, [Public Law 112-105](https://www.congress.gov/bill/112th-congress/senate-bill/2038), requires disclosure of covered securities transactions over $1,000 by a member, their spouse, or a dependent child. That includes options. The filings are published by the [Clerk of the House](https://disclosures-clerk.house.gov) and by the Senate's electronic system. This guide walks through what an options row actually tells you, and what it does not.
## What an option is, in one paragraph
An option is a contract. A call gives the holder the right to buy an underlying security at a set price, called the strike, before or at expiration. A put gives the holder the right to sell at the strike. There are two sides to every contract. The buyer pays a premium and holds the right. The seller, also called the writer, receives the premium and takes on the obligation. Direction comes from the combination of which type of contract it is and which side of it the filer took. That is the part a stock-only reading misses.
## The four base positions
Four combinations cover most of what you will see in a PTR. Hold them in mind before reading any options row.
**Buying a call** is a bullish position. The holder profits if the underlying rises above the strike by enough to cover the premium.
**Buying a put** is a bearish position. The holder profits if the underlying falls below the strike. This is the one that trips people up most. The transaction code says purchase, but the position is a bet on a decline.
**Selling a call** is neutral to bearish. If the filer already owns the stock, this is a covered call, a way to collect premium and cap upside. If they do not own it, it is a naked call, a direct bet against the stock.
**Selling a put** is neutral to bullish. The writer collects premium and takes on the obligation to buy the stock at the strike if it falls. It is often used to generate income or to set a target entry price.
So "purchase" is not automatically bullish, and "sale" is not automatically bearish. You need the contract type and the side together.
## Where the details live in the row
Options land in the same transaction table as everything else. The columns behave the same, but two of them carry extra weight.
**Asset.** For an option, the asset name usually describes the whole contract. You should expect to see the underlying company, the word call or put, a strike price, and an expiration date. Formats vary by filer and by whether the report was e-filed or scanned, so the same contract can be written several ways. Read this field carefully, because the single word call or put changes the entire meaning of the row.
**Transaction type.** Still a single letter in most filings. P for purchase, S for sale, often S (partial) for a partial sale, and E for an exchange. On an options row, this letter tells you which side of the contract the filer took, buyer or seller. It does not, on its own, tell you whether the trade opened a new position or closed an existing one. That is the next problem.
## Open versus close, the ambiguity the codes hide
A stock purchase and a stock sale are roughly symmetric. An options purchase or sale is not, because it can either open a position or close one, and the PTR code often does not distinguish the two.
Consider a sale of a call. It could be a filer writing a fresh covered call against stock they hold, which is a new short-call position. It could also be a filer closing out a long call they bought earlier, which simply exits a bullish bet. Same letter, opposite meaning for the portfolio. The filing rarely spells out which one it is. Some filers add a note in a description field, but many do not.
The honest way to read this is to treat a single options row as incomplete on its own. Look for the matching trade. A call bought in March and sold in June is most likely one bullish position opened and then closed. A call sold with no prior purchase on record, against a stock the member is known to hold, is more likely a covered write. You are reconstructing the position from the sequence, not reading it off one line.
## Exercise and assignment rows
Options can end in more ways than a stock trade. They can expire, be closed by an offsetting trade, be exercised by the holder, or be assigned to the writer. Exercise and assignment often appear as their own rows, and they are easy to misread.
When a filer exercises a call, they use the contract to buy the underlying stock at the strike. You may see this reported as an exchange, coded E, or as a linked pair of entries, one retiring the option and one showing the resulting stock. The economic result is that the option is gone and a stock position has appeared. If you count the exercise as a fresh, independent stock purchase at market, you overstate new buying and misread the timing, because the price was fixed by the strike, not by that day's market.
Assignment is the mirror image on the writer's side. A short put that gets assigned turns into a stock purchase at the strike. A short call that gets assigned turns into a stock sale at the strike. Again, the row may look like a plain stock trade, but it is the mechanical result of an option the filer sold earlier, not a fresh decision made that day.
## Premium is not exposure
One more trap sits in the amount column. The disclosed dollar range on an options row is a range for the transaction itself, which for a bought option is the premium paid. The premium is usually far smaller than the value of the stock the contract controls. A modest premium in the $1,001 to $15,000 bracket can represent exposure to a much larger notional amount of stock, depending on the strike and the size of the position.
This cuts both ways. Judging an options trade as small because the disclosed bracket is small can understate the real bet. Treating the premium as if it were the stock position overstates nothing but confuses the type of risk. The amount tells you what changed hands, not how much market exposure the contract carries.
## A short checklist for options rows
- Read the asset field for the word call or put before anything else.
- Combine the contract type with the transaction code to get direction, not the code alone.
- Remember that a purchase can be bearish, a put, and a sale can be bullish, a written put.
- Do not assume a row opens a position. Look for the matching trade to tell open from close.
- Treat exercise and assignment rows as the result of an earlier option, not a fresh market trade.
- Read the amount as premium or transaction value, not as market exposure.
You can verify any of this against the primary record yourself. House filings are free to search at the [Clerk of the House disclosure site](https://disclosures-clerk.house.gov), and the underlying law and its reporting rules are on [congress.gov](https://www.congress.gov). Reading a handful of real options rows against these four base positions is the fastest way to stop misreading direction.
*The [Congress Stock Trades Report](/reports/congress.html) parses these filings, including options rows, into one scored and ranked document where every trade links back to the official PDF so you can check the direction yourself. [Get the free preview](https://datasignalslab.lemonsqueezy.com/checkout/buy/2002ea9c-0e57-4e3d-ab50-3dc12a865d07).*
*DataSignals Lab publishes data and research. This is not investment advice.*
---
# How Fast Are Congress Trades Disclosed? Measuring the Real Filing Lag
URL: https://datasignalslab.com/blog/how-fast-are-congress-trades-disclosed-measuring-the-real-filing-lag/
You see a headline. A senator bought shares in a defense contractor. The story spreads fast. Then you check the fine print. The trade happened weeks ago. The stock already moved. This gap between trade and disclosure is the single most important thing to understand before you follow congressional trades. This article explains where the gap comes from, how large the law allows it to be, and how you can measure the real lag yourself using the public filings.
## The rule: 45 days is a ceiling, not a promise
The legal framework comes from the STOCK Act of 2012. The full name is the Stop Trading on Congressional Knowledge Act. You can read the bill text on [congress.gov](https://www.congress.gov/bill/112th-congress/senate-bill/2038). Before the STOCK Act, members of Congress only disclosed their holdings once a year. After it, they must file a Periodic Transaction Report, usually called a PTR, for individual trades.
The timing rule has two parts. A member must file within 30 days of becoming aware of a transaction. And in no case later than 45 days after the transaction itself. The rule applies to purchases, sales, and exchanges of stocks, bonds, and other covered securities above a $1,000 threshold. It covers the member, their spouse, and dependent children.
Notice the structure of that rule. The 45 days is an outer bound. Nothing stops a member from filing the next day. Some do. Nothing forces a member to file before day 45 either. Some wait until the deadline. And some file late. The penalty for a late filing starts at a modest standard fee, historically $200, which the ethics committees can waive. That penalty structure matters. It means the deadline is enforced softly, and late filings are a known part of the data.
So the honest answer to "how fast are congress trades disclosed" is this. The law says 45 days at most. Practice varies member by member and filing by filing. Anyone who quotes you a single average number without showing their method is skipping the interesting part.
## Where the raw data lives
Every PTR is public. The House publishes them through the Clerk's financial disclosure portal at [disclosures-clerk.house.gov](https://disclosures-clerk.house.gov/). The Senate publishes them through its electronic financial disclosure system at [efdsearch.senate.gov](https://efdsearch.senate.gov/search/). Both are free. You do not need an account to search, though the Senate system asks you to accept an access agreement each session.
Each PTR contains the fields you need to measure the lag. There is a transaction date for each trade. There is a notification or filing date for the report itself. There is the asset name, usually with a ticker. There is the transaction type, meaning purchase, sale, or exchange. And there is an amount range rather than an exact figure. The ranges are bands like $1,001 to $15,000, then $15,001 to $50,000, and so on upward. You never see the exact dollar amount.
## How to measure the real lag yourself
The filing lag for a single trade is simple arithmetic. Take the filing date. Subtract the transaction date. The result is the number of calendar days the market waited before that trade became public.
Doing this at scale takes a few steps. Here is the method in plain terms.
First, collect the PTRs. The House Clerk site offers yearly index files that list every disclosure filing with links to the documents. The Senate site is searchable by date range. Most House PTRs filed in recent years are electronic and parseable. Some filings, especially older ones, are scanned paper documents. Those need manual reading or OCR, and any serious measurement should state how it handled them.
Second, extract both dates for every transaction. One PTR can contain many trades with different transaction dates. Compute the lag per transaction, not per document.
Third, look at the distribution, not just one summary number. A mean can be dragged around by a handful of very late amendments. The median tells you what a typical trade looks like. The 90th percentile tells you how bad the slow tail is. Plotting a histogram of lags usually reveals a cluster well inside the deadline and a long tail beyond it.
Fourth, handle amendments carefully. Members sometimes file an amended PTR that corrects or adds trades from months earlier. If you treat an amendment as the first disclosure of a trade, the lag can run far past 45 days. That is real information about how slowly the trade reached the public. But you should separate original filings from amendments in your analysis, because mixing them changes the story.
Fifth, decide what date you actually care about. The filing date is when the document was submitted. The date the portal published it, and the date any downstream tracker picked it up, can add more delay. If your question is "when could a follower realistically have acted," measure to the moment the data was retrievable, not the moment it was signed.
If you do not want to build this yourself, several free trackers republish the filings, including Capitol Trades and Quiver Quantitative. They are convenient. But for measuring lag precisely, the official portals are the ground truth, and they cost nothing.
## What the 45-day ceiling means for followers
Now the practical question. If a trade can be up to 45 days old when you see it, what is left for you?
Compare the situation to corporate insiders. Company officers and directors report their trades on Form 4, which is generally due within two business days of the transaction. The SEC explains this on its [Form 4 overview page](https://www.sec.gov/about/forms/form4.pdf) and in its insider reporting rules. Two business days versus up to 45 calendar days is an enormous difference. Insider filings are near real time. Congressional filings are history by comparison.
That difference shapes what kind of signal can survive the lag.
Short-term information decays fast. If a trade was driven by something the market learned within days, the disclosure arrives long after the move. Copying it as a quick trade means buying old news. The lag alone is enough to kill most fast edges, before you even ask whether an edge existed.
Longer-horizon positioning decays slowly. If a member builds a position they expect to hold for quarters or years, a few weeks of delay matters much less. The disclosure still tells you where a well-connected person is putting real money for the long run.
Aggregation beats single filings. One delayed trade is weak evidence. Several members buying the same name or the same sector within a window is more interesting, even seen late. Clusters change slowly, so the lag hurts them less.
The reported ranges add their own noise. A purchase in the $1,001 to $15,000 band could be pocket change for a wealthy member. Position size relative to the member's disclosed wealth says more than the band alone, and even that is a rough estimate.
There is one more consequence of the ceiling worth stating plainly. Because members choose when to file within the window, the lag itself is a behavioral signal. A member who consistently files fast is easier to follow. A member who consistently files on day 44, or amends months later, produces data you should discount. When you measure lags per member, you are also building a reliability score.
## Honest limits of this data
A few caveats keep the picture truthful. PTRs do not state why a trade happened. Many congressional trades are made by spouses, advisers, or managed accounts the member does not direct day to day. Some members use blind trusts or broad funds, which generate little tradeable information at all. And academic studies disagree on whether congressional portfolios beat the market once you account for risk and timing. The disclosures are a transparency tool first. Any signal you extract from them is a hypothesis to test, not a guarantee. Nothing here is investment advice.
The core takeaway is simple. The 45-day rule defines the worst case, not the typical case. The real lag is measurable, member by member, from free public filings. Measure it before you trust any strategy built on following these trades, because the age of the information is as important as the information itself.
If you want this work done for you, our Congress Stock Trades report tracks new PTR filings, records both the transaction and disclosure dates, and scores trades with the filing lag built in, so you always know how fresh a signal really is. See the latest data on the [Congress Stock Trades report](/reports/congress.html).
---
# Committee Assignments and Stock Trades: What to Watch and Why It Matters More Than Trade Size
URL: https://datasignalslab.com/blog/committee-assignments-and-stock-trades-what-to-watch-and-why-it-matter/
A member of Congress buys shares in a pharmaceutical company. Is that interesting? On its own, not very. Hundreds of members hold stocks, and most trades are routine portfolio moves. Now add one fact. The member sits on the committee that oversees drug pricing, and a markup on a pricing bill is scheduled for next month. The trade did not change. The context did. This article explains why committee assignments are the single most useful piece of context for reading congressional trades, how to look them up yourself, and where the limits of this signal sit.
## Why the committee matters more than the dollar amount
Start with what a committee actually is. Congress does most of its real work in committees, not on the chamber floor. Committees hold hearings, question executives and regulators, draft and amend bills, and conduct oversight of federal agencies. A member of the House Committee on Energy and Commerce spends their working weeks inside the details of healthcare, telecom, and energy policy. A member of the Senate Committee on Armed Services reviews defense budgets and weapons programs. You can browse the full list of House committees at [house.gov/committees](https://www.house.gov/committees) and the Senate equivalents at [senate.gov](https://www.senate.gov/committees/committees_home.htm).
This is where informational advantage would live, if it lives anywhere. Committee members receive briefings before the public does. They see draft legislation before it is introduced. They know which amendments have support and which will die quietly. They hear testimony in closed sessions. None of this means a given trade used that knowledge. It means the opportunity is structurally concentrated in committees, not spread evenly across all 535 members.
Now compare that to trade size. Congressional disclosures never show exact amounts. A Periodic Transaction Report, the filing required for individual trades, reports each transaction in a band such as $1,001 to $15,000 or $50,001 to $100,000. A trade at the top of one band and the bottom of the next can look identical or wildly different depending on where the cutoffs fall. Size also correlates strongly with personal wealth. Wealthy members make large trades because they have large portfolios, not because they know something. A big number in a filing tells you the member is rich. The committee assignment tells you what the member knows about.
So the practical rule is simple. Sector overlap beats dollar size. A modest purchase of a bank stock by a member of the House Financial Services Committee is more worth your attention than a large purchase of the same stock by a member whose committees handle agriculture and veterans affairs.
## The legal backdrop in one paragraph
The disclosure regime comes from the STOCK Act of 2012, which you can read in full on [congress.gov](https://www.congress.gov/bill/112th-congress/senate-bill/2038). The law affirmed that members of Congress are not exempt from insider trading rules and required faster disclosure of trades through PTRs. Members must file within 30 days of learning about a transaction and never later than 45 days after the trade itself. The law covers the member, their spouse, and dependent children. It did not ban stock ownership, and it did not require members to recuse from votes that touch their holdings. Several bills proposing an outright trading ban have been introduced since, but as of this writing members may still trade individual stocks.
## How to connect a trade to a committee, step by step
Everything you need is public and free. Here is the workflow.
First, get the trade. House PTRs are published by the Clerk at [disclosures-clerk.house.gov](https://disclosures-clerk.house.gov/). Senate filings are in the electronic financial disclosure system at [efdsearch.senate.gov](https://efdsearch.senate.gov/search/). Each filing shows the asset, the transaction type, the date, and the amount band.
Second, get the member's committee assignments. The most reliable single source is the member's profile page on [congress.gov](https://www.congress.gov/members), which lists current committee and subcommittee memberships. The committee's own website will also list its members and, importantly, its subcommittees.
Third, map the traded company to a sector, and the sector to committee jurisdiction. This step takes judgment. Committee jurisdictions are defined in chamber rules and they are broad. Energy and Commerce alone touches healthcare, environment, telecom, and consumer products. A defense contractor maps cleanly to Armed Services. A diversified conglomerate maps to almost everything, which means it maps to nothing useful.
Fourth, go one level deeper than the full committee. Subcommittee assignments are sharper signals than committee assignments. A member of the Subcommittee on Health sees drug policy up close. A member of the same full committee who sits only on the communications subcommittee does not. Leadership roles sharpen the signal further. Chairs and ranking members control agendas, schedule hearings, and negotiate bill text. Their exposure to nonpublic information is the deepest in the building.
Fifth, check the calendar. A trade in a sector the member oversees is one data point. A trade shortly before a scheduled hearing, a markup, or an agency budget decision in that same sector is a stronger pattern. Committee websites publish hearing schedules in advance, so you can line up trade dates against committee activity yourself.
## What this signal cannot tell you
Honesty requires a clear list of limits, because this signal breaks in specific ways.
The overlap proves nothing by itself. A member of the Financial Services Committee who buys bank stocks may simply like banks. People tend to invest in industries they understand, and committee work creates familiarity that is entirely legal. Correlation between committee and portfolio is expected even with zero misconduct.
Many members do not direct their own trades. Some use financial advisors with discretionary authority. Some hold assets in blind trusts or broad index funds. Some disclosed trades belong to a spouse who has a career and a portfolio of their own. A PTR marks spouse and dependent trades, so check that field before drawing conclusions about the member personally.
The disclosure lag blunts everything. You learn about a trade up to 45 days after it happened, and sometimes later when filings are amended or late. Whatever information edge existed at trade time has mostly expired by disclosure time. That limits both the fairness concern and any copying strategy.
The academic evidence on whether members of Congress beat the market is genuinely mixed. Early studies found abnormal returns, especially in the Senate. Later studies using more recent data found little or no outperformance for the average member. Nobody serious claims the average congressional trade is smart money. The interesting question is whether a small subset of trades, filtered by committee relevance and timing, behaves differently. That is a filtering problem, which is exactly why committee context matters.
Jurisdiction mapping is fuzzy. Big companies span sectors. Committee boundaries overlap. Two reasonable people can disagree about whether a given trade falls inside a member's oversight. Treat any automated committee-to-ticker match as a starting point for reading the actual filing, not as a verdict.
## Free tools, and what to build yourself
You do not need to pay anyone to do this analysis. The official portals above are the ground truth for trades. Congress.gov is the ground truth for assignments. For convenience, free trackers such as Capitol Trades and Quiver Quantitative republish congressional trades in searchable form, and some let you filter by committee. Their data still originates from the same official filings, so for anything that matters, verify against the source document.
If you want to build the pipeline yourself, the shape is straightforward. Pull PTRs from the House and Senate portals. Pull committee rosters from congress.gov. Maintain a mapping from tickers to sectors and from sectors to committee jurisdictions. Then score each trade on overlap, subcommittee depth, leadership role, and proximity to committee events. The scoring is where all the judgment lives, and it is worth writing your assumptions down so you can revisit them.
One thing this article is not. It is not investment advice. Congressional trading data is a transparency tool and a research input, not a trading system, and past patterns in these filings do not predict future returns.
If you would rather not stitch the filings, rosters, and calendars together by hand, our [Congress Stock Trades report](/reports/congress.html) does the collection for you. It pulls every new PTR from the official House disclosure portal, links each trade to the member behind it, and scores the filings so the committee-relevant ones are easy to spot. Start there, then verify anything interesting against the primary sources linked above.
---
# The 45 Day Rule and Why It Matters
URL: https://datasignalslab.com/blog/the-45-day-rule-and-why-it-matters/
Every dataset of congressional stock trades carries one built-in flaw. The trades are old by the time you see them. The reason is a deadline written into federal law, commonly called the 45-day rule. Understanding exactly what the rule says, and what it does not say, is the difference between using this data well and using it badly.
## What the rule actually says
The rule comes from the Stop Trading on Congressional Knowledge Act of 2012, Public Law 112-105, available in full on [congress.gov](https://www.congress.gov). The STOCK Act requires members of Congress to file a Periodic Transaction Report, or PTR, for covered securities transactions over $1,000 made by the member, their spouse, or a dependent child.
The deadline has two parts, and most summaries get this slightly wrong:
1. The report is due **no later than 30 days after the member becomes aware of the transaction**.
2. In no case may it be filed **later than 45 days after the transaction date itself**.
So 45 days is the outer wall, not the standard. A member who places a trade personally knows about it immediately, which starts the 30-day clock on day one. The 45-day limit exists for cases where someone else executes the trade, such as a spouse's account or a financial adviser acting with discretion, and the member learns about it later. Awareness can lag the trade, but the law caps the total delay at 45 days regardless.
## Why the rule exists at all
Before the STOCK Act, members of Congress disclosed their trades once a year in an annual financial disclosure report. A purchase made in January could stay invisible to the public until the following spring. By the time anyone could examine it, the trade was history, and any connection to legislation, briefings, or committee work was buried under more than a year of news.
The STOCK Act was passed in 2012 after sustained public attention on congressional trading. Its transaction reporting section compressed the disclosure window from roughly a year to a month and a half at most. That single change is what makes congressional trading data a live dataset instead of an annual archive. Sites, research projects, and data products that track these trades all exist because of this one deadline.
The law applies the same periodic reporting logic to senior congressional staff and to many executive branch officials. But the members themselves are where public interest concentrates, and their filings are what the House Clerk publishes at [disclosures-clerk.house.gov](https://disclosures-clerk.house.gov) and the Senate publishes at [efdsearch.senate.gov](https://efdsearch.senate.gov).
## What the delay looks like in practice
The gap between trade date and public visibility varies from filing to filing. Three dates matter:
- **The transaction date**: when the trade executed.
- **The notification date**: when the member became aware of it. This appears on the PTR alongside the transaction date.
- **The filing date**: when the report reached the Clerk or the Senate system and became public.
A diligent member who trades their own account and files promptly might surface a trade within a week or two. A member relying on the full legal window surfaces it a month and a half after the fact. Both are compliant. When you look at a feed of congressional trades, you are looking at a mix of both, which means the effective staleness of the data is uneven across rows.
This is why serious use of the data always compares the transaction date against the filing date. The spread between them tells you how much of the legal window the filer used, and it tells you how old the information was before anyone outside the member's household could see it.
## Late filings and the $200 fee
The standard penalty for missing the deadline is a $200 late filing fee. The fee attaches to the late report, not to each transaction in it, and ethics committees have discretion to waive it. News organizations have repeatedly documented members filing weeks or months late, paying the small fee or facing no consequence at all.
For a data user, late filings have a practical consequence beyond the ethics question. A trade can appear in the public record long after the 45-day wall has passed. Any pipeline that assumes all disclosed trades are at most 45 days old will occasionally be wrong. Robust handling means reading the transaction date from the filing itself rather than inferring it from when the document appeared.
Amendments create a similar wrinkle. Members can and do file amended PTRs that correct tickers, amounts, dates, or transaction types on earlier reports. A dataset that never reconciles amendments slowly accumulates errors.
## What the lag means for anyone using the data
The 45-day rule shapes what questions the data can answer.
**Questions the data answers well.** What did members trade last quarter? Is there a pattern of activity in a sector over months? Are multiple members accumulating the same ticker across several filings? Did a member trade in an area their committee oversees? These are pattern and accountability questions, and a few weeks of lag barely affects them.
**Questions the data answers poorly.** Did a member buy something this morning? Can a disclosed trade be copied at the member's price? Almost never. By the time a purchase is public, the stock has had up to 45 days to move. If the trade reflected any short-lived information, that information is likely already in the price. Academic work on congressional trading, including post-2012 studies of trades disclosed under the STOCK Act, has generally found no reliable excess returns from following disclosed trades, and the disclosure lag is one plausible reason.
The honest way to treat the lag is as a filter on strategy. Fast imitation is structurally hopeless. Slow signals, such as sustained accumulation by several members, sector-level shifts, or unusually large purchases relative to a member's history, degrade far less over 45 days.
## Freshness as a scoring input
Because staleness varies row by row, a useful dataset should not treat all disclosed trades as equal. A trade disclosed 5 days after execution and a trade disclosed 44 days after execution are different objects, even if the ticker and size match. The first is recent enough that the position likely still exists in something like its original form. The second is a historical record.
This is why freshness belongs in any scoring of congressional trades. Weighting recent transactions above older ones, within and across filings, keeps attention on the part of the record that still describes the present. Combined with trade size, it produces a ranking where a large purchase from last week sits above a small sale from six weeks ago, which matches how a careful human reader would prioritize the same documents.
The 45-day rule is a compromise between member privacy and public accountability, and it defines the physics of this dataset. You cannot remove the lag. You can only measure it, score around it, and ask questions the lag does not destroy.
*The [Congress Stock Trades Report](/reports/congress.html) works within the 45-day window by scoring every disclosed trade for freshness and size in one ranked document. [Get the free preview](https://datasignalslab.lemonsqueezy.com/checkout/buy/2002ea9c-0e57-4e3d-ab50-3dc12a865d07).*
*DataSignals Lab publishes data and research. This is not investment advice.*
---
# Reading a Periodic Transaction Report
URL: https://datasignalslab.com/blog/reading-a-periodic-transaction-report/
The Periodic Transaction Report, or PTR, is the document behind every congressional stock trading headline. When a site reports that a representative bought a semiconductor stock, the primary source is a PTR filed with the Clerk of the House or with the Senate's disclosure office. Anyone who works with this data eventually needs to read the actual documents. This guide walks through one, field by field.
## Where to find a PTR
House filings are published by the Clerk of the House at [disclosures-clerk.house.gov](https://disclosures-clerk.house.gov). The site offers a search by name, state, and filing year, and each result links to a PDF. Senate filings live in the Senate's electronic financial disclosure system at [efdsearch.senate.gov](https://efdsearch.senate.gov), which requires accepting an agreement before searching and places restrictions on reuse of the data.
The legal basis for the PTR is the STOCK Act of 2012, Public Law 112-105, whose text is on [congress.gov](https://www.congress.gov). It requires a report for securities transactions over $1,000 by a member, their spouse, or a dependent child, filed within 30 days of the member becoming aware of the transaction and never more than 45 days after the transaction itself.
## The header
The top of a PTR identifies the filer and the filing. You will see the member's name, their state and district for House filings, the filing type, and the filing date. The filing type field distinguishes an original PTR from an amendment. This distinction matters more than it looks. Amendments restate or correct earlier reports, and a dataset that counts an amended transaction and its original as two separate trades will double count.
The filing date in the header is when the document was submitted, not when any trade happened. The gap between transaction dates inside the report and the filing date in the header tells you how much of the legal disclosure window the member used.
## The transaction table
The body of the report is a table with one row per transaction. The columns are consistent across filings, even when the visual layout differs.
**Owner.** A short code showing whose account the trade was in. Common values are SP for spouse, DC for dependent child, and JT for a joint account. When the column is blank or shows the member indicator, the trade was in the member's own account. Owner codes are essential context. A pattern of trades marked SP describes a spouse's portfolio decisions, and public discussion that attributes those trades directly to the member is being imprecise.
**Asset.** The full name of the security, and for listed stocks usually the ticker symbol in parentheses. Assets are not limited to common stock. PTRs also disclose bonds, options, exchange traded funds, and other covered securities. Option disclosures often describe the contract in the asset name, including strike and expiration, though formats vary by filer.
**Transaction type.** Usually a single letter. P means purchase. S means sale. Many filings distinguish a full sale from a partial sale, often written as S (partial). E marks an exchange. The purchase and sale codes carry different information value. A purchase is a positive decision to put money into one specific security. A sale can mean many things: taking profit, cutting a loss, raising cash, rebalancing, or paying a tax bill.
**Date.** The transaction date, when the trade executed.
**Notification date.** The date the member became aware of the transaction. For self-directed trades this matches the transaction date. For trades executed by a spouse or an adviser it can be later. The 30-day filing clock runs from this date.
**Amount.** A dollar range, never an exact figure. The brackets follow the standard disclosure schedule: $1,001 to $15,000, $15,001 to $50,000, $50,001 to $100,000, $100,001 to $250,000, $250,001 to $500,000, $500,001 to $1,000,000, and further ranges beyond $1,000,000.
Some filings include a description field with free-text notes, such as an indication that a holding belongs to a trust or that a transaction relates to an initial public offering.
## Making sense of amount ranges
The ranges are the single most misunderstood part of these documents. Three points keep interpretation honest.
First, any exact dollar figure you see attached to a congressional trade in a dataset is an estimate. A common convention is to use the midpoint of the disclosed bracket, so a trade in the $15,001 to $50,000 range is treated as roughly $32,500. That is a reasonable modeling choice, but it is a choice, not a disclosure.
Second, the ranges are wide at the top. The bracket structure means uncertainty grows with size. A very large disclosed trade is known to be very large, but its exact size can be uncertain by hundreds of thousands of dollars.
Third, large trades are often split. A filer buying heavily into one stock over several days may report multiple rows in the same bracket. Summing midpoints across rows gives a rough total, with the uncertainty compounding accordingly.
## E-filed versus scanned reports
House PTRs come in two physical forms. Electronically filed reports are generated from structured data, and the text in the PDF can be extracted reliably. Paper reports, which are handwritten or typed forms scanned to PDF, still exist in the historical record. Extracting data from scans requires optical character recognition, which introduces errors in exactly the fields where errors hurt most: tickers, dates, and amounts.
This is why data provenance matters when you use parsed congressional trading data. A dataset built only from e-filed reports trades some coverage for accuracy. A dataset that includes OCR output from scans has broader coverage and a higher, usually unmeasured, error rate.
## Amendments and corrections
Members file amended PTRs to fix mistakes: a wrong ticker, a wrong date, a missing transaction, a misstated amount range. The amendment appears as a new document referencing the original period. Careful reading means checking whether a striking transaction was later amended, and careful data engineering means reconciling amendments so the final dataset reflects the corrected record rather than the first draft.
## A short checklist for reading any PTR
- Check the filing type first. Original or amendment?
- Compare transaction dates with the filing date to see how stale the information was at publication.
- Read owner codes before attributing trades to the member personally.
- Treat every amount as a range, and treat any single number derived from it as an estimate.
- Distinguish purchases from sales, and full sales from partial ones.
- For older House filings, note whether the document is e-filed or scanned before trusting extracted text.
Reading one PTR takes a few minutes. The difficulty is scale. Hundreds of filers, thousands of documents per year, two different publishing systems, amendments arriving out of order, and a mix of machine-readable and scanned sources. That is the gap between the official record and a clean dataset, and it is exactly the gap that parsing and scoring pipelines exist to close.
*The [Congress Stock Trades Report](/reports/congress.html) does this reading for you, turning raw PTR filings into one scored, ranked document where every row links back to the official PDF. [Get the free preview](https://datasignalslab.lemonsqueezy.com/checkout/buy/2002ea9c-0e57-4e3d-ab50-3dc12a865d07).*
*DataSignals Lab publishes data and research. This is not investment advice.*
---
# How Congressional Trading Disclosures Work
URL: https://datasignalslab.com/blog/how-congressional-trading-disclosures-work/
Members of the US Congress are allowed to buy and sell stocks. What they cannot do is keep those trades secret. Federal law requires them to disclose their personal securities transactions, and those of their spouses and dependent children, in public filings. This article explains the whole system: which laws apply, who must file, what the reports contain, where they are published, and what the data can and cannot tell you.
## The legal foundation
Two laws matter here.
The first is the Ethics in Government Act of 1978. It created the annual financial disclosure regime for senior federal officials, including members of Congress. Under that law, members file a yearly report listing their assets, liabilities, income sources, and transactions.
The second is the Stop Trading on Congressional Knowledge Act, better known as the STOCK Act. It became Public Law 112-105 when it was signed on April 4, 2012. You can read the full text on [congress.gov](https://www.congress.gov). The STOCK Act did two big things. It affirmed that members of Congress and their staff are covered by insider trading law and owe a duty of trust regarding nonpublic information they learn through their positions. And it created a new, much faster disclosure requirement: the Periodic Transaction Report, or PTR.
Before the STOCK Act, the public learned about a member's trades once a year, in the annual report. A stock bought in February might not become public until the following May or June. The PTR changed that. Trades now surface within weeks, not a year later.
## Who must file, and for whom
The reporting duty covers every member of the House of Representatives and every senator. It also extends beyond the member personally. Reportable transactions include those made by:
- The member
- The member's spouse
- The member's dependent children
This matters in practice. Some of the most watched congressional trading activity has come from spouse accounts rather than from the members themselves. A PTR marks each transaction with an owner code so you can tell whose account it was.
The rules also apply to senior congressional staff and to many executive branch officials, but the public attention, and most of the data products built on these filings, focus on the members.
## The two report types
Congressional trading data comes from two documents.
**The annual financial disclosure report** covers a full calendar year. It lists assets and their value ranges, income, liabilities, positions held outside Congress, and transactions during the year. Members file it each spring for the prior year. It is comprehensive but slow.
**The Periodic Transaction Report** is the fast channel. A member must file a PTR when they, their spouse, or a dependent child buys, sells, or exchanges stocks, bonds, or other covered securities in an amount over $1,000. The deadline is strict: no later than 30 days after the member becomes aware of the transaction, and in no case later than 45 days after the transaction date itself.
The $1,000 threshold means small trades never appear. The 45-day outer limit means the data always carries some lag. Both facts shape how the data should be used.
## What a PTR actually contains
Each transaction line in a PTR includes:
- **The asset**: the security's full name and, for stocks, usually the ticker symbol.
- **The transaction type**: purchase, sale, partial sale, or exchange.
- **The transaction date**: when the trade happened.
- **The notification date**: when the member learned of it, which starts the 30-day clock.
- **The amount, as a range**: disclosures never show exact dollar figures. They use brackets such as $1,001 to $15,000, $15,001 to $50,000, $50,001 to $100,000, and so on up through ranges above $1,000,000.
- **The owner**: member, spouse, dependent child, or joint account.
The ranges are the most important limitation to understand. A filing that says $50,001 to $100,000 could be a $51,000 trade or a $99,000 trade. Any dataset that shows a single dollar amount for a congressional trade is showing an estimate derived from the bracket, not a disclosed figure.
## Where the filings live
The two chambers publish separately, and the difference between them is significant.
**The House of Representatives** publishes disclosures through the Clerk of the House at [disclosures-clerk.house.gov](https://disclosures-clerk.house.gov). You can search by member name and year and download each report as a PDF. Newer reports are filed electronically and are machine-readable. Older reports may be scanned images of paper forms, which are much harder to process reliably.
**The Senate** publishes through its electronic financial disclosure system at [efdsearch.senate.gov](https://efdsearch.senate.gov). The search works, but the site requires an agreement step before access, and its terms restrict how the data may be reused. Anyone building on Senate data needs to read those terms carefully.
Because both systems publish per-member, per-report documents rather than a single clean feed, working with this data at scale means collecting many individual filings and parsing them into a consistent structure.
## Enforcement and penalties
The standard penalty for filing a PTR late is a $200 fee. Ethics committees can waive it, and reporting over the years has documented many late filings. The fee applies to the late report, not to each transaction inside it.
Larger violations are possible in theory. The STOCK Act confirmed that insider trading law applies to Congress, so trading on material nonpublic information learned through official duties can be pursued as securities fraud. In practice, disclosure violations are far more common than fraud cases, and the routine consequence is the $200 fee.
Knowing this helps set expectations for the data. The system produces broadly reliable public records, but deadlines are sometimes missed, amendments are common, and the incentive structure tolerates sloppiness at the margins.
## What the data is good for
Congressional trading disclosures are transparency data. They answer questions like:
- Which members trade actively, and in what?
- Did anyone in Congress buy or sell a specific ticker recently?
- Are several members moving in the same direction on the same stock?
- Did a member trade in a sector their committee oversees?
Researchers use the filings to study conflicts of interest. Journalists use them to report on specific trades around specific events. Market watchers use them as one input among many, usually focused on large purchases, since a purchase is a clearer statement of conviction than a sale, which can happen for tax, liquidity, or diversification reasons.
The data has real limits. It arrives with up to a 45-day delay. Amounts are ranges. The academic evidence on whether copying these trades earns excess returns is mixed, with early studies finding outperformance and later studies finding none. Treat the filings as a record of what elected officials did with their money, not as a trading system.
## From raw filings to usable data
Getting from the official sources to a usable dataset takes several steps: collecting new filings as they appear, extracting the transaction tables from PDFs, normalizing tickers and asset names, classifying buys and sells, estimating sizes from the disclosed brackets, and keeping every record traceable to the underlying document. Doing that once for one member is easy. Doing it continuously across hundreds of filers is the actual work.
*The [Congress Stock Trades Report](/reports/congress.html) turns these filings into one scored, ranked document. [Get the free preview](https://datasignalslab.lemonsqueezy.com/checkout/buy/2002ea9c-0e57-4e3d-ab50-3dc12a865d07).*
*DataSignals Lab publishes data and research. This is not investment advice.*
---
# From Filing to Signal: How Scoring Works
URL: https://datasignalslab.com/blog/from-filing-to-signal-how-scoring-works/
A single week of congressional disclosures can contain hundreds of transactions. Most are routine: small rebalancing trades, index fund purchases, partial sales for liquidity. Buried among them are the rows a careful reader would actually stop at, such as an unusually large purchase filed quickly, or three members buying the same ticker within days of each other. Scoring is the process of making the interesting rows rise to the top automatically. This article explains how the DataSignals scoring method works, step by step, from official filing to ranked row.
## Step 1: Start from the official record
Everything begins with the primary source. House members file Periodic Transaction Reports with the Clerk of the House, published at [disclosures-clerk.house.gov](https://disclosures-clerk.house.gov). The Senate publishes through its own system at [efdsearch.senate.gov](https://efdsearch.senate.gov), with terms that restrict commercial reuse. The legal requirement behind both comes from the STOCK Act of 2012, Public Law 112-105, on [congress.gov](https://www.congress.gov): transactions over $1,000 must be disclosed within 30 days of the member learning of them, and never later than 45 days after the trade.
Working from the official record rather than from second-hand aggregations has one decisive property. Every parsed row can carry a link back to the exact PTR PDF it came from. In the DataSignals dataset, every row does. If a score looks surprising, you can open the underlying filing and check the primary source yourself in seconds. Data without that traceability asks for trust. Data with it only asks for verification.
## Step 2: Parse the filing into fields
A PTR is a document, not a dataset. Parsing extracts the fields that scoring needs:
- The member's name and chamber
- The asset name and ticker
- The transaction type: purchase, sale, or exchange
- The transaction date and the filing date
- The disclosed amount range
- The owner code: member, spouse, dependent child, or joint
Parsing also handles the unglamorous cases. Amendments must supersede the originals they correct. Tickers need normalization. Non-stock assets such as bonds and options need classification. Filings that are scanned images rather than e-filed documents need to be treated differently from machine-readable ones, because extracted text from scans carries a real error rate.
## Step 3: Estimate size from the disclosed range
Congressional disclosures never state exact amounts. They use brackets: $1,001 to $15,000, $15,001 to $50,000, $50,001 to $100,000, and larger ranges beyond. Scoring needs a number, so the method uses the midpoint of the disclosed range as the size estimate. A trade in the $15,001 to $50,000 bracket is treated as roughly $32,500. A trade in the $100,001 to $250,000 bracket is treated as roughly $175,000.
The midpoint is a modeling convention, and it is presented as one. The true amount could sit anywhere in the bracket. But the convention is unbiased in a useful sense, it is applied identically to every row, and it preserves the ordering that matters: a trade disclosed in a higher bracket always scores as larger than a trade disclosed in a lower one. Size matters for scoring because it separates conviction from noise. A five-figure purchase is a decision. A four-figure one is often just housekeeping.
## Step 4: Weight by freshness
The second scoring input is time. Because the law allows up to 45 days between trade and disclosure, and because members use varying amounts of that window, the trades in any batch of filings differ widely in age. A purchase executed last week and a purchase executed six weeks ago are not equally informative about the present, even at the same size.
Freshness weighting encodes that. Recent filings score higher, and the score decays as the transaction date recedes. The effect on the ranked output is exactly what a human prioritizer would want: a large purchase from days ago outranks an equally large purchase from over a month ago, and a stale small trade sinks to the bottom regardless of who made it.
## Step 5: Combine into an impact score
Size and freshness combine into a single impact score per transaction. The design goal is a one-number answer to the question a reader brings to each row: how much should this trade command my attention right now? Big and recent scores high. Small or old scores low. Big but old and small but recent land in between.
A single scalar cannot capture everything, and it does not try to. What it does is convert an unsorted pile of transactions into a ranked list, so that attention starts at the top instead of being spent evenly across hundreds of routine rows.
## Step 6: Look across members for consensus
Individual trades are one layer. The second layer looks across filers. When multiple members trade the same ticker within the covered window, the dataset builds a consensus view for that ticker, including the balance between buying and selling.
The buy/sell lean is the key output. Five members buying a ticker and none selling is a different picture than three buying and three selling. The consensus view does not claim the members coordinated, and it does not claim they are right. It states an observable fact of the record: this many filers, this direction, this recently. Independent decisions pointing the same way are simply a stronger pattern than one decision alone, which is the same logic that makes cluster detection standard practice in corporate insider analysis.
Consensus also acts as a natural noise filter. Any one member's trade can be explained by personal circumstances. It is harder for personal circumstances to explain several members converging on one ticker in one direction in one window.
## What the scoring does not claim
Clear boundaries make data usable, so these are stated plainly.
The score measures the observable properties of a disclosure: how large, how recent, how many filers, which direction. It does not predict returns. No backtest results are attached to the scores, and none are implied. The academic evidence on modern congressional trading, discussed at length elsewhere on this site, finds no average outperformance in trades disclosed under the STOCK Act. Scoring exists to organize attention over a public record, not to promise that the top of the list will beat the market.
The score also inherits the limits of the underlying disclosures. Amounts are ranges, so sizes are estimates. Trades can be up to 45 days old at disclosure, and occasionally older when filings are late. Owner codes mean some trades belong to spouses or dependent children rather than the members themselves. The dataset preserves these facts rather than papering over them, and the link on every row leads back to the official PTR PDF where the original ranges, dates, and codes are stated.
## The pipeline in one view
From end to end: collect filings from the official source, parse documents into structured fields, reconcile amendments, estimate sizes from range midpoints, weight by freshness, combine into an impact score, aggregate into per-ticker consensus with a buy/sell lean, and keep every row traceable to its source PDF. Each step is simple. The value is in running all of them, continuously, so the ranked output is ready when the filings land.
*The [Congress Stock Trades Report](/reports/congress.html) is the output of this pipeline, turning raw filings into one scored, ranked document. [Get the free preview](https://datasignalslab.lemonsqueezy.com/checkout/buy/2002ea9c-0e57-4e3d-ab50-3dc12a865d07).*
*DataSignals Lab publishes data and research. This is not investment advice.*
---
# The Biggest Startup Capital Raises — Week 29, 2026 (SEC Form D)
URL: https://datasignalslab.com/blog/biggest-startup-raises-week-29-2026/
Every week, companies quietly disclose fresh capital raises in **SEC Form D filings** — often days before (and frequently without) any press coverage. We pull the filings straight from EDGAR, filter out pooled investment funds (the hedge/PE/VC noise that makes up most Form D volume), and rank what is left: the real operating-company startup-funding signal.
**This window (July 9–10, 2026): 60 qualifying raises, $9.5B in disclosed capital.**
## Top 10 by amount raised
| # | Company | Raised | Industry | State | Score |
|---|---------|--------|----------|-------|-------|
| 1 | Nhit: Strategic Alpha Trust | $4.9B | Investing | Massachusetts | 92 |
| 2 | Nhit: CORE Fixed Income Trust | $2.1B | Investing | Massachusetts | 92 |
| 3 | CRA Funding 1, LLC | $679.4M | Other Banking And Financial Services | NEW YORK | 85 |
| 4 | Ondas Inc. | $675.5M | Other | Florida | 100 |
| 5 | BP Commercial Funding Trust II, Series Spl-vi | $216.5M | Other Banking And Financial Services | NEW YORK | 79 |
| 6 | Imperative Care, Inc. | $111.3M | Other Health Care | California | 79 |
| 7 | Argent Vale Capital Inc. | $100.0M | Investing | NEW YORK | 100 |
| 8 | Black Tower Technology Inc. | $100.0M | Investing | NEW YORK | 100 |
| 9 | Databento Inc. | $97.0M | Other Technology | UTAH | 90 |
| 10 | Mutual Bancorp | $85.0M | Commercial Banking | Massachusetts | 86 |
Score = size × freshness × type (0–100). Every row in the full report links to the original SEC filing.
## The headline raise: Nhit: Strategic Alpha Trust
Nhit: Strategic Alpha Trust reported **$4.9B** raised across 80 investors (Investing, Massachusetts). Disclosed executives / related persons include N/A Loomis Sayles Trust Company, LLC. As always with Form D: amounts are self-reported by the issuer, and a filing is a disclosure — not an endorsement.
## Get the full picture
- **[The full ranked report — $19, refreshes daily](/reports.html)**: every qualifying raise with amounts, executives, securities type and direct SEC links. One-off purchase, your link always opens the latest edition.
- **Live data, any day or sector**: the underlying tool is self-serve on Apify at [$0.20 per result](https://apify.com/datasignalslab/startup-funding-form-d-monitor?fpr=wnlxst), and free to call for AI agents via our [MCP server](/datasignals-mcp.html).
*Data source: official SEC EDGAR Form D. This is data and research, not investment advice.*
---
# Startup Funding Data Sources Compared
URL: https://datasignalslab.com/blog/startup-funding-data-sources-compared/
Anyone who needs startup funding data faces the same shortlist: SEC EDGAR, Crunchbase, PitchBook, the tech press, and a growing set of filing-based feeds. Each one answers a different question, and each has real weaknesses that vendors rarely advertise. This comparison lays them out honestly, including where a Form D feed fits and where it does not.
The dimensions that matter are coverage (which deals appear), timing (how soon after the money moves), depth (what you learn about each deal), cost, and format (browsing versus programmatic access).
## SEC EDGAR: free, official, raw
EDGAR is the SEC's filing system and the primary source for US private-raise data. Most US companies raising under Regulation D must file a [Form D](https://www.sec.gov/education/smallbusiness/exemptofferings/formd) within 15 days after the first sale of securities. Every filing since March 2009 is electronic, public, and free, searchable through [EDGAR full-text search](https://efts.sec.gov/LATEST/search-index?q=%22form%20d%22) and downloadable through daily indexes.
**Strengths.** It is the source of truth. The data is a legal filing by the issuer, not an aggregator's guess. Timing is the best available, since the filing deadline runs from the first sale and Form D filings often precede any press coverage. Cost is zero. Coverage includes the long tail of raises that never get announced anywhere, which for sales and research use cases is the most valuable slice.
**Weaknesses.** It is raw. Hundreds of filings arrive daily and most are pooled investment funds rather than startups. There is no ranking, no deduplication of amendments, no valuation, no investor names, and no company description beyond an industry checkbox. Coverage is US-only and misses raises done outside Regulation D. Using EDGAR at scale means building a parser and a filter pipeline yourself.
**Best for.** Engineers and analysts willing to build, and anyone verifying a specific deal at the source.
## Crunchbase: broad, browsable, announcement-driven
Crunchbase is the best-known startup database. It combines contributed data, machine collection, and editorial work into company profiles with funding histories, and it covers companies worldwide. Paid access starts with the Pro tier, with a Business tier and API access above it. Current pricing is on [crunchbase.com](https://www.crunchbase.com/).
**Strengths.** Breadth and usability. Profiles include founders, investor lists, round labels like seed or Series B, and industry tags, connected across companies and people. International coverage goes far beyond what US filings can show. For quickly answering "who has this company raised from," it is efficient.
**Weaknesses.** Crunchbase largely reflects announced information. Rounds typically appear when they are made public, which can be weeks or months after the money moved, and quiet raises may never appear. Round amounts and investor lists depend on what was disclosed or contributed, so completeness varies by company. Filing-level precision, such as the exact exemption claimed or the disclosed amount sold, is not what the product is for.
**Best for.** Market mapping, competitive research, and enrichment when timing is not critical.
## PitchBook: deep, institutional, expensive
PitchBook, owned by Morningstar, is the institutional research platform for private markets. It covers venture, private equity, and M&A with analyst-curated data: valuations, cap-table estimates, fund performance, and detailed investor profiles. There is no public price list. Access is sold as an annual subscription, quoted per seat and per module through their sales team, as described on [pitchbook.com](https://pitchbook.com/pricing).
**Strengths.** Depth that no free source approaches. Valuation data, deal terms, fund returns, and analyst coverage make it the standard tool inside venture funds, investment banks, and corporate development teams.
**Weaknesses.** Cost puts it out of reach for individuals and most small teams, and quotes are annual commitments. Like every aggregator, its coverage of unannounced deals depends on research reach, and some quiet raises surface late or not at all. It is a research platform first, so lightweight programmatic use of a single signal means paying for the whole terminal.
**Best for.** Professional investors and advisors whose work justifies an institutional data budget.
## The tech press and newsletters: context, not coverage
TechCrunch, Axios, Business Insider, and sector newsletters remain how most people hear about funding rounds. The reporting adds what no database has: the story, the strategy, quotes from founders and investors.
**Strengths.** Free or cheap, zero effort, and rich context on the deals covered.
**Weaknesses.** The press covers a curated sliver of funding activity, weighted toward large rounds, known investors, and companies with PR support. Timing follows the company's announcement plan, not the transaction. Many raises visible in filings never receive a single article. As a data source, press coverage is a biased sample delivered late.
**Best for.** Staying generally informed, and understanding the deals that do get covered.
## Form D feeds: the filing signal, parsed
Between raw EDGAR and the aggregators sits a fourth category: feeds that parse Form D filings into usable data. The Startup Capital Raises Report is one of these. It parses every qualifying Form D raise of $1 million or more, filters out pooled investment funds, and ranks operating-company raises by a funding score based on size, freshness, and securities type. Each row shows the disclosed executives and links to the official filing on EDGAR, and the report refreshes daily.
**Strengths.** Filing-level timing without filing-level labor. The fund filtering and ranking solve the two problems that make raw EDGAR impractical for daily use, and the link to each official filing keeps every row verifiable at the source.
**Weaknesses.** The same limits as the underlying filings. US raises under Regulation D only, no valuations, no investor names, and no coverage of deals that skip or delay the filing. A Form D feed complements a database like Crunchbase rather than replacing it, since one is early and official while the other is broad and descriptive.
**Best for.** Sales teams, recruiters, investors, and journalists who care about knowing early and want each claim traceable to an official document.
## Head-to-head summary
- **Timing.** EDGAR and Form D feeds first, by design of the 15-day filing rule. Press and aggregators follow announcements.
- **Coverage of quiet deals.** EDGAR and Form D feeds capture unannounced US raises. Press mostly misses them. Crunchbase and PitchBook catch some, later.
- **International coverage.** Crunchbase and PitchBook clearly win. Filings-based sources are US-only.
- **Depth per deal.** PitchBook wins, then Crunchbase. Filings give facts, not narrative or valuation.
- **Cost.** EDGAR is free. Crunchbase Pro is a monthly-scale subscription. PitchBook is a quoted annual contract. Form D feeds sit near the low end.
- **Verifiability.** Filings win outright. Every Form D row can be checked against the official document. Aggregator entries depend on sourcing you cannot always inspect.
## How to choose
Match the source to the question. If the question is "what happened in this space last year," an aggregator is the right tool. If the question is "which companies raised money this week, including the ones nobody wrote about," the answer lives in filings, either through your own EDGAR pipeline or through a parsed feed. If the question involves valuations and deal terms at professional depth, that is PitchBook territory and it costs accordingly.
Many workflows combine them: a filings feed for early detection, an aggregator for enrichment, and the press for narrative. The mistake is expecting one source to do all three jobs, because none of them does.
*The [Startup Capital Raises Report](/reports/formd.html) covers the filings side of this comparison, ranking every new Form D raise before the press writes about it. [Get the free preview](https://datasignalslab.lemonsqueezy.com/checkout/buy/ebaa8db4-4d75-4a3c-a241-7b481025c3e9).*
*DataSignals Lab publishes data and research. This is not investment advice.*
---
# Best Congress Trading Trackers Compared
URL: https://datasignalslab.com/blog/best-congress-trading-trackers-compared/
Every congressional trading tracker works from the same raw material: Periodic Transaction Reports filed under the STOCK Act ([Pub. L. 112-105](https://www.congress.gov/bill/112th-congress/senate-bill/2038)) and published by the [House Clerk](https://disclosures-clerk.house.gov) and the [Senate eFD system](https://efdsearch.senate.gov). No tracker has data the others cannot get. What differs is how each one parses, presents and prioritizes the filings, and what you pay for that work.
This comparison covers the main options honestly, including the free ones, because for many use cases a free tool is the right answer.
## The official sources: House Clerk and Senate eFD
Start with the source, because every other tool is a layer on top of it.
The House Clerk's disclosure site lets you search any representative by name and year and download their filings as PDFs. The Senate's eFD system does the same for senators. Both are free, official, and complete by definition. When any tracker shows you a trade, this is where it came from, and when trackers disagree, the filing on the official site settles it.
**What they do well.** Provenance. There is no substitute for reading the actual report, especially for trades that make headlines. The House site also publishes annual index files that list all filings, which is the entry point for anyone building their own pipeline.
**Where they fall short.** Usability. The filings are PDFs, amounts are ranges, some House reports are paper scans, and there is no aggregation, no alerts, no ranking, and no cross-member view. The official sites answer "what did this member file" and nothing else.
**Best for.** Verifying specific trades, and as the ground truth behind any other tool.
## Capitol Trades
Capitol Trades is a free web platform focused specifically on congressional trading. It parses filings from both chambers and presents them as a clean, searchable feed with politician profiles, issuer pages and filters by party, chamber, transaction type and date.
**What it does well.** Focus and polish. It does one thing, congressional trades, with a fast interface and no account required. For casually checking what a member has traded recently, or browsing the latest filings over coffee, it is arguably the most direct free option available.
**Where it falls short.** It is a browsing tool, not a data tool. There is no scoring or ranking beyond sorting, and getting structured data out for your own analysis is not what the free interface is built for.
**Best for.** Free, no-friction browsing of recent congressional trades.
## Quiver Quantitative
Quiver Quantitative is a broader alternative data platform where congressional trading is one dataset among many, alongside things like government contracts, lobbying disclosures and corporate insider activity. Much of the congressional data is viewable free on the site, and paid tiers add features such as data exports, API access and strategy tools.
**What it does well.** Breadth and context. If you want to look at a company through several government-related lenses at once, having congressional trades next to lobbying and contract data in one place is genuinely useful. The site also publishes accessible writeups around the data.
**Where it falls short.** Congressional trading is a feature, not the whole product, and the experience reflects that. Programmatic access sits behind the paid tiers.
**Best for.** Users who want congressional trades as part of a wider alternative data toolkit.
## Unusual Whales
Unusual Whales is best known for options flow data, and it added congressional trade tracking as part of its platform. It publishes widely shared reports on congressional trading performance and launched the two ETFs, NANC and KRUZ, that build portfolios from Democratic and Republican filings respectively. Congressional data appears on the free side of the site, with the full platform sold as a paid subscription.
**What it does well.** Reach and commentary. Its congressional trading coverage is engaging and widely cited, and the ETFs are a real-money expression of the copy-Congress idea that anyone can evaluate through public fund data.
**Where it falls short.** The platform is options-flow-first, so someone who only wants congressional filings is buying into a much larger product. As with any commentary-driven source, the framing arrives with the data.
**Best for.** Traders already interested in options flow who want congressional data in the same subscription.
## DataSignals Congress Stock Trades Report
The DataSignals approach is different in shape: not a dashboard to browse, but a scored document generated from the filings.
The [Congress Stock Trades Report](/reports/congress.html) parses official House Clerk PTR filings, ranks each trade with an impact score based on size and freshness, shows consensus tickers traded by multiple members, and refreshes weekly. The intent is to replace a browsing session with a ranked read: the highest-impact recent trades at the top, the crowd-agreement signal made explicit, and every item traceable to the underlying filing.
It is also built for automation. The underlying data is available for agent access via MCP, so the same congressional trade data that feeds the report can be queried programmatically alongside other DataSignals feeds.
**What it does well.** Prioritization and delivery. Scoring and ranking answer the question dashboards leave to you: which of this week's filings actually matter. Bundling and the weekly refresh make it a recurring research input rather than a site to remember to visit.
**Where it falls short.** It is not a real-time browsing interface, and it does not try to be. Someone who wants to click through every member's history interactively is better served by Capitol Trades or Quiver.
**Best for.** A weekly, decision-ready summary of what Congress traded, and pipelines that want scored data rather than raw filings.
## How to choose
The honest decision tree is short.
If you want to verify one specific trade, go to the [official source](https://disclosures-clerk.house.gov). If you want free browsing of the latest congressional trades, Capitol Trades does that well. If you want congressional trades inside a broader government-data toolkit, Quiver Quantitative fits. If you are an options trader who wants congressional data bundled with flow data, Unusual Whales fits. If you want the filings condensed into one scored, ranked, weekly document, or exposed for agent access via MCP, that is what the DataSignals report was built for.
Two closing cautions apply to every tool on this list, free or paid. First, all of them inherit the STOCK Act's 45-day disclosure lag, so nothing here shows trades in real time, whatever the marketing suggests. Second, the academic evidence that copying congressional trades beats the market is weak, with the most recent large study, Belmont, Sacerdote, Sehgal and Van Hoek (2022) in the Journal of Public Economics, finding no outperformance in trades from 2012 to 2020. These are transparency and research tools. Judged that way, several of them, including the free ones, are very good.
*The [Congress Stock Trades Report](/reports/congress.html) turns these filings into one scored, ranked document. [Get the free preview](https://datasignalslab.lemonsqueezy.com/checkout/buy/2002ea9c-0e57-4e3d-ab50-3dc12a865d07).*
*DataSignals Lab publishes data and research. This is not investment advice.*
---
# What the STOCK Act Actually Requires
URL: https://datasignalslab.com/blog/what-the-stock-act-actually-requires/
The STOCK Act gets invoked constantly in debates about congressional trading, usually by people arguing it goes too far or nowhere near far enough. Both arguments tend to skip what the law actually says. The text is specific: who files, what counts, how fast, and what happens when someone misses the deadline.
This article lays out the requirements in plain terms, with the gaps included.
## Where the law came from
In November 2011, a 60 Minutes segment reported on stock trading by members of Congress and asked why lawmakers seemed to sit outside the insider trading rules that applied to everyone else. Bills to address congressional trading had existed for years and gone nowhere. After the broadcast, they moved fast.
The Stop Trading on Congressional Knowledge Act, or STOCK Act, passed the Senate 96 to 3 and the House 417 to 2, and was signed into law on April 4, 2012 as [Public Law 112-105](https://www.congress.gov/bill/112th-congress/senate-bill/2038). The speed and the near-unanimous votes reflected the politics of the moment. Almost no one wanted to be recorded voting against it.
## The core affirmation: insider trading law applies
The first thing the STOCK Act does is remove an ambiguity. It affirms that members of Congress and their employees are not exempt from federal insider trading prohibitions, and it states that members owe a duty of trust and confidence regarding material nonpublic information they obtain through their positions.
Before 2012, some legal scholars argued that classic insider trading theory did not map cleanly onto legislators, because insider trading law is built on breaches of duty and it was unclear what duty a member of Congress breached by trading on legislative knowledge. The STOCK Act closed that argument by writing the duty into law. Enforcement of insider trading itself remains with the [SEC](https://www.sec.gov) and the Department of Justice, the same as for corporate insiders.
This part of the law matters, but it is invisible day to day. The part everyone interacts with is disclosure.
## The disclosure requirement in detail
The STOCK Act added a rapid reporting layer on top of the annual financial disclosures that already existed under the Ethics in Government Act. The new filings are called Periodic Transaction Reports, or PTRs. The mechanics:
**Who files.** Members of Congress, candidates for Congress, and senior congressional staff who meet the existing financial disclosure thresholds. The law also extended similar requirements to senior executive branch officials.
**What gets reported.** Any purchase, sale or exchange of stocks, bonds, commodity futures and other securities where the transaction amount exceeds $1,000. Transactions by the filer's spouse and dependent children are covered, not just the filer's own. Widely held investment funds such as mutual funds are exempt from the rapid reporting requirement.
**How fast.** The report is due within 30 days of the filer becoming aware of the transaction, and in no case later than 45 days after the transaction date. The 45-day outer bound is the number that matters in practice, because it defines the maximum lag between a trade and its public disclosure.
**What the report contains.** The asset, the transaction type, the transaction date, and the amount as a range. The ranges run in brackets, starting at $1,001 to $15,000 and stepping up through brackets in the millions. Exact amounts are never required.
**Where it goes.** House reports are filed with the Clerk of the House and published at [disclosures-clerk.house.gov](https://disclosures-clerk.house.gov). Senate reports go through the Senate's electronic filing system and are published at [efdsearch.senate.gov](https://efdsearch.senate.gov). Both are free to search.
## The penalty: a $200 late fee
Miss the deadline and the standard consequence is a late filing fee of $200 for a first late report, paid to the US Treasury. The supervising ethics office can waive the fee in extraordinary circumstances, and repeat or extended lateness can bring additional fees or referral to the ethics committees.
That number does the most work in any honest assessment of the law. A member whose household trades hundreds of thousands of dollars in a quarter faces the same $200 fee for a late report as a member with a single small trade. Journalists have documented, year after year, dozens of members filing late, and the fee has not changed since 2012. Knowing violations of the underlying disclosure statute can in theory bring civil penalties, and false statements in a filing can bring criminal exposure, but the routine enforcement reality for late PTRs is the $200 fee.
The disclosure requirement still has force, just not through the fee. The force comes from publicity. A late or missing filing is itself a story, and the reporting that surfaces those violations depends entirely on the filings being public.
## The 2013 rollback
One piece of the original law was cut back a year later. The 2012 act called for the disclosures of a broad set of filers, including senior staff, to be posted in searchable, sortable online databases. In April 2013, Congress passed S. 716 ([Public Law 113-7](https://www.congress.gov/bill/113th-congress/senate-bill/716)), which eliminated the online database requirement for most executive branch employees and congressional staff, citing security concerns raised in a National Academy of Public Administration review. Members of Congress themselves remained subject to online posting.
The practical result today: trades by members are searchable online, while equivalent visibility into senior staff requires requesting records rather than browsing a database.
## What the STOCK Act does not do
The gaps define the current reform debate as much as the requirements do.
It does not ban trading. A member can buy and sell individual stocks in companies directly affected by their committee work, as long as the trades are disclosed on time. The law bets on transparency rather than prohibition.
It does not require exact amounts. Ranges were carried over from the annual disclosure regime, so the public record shows brackets, not figures.
It does not produce clean data. Filings are PDFs, some House members still file on paper, tickers and asset names are inconsistent, and options and bonds are described in free text. The law mandates disclosure, not usability.
And it has produced few enforcement actions. Insider trading cases against members of Congress remain rare, in part because proving that a specific trade relied on specific material nonpublic information is genuinely hard.
Multiple bills in recent Congresses have proposed going further, most commonly by banning individual stock ownership by members and requiring divestment or blind trusts. As of mid-2026, no such ban has become law. The STOCK Act's disclosure regime remains the operative system.
## Why the details matter for anyone using the data
Every congressional trading tracker, dashboard and dataset inherits the STOCK Act's shape. The 45-day deadline defines how fresh the data can be. The $1,000 threshold defines what is visible at all. The ranges define why every trade size is an estimate. The two separate filing systems define why House and Senate coverage often differ. Understanding the law is understanding the data.
Read a filing with those constraints in mind and it becomes what it actually is: a delayed, bracketed, self-reported record that is still, despite everything, one of the most direct transparency feeds in American government.
*The [Congress Stock Trades Report](/reports/congress.html) turns these STOCK Act filings into one scored, ranked document. [Get the free preview](https://datasignalslab.lemonsqueezy.com/checkout/buy/2002ea9c-0e57-4e3d-ab50-3dc12a865d07).*
*DataSignals Lab publishes data and research. This is not investment advice.*
---
# 13F Filing Deadline: Dates and the 45-Day Lag
URL: https://datasignalslab.com/blog/13f-deadlines-and-the-45-day-lag/
Every 13F headline you read describes the past. The rule that creates the data also delays it: institutional investment managers must file Form 13F within 45 days after the end of each calendar quarter, and most of them use nearly all of that time. Understanding the deadline calendar and the lag it creates is the difference between using 13F data well and being misled by it.
## The rule in one paragraph
Section 13(f) of the Securities Exchange Act of 1934 requires institutional investment managers with investment discretion over at least $100 million in Section 13(f) securities to report those holdings quarterly. The report is Form 13F, and it is due within 45 days after the end of the calendar quarter. Holdings are reported as of the last day of the quarter. The SEC lays this out in its [Form 13F FAQ](https://www.sec.gov/divisions/investment/13ffaq), and every filing is public on [SEC EDGAR](https://www.sec.gov/cgi-bin/browse-edgar).
The requirement dates to 1975, when Congress added Section 13(f) to give regulators and the public visibility into the growing influence of institutional investors. The 45 day window was part of the design from the start, intended to give managers time to compile accurate reports while limiting how much of their current positioning they reveal.
## The filing calendar
Because quarters end on fixed dates, the deadlines are fixed too:
- **Q4 (December 31)**: due February 14
- **Q1 (March 31)**: due May 15
- **Q2 (June 30)**: due August 14
- **Q3 (September 30)**: due November 14
When a deadline falls on a weekend or a federal holiday, it rolls to the next business day under the SEC's filing date rules. February is the deadline most often affected, since February 14 lands near the Washington's Birthday holiday.
Managers may file any time inside the window, and a few do file early. In practice, the large majority file in the final days, and deadline day itself brings a flood of filings. This clustering means 13F season is effectively four short bursts a year: mid-February, mid-May, mid-August and mid-November. If you track funds, those are the weeks that matter. As of early July 2026, the most recent completed season covered Q1 2026 holdings, filed by May 15, and the next wave arrives by August 14 with Q2 positions.
## How stale is the data, really
Do the arithmetic on the worst case. A manager buys a stock on the first day of a quarter. The position is reported as of quarter end, roughly 90 days later. The filing lands 45 days after that. By the time the public sees the position, the purchase can be about 135 days old. Even the best case, a trade made on the final day of the quarter and filed at the deadline, is 45 days old on publication.
Then there is what the snapshot skips entirely. A position opened and closed inside a single quarter never appears at all. A manager can also request confidential treatment from the SEC to omit a position it is still accumulating, with disclosure arriving later through an amendment. So the filing you read is delayed, and occasionally incomplete on top of that.
None of this is a scandal. It is the rule working as designed. The 45 day lag exists partly to protect filers: forcing large managers to reveal fresh positions immediately would invite front-running of their remaining buy orders and expose their research to free-riders in real time. The lag is the compromise that makes public disclosure politically and commercially survivable.
## What the lag means for anyone following filings
The lag does not make 13F data useless. It changes what the data is good for, and for whom.
**Holding period is everything.** For a fast-trading multi-strategy fund, a 45-plus day old snapshot may bear little resemblance to the current book. For a concentrated long-term investor whose positions last years, the snapshot is usually still accurate when it becomes public. The same lag that destroys the signal in one filing barely dents it in another. Weight your attention toward managers whose holding periods comfortably exceed the reporting delay.
**Prices have moved.** A stock that a fund bought during the quarter may be far above its cost basis by filing day, especially if the filing itself attracts buyers. Copying a position after publication means taking a different entry at a different price with different risk. The filing tells you what was attractive to someone at some earlier price, not what is attractive today.
**Direction beats level.** Because the snapshot is old, the most durable information is the direction of change: new buys, meaningful adds, exits. A manager initiating a position late in a quarter and still holding it at quarter end has, at minimum, expressed a view that survived until the snapshot date. Quarter-over-quarter deltas age better than absolute position lists.
**Convergence ages best of all.** When several managers independently initiated or added to the same stock in the same quarter, the lag applies to all of them equally, and the overlap itself is information that does not decay as fast as any single position. Multiple experienced teams reached the same conclusion in the same window. That is a research lead worth following up even months later.
## Practical tips for filing season
A few habits make the four annual bursts more productive.
Check filings at the source. Deadline-day media coverage compresses thousands of rows into a headline, and details like put and call flags or share class distinctions get lost. The [EDGAR full-text search](https://www.sec.gov/edgar/search/) and company search give you the actual tables within minutes of filing.
Watch for amendments. Form 13F-HR/A filings correct or complete earlier reports, and confidential positions surface there. A fund's story for a quarter is not always finished on deadline day.
Use free trackers for breadth, primary filings for depth. [Dataroma](https://www.dataroma.com) is a convenient free view of well-known long-term investors, and [WhaleWisdom](https://whalewisdom.com) covers the wider filer universe with some features paid. Verify anything decision-relevant against EDGAR.
And set your expectations by the calendar. Between seasons, 13F data goes quiet for roughly ten weeks at a stretch. The productive rhythm is to process each wave thoroughly in the days after the deadline, extract the deltas and overlaps, and then do the slow fundamental work on the handful of names that earned attention.
*The [Smart-Money 13F Consensus Report](/reports/13f.html) is rebuilt on that rhythm: each filing season it distills the latest filings of 18 top managers into one ranked consensus table. [Get the free preview](https://datasignalslab.lemonsqueezy.com/checkout/buy/8817c6c0-1d17-4b32-8238-e5339aa39310).*
*DataSignals Lab publishes data and research. This is not investment advice.*
---
# Reg D Rule 506(b) vs 506(c): What Actually Differs
URL: https://datasignalslab.com/blog/regulation-d-exemptions-explained/
Almost every US startup round runs on Regulation D. It is the set of SEC rules that lets companies sell securities without a registered public offering, and it is the reason a seed round takes weeks of paperwork instead of the year-long process of an IPO. Inside Regulation D, two rules dominate: Rule 506(b) and Rule 506(c). They look similar, differ in two important ways, and the choice between them is visible in public filings.
This guide explains the framework in plain terms and then focuses on the 506(b) versus 506(c) split.
## The starting point: registration or exemption
The Securities Act of 1933 sets a default rule. Every offer and sale of securities must be registered with the SEC unless an exemption applies. Registration means a full prospectus, audited financials, and SEC review. That makes sense for public offerings and makes no sense for a startup raising $3 million from a handful of investors.
Regulation D, adopted by the SEC in 1982, provides a set of safe harbors that make private capital raising practical. A company relying on Regulation D must file a notice with the SEC, called [Form D](https://www.sec.gov/education/smallbusiness/exemptofferings/formd), within 15 days after the first sale of securities. The filing is public on EDGAR, which is why Regulation D activity can be tracked.
Regulation D contains three offering exemptions worth knowing.
## Rule 504: the small offering exemption
Rule 504 allows a company to raise up to $10 million in a 12-month period. The SEC raised this cap from $5 million to $10 million in amendments that took effect in March 2021. Rule 504 offerings remain subject to state securities registration in most cases, which limits their practical appeal. Startups and funds mostly skip past it, and Rule 504 accounts for a small slice of Regulation D activity.
## Rule 506(b): the classic private placement
Rule 506(b) is the workhorse of private capital. Its key features:
- **No cap on the amount raised.** A company can raise any amount.
- **No general solicitation.** The company may not advertise the offering publicly. No tweets about the raise, no demo-day pitch to a general audience, no "we're raising" landing page. Investors must come through pre-existing relationships or private networks.
- **Investor limits.** The company may sell to an unlimited number of accredited investors and to up to 35 non-accredited purchasers. Any non-accredited purchasers must be sophisticated, meaning they have enough financial knowledge to evaluate the investment, and their inclusion triggers disclosure obligations comparable to registered offerings. In practice, most 506(b) rounds take accredited investors only, precisely to avoid those disclosure requirements.
- **Self-certification is standard.** The company must have a reasonable belief that its investors are accredited, and in a 506(b) offering a questionnaire in which the investor certifies their own status is the accepted norm. See the SEC's overview of [private placements under Rule 506(b)](https://www.sec.gov/resources-small-businesses/exempt-offerings/private-placements-rule-506b).
## Rule 506(c): general solicitation allowed
Rule 506(c) was created by the JOBS Act and took effect in September 2013. It removed the advertising ban in exchange for a stricter investor requirement:
- **General solicitation is permitted.** The company may advertise the offering publicly, on its website, in media, at events, anywhere.
- **All purchasers must be accredited investors.** No 35-person allowance for non-accredited purchasers. Everyone who buys must be accredited.
- **Verification is mandatory.** The company must take reasonable steps to verify that purchasers are accredited. Self-certification alone has historically not been enough under 506(c). The rule lists non-exclusive safe-harbor methods: reviewing tax documents showing income, reviewing bank and brokerage statements showing net worth, or obtaining a written confirmation from a registered broker-dealer, investment adviser, licensed attorney, or CPA. See the SEC's page on [general solicitation under Rule 506(c)](https://www.sec.gov/resources-small-businesses/exempt-offerings/general-solicitation-rule-506c).
One recent development softened the verification burden. In a March 2025 no-action letter, SEC staff indicated that an issuer can reasonably rely on high minimum investment amounts, at least $200,000 for natural persons and at least $1 million for entities, combined with written representations from the purchaser, as reasonable steps to verify accredited status. That guidance made 506(c) more workable for large-check offerings, though the verification requirement itself still stands and still separates 506(c) from 506(b).
## The two differences that matter
Strip away the details and the split comes down to two trades:
1. **Publicity.** 506(b) forbids public advertising of the offering. 506(c) allows it.
2. **Verification.** 506(b) accepts a reasonable belief based on self-certification. 506(c) demands reasonable verification steps, with documentation.
Everything else is shared. Both allow unlimited raise sizes. Both produce a Form D filing. Both create restricted securities that investors cannot freely resell. Both are federal covered securities, meaning states cannot impose their own registration requirements on the offering, though states can require notice filings and fees. Both are subject to the bad actor disqualification in Rule 506(d), which blocks the exemption when the company or its covered persons have certain securities-law convictions or sanctions.
## Why most companies still choose 506(b)
Despite the advertising freedom of 506(c), the traditional 506(b) route remains dominant in venture rounds. The reasons are practical:
- Most startups do not need to advertise. Their investors come through networks, and the round is oversubscribed or dead long before advertising would help.
- Verification adds friction. Asking a venture fund for verification paperwork is easy. Asking twenty angel investors for tax returns or CPA letters is awkward, and some walk away.
- Lawyers default to the established path. 506(b) has decades of practice behind it.
506(c) shines in specific cases: crowdfunding-style platforms for accredited investors, real estate syndications marketed online, funds that promote publicly, and any raise where the company wants to talk about the round while it is still open.
## Reading the exemption on a Form D
Every Form D states which exemption the issuer claims, in Item 6. This makes the 506(b) versus 506(c) choice a public data point, and it carries signal:
- A **506(b)** filing suggests a conventional private round through existing networks. This is the default for venture-backed startups.
- A **506(c)** filing tells you the company reserved the right to market publicly. Expect to see the raise promoted, or expect a platform-based offering.
- A **Rule 504** filing indicates a small raise, capped at $10 million over 12 months.
Combined with the other fields, offering amount, securities type, industry group, and the date of first sale, the exemption box helps separate a quiet institutional round from a publicly marketed syndication. The Startup Capital Raises Report carries this context for every operating-company raise of $1 million or more it parses from the daily Form D feed. It filters out pooled investment funds, ranks raises by a funding score built on size, freshness, and securities type, shows the disclosed executives, and links every row to the official filing on EDGAR. You can also query the raw filings yourself through [EDGAR full-text search](https://efts.sec.gov/LATEST/search-index?q=%22form%20d%22).
## The honest caveats
This is an overview, not legal advice, and offering exemptions have details and edge cases that matter in practice. Companies sometimes claim multiple exemptions or amend filings as a round evolves. A claimed exemption on a Form D is the issuer's own assertion, and the SEC does not review or approve it. For anyone raising capital, the rules are a matter for securities counsel. For anyone reading filings, the exemption box is a reliable and underused piece of public signal.
*The [Startup Capital Raises Report](/reports/formd.html) shows the exemption, amount, and executives for every new Form D raise before the press writes about it. [Get the free preview](https://datasignalslab.lemonsqueezy.com/checkout/buy/ebaa8db4-4d75-4a3c-a241-7b481025c3e9).*
*DataSignals Lab publishes data and research. This is not investment advice.*
---
# How to Follow Berkshire Hathaway's 13F Portfolio
URL: https://datasignalslab.com/blog/follow-berkshire-hathaway-13f/
No quarterly disclosure gets read more closely than Berkshire Hathaway's Form 13F. Within minutes of each filing hitting SEC EDGAR, headlines announce what Berkshire bought and sold. Most of those headlines are written in a hurry, and some of them are wrong in predictable ways. This guide shows how to go straight to the source, read the filing correctly, and avoid the standard mistakes.
## Why Berkshire's 13F matters
Berkshire Hathaway is required to file because it is an institutional investment manager with far more than $100 million in US-listed 13(f) securities. Under Section 13(f) of the Securities Exchange Act, such managers must report their long US equity holdings within 45 days after each calendar quarter ends. The SEC's [Form 13F FAQ](https://www.sec.gov/divisions/investment/13ffaq) covers the rule in detail.
Two things make Berkshire's filing unusually informative compared with most hedge fund filings. The portfolio is concentrated, with a handful of positions dominating the total, so changes are meaningful rather than noise. And the holding periods are long, often measured in years or decades, which softens the biggest weakness of 13F data: the reporting lag. A fast-trading fund may be out of a position before its filing is public. Berkshire's positions are usually still there.
The filing also marks an era change. Warren Buffett handed the chief executive role to Greg Abel at the start of 2026 while remaining chairman, which gives each new filing an extra question to answer: what does the portfolio look like as the transition beds in.
## Where to find the filing
Berkshire files under CIK 0001067983. The fastest route is the [EDGAR company search](https://www.sec.gov/cgi-bin/browse-edgar): enter Berkshire Hathaway or the CIK, then filter on form type 13F-HR. Each filing page includes the cover page and the information table, which EDGAR renders both as raw XML and as a readable table.
Filings arrive on a predictable rhythm. The deadline is 45 days after quarter end, which puts the four filings in mid-February, mid-May, mid-August and mid-November, shifted to the next business day when the date falls on a weekend or holiday. Berkshire, like most large filers, tends to file close to the deadline.
If you prefer a friendlier view, [Dataroma](https://www.dataroma.com) presents Berkshire's holdings and quarterly activity in clean tables, and [WhaleWisdom](https://whalewisdom.com) adds history and comparisons. Both are built from the same EDGAR filings, so anything important should be verified against the original.
## How to read the quarterly changes
Pull the latest filing and the prior one, then match positions by CUSIP. Classify each into new buys, adds, trims and exits, and always compare share counts rather than dollar values. A position's reported value moves with the stock price, so a rising value does not mean Berkshire bought anything.
Weight matters more than count. Berkshire's equity portfolio is famously top-heavy. Long-running core positions such as Apple, American Express, Coca-Cola, Bank of America and Chevron have dominated the filing for years, and a small percentage change in one of them can involve more money than an entire new position further down the list. Read the filing in two passes: first the big holdings for any change at all, then the bottom of the table for new names.
New positions get the headlines for good reason. As one example, Berkshire's Q1 2026 filing disclosed new positions including Alphabet, Delta Air Lines and Macy's, each traceable to the information table of the filing itself. When a firm known for decade-long holding periods starts a position, the entry is worth studying even though the filing arrives weeks after the purchases.
## What Berkshire's 13F does not show
The filing covers long positions in US-listed 13(f) securities only. Several important parts of the Berkshire picture live elsewhere.
**Cash and Treasury bills.** Berkshire's cash pile is one of its most discussed numbers, and it never appears in the 13F. It is disclosed in the quarterly 10-Q and annual 10-K reports, filed under the same CIK on EDGAR.
**Foreign-listed holdings.** Positions listed only on non-US exchanges do not appear. Berkshire's long-standing BYD stake, listed in Hong Kong, was never in the 13F and its changes were disclosed through Hong Kong exchange filings. Its stakes in the five large Japanese trading houses are likewise disclosed outside the 13F.
**Wholly owned businesses.** GEICO, BNSF, the energy companies and dozens of other operating subsidiaries are not portfolio positions and never appear. The 13F covers only the marketable securities portfolio.
**Who made each decision.** The filing does not distinguish stock picks made by Buffett from those made by the investment managers Todd Combs and Ted Weschler. Smaller positions are commonly attributed to the two managers by observers, but the filing itself never says.
One more wrinkle: confidential treatment. The SEC can allow a manager to omit a position from the public filing while it is still being built, with disclosure following later. Berkshire has used this mechanism in the past, notably while accumulating its stake in Chubb, which was revealed in May 2024 after quarters of speculation about a hidden position. So a Berkshire 13F is occasionally incomplete on the day it is published, with the missing piece arriving in a later filing or amendment.
## Following Berkshire without fooling yourself
The lag is the first discipline. A February filing shows the portfolio as of December 31. Prices have moved since, and in rare cases the position has too. Buying a stock purely because it appeared in the filing means paying a different price than Berkshire did, sometimes a much higher one after the announcement pop.
The size mismatch is the second. Berkshire manages hundreds of billions of dollars. Its opportunity set is limited to very large companies, and its position sizing logic does not transfer to a small portfolio. What does transfer is the research lead: a new Berkshire position is a well-vetted candidate for your own analysis of the business, its pricing power and its valuation.
The third discipline is context across managers. Berkshire is one voice, an exceptionally good one, but still one. When a Berkshire new buy also shows up as a new buy or an add at other respected funds in the same quarter, the case for spending your research time on that company strengthens. That cross-fund view is exactly what a single filing cannot give you, and building it by hand means repeating the delta analysis for every fund you follow, every quarter.
*The [Smart-Money 13F Consensus Report](/reports/13f.html) puts Berkshire's filing in that context: it distills the latest filings of 18 top managers, Berkshire included, into one ranked consensus table. [Get the free preview](https://datasignalslab.lemonsqueezy.com/checkout/buy/8817c6c0-1d17-4b32-8238-e5339aa39310).*
*DataSignals Lab publishes data and research. This is not investment advice.*
---
# Senator Trades Versus the Market: What the Data Shows
URL: https://datasignalslab.com/blog/senator-trades-versus-the-market/
The idea that senators beat the market is one of the most durable beliefs in retail investing. It powers tracker accounts, dashboards, two exchange-traded funds and a steady stream of headlines. It also rests on a real academic finding. The problem is that the finding is from data collected three decades ago, and the research since then points the other way.
This article walks through the actual studies, in order, and then looks at what the disagreement means for anyone using congressional trading data today.
## The study that started it: Ziobrowski (2004)
The claim has a specific origin. Ziobrowski, Cheng, Boyd and Ziobrowski published "Abnormal Returns from the Common Stock Investments of the U.S. Senate" in the Journal of Financial and Quantitative Analysis in 2004. They built portfolios from senators' disclosed trades between 1993 and 1998 and measured performance against the market.
The result was striking. A portfolio mimicking senators' purchases beat the market by roughly 85 basis points per month. Stocks senators sold went on to lag. The authors interpreted the pattern as consistent with an information advantage: senators are close to government decisions that move markets, and their trades looked like they reflected that.
A follow-up study by the same authors, published in Business and Politics in 2011, examined House members' trades from 1985 to 2001 and found abnormal returns that were smaller than the Senate result but still positive.
These two papers are the academic foundation of the copy-Congress idea. They were widely covered, they featured in the 2011 political fight over congressional trading, and they are still the citations behind most "senators beat the market" claims. It is worth being precise about what they cover: hand-collected disclosure data from the 1980s and 1990s, before electronic filing, before the STOCK Act, and before anyone was watching.
## The first counterevidence: Eggers and Hainmueller (2013)
Andrew Eggers and Jens Hainmueller published "Capitol Losses: The Mediocre Performance of Congressional Stock Portfolios" in the Journal of Politics in 2013. They studied congressional stock portfolios from 2004 to 2008 and also reexamined the earlier period.
Their findings were the opposite of Ziobrowski's. They found no evidence of informed trading or above-market returns for Congress as a whole or for any subset of members they examined. The average congressional investor in their sample underperformed the market by 2 to 3 percent per year. Their conclusion was blunt: most members would have been better off in index funds, and widespread congressional insider trading looked more like myth than reality in their data.
Two careful researchers looking at adjacent periods reached opposite conclusions. That alone should adjust anyone's confidence in the simple version of the story.
## The modern verdict: post-STOCK Act studies
The STOCK Act of 2012 ([Pub. L. 112-105](https://www.congress.gov/bill/112th-congress/senate-bill/2038)) created the modern disclosure regime: transactions over $1,000 reported within 45 days, published by the [House Clerk](https://disclosures-clerk.house.gov) and the [Senate eFD system](https://efdsearch.senate.gov). That produced far better data, and researchers used it.
The largest study of the disclosure era is Belmont, Sacerdote, Sehgal and Van Hoek, "Do senators and house members beat the stock market? Evidence from the STOCK Act," published in the Journal of Public Economics in 2022. Using trades from January 2012 through December 2020, they found no evidence of superior investment performance, in aggregate or among senators specifically accused of informed trading. Stocks bought by House members underperformed on average by 26 basis points over a six-month horizon. Even the best-performing trades in their sample were consistent with random stock picking rather than skill. An earlier version circulated as an NBER working paper in 2020 under a title that summarizes the finding: "Relief Rally: Senators As Feckless As the Rest of Us at Stock Picking."
The modern evidence, on the best data available, does not show senators beating the market.
## Why the studies disagree
Several explanations are plausible, and they matter for how you use the data.
**The world changed.** The 1993 to 1998 Senate was trading in an environment with paper disclosures, little media attention and no rapid reporting requirement. After 2012, every trade becomes public within 45 days and tracker accounts amplify it within minutes of filing. If an edge existed, scrutiny is exactly the kind of thing that shrinks it. Research on Senate behavior after the 2011 60 Minutes broadcast and the STOCK Act suggests trading activity itself changed under the spotlight.
**Averages hide variation.** Finding no edge on average does not mean no individual trade was ever informed. It means informed trades, if they exist, are rare enough not to move the aggregate. Individual enforcement questions and portfolio-level statistics are different subjects.
**Old data was thin.** The early studies relied on hand-collected records with unavoidable gaps. Modern studies work from a far more complete electronic record. When better data reverses a finding, the newer result usually deserves more weight.
## The real-world test: the copy-Congress ETFs
Since February 2023 the question has had a live experiment. Two ETFs, NANC and KRUZ, build portfolios from the disclosed trades of Democratic and Republican members and their spouses respectively. Both funds necessarily buy after disclosure, which means after the up-to-45-day lag, exactly like any copier.
Their ongoing performance is public fund data anyone can check against a benchmark. Whatever the numbers show in any given period, the structural point stands: a disclosure-based portfolio holds what members held weeks ago, concentrated in whatever those members happened to own, which for the Democratic fund has meant a heavy tilt toward large-cap technology. Separating "congressional information" from "held big tech in a big tech bull market" is exactly the kind of question the academic studies are designed to answer, and their answer so far is that the information effect is not there.
## What this means for using congressional trading data
The honest conclusion is not that the data is useless. It is that the data answers different questions than the popular narrative assumes.
As a mechanical trading signal, congressional trades have weak support. The disclosure lag runs up to 45 days, amounts are ranges rather than exact figures, and the best modern study finds no outperformance to copy in the first place.
As transparency and research data, the filings are excellent and getting better. They show which members hold positions in sectors they oversee. They surface consensus, when multiple members buy the same ticker in the same window, which is a research lead regardless of whether it predicts returns. They give journalists the raw material that has repeatedly produced accountability reporting. And they provide context: knowing that Congress is accumulating a sector tells you something about Washington's attention even if it tells you nothing tradable.
That is the frame worth keeping. Senators are, on the current evidence, roughly as good at picking stocks as everyone else. Their trades are still worth reading, for what they reveal rather than for what they promise.
*The [Congress Stock Trades Report](/reports/congress.html) turns these filings into one scored, ranked document. [Get the free preview](https://datasignalslab.lemonsqueezy.com/checkout/buy/2002ea9c-0e57-4e3d-ab50-3dc12a865d07).*
*DataSignals Lab publishes data and research. This is not investment advice.*
---
# How to Find Startup Funding Rounds Before the Press
URL: https://datasignalslab.com/blog/find-startup-funding-rounds-before-the-press/
Funding news follows a predictable path. A startup closes a round, the founders and investors agree on announcement timing, a PR firm pitches the story, and weeks later a tech news site publishes it. By the time the article is live, every competitor, recruiter, and salesperson reads it at the same moment.
There is an earlier point in that timeline, and it is public. Most US startup raises trigger a mandatory SEC filing called Form D, due within 15 days after the first sale of securities in the offering. That filing lands on EDGAR regardless of the company's press plans. Form D filings often precede any press coverage, and many raises never get press at all. This article shows how to work that source, first by hand and then in an automated way.
## Why the press lags the filing
The [Form D requirement](https://www.sec.gov/education/smallbusiness/exemptofferings/formd) is tied to the first sale, not to the closing or the announcement. Rounds frequently have a first close months before the final close. A company might take its first checks in March, file Form D in early April, keep raising through the summer, and announce in September once the story is polished.
Some companies never announce. Bridge rounds, down rounds, extensions, raises by companies outside the tech press's beat, and debt raises rarely get coverage. Regional manufacturers, healthcare operators, and industrial companies raise millions under Regulation D without a single article being written. For anyone selling to funded companies or mapping a sector, these invisible raises are exactly the interesting ones, because nobody else is calling them.
## The source: EDGAR
Every Form D since March 2009 is filed electronically on EDGAR, the SEC's filing system. Access is free and requires no account. The relevant entry points:
- **[EDGAR full-text search](https://efts.sec.gov/LATEST/search-index?q=%22form%20d%22)**, with a human-friendly interface at [sec.gov/edgar/search](https://www.sec.gov/edgar/search/). Filter by form type "D" and a date range to see everything filed in a window.
- **Daily index files.** EDGAR publishes machine-readable indexes of every filing, updated throughout the day. Automated monitors work from these.
- **Company pages.** Each issuer has a filing history page, useful for checking whether a specific company has raised before.
The filings themselves are structured XML with a rendered HTML view, so the key fields, such as offering amount and amount sold, are extractable without guesswork.
## A manual workflow that works
If you want to watch a sector or a region without writing code, this routine takes about 20 minutes a day:
1. Open EDGAR full-text search, set form type to D, and set the date to today or yesterday.
2. Scan the issuer names. Skip anything with "Fund", "Partners", "Capital", "LP", or "LLC" naming patterns typical of investment vehicles. This rough filter is imperfect but fast.
3. Open the remaining filings. Check Item 4, the industry group. Anything marked "Pooled Investment Fund" is a fund raising from its limited partners, not a startup.
4. Check Item 13 for the total offering amount and the amount sold so far. A $50,000 raise and a $50 million raise look identical in the list view.
5. Check Item 3 for the executives and directors. This tells you who to contact and often reveals which known founders are behind a stealth entity.
6. Check Item 7 for the date of first sale. A first sale last week is fresh news. A first sale eight months ago on an amended filing is not.
Do this daily and you will regularly see raises weeks before any coverage, and you will see many that never get covered.
## The problems with the manual approach
Volume is the first problem. Hundreds of Form D notices arrive on a typical weekday, and the large majority are pooled investment funds. Manually separating operating companies from fund vehicles is tedious, and name-based filtering misses cases in both directions.
Ranking is the second problem. A raw date-sorted list treats a $1 million convertible note extension the same as a $200 million equity round filed the same morning. Deciding what deserves attention means weighing the size of the raise, how fresh the first sale is, and what kind of securities were sold.
Continuity is the third problem. New notices and amendments look similar in the feed. An amendment to a year-old offering is easy to mistake for a new raise if you only glance at the filing date.
## Automating it
The build-it-yourself version looks like this:
- Poll the EDGAR daily index for new Form D submissions.
- Fetch and parse the XML of each filing.
- Drop filings where the industry group is pooled investment fund, and drop amendments that add no new sales.
- Apply a size threshold so micro-raises do not bury the significant ones.
- Extract issuer name, location, executives, exemption claimed, securities type, total offering amount, and amount sold.
- Store results and diff against previous days.
The SEC asks automated clients to identify themselves with a user agent and respect rate limits, and the data itself is public domain. This is a genuinely feasible weekend project for a developer, and for one-off research it is the right answer.
The already-built version is a parsed feed. The Startup Capital Raises Report runs this exact pipeline daily. It parses every qualifying Form D raise of $1 million or more, filters out pooled investment funds, and ranks operating-company raises by a funding score that weighs size, freshness, and securities type. Each row shows the disclosed executives and links to the official filing on EDGAR, so every claim is one click from its source.
## What to do with an early signal
The value of catching a raise early depends on who you are:
- **B2B sales.** A company that just raised has budget and mandates to grow. Reaching them in the window between the filing and the announcement means reaching them before the inbound flood that follows press coverage.
- **Recruiting.** Funded companies hire. The Form D lists executives, which gives a warm entry point.
- **Investors.** Seeing which companies in a thesis area are raising, and under which exemption, maps the competitive funding landscape earlier than any database that waits for announcements.
- **Journalists and researchers.** The filing is a citable primary source. "According to a Form D filed Tuesday" is a stronger sentence than "according to a person familiar with the matter."
## The honest caveats
Form D is an early signal, not a complete one. The filing does not include valuation or investor identities. The amount sold can be a partial first-tranche number that grows in later amendments. Some raises never produce a Form D at all, because Rule 506 remains available even when a company files late or not at all, and because some offerings rely on other exemptions entirely. And a filing tells you that money moved, not that the company is good. Early information still requires judgment.
None of that changes the core arithmetic. The filing deadline is 15 days after first sale. Press cycles run weeks to months behind that, when they run at all. Whoever reads the filings first simply knows first.
*The [Startup Capital Raises Report](/reports/formd.html) surfaces every new Form D raise before the press writes about it, ranked and refreshed daily. [Get the free preview](https://datasignalslab.lemonsqueezy.com/checkout/buy/ebaa8db4-4d75-4a3c-a241-7b481025c3e9).*
*DataSignals Lab publishes data and research. This is not investment advice.*
---
# How to Track Hedge Fund Portfolios
URL: https://datasignalslab.com/blog/how-to-track-hedge-fund-portfolios/
Hedge funds do not publish their portfolios voluntarily. The law does it for them, once a quarter. Any institutional investment manager with at least $100 million in US-listed 13(f) securities must disclose its long equity positions on SEC Form 13F within 45 days after quarter end. The SEC documents the requirement in its [Form 13F FAQ](https://www.sec.gov/divisions/investment/13ffaq). That single rule makes hedge fund tracking possible for anyone with a browser and some patience. Here is how to do it properly, from raw filings to a repeatable workflow.
## Step 1: Find the right filer on EDGAR
Everything starts at SEC EDGAR, the commission's free filing database. Use the [EDGAR company search](https://www.sec.gov/cgi-bin/browse-edgar) and filter on form type 13F-HR.
The first hurdle is naming. Funds market themselves under one name and file under another. The entity that files is the management company, not the fund vehicle you read about in the press. Some firms also have multiple filing entities. To resolve this, search a few name variants, or use [EDGAR full-text search](https://www.sec.gov/edgar/search/) to find the filing and read the cover page, which names the manager.
Once you find the right entity, note its CIK number. The CIK is permanent. With it you can pull every 13F the manager has ever filed, going back to when it first crossed the $100 million threshold.
## Step 2: Pull two quarters, not one
A single 13F is a photo. Two consecutive 13Fs are a story. The entire value of hedge fund tracking sits in the difference between the latest filing and the one before it.
Open both filings and export or copy the information tables. Each row carries the issuer name, share class, CUSIP, market value, share count, an option flag for puts and calls, and discretion details. Match rows across the two quarters by CUSIP, not by name, because names are abbreviated inconsistently between filings.
Then classify every position:
- **New buys**: present now, absent last quarter.
- **Adds**: share count increased.
- **Trims**: share count decreased.
- **Exits**: present last quarter, gone now.
Two technical points matter. First, compare share counts rather than dollar values. A rising stock inflates the value of an unchanged position and looks like buying when it is not. Second, watch for stock splits, which can make an unchanged position look like a massive add. If a share count jumps by a suspiciously clean multiple, check for a split before drawing conclusions.
## Step 3: Weigh positions, not just names
A list of new buys is not enough. Context decides what a position means.
Position weight is the first filter. For a concentrated manager running ten to twenty names, a new 5 percent position is a major statement. For a giant multi-strategy fund whose filing runs to thousands of rows, a small line may be a hedge, a pairs trade leg, or an index adjustment that nobody senior ever discussed.
The option flag is the second filter. A put position on a stock is not a bullish holding. Filings from large multi-strategy and market-making-adjacent firms are full of listed options, and skipping the put or call column turns the data into noise.
Fund style is the third filter. A long-horizon value investor's new buy tends to still be in the book when you read about it. A fast-trading fund may have exited before the filing even became public. Match the signal to the manager's holding period.
## Step 4: Know what you cannot see
13F data has hard limits, and tracking hedge funds honestly means keeping them in view at all times.
The filing shows only long positions in US-listed 13(f) securities. Short positions are absent, so a fund can appear long a stock it is actually net short against through other instruments. Cash is absent. Bonds, loans and credit are absent. Foreign-listed shares are absent unless they trade as ADRs on a US exchange. Swaps and private derivatives are absent.
Timing is the other limit. Positions are reported as of quarter end, and most managers file at or near the 45-day deadline. By the time you read a filing, the snapshot can be up to 135 days stale, and anything could have changed since. Managers can also request confidential treatment from the SEC to delay disclosure of a position they are still accumulating, so an occasional large stake surfaces only later in an amendment.
For these reasons, treat 13F tracking as a research pipeline, not a trade feed. Studies of naive 13F-copying strategies report mixed and often disappointing results, largely because of the lag. The durable value is different: filings tell you where sophisticated investors with large research budgets have committed real capital, and that is an excellent starting point for your own work on a company.
## Free tools that speed this up
Three free resources cover most needs.
**SEC EDGAR** is the primary source and the final word. Every number you use should be traceable to a filing there. The [full-text search](https://www.sec.gov/edgar/search/) covers filings from 2001 onward.
**[Dataroma](https://www.dataroma.com)** tracks a curated list of well-known, mostly value-oriented superinvestors. It shows each manager's holdings, recent buys and sells, and which stocks appear across multiple tracked portfolios. It is free and easy to read, with the trade-off that its filer universe is small and curated.
**[WhaleWisdom](https://whalewisdom.com)** covers the broad filer universe with screening, filer comparisons and history. Some of its features sit behind a paid subscription, but basic filer lookups are free.
All of these sit on top of the same EDGAR filings. Use the convenient view to scan, and the primary filing to verify anything you plan to act on.
## Step 5: Track across funds, not just within one
Following one fund tells you what one team thinks. The stronger signal comes from overlap. When several managers with different mandates and different styles all initiate or add to the same stock in the same quarter, that convergence is worth more attention than any single filing.
Building that view by hand means repeating the two-quarter delta workflow for every fund you follow, then joining the results by CUSIP and counting how many funds hold each name and how many were buyers. It is straightforward but tedious, and it has to be redone every filing season, four times a year, within days of the deadline if you want the information while it is fresh.
That cross-fund aggregation step is exactly where automation earns its keep. Pick a fixed set of managers whose judgment you respect, rebuild the consensus every quarter, and spend your saved time on the part machines cannot do: understanding why the businesses at the top of the list might be attractive.
*The [Smart-Money 13F Consensus Report](/reports/13f.html) runs that workflow for you and distills the latest filings of 18 top managers into one ranked consensus table. [Get the free preview](https://datasignalslab.lemonsqueezy.com/checkout/buy/8817c6c0-1d17-4b32-8238-e5339aa39310).*
*DataSignals Lab publishes data and research. This is not investment advice.*
---
# Do Congress Members Beat the Market
URL: https://datasignalslab.com/blog/do-congress-members-beat-the-market/
The premise behind every congressional trading tracker is simple. Members of Congress hear things early. They sit in briefings, write legislation, and oversee industries. If any group of retail investors should have an edge, it is this one. So do they actually beat the market? The academic record gives a more complicated answer than the headlines suggest, and the honest summary is that the evidence flipped over time.
## The studies that started it all
The modern debate begins with Alan Ziobrowski and coauthors. Their first study, "Abnormal Returns from the Common Stock Investments of the U.S. Senate," was published in the Journal of Financial and Quantitative Analysis in 2004. It examined senators' stock transactions from 1993 through 1998, reconstructed from the annual disclosure reports that were the only public record at the time.
The result was striking. A portfolio mimicking senators' purchases beat the market by roughly 85 basis points per month. The stocks senators bought outperformed after they bought them, and the stocks they sold lagged after they sold. Returns of that size, compounding to double digits annually, resemble what researchers had previously measured for corporate insiders trading their own companies' stock. The authors read the pattern as consistent with senators trading on an information advantage.
A follow-up study by the same team, published in the journal Business and Politics in 2011, applied similar methods to the House of Representatives. Using roughly 16,000 transactions by around 300 House members from 1985 to 2001, it found that a portfolio mimicking House purchases beat the market by about 55 basis points per month, close to 6 percent per year. Smaller than the Senate result, but still large.
These two papers created the public narrative. They were widely cited in press coverage, appeared in congressional testimony, and contributed to the political momentum behind the STOCK Act of 2012, Public Law 112-105, which you can read on [congress.gov](https://www.congress.gov). That law affirmed that insider trading rules apply to Congress and created the 45-day Periodic Transaction Report that makes today's near-real-time tracking possible.
## The pushback
The Ziobrowski results did not survive unchallenged. In 2013, Andrew Eggers and Jens Hainmueller published "Capitol Losses: The Mediocre Performance of Congressional Stock Portfolios" in the Journal of Politics. They studied congressional stock investments from 2004 to 2008 with a different approach, measuring the actual portfolios members held rather than constructing calendar-time mimicking portfolios from transactions alone.
Their finding pointed the other way. Members of Congress underperformed passive index benchmarks by 2 to 3 percent per year over the period. The average member would have done better in an index fund. The authors concluded that the evidence for congressional insider trading was surprisingly weak and that members were, as a group, rather poor investors, not obviously different from ordinary retail traders.
The two bodies of work are not strictly contradictory, because they cover different periods and use different methods. But the contrast established the core methodological lesson of this literature: results depend heavily on the sample window, on whether you weight by trade size, and on whether you measure hypothetical mimicking portfolios or actual wealth outcomes.
## The post-STOCK Act evidence
The STOCK Act improved the data dramatically. Transactions now become public within 45 days instead of up to 18 months later, which lets researchers study cleaner samples with precise dates. The most direct modern test is by William Belmont, Bruce Sacerdote, Ranjan Sehgal, and Ian Van Hoek. Their work circulated as a National Bureau of Economic Research working paper in 2020 under the memorable title "Relief Rally: Senators As Feckless As the Rest of Us at Stock Picking," and a version was published in the Journal of Public Economics in 2022 as "Do senators and house members beat the stock market? Evidence from the STOCK Act."
Using trades disclosed under the STOCK Act from 2012 onward, they found no outperformance. Stocks purchased by senators slightly underperformed comparable stocks matched on industry and size over horizons from one to six months. Stocks bought by House members also underperformed modestly over six months. The authors found no evidence that members earned excess returns in industries overseen by their committees, and no superior performance even among members who had been publicly accused of informed trading.
Taken at face value, the post-2012 record says the average disclosed congressional trade contains no exploitable information.
## Why the results changed
Several explanations fit the pattern, and they are not mutually exclusive.
**Disclosure changed behavior.** The pre-2004 samples come from an era when trades stayed hidden for over a year and enforcement attention was minimal. The STOCK Act put every trade on a 45-day public clock and put the words insider trading and Congress in the same sentence in federal law. Members who once traded aggressively on their information may simply have stopped.
**Methods differ.** Transaction-based mimicking portfolios, holdings-based portfolio returns, and matched-stock comparisons answer related but distinct questions. Some of the gap between studies is measurement, not behavior.
**Averages hide tails.** Every study above describes the average member or the average trade. None of them rules out the possibility that specific trades by specific members at specific times were informed. A large purchase by a member whose committee oversees the sector, filed days before material news, can still be interesting even if the population-level average is zero. The population studies just say you cannot profit by blindly copying everyone.
## What this means for following congressional trades
The honest conclusions from twenty years of research:
- The famous outperformance numbers, 85 basis points per month for senators and 55 for House members, come from pre-2002 data and were published in 2004 and 2011. They describe a disclosure regime that no longer exists.
- Post-STOCK Act studies of the modern data find no average edge, and if anything slight underperformance of the stocks members buy.
- The disclosure lag compounds the problem for imitators. Even where a trade did contain information, a copier acting up to 45 days later buys after the market has had weeks to price it in.
- The data remains genuinely valuable for accountability, research, and screening. Which members trade heavily in sectors they oversee, whether multiple members converge on the same ticker, and how trading activity clusters around legislative events are all questions the filings answer regardless of whether copying trades is profitable.
So do Congress members beat the market? The best current evidence says no, not on average, not anymore. What the disclosures offer is not a shortcut to alpha but a public record worth watching, and the interesting work is in filtering thousands of routine transactions down to the few that deserve attention.
*The [Congress Stock Trades Report](/reports/congress.html) applies that filtering, turning raw filings into one scored, ranked document. [Get the free preview](https://datasignalslab.lemonsqueezy.com/checkout/buy/2002ea9c-0e57-4e3d-ab50-3dc12a865d07).*
*DataSignals Lab publishes data and research. This is not investment advice.*
---
# The Biggest Startup Capital Raises: Week 28, 2026 (SEC Form D)
URL: https://datasignalslab.com/blog/biggest-startup-raises-week-28-2026/
Every week, companies quietly disclose fresh capital raises in **SEC Form D filings**, often days before (and frequently without) any press coverage. We pull the filings straight from EDGAR, filter out pooled investment funds (the hedge/PE/VC noise that makes up most Form D volume), and rank what is left: the real operating-company startup-funding signal.
**This window (July 1–2, 2026): 60 qualifying raises, $1.2B in disclosed capital.**
## Top 10 by amount raised
| # | Company | Raised | Industry | State | Score |
|---|---------|--------|----------|-------|-------|
| 1 | Sealy Industrial Partners IV, LP | $441.3M | Other Real Estate | Louisiana | 89 |
| 2 | NLC VA 14 Raleigh Healthcare DST | $165.3M | Other Real Estate | NEW Hampshire | 79 |
| 3 | Prime Intellect, Inc. | $82.5M | Other | Delaware | 90 |
| 4 | Epping Forest Opportunity Fund I, LLC | $64.1M | Investing | Florida | 76 |
| 5 | Captura Corp. | $53.5M | Other Technology | California | 90 |
| 6 | Payit Holdings, LLC | $46.5M | Other | Missouri | 69 |
| 7 | Jaguar Health, Inc. | $40.8M | Pharmaceuticals | California | 83 |
| 8 | Georgia Banking CO INC | $38.8M | Commercial Banking | Georgia | 83 |
| 9 | Epsilon Group New Holdings Ltd | $32.8M | Other | Jersey | 69 |
| 10 | Myrtelle Inc. | $16.7M | Biotechnology | NEW YORK | 77 |
Score = size × freshness × type (0–100). Every row in the full report links to the original SEC filing.
## The headline raise: Sealy Industrial Partners IV, LP
Sealy Industrial Partners IV, LP reported **$441.3M** raised across 2891 investors (Other Real Estate, Louisiana). Disclosed executives / related persons include N/A Sealy Industrial Partners IV GP, LLC, Mark Sealy, Scott Sealy, Sr.. As always with Form D: amounts are self-reported by the issuer, and a filing is a disclosure, not an endorsement.
## Get the full picture
- **[The full ranked report for $19, refreshes daily](/reports.html)**: every qualifying raise with amounts, executives, securities type and direct SEC links. One-off purchase, your link always opens the latest edition.
- **Live data, any day or sector**: the underlying tool is self-serve on Apify at [$0.20 per result](https://apify.com/datasignalslab/startup-funding-form-d-monitor?fpr=wnlxst), and free to call for AI agents via our [MCP server](/datasignals-mcp.html).
*Data source: official SEC EDGAR Form D. This is data and research, not investment advice.*
---
# What Is SEC Form D: A Plain English Guide
URL: https://datasignalslab.com/blog/what-is-sec-form-d-plain-english/
Every year, tens of thousands of US companies raise money from private investors without registering the offering with the SEC. Most of them still have to tell the SEC that the raise happened. The document they use is Form D. It is short, it is public, and it often appears before anyone else knows about the deal.
This guide explains what Form D is, who files it, what it contains, and how to find filings yourself.
## The one-sentence definition
Form D is a brief notice that a company files with the SEC when it sells securities in an offering that is exempt from registration under [Regulation D](https://www.sec.gov/education/smallbusiness/exemptofferings/formd). It is not an application and not a request for approval. It is a notice that a sale has already started.
That last point matters. A registered offering, like an IPO, involves a long review process before any shares are sold. A Form D works the other way around. The company sells first and notifies the SEC afterward.
## Why companies file it
The Securities Act of 1933 requires that every offer and sale of securities in the United States either be registered with the SEC or qualify for an exemption. Registration is expensive and slow. So almost all startup fundraising happens under exemptions, and the most popular set of exemptions is Regulation D.
Regulation D contains several rules, mainly Rule 504, Rule 506(b), and Rule 506(c). Each one lets a company raise capital privately under specific conditions. Rule 503 of Regulation D then requires the company to file a notice of the offering with the SEC. That notice is Form D.
In practice this covers a huge share of American startup activity. Seed rounds, Series A through late-stage venture rounds, private debt raises, and fund formations all commonly rely on Regulation D. When a startup announces a $20 million Series B in the press, there is very often a Form D sitting on EDGAR that describes the same raise.
## The 15-day deadline
The SEC requires companies to file Form D within 15 days after the first sale of securities in the offering. The clock starts when the first investor is irrevocably committed to invest, not when the round closes or when the company decides to announce.
This deadline is the reason Form D is useful as a signal. Companies control their press timing. They do not control the Form D timing in the same way. A startup that quietly closed the first check of its round two weeks ago may have a Form D on EDGAR today, while the press release is planned for next quarter or never comes at all. Form D filings often precede any press coverage, and many raises never get press at all.
Filings are electronic. Since March 2009, all Form D notices must be submitted through EDGAR, the SEC's electronic filing system. That makes them searchable within minutes of filing.
## What is inside a Form D
Form D is deliberately short. It asks for basic facts about the issuer and the offering across 16 numbered items. The most useful pieces are:
- **Issuer identity and location.** Legal name, entity type, year of incorporation, and principal place of business.
- **Related persons.** Executive officers, directors, and promoters, with addresses. This is where you learn who runs the company.
- **Industry group.** A checkbox classification such as biotechnology, computer hardware, commercial real estate, or pooled investment fund.
- **The exemption claimed.** Which rule the company is relying on, most often Rule 506(b) or Rule 506(c).
- **Date of first sale.** When the first investor committed.
- **Types of securities.** Equity, debt, convertible notes, options, or interests in a fund.
- **Offering amounts.** The total offering amount, the amount already sold, and the amount remaining. Companies can check "indefinite" for the total.
- **Number of investors.** How many investors have participated so far, and whether any are non-accredited.
- **Sales compensation.** Whether brokers or finders were paid, and who they are.
A separate guide on this site walks through [how to read each item in detail](/blog/how-to-read-a-form-d-filing/).
## What is not inside a Form D
Just as important is what the form leaves out. There are no financial statements. No revenue figures beyond a broad range checkbox that most companies decline to answer. No valuation. No investor names, only the count. No pitch deck, no business description beyond the industry checkbox.
Form D is a notice, not a disclosure document. The SEC does not review or approve the offering, and a filed Form D says nothing about the quality of the company. It says only that the company reported an exempt offering.
There is also a coverage gap to be honest about. Rule 506 exemptions are self-executing, meaning the exemption is available even when the company files late or does not file. Enforcement of the filing requirement is light. So Form D captures a large share of private raises, not all of them. Some well-advised companies also raise under Section 4(a)(2) directly without using Regulation D, and those raises produce no Form D at all.
## How to find Form D filings
Everything is free on EDGAR. Three ways in:
1. **Company search.** If you know the company, look it up on [EDGAR company search](https://www.sec.gov/cgi-bin/browse-edgar?action=getcompany) and filter by form type "D".
2. **Full-text search.** [EDGAR full-text search](https://efts.sec.gov/LATEST/search-index?q=%22form%20d%22) lets you query filing contents directly, and the [search interface](https://www.sec.gov/edgar/search/) supports filtering by form type and date.
3. **Daily indexes.** EDGAR publishes daily filing indexes that list every new submission, including every Form D. This is how automated monitoring works.
The raw feed has real friction, though. A typical weekday brings hundreds of new Form D filings. The majority are pooled investment funds, meaning hedge funds, private equity vehicles, and real estate funds raising from their limited partners. Those are legitimate filings but they are not startups. Finding the operating companies means parsing the XML of each filing, checking the industry classification and fund indicators, and extracting the amounts.
## Why anyone watches this feed
Different readers use Form D for different reasons:
- **Investors** watch for companies raising in their sector before the deals become common knowledge.
- **Sales teams** treat a fresh raise as a buying signal, since funded companies spend.
- **Journalists** use filings to confirm or break funding stories.
- **Founders and analysts** use the feed as a map of who is raising, how much, and under which exemption.
The common thread is timing. By the time a round is on a tech news site, everyone has it. The Form D existed first.
## Doing it yourself vs using a parsed feed
You can build this pipeline yourself. Pull the EDGAR daily index, fetch each Form D, parse the XML, drop the pooled investment funds, and keep the raises above your size threshold. It is a real engineering task but a well-documented one, and the data is free.
The alternative is a feed that has already done the parsing. The Startup Capital Raises Report parses every qualifying Form D raise of $1 million or more, filters out pooled investment funds, and ranks the remaining operating-company raises by a funding score based on size, freshness, and securities type. Each row shows the disclosed executives and links back to the official filing on EDGAR, and the report refreshes daily.
## The honest caveats
Form D tells you a raise happened, not why or at what valuation. Amounts sold can be partial snapshots, since companies may file when only the first tranche has closed. Some offerings check "indefinite" for the total amount. And as noted above, not every private raise produces a Form D. Treat the feed as an early, incomplete, but official signal rather than a complete census of private markets.
*The [Startup Capital Raises Report](/reports/formd.html) ranks every new Form D raise before the press writes about it. [Get the free preview](https://datasignalslab.lemonsqueezy.com/checkout/buy/ebaa8db4-4d75-4a3c-a241-7b481025c3e9).*
*DataSignals Lab publishes data and research. This is not investment advice.*
---
# What Is a 13F Filing and How to Read It
URL: https://datasignalslab.com/blog/what-is-a-13f-filing-how-to-read-it/
Four times a year, the world's largest investment managers hand the public a snapshot of their US stock portfolios. That snapshot is Form 13F. It is free, it is official, and it is the raw material behind every "what is Buffett buying" headline you have ever read. Yet most people have never opened one. This guide explains what a 13F filing is, where to find it, and how to read it without fooling yourself.
## What a 13F filing is
Form 13F is a quarterly report required by Section 13(f) of the Securities Exchange Act of 1934. Congress added the requirement in 1975 to give the public and regulators visibility into the holdings of large institutional managers.
The rule applies to institutional investment managers that exercise investment discretion over at least $100 million in so-called Section 13(f) securities. That group includes hedge funds, mutual fund managers, pension managers, banks, insurance companies and large family offices. The filing is due within 45 days after the end of each calendar quarter. The SEC explains the mechanics in its official [Form 13F FAQ](https://www.sec.gov/divisions/investment/13ffaq).
Section 13(f) securities are, roughly, US exchange-traded stocks, certain ETFs, certain convertible bonds, and exchange-traded options on those securities. The SEC publishes an official list of covered securities every quarter. If a security is not on that list, it does not appear in the filing.
Three form variants exist. Form 13F-HR is the standard holdings report. Form 13F-NT is a notice that says another manager reports the holdings instead. Form 13F-HR/A is an amendment that corrects or restates an earlier filing.
## Where to find 13F filings
Every 13F filing lives on SEC EDGAR, the commission's free public filing system. Two entry points work well.
First, the [EDGAR company search](https://www.sec.gov/cgi-bin/browse-edgar) lets you look up a manager by name or CIK number and filter by form type 13F-HR. Each filer has one CIK, so once you know it you can pull every quarter of history.
Second, [EDGAR full-text search](https://www.sec.gov/edgar/search/) lets you search across filings. It is useful when you know a fund's brand name but not its exact legal filer name, which are often different.
One practical warning. Fund brands and legal filers rarely match one to one. A famous investor may file under a management company name you have never heard of, and one firm can have multiple filing entities. Confirm you have the right CIK before you compare quarters.
## How to read the holdings table
The heart of a 13F is the information table. Since 2013 it is filed as structured XML, and EDGAR also renders it as a readable web page. Each row is one position and carries these columns:
- **Name of issuer and title of class.** The company and the share class, for example common stock versus a specific class.
- **CUSIP.** The security identifier. This is the reliable key for matching positions across funds and across quarters, because company names get abbreviated inconsistently.
- **Value.** The market value of the position at quarter end.
- **Shares or principal amount.** The position size in shares.
- **Put or call flag.** If the row is an option position, this column says whether it is a put or a call. A put row is not a bullish bet, so never skip this column.
- **Investment discretion and voting authority.** Whether the manager decides alone or shares control, and how voting power splits.
Reading one filing in isolation tells you what a fund held on the last day of the quarter. The real information is in the change between two filings. Line up the current quarter against the prior quarter by CUSIP and classify every position into four buckets:
1. **New buys.** In the current filing, absent from the prior one.
2. **Adds.** Held in both, share count went up.
3. **Trims.** Held in both, share count went down.
4. **Exits.** In the prior filing, gone from the current one.
Also look at position weight. A new buy that is 8 percent of a concentrated portfolio says far more than a 0.1 percent line in a fund with three thousand rows. Compare share counts, not dollar values, when judging whether a fund bought or sold. A position's dollar value can rise purely because the stock went up.
## What a 13F does not show
This is where most misreadings happen. A 13F filing shows long positions in US-listed 13(f) securities and nothing else. It does not show:
- **Short positions.** A fund can be net short a stock it appears to hold.
- **Cash.** There is no cash line in a 13F.
- **Bonds and credit.** Corporate bonds, treasuries and loans are absent.
- **Foreign-listed shares.** A stock listed only in Hong Kong or Frankfurt does not appear, even if the position is enormous. ADRs traded on US exchanges do appear.
- **Derivatives beyond listed options.** Swaps and other private contracts are invisible.
- **Intra-quarter trading.** The filing is a quarter-end snapshot. A fund can buy and fully sell a stock inside a quarter and leave no trace.
There is also the timing gap. Because the deadline is 45 days after quarter end, and most managers file at or near the deadline, the positions you read can be up to 135 days old at the moment you read them. A manager may also request confidential treatment from the SEC to delay disclosure of a position it is still building, which is why a large new stake sometimes appears in an amendment months later.
## Common mistakes to avoid
A few errors show up constantly when people first work with 13F data.
Treating a put option row as a long position is the classic one. The put or call column exists for a reason. Confusing dollar-value changes with buying is the second. Always compare shares. Ignoring 13F-NT and combined filings is the third, because holdings may sit under a different filer than expected. And assuming a filing reflects today's portfolio is the fourth. It reflects a date at least six weeks in the past.
Finally, remember what the data is for. Academic and practitioner studies of 13F-copying strategies come to mixed and often negative conclusions, largely because of the reporting lag. The filings are best used as a research lead. They tell you where experienced, well-resourced investors have put real money, and that is a strong reason to study a company. It is not a reason to buy it blind.
## Free tools that make this easier
You can do everything above with EDGAR alone, and every serious workflow should end at the primary source. For convenience, two free sites are worth knowing. [Dataroma](https://www.dataroma.com) tracks a curated set of value-oriented superinvestors and presents their holdings and changes cleanly. [WhaleWisdom](https://whalewisdom.com) covers a much broader filer universe with screening and history, with some features behind a paid tier. Both build on the same EDGAR filings you can verify yourself.
The next step beyond reading one fund is comparing several. When multiple managers with different styles file the same new buy in the same quarter, that overlap is the most useful signal 13F data produces, and it is invisible when you read filings one at a time.
*The [Smart-Money 13F Consensus Report](/reports/13f.html) does that comparison for you: it distills the latest filings of 18 top managers into one ranked consensus table. [Get the free preview](https://datasignalslab.lemonsqueezy.com/checkout/buy/8817c6c0-1d17-4b32-8238-e5339aa39310).*
*DataSignals Lab publishes data and research. This is not investment advice.*
---
# What Is 13F Consensus and Why Crowded Positions Matter
URL: https://datasignalslab.com/blog/what-is-13f-consensus/
One hedge fund buying a stock is an opinion. Five funds buying the same stock in the same quarter is a pattern. 13F consensus is the practice of reading institutional filings across funds instead of one at a time, to find the positions where multiple respected managers agree. It is the most useful thing you can do with 13F data, and also the easiest to misuse. This article explains how consensus is measured, what it signals, and why crowded positions deserve both attention and caution.
## The raw material: quarterly 13F filings
The data source is SEC Form 13F. Institutional investment managers with at least $100 million in US-listed 13(f) securities must report their long equity holdings within 45 days after each calendar quarter ends, as described in the SEC's [Form 13F FAQ](https://www.sec.gov/divisions/investment/13ffaq). Every filing is public and free on [SEC EDGAR](https://www.sec.gov/cgi-bin/browse-edgar).
A single filing answers one question: what did this fund hold at quarter end. Consensus analysis asks a different question across a chosen set of funds: which stocks do several of them hold, and which of those were they actively buying. The unit of analysis shifts from the fund to the stock.
## How consensus is measured
A basic consensus build has four steps.
**Pick the funds deliberately.** Consensus is only as meaningful as the panel behind it. A useful panel mixes styles, so agreement between funds is informative rather than automatic. A concentrated value investor, a macro fund, an activist and a distressed specialist agreeing on one stock says more than four clones of the same strategy agreeing. Panels are usually small, six to twenty funds, because beyond that the aggregate drifts toward looking like an index.
**Extract and normalize holdings.** Pull each fund's latest 13F and the prior quarter's, and match positions by CUSIP rather than by company name, since names are abbreviated inconsistently across filings. Respect the put and call flags, because an option row is not a plain long position.
**Compute the deltas.** For each fund, classify every position as a new buy, an add, a trim, an exit or unchanged, comparing share counts rather than dollar values so that price moves are not mistaken for trading.
**Aggregate by stock.** For every stock, count how many funds in the panel hold it, and how many were buyers this quarter. A simple conviction score combines the two: breadth of ownership plus recent buying activity. A stock held by five of six funds where three added or initiated ranks above a stock held by five funds that all sat still, which in turn ranks above a name held by two.
The ranking that falls out of this is the consensus table. The top of the table is where the panel's independent research processes converged in the same quarter.
## Why crowded positions matter: the bull case
The case for paying attention to consensus rests on what a 13F position actually represents. Each line is real capital committed after a research process, by a team with resources most individual investors do not have. When several such teams with different mandates reach the same name in the same window, three useful things follow.
First, the idea has survived multiple independent filters. Different funds have different analysts, different valuation frameworks and different risk committees. Convergence means the thesis is robust to more than one way of looking at it.
Second, buying activity dates the signal. Broad ownership alone can be stale, a legacy of positions built years ago. Fresh adds and new buys across funds indicate that the thesis is attractive at recent prices, which partially offsets the 45 day reporting lag baked into all 13F data.
Third, consensus compresses your research funnel. Nobody can study every stock. A ranked consensus table across respected managers is a defensible way to pick the ten names worth your next month of reading, which is a far more realistic use of 13F data than mechanical copying. Studies of naive 13F-copying strategies report mixed and often negative results, largely because of the reporting lag, so the honest framing is that consensus tells you where to look, not what to buy.
## Why crowded positions matter: the risk case
Crowding is not only a signal. It is also a risk factor, and any serious use of consensus data holds both ideas at once.
A stock heavily owned by similar institutions embeds a shared assumption. If that assumption breaks, many large holders may want out through the same narrow door at the same time, and the exit itself moves the price far more than the news alone would justify. Episodes of rapid, correlated unwinding in widely held hedge fund positions are a recurring feature of markets, and funds themselves track crowding metrics precisely to manage this exposure.
There is also a valuation effect. By the time a stock reaches the top of every consensus screen, a lot of informed buying has already happened. The expected return left on the table for a latecomer is smaller than what the early funds captured, and the downside if the crowd turns is larger.
The practical takeaway is not to avoid crowded names, and not to chase them, but to read the crowding level as context. High consensus with fresh buying flags a strong, current, shared thesis, and also flags that the position will be volatile if the thesis cracks. High ownership with net trimming across the panel is a very different message from the same ownership count, which is exactly why buying activity belongs in the score.
## Building consensus yourself, or reading it ready-made
Everything above can be done by hand with free tools. [EDGAR full-text search](https://www.sec.gov/edgar/search/) and company search give you every filing. [Dataroma](https://www.dataroma.com) shows overlap across its curated superinvestors for free, including which stocks appear in many tracked portfolios. [WhaleWisdom](https://whalewisdom.com) offers broader screening across the full filer universe, with some features paid.
The manual route is a spreadsheet exercise repeated every filing season: two filings per fund, CUSIP matching, delta classification, then aggregation. It is genuinely doable and instructive to build at least once. It is also the kind of work that quietly stops happening by the third quarter you have to redo it.
The ready-made route is a maintained report. The Smart-Money 13F Consensus Report tracks 18 widely followed managers, from Berkshire Hathaway, Citadel and Bridgewater to Renaissance Technologies, Duquesne and Point72, selected on assets under management and public prominence, never on past results. It ranks 50 stocks by a conviction score based on how many of the funds hold each name plus their buying activity, lists each fund's new buys, adds, exits and trims, and refreshes each filing season. Every figure traces back to the underlying EDGAR filing, so any line can be verified at the source in a minute.
However you build it, the discipline is the same. Consensus is the start of the research process, not the end. The table tells you where smart money agrees. The work of deciding whether they are right is still yours.
*The [Smart-Money 13F Consensus Report](/reports/13f.html) distills the latest filings of 18 top managers into one ranked consensus table. [Get the free preview](https://datasignalslab.lemonsqueezy.com/checkout/buy/8817c6c0-1d17-4b32-8238-e5339aa39310).*
*DataSignals Lab publishes data and research. This is not investment advice.*
---
# Nancy Pelosi Stock Trades and the PTR Filings
URL: https://datasignalslab.com/blog/nancy-pelosi-stock-trades-explained/
Nancy Pelosi is the most watched investor in American politics. Dedicated tracker accounts repost her filings within minutes. An exchange-traded fund carries a ticker that nods to her name. Financial media covers her disclosures the way it covers earnings reports. Yet most of what circulates about "Pelosi trades" skips the basics: who places the trades, what the filings actually contain, and whether following them has ever been shown to work.
This article walks through all of it, using only what the official record supports.
## Who actually places the trades
Nancy Pelosi has represented San Francisco in the House of Representatives since 1987. She served twice as Speaker of the House, the first woman to hold the job. She does not run a brokerage account full of tech stocks herself.
The trades that appear in her disclosures are made by her husband, Paul Pelosi. He runs Financial Leasing Services, a San Francisco investment and consulting firm, and has been a professional investor for decades. Under federal disclosure law, a House member must report transactions made by a spouse or dependent child, not just their own. Each transaction line in a filing carries an owner code, and the trades in Pelosi's reports are marked as spouse transactions.
Pelosi's office has stated repeatedly that she owns no stock herself and has no involvement in or prior knowledge of the transactions. Critics respond that a household shares financial interests either way. Both positions are part of the public debate. The filings themselves do not settle it. They only record what was bought and sold.
## Where the disclosures come from
Every trade attributed to Pelosi traces back to one source: the Periodic Transaction Reports she files with the Clerk of the House. These are published on the House Clerk's financial disclosure site at [disclosures-clerk.house.gov](https://disclosures-clerk.house.gov). Anyone can search her name and read every report, free, without an account.
The filing obligation comes from the STOCK Act of 2012 ([Pub. L. 112-105](https://www.congress.gov/bill/112th-congress/senate-bill/2038)). The law requires members of Congress to report securities transactions over $1,000 within 30 days of becoming aware of them, and in no case later than 45 days after the trade date. It also affirmed that members and their staff are covered by the insider trading prohibitions in federal securities law, which the [SEC](https://www.sec.gov) enforces for everyone else.
That 45-day window matters. By the time a Pelosi trade becomes public, the position may be weeks old. Anyone reacting to the disclosure is reacting to stale information, and the price has had plenty of time to move.
## What the filings actually show
A Periodic Transaction Report is less precise than people assume. Three details get lost in most coverage.
**Amounts are ranges, not numbers.** Disclosures use brackets such as $1,001 to $15,000, $50,001 to $100,000, and $1,000,001 to $5,000,000. A headline that states an exact dollar figure for a Pelosi trade is an estimate someone made from a bracket. The filing never contains an exact amount.
**Options appear often.** Many of the most discussed Pelosi transactions are not simple stock purchases. They are call options, frequently long-dated calls on large technology companies. An option position has a strike price and an expiry, which changes both the risk and the meaning of the trade. A report line might show the purchase of call options with a specific strike and expiration date, and that structure is part of the story.
**The report shows the trade, not the reasoning.** There is no field for intent. A sale can be a tax decision, a portfolio rebalance, or a bet. The filing cannot tell you which.
## The trades that made headlines
Several disclosures drew national attention because of their size, their timing, or both.
The filings have shown large call option purchases in major technology companies, including Nvidia, Apple, Microsoft and Alphabet, often in the upper disclosure brackets. Because Paul Pelosi concentrates in large-cap tech and uses options, the positions read as high conviction, and that is exactly what makes them shareable on social media.
One episode stands out. In July 2022, Paul Pelosi sold Nvidia shares shortly before the House voted on major semiconductor legislation. The sale followed public criticism of an earlier Nvidia purchase, and the disclosure indicated the shares were sold at a loss. Pelosi's office said the sale was made to avoid the appearance of a conflict. The episode became a reference point in the congressional trading debate on all sides, cited both by people who think the system works because the trade was disclosed and by people who think a member's household should not hold the stock at all.
The attention also produced a product. In February 2023, Unusual Whales and Subversive Capital launched two ETFs that build portfolios from congressional disclosures, one tracking Democratic filers and one tracking Republican filers. The Democratic fund trades under the ticker NANC, a direct nod to Pelosi.
## Pelosi is retiring
On November 6, 2025, Pelosi announced that she will not seek reelection in 2026. Her term ends in early January 2027, closing out nearly four decades in the House.
For people who track her filings, two things follow. First, she remains a sitting member until the term ends, so the STOCK Act filing requirements still apply to her household's trades through the end of her service. Second, once she leaves office, the periodic reporting obligation ends with the seat. Former members do not file Periodic Transaction Reports. The public window into the Pelosi household's trading closes when she leaves.
That is a useful reminder of what this data is. It is not a permanent feed from a famous investor. It is a transparency requirement attached to public office, and it lasts exactly as long as the office does.
## Does copying the trades actually work
The academic record on congressional trading is more sobering than the social media narrative.
Early research found an edge. Ziobrowski, Cheng, Boyd and Ziobrowski (2004), published in the Journal of Financial and Quantitative Analysis, studied Senate trades from 1993 to 1998 and found that a portfolio mimicking senators' purchases beat the market by roughly 85 basis points per month. A follow-up study by the same authors (2011) found smaller but still positive abnormal returns for House members.
Later research, using data from the disclosure era, found the opposite. Eggers and Hainmueller (2013), in a Journal of Politics paper titled Capitol Losses, examined congressional portfolios from 2004 to 2008 and found no evidence of informed trading. The average congressional investor in their sample underperformed the market by 2 to 3 percent per year. Belmont, Sacerdote, Sehgal and Van Hoek (2022), in the Journal of Public Economics, studied trades from 2012 to 2020, after the STOCK Act took effect, and found no evidence of superior performance in aggregate, with stocks bought by House members slightly underperforming over a six-month horizon.
None of these studies looked at Pelosi's filings in isolation, and a single household's results can differ from the average. But the base rate matters. The published evidence does not support the idea that congressional trades, followed mechanically after a 45-day delay, beat the market.
## How to follow the filings yourself
The raw path is simple. Search Pelosi's name on the [House Clerk disclosure site](https://disclosures-clerk.house.gov), open each Periodic Transaction Report, and read the transaction lines. The reports are free and official. The work is in the repetition: new filings arrive on their own schedule, amounts need estimating from brackets, and one member's trades mean more in the context of what the rest of the chamber is doing.
That context layer is what turns filings into research. Which trades are large relative to the bracket scale. Which are recent enough to still matter. Which tickers show up across multiple members in the same window, not just in one famous household.
*The [Congress Stock Trades Report](/reports/congress.html) turns these filings into one scored, ranked document. [Get the free preview](https://datasignalslab.lemonsqueezy.com/checkout/buy/2002ea9c-0e57-4e3d-ab50-3dc12a865d07).*
*DataSignals Lab publishes data and research. This is not investment advice.*
---
# How to Track Congress Stock Trades in 2026
URL: https://datasignalslab.com/blog/how-to-track-congress-stock-trades-2026/
Members of the US Congress must disclose their personal securities trades. The disclosures are public, free, and official. Yet most people who want this data end up on a third-party dashboard without ever seeing the source, and without understanding what the filings can and cannot tell them.
This guide covers the full path: where the data originates, how the filings work, what the known limitations are, and how to set up a tracking workflow in 2026 that does not fall apart on the details.
## Why the data exists at all
The legal basis is the STOCK Act of 2012 ([Pub. L. 112-105](https://www.congress.gov/bill/112th-congress/senate-bill/2038)). The law requires members of Congress to report any securities transaction over $1,000 within 30 days of becoming aware of it, and no later than 45 days after the trade date. The requirement covers the member, the member's spouse, and dependent children. It also affirmed that members and congressional staff fall under the same insider trading laws the [SEC](https://www.sec.gov) applies to everyone else.
These filings are called Periodic Transaction Reports, or PTRs. They are separate from the annual financial disclosure each member files, which lists holdings once a year. PTRs are the trade-level feed, and they are what every congressional trading tracker is built on.
## The two official sources
Congress has two chambers, and each publishes its own disclosures. The difference between them shapes everything downstream.
**House of Representatives.** The Clerk of the House publishes PTRs at [disclosures-clerk.house.gov](https://disclosures-clerk.house.gov). You can search by name and year, and download each report. Reports filed electronically are machine-readable PDFs. Some members still file on paper, and those arrive as scanned images that require manual reading or error-prone OCR. The House also publishes annual index files listing all disclosures for a year, which is the practical entry point for anyone processing filings in bulk.
**Senate.** The Senate runs its own electronic filing system at [efdsearch.senate.gov](https://efdsearch.senate.gov). Searching requires agreeing to terms on each visit, and the site is built for human visitors rather than automated collection. Anyone using Senate data should read the site's terms carefully, because they restrict how the data may be used. This asymmetry is why some datasets cover the House only. House data offers cleaner provenance.
Both sources are free. Neither requires an account. Everything a paid dashboard shows you about congressional trades started as a PDF on one of these two sites.
## How to read a Periodic Transaction Report
Open any PTR and you will find a table of transactions. Each line carries a handful of fields, and three of them are commonly misread.
**Amount is a range.** Filings report brackets, not exact figures: $1,001 to $15,000, $15,001 to $50,000, $50,001 to $100,000, and so on up through brackets in the millions. Any exact dollar figure you see attached to a congressional trade is an estimate derived from a bracket. Ranges are also why trade sizes should be compared by bracket, not by a false-precision number.
**Owner is not always the member.** A code on each line marks whether the transaction belongs to the member, the spouse, or a dependent child. Many of the most famous congressional trades are spouse transactions. That does not make them irrelevant, since the law treats household trades as reportable for a reason, but it is a fact worth keeping visible.
**Dates come in pairs.** Each line has a transaction date and each report has a filing date. The gap between them is the disclosure lag, and by law it can be as long as 45 days. When you see a trade today, the position may be six weeks old.
Asset descriptions add one more wrinkle. A line can describe common stock, but it can also describe call or put options with a strike and expiry, bonds, or fund shares. Ticker symbols are not always clean, and asset names need normalizing before any aggregation makes sense.
## The limitations you cannot work around
Honest tracking starts with what the data cannot do.
The 45-day lag is structural. No tool can show you a congressional trade before it is filed, and the law gives filers up to 45 days. Anyone promising real-time congressional trades is describing filing alerts, not trade alerts.
Compliance is imperfect. Journalists have repeatedly documented members filing late or with errors. The penalty for a late filing starts at a $200 fee, which is small enough that late reports keep happening. A tracking workflow should treat the feed as mostly complete rather than perfectly complete.
And the trades are not a strategy by themselves. Published academic work is mixed at best on whether members outperform, with the most recent large study, Belmont, Sacerdote, Sehgal and Van Hoek (2022) in the Journal of Public Economics, finding no evidence of superior returns in trades from 2012 to 2020. Congressional trading data is transparency data. It is useful for research, journalism, screening and context, not as a mechanical trading system.
## Three ways to track in 2026
**Option one: go direct.** Bookmark the [House Clerk site](https://disclosures-clerk.house.gov) and the [Senate eFD site](https://efdsearch.senate.gov), and check the members you care about. This costs nothing and gives you the primary record. It works well for a handful of names. It does not scale, because you are opening PDFs one at a time and doing the bracket math yourself.
**Option two: use a free dashboard.** Sites such as Capitol Trades and Quiver Quantitative parse the filings and present them in searchable form, with member profiles and recent trade lists. This is the right tool for casual browsing and for quickly answering questions like what a specific member bought last quarter. The tradeoff is that you get their interface and their prioritization, and exporting structured data for your own analysis typically requires a paid tier.
**Option three: use a scored feed.** For anyone who wants a decision-ready document or a data pipeline rather than a browsing session, the useful layer sits above parsing: estimating size from brackets, weighting recency, ranking trades so a large fresh purchase outranks a small stale sale, and surfacing consensus tickers that multiple members traded in the same window. That aggregation step is where raw filings turn into research material.
## What to actually look for
Whatever tool you use, the filings reward a few specific habits.
Watch size and freshness together. A trade in a top bracket filed this week deserves more attention than ten small trades from last month. Ranking by a combined score beats scanning a chronological list.
Watch consensus. One member buying a ticker is an anecdote. Several members buying the same ticker within a few weeks is a pattern worth investigating, even if the explanation turns out to be mundane.
Watch committee context. A trade in a sector the member's committee oversees carries a different weight than a generic index fund purchase. The filings do not flag this for you, which is exactly why the context layer matters.
And always keep the path back to the source. Every parsed trade should be traceable to the underlying PTR on the official site, because the filing is the record and everything else is interpretation.
*The [Congress Stock Trades Report](/reports/congress.html) turns these filings into one scored, ranked document. [Get the free preview](https://datasignalslab.lemonsqueezy.com/checkout/buy/2002ea9c-0e57-4e3d-ab50-3dc12a865d07).*
*DataSignals Lab publishes data and research. This is not investment advice.*
---
# How to Read a Form D Filing
URL: https://datasignalslab.com/blog/how-to-read-a-form-d-filing/
A Form D filing is short. Rendered on EDGAR it runs a few pages, organized into 16 numbered items. Reading one takes two minutes once you know which fields carry signal and which are boilerplate. This guide walks through the form the way an analyst reads it, in the order that answers the practical questions: who raised, how much, when, and under what terms.
For background, [Form D](https://www.sec.gov/education/smallbusiness/exemptofferings/formd) is the notice a company files with the SEC within 15 days after the first sale of securities in an offering exempt under Regulation D. It is a notice of a raise, not a disclosure document, so expect facts rather than narrative.
## Start with the header: new notice or amendment
Before reading any item, check Item 7, the type of filing. A Form D is either a **new notice** or an **amendment** (shown as D/A in EDGAR listings). This single flag changes the meaning of everything else.
A new notice reports a fresh offering. An amendment updates an earlier one. Companies must amend to correct material mistakes, to reflect certain material changes, and annually if the offering is still ongoing. An amendment showing a higher amount sold means the round grew. An annual amendment with unchanged numbers means nothing new happened. Treating a D/A as breaking news is the most common mistake in reading this feed.
## Item 1 and 2: who and where
Item 1 gives the issuer's legal name, entity type, jurisdiction of incorporation, and year of incorporation. Item 2 gives the principal place of business and a phone number.
Signal to extract: the entity age. A company incorporated this year raising its first round is a different story from a ten-year-old entity raising again. Stealth startups often file under bland legal names, so the name itself may tell you little, which is why the next item matters more.
## Item 3: related persons
Item 3 lists the executive officers, directors, and promoters, each with an address. This is often the most valuable field in the filing.
For a stealth company, the related persons are frequently the only way to identify who is behind it. A generic entity called something like "Project Metric Inc." becomes interesting when its listed directors are two known operators from a large tech company. For sales and recruiting use cases, this is the contact list. Note that investors are not listed here, only people connected to the issuer. Sometimes a director seat held by a partner at a venture firm hints at the lead investor, but the form never states it directly.
## Item 4 and 5: industry and size
Item 4 is a checkbox industry classification: banking, biotechnology, computers, energy, real estate, and so on. One checkbox deserves special attention: **Pooled Investment Fund**. When it is checked, the filer is a fund raising from limited partners, not an operating company. On a typical day, most Form D filings are funds. Any startup-focused reading of the feed starts by filtering these out.
Item 5 asks for the issuer's revenue range, or aggregate net asset value for funds. Most operating companies check "Decline to Disclose," so treat any actual answer as a bonus rather than an expectation.
## Item 6: the exemption claimed
Item 6 states which exemption the offering relies on. The common values are Rule 506(b) and Rule 506(c), with Rule 504 appearing for small raises capped at $10 million.
The practical difference: 506(b) forbids general solicitation and is the default for conventional venture rounds. 506(c) permits public advertising but requires all purchasers to be verified accredited investors. A 506(c) filing suggests the raise may be marketed openly. The plain-English [Form D overview](/blog/what-is-sec-form-d-plain-english/) covers where these rules come from.
## Item 7 and 8: timing and duration
Alongside the new-versus-amendment flag, Item 7 contains the **date of first sale**, the day the first investor became irrevocably committed. Compare it to the filing date. A first sale within the last two weeks means the filing is fresh, consistent with the 15-day deadline. A first sale many months before the filing date usually means a late filing or an amendment cycle.
Item 8 asks whether the offering is intended to last more than one year. Funds often say yes. Startups usually say no.
## Item 9 and 10: what is being sold
Item 9 lists the types of securities: equity, debt, options or warrants, security-to-be-acquired (which covers convertible instruments such as SAFEs and convertible notes), and pooled fund interests.
This field separates round types. Straight equity suggests a priced round. The convertible category suggests a SAFE or note round, typical at seed stage. Debt can mean venture debt or a bridge. Item 10 asks whether the offering is connected to a business combination such as a merger, which is rare and worth noticing when checked.
## Item 11 and 12: minimum investment and middlemen
Item 11 states the minimum investment accepted from an outside investor. A $0 or low minimum is normal for venture rounds. A high minimum, such as $100,000 or more, points to an institutional or syndication structure.
Item 12 discloses sales compensation: brokers, dealers, or finders being paid in connection with the offering, with their CRD numbers. Venture rounds are usually empty here. Filled-in recipients are common in real estate syndications and retail-facing private placements, where placement agents take a cut.
## Item 13: the money
Item 13 is the headline: **total offering amount**, **amount sold**, and **amount remaining**.
Read it carefully:
- The total can be a target, a ceiling, or "indefinite." An indefinite total is common for funds and continuous offerings.
- The amount sold is a snapshot as of the filing date. A company that files quickly after a first close may show a small sold amount that grows in later amendments.
- Sold equal to total usually means a completed round. Sold far below total means the raise is open, which is itself information: the company is still out raising.
When a press release later announces a round, the announced figure often matches the Form D amount sold, sometimes months after the filing was public.
## Item 14, 15 and 16: investors and proceeds
Item 14 gives the number of investors who have already invested, and how many are non-accredited. Two investors buying $30 million reads institutional. Ninety investors buying $2 million reads like an angel or crowd-style round. Any non-accredited count above zero is rare and means the issuer took on extra disclosure obligations under 506(b).
Item 15 states sales commissions and finders' fees, and Item 16 states the portion of proceeds used for payments to executive officers, directors, and promoters. Both are usually zero for startups. Non-zero values in Item 16 deserve attention, since they mean part of the raise flows to insiders rather than the business.
## A two-minute reading order
Putting it together, an efficient pass over any Form D:
1. Item 7: new or amendment, and date of first sale.
2. Item 4: pooled fund or operating company.
3. Item 13: total, sold, remaining.
4. Item 3: who runs it.
5. Item 6 and 9: exemption and security type.
6. Items 11 through 16: skim for anomalies.
That order resolves the main questions fast and flags the filings worth a deeper look.
## Reading one versus reading all of them
Reading a single filing is easy. The hard part is scale, since hundreds of Form D notices arrive on a typical weekday and most are funds. You can search and read them free through [EDGAR full-text search](https://efts.sec.gov/LATEST/search-index?q=%22form%20d%22). For a pre-filtered view, the Startup Capital Raises Report parses every qualifying Form D raise of $1 million or more, filters out pooled investment funds, and ranks operating-company raises by a funding score built on size, freshness, and securities type. Each row shows the disclosed executives and links to the official filing, and the data refreshes daily. Form D filings often precede any press coverage, and many raises never get press at all, so the filings are frequently the only record of a raise.
## The honest caveats
Form D reports what the issuer chose to state, and the SEC does not verify or approve the contents. Amounts can be partial snapshots. Some fields, like revenue range, are usually declined. No valuation and no investor names appear anywhere on the form. Reading a filing tells you a raise happened and its basic shape. What the raise means still takes judgment.
*The [Startup Capital Raises Report](/reports/formd.html) reads every new Form D for you and ranks the raises before the press writes about them. [Get the free preview](https://datasignalslab.lemonsqueezy.com/checkout/buy/ebaa8db4-4d75-4a3c-a241-7b481025c3e9).*
*DataSignals Lab publishes data and research. This is not investment advice.*
---
# The Biggest Startup Capital Raises: Week 27, 2026 (SEC Form D)
URL: https://datasignalslab.com/blog/biggest-startup-raises-week-27-2026/
Every week, companies quietly disclose fresh capital raises in **SEC Form D filings**, often days before (and frequently without) any press coverage. We pull the filings straight from EDGAR, filter out pooled investment funds (the hedge/PE/VC noise that makes up most Form D volume), and rank what is left: the real operating-company startup-funding signal.
**This window (June 30 – July 1, 2026): 59 qualifying raises, $2.7B in disclosed capital.**
## Top 10 by amount raised
| # | Company | Raised | Industry | State | Score |
|---|---------|--------|----------|-------|-------|
| 1 | Baseten Labs, Inc. | $1.1B | Other Technology | California | 100 |
| 2 | Fluidstack Ltd | $730.0M | Other Technology | United Kingdom | 100 |
| 3 | Bonaventure Multifamily Income Trust, Inc. | $144.4M | Reits And Finance | Virginia | 83 |
| 4 | CHE Holdings I LP | $90.0M | Retailing | NEW YORK | 90 |
| 5 | Prime Intellect, Inc. | $82.5M | Other | Delaware | 90 |
| 6 | Wander.com, Inc. | $64.1M | Other Technology | Texas | 76 |
| 7 | BREX Net Lease Industrial I DST | $45.8M | Other Real Estate | NEW YORK | 69 |
| 8 | Waveland Resource Partners Viii, LP | $44.2M | Oil And Gas | California | 69 |
| 9 | Teitf Vancouver FEE Owner, LLC | $42.4M | Commercial | NEW YORK | 69 |
| 10 | ADC Fernley, LLC | $41.6M | Other | California | 69 |
Score = size × freshness × type (0–100). Every row in the full report links to the original SEC filing.
## The headline raise: Baseten Labs, Inc.
Baseten Labs, Inc. reported **$1.1B** raised across 41 investors (Other Technology, California). Disclosed executives / related persons include Tuhin Srivastava, Sarah Guo, Shravan Narayen. As always with Form D: amounts are self-reported by the issuer, and a filing is a disclosure, not an endorsement.
## Get the full picture
- **[The full ranked report for $19, refreshes daily](/reports.html)**: every qualifying raise with amounts, executives, securities type and direct SEC links. One-off purchase, your link always opens the latest edition.
- **Live data, any day or sector**: the underlying tool is self-serve on Apify at [$0.20 per result](https://apify.com/datasignalslab/startup-funding-form-d-monitor?fpr=wnlxst), and free to call for AI agents via our [MCP server](/datasignals-mcp.html).
*Data source: official SEC EDGAR Form D. This is data and research, not investment advice.*
---
# How to Track Startup Funding Before the Press Release (SEC Form D)
URL: https://datasignalslab.com/blog/track-startup-funding-sec-form-d/
TechCrunch covers maybe a few dozen funding rounds a week. The SEC receives **hundreds of Form D filings every business day**. The gap between those two numbers is information almost nobody is looking at.
## What a Form D actually is
When a US company raises capital privately under Regulation D - the exemption behind virtually all startup rounds - it must file a Form D with the SEC within 15 days of the first sale. The filing is public, structured XML in EDGAR, and contains the company, its industry, the amount offered and sold, the type of securities, the minimum investment, the number of investors already in, and the executives and directors behind the company.
In other words: the official record of a raise, often days before any coverage - and for the majority of raises that never get covered, the only record there will ever be.
## The catch nobody mentions: funds
Here is what every raw Form D scraper gets wrong. By filing count, **most Form Ds are not startups** - they are pooled investment funds: hedge funds, PE vehicles and VC funds raising their own capital, often as endless series ("Fund V, Series 39"). If you do not filter those, your "startup funding feed" is mostly fund-administration noise.
The fix is in the filing itself: fund offerings declare the industry group "Pooled Investment Fund" and claim the Investment Company Act 3C exemption. Classify on those two fields and the noise drops away.
## What a filtered, scored day looks like
A real scan of one recent EDGAR day, operating companies only, $1M+ (from the [Startup Funding Monitor](/startup-funding-form-d.html)):
| Score | Amount | Industry | Company |
|---|---|---|---|
| 100 | $339.6M | Other Energy | EnerVenue Holdings |
| 100 | $270.5M | Other Technology | CesiumAstro |
| 86 | $30.5M | REITS and Finance | Brixmor Operating Partnership |
| 81 | $11.6M | Insurance | HMA II |
| 75 | $10.4M | Other Technology | Curiosities Inc. |
CesiumAstro (space communications) and EnerVenue (energy storage): nine-figure raises, with named executives parsed from the filings, visible in EDGAR before mainstream coverage. The score blends amount (log-scaled), new filing vs amendment, equity vs debt and freshness of the first sale.
## Why this beats news-based funding feeds
- **Coverage**: news-based feeds only know what a journalist wrote up. Form D captures raises no outlet covers - regional companies, insurance, energy, industrial tech.
- **Timing**: the filing precedes coverage when coverage exists at all.
- **Structure**: amounts come from XML fields, not from parsing "raised eight figures" out of a headline.
- **Provenance**: every record links to the SEC filing itself.
The honest limits: Form D only covers Regulation D offerings (some raises use other routes), filings can lag the first sale by up to 15 days, and amounts reflect what is reported at filing time.
## Do it yourself or as a feed
The source is free: EDGAR publishes a daily index of all filings, and each Form D contains a structured `primary_doc.xml`. You will need to handle the index format, the fund classification, "Indefinite" offering amounts and SEC fair-access rate limits.
Or skip the plumbing: the [Startup Funding Monitor](https://apify.com/datasignalslab/startup-funding-form-d-monitor?fpr=wnlxst) does the scan as a feed - $0.20 for a full ranked day of analyzed filings, JSON out, with the fund filter, scoring and executives included. AI agents can call it through our free [MCP server](/datasignals-mcp.html) ("who raised more than $10M this week?"), and it chains naturally into the [Form 4 insider scanner](/sec-form4-insider-buying.html) and [13D/G activist monitor](/sec-13dg-activist-stakes.html) for the full money-flow picture.
*Data for research, screening and monitoring - not investment advice.*
---
# Which Stocks Did Multiple Hedge Funds Buy Last Quarter? 13F Consensus, With Real Data
URL: https://datasignalslab.com/blog/hedge-fund-13f-consensus-q1-2026/
Every quarter, institutional managers with over $100M must disclose their US equity holdings in a 13F filing. Most tools dump those holdings as thousands of raw rows per fund. The more interesting question is the one across funds: **what do several smart-money managers agree on right now?**
We ran six well-known funds through the [Smart Money 13F](/smart-money-13f.html) consensus analysis - Berkshire Hathaway, Pershing Square, Third Point, Appaloosa, Coatue and Tiger Global - based on their most recent 13F filings (Q1 2026). This post shows the actual output.
## The consensus picks
| Stock | Funds holding | Of which bought/added | Combined value |
|---|---|---|---|
| Taiwan Semiconductor (TSM) | 4 of 6 | **3** | $5.6B |
| Alphabet (GOOGL) | 4 of 6 | 2 | $16.9B |
| Amazon (AMZN) | 4 of 6 | 2 | $5.3B |
| Meta Platforms (META) | 4 of 6 | 2 | $3.7B |
| ASML | 3 of 6 | 2 | $0.7B |
| Microsoft (MSFT) | 4 of 6 | 1 | $4.0B |
| Nvidia (NVDA) | 4 of 6 | 1 | $3.5B |
The single most interesting line is Taiwan Semiconductor: held by four of the six funds, and **three of them were buyers** in the latest quarter (Third Point, Appaloosa, Coatue and Tiger Global hold it; three increased or initiated). When managers with very different styles - an event-driven fund, a distressed specialist and two growth funds - converge on the same name in the same quarter, that is a research lead worth understanding.
## Notable new positions this quarter
The per-fund delta analysis (new buys vs prior quarter) showed, among others:
- **Berkshire Hathaway**: new positions in Alphabet, Delta Air Lines and Macy's.
- **Pershing Square**: a new position in Microsoft.
- **Third Point**: new in Meta, Alphabet, SPDR Gold Trust and TransDigm.
- **Appaloosa**: new in SanDisk.
- **Coatue**: new in Equinix, ASML, Visa and Qualcomm.
- **Tiger Global**: new in MercadoLibre, Lumentum and Intel.
Berkshire initiating Alphabet made headlines; the fact that Third Point started a new Alphabet position in the same quarter is the kind of cross-fund confirmation a consensus view surfaces immediately and headline-reading does not.
## The caveats that matter
Honesty first: 13F data has real limitations. Filings arrive up to 45 days after quarter end, so positions may have changed. They show only US long equity positions - no shorts, no bonds, no international lines, no options context. And consensus is a research lead, not a buy list: peer-reviewed work consistently finds that naively copying 13F filings is not a reliable strategy. The value is knowing **where to look**, faster.
## Reproduce this analysis
This entire post is the output of one $0.20-per-fund run. Give the [Smart Money 13F Actor](https://apify.com/datasignalslab/smart-money-13f?fpr=wnlxst) a list of fund CIKs and it returns each fund's new buys, increases, trims and exits plus the cross-fund consensus ranking - as JSON, with every fund linking back to its actual SEC filing. AI agents can run the same analysis through our free [MCP server](/datasignals-mcp.html).
Compare that with a WhaleWisdom subscription at roughly $30/month: if you need this once a quarter for a handful of funds, pay-per-result is an order of magnitude cheaper. (Need 20 quarters of history? Then WhaleWisdom is the better tool - see our [honest comparison](/compare.html).)
*Data for research, screening and monitoring - not investment advice. Historical patterns do not guarantee future results.*
---
# A Free MCP Server That Gives AI Agents 13 Live Financial Data Feeds
URL: https://datasignalslab.com/blog/free-mcp-server-financial-data-ai-agents/
Most AI agents are great at reasoning and bad at knowing things. Ask one "which companies had insider buying clusters this week?" and it will either hallucinate or try to scrape the web and time out.
The boring fix: an MCP (Model Context Protocol) server that exposes 13 scored financial and market-data feeds behind one endpoint, so Claude, Cursor or any MCP-capable agent can pull real, structured, source-linked data on demand.
## What the agent gets
One connection, thirteen data feeds: insider buying clusters (SEC Form 4), planned insider sales (Form 144), hedge-fund 13F consensus, 8-K material events, 13D/G activist stakes (including the activist's stated purpose from Item 4), biotech trial catalysts, FDA drug actions, US government contract awards, congress trading, NIH research funding, new SEC Form D startup-funding raises, a crypto momentum scanner and App Store review intelligence.
All sources are official and public - SEC EDGAR, openFDA, ClinicalTrials.gov, USAspending.gov, House Clerk, NIH RePORTER, CoinGecko, Apple App Store. Every result links back to the original filing, so the agent can cite its source instead of asserting from memory.
## Why MCP instead of a REST API
Integration cost. With a REST API, every data source means auth, schema reading and glue code. With MCP, the agent discovers the tools and reads their descriptions - the descriptions ARE the documentation. We write them for the model ("use this when the user asks about activist investors..."), not for humans.
The server itself is **free**. The underlying data tools bill $0.20 per result through Apify's pay-per-event model, only when a tool is actually called. No subscription, which matters for agents that might query a feed twice a month. Charging at both the server layer and the data layer would punish exactly the users we want.
## The architecture: a proxy over standby
The server runs as an Apify Actor in standby mode - a persistent process speaking streamable HTTP MCP at `/mcp`. Each tool is a thin proxy that calls the corresponding data Actor and returns its dataset. We chose proxying over reimplementing the logic in the server for three reasons: one source of truth (the data Actors already run and are monitored daily), independent billing per tool, and crash isolation - a bug in one parser cannot take down the endpoint.
The underlying data tools run as transparent [Apify](https://apify.com/datasignalslab?fpr=wnlxst) Actors, each monitored daily with a source link on every result.
## Gotchas that cost real time
- **FastMCP 3.x positional arguments**: `FastMCP("name", "x")` silently treats the second positional as `instructions`. Use keyword arguments.
- **Standby permissions**: Apify standby Actors run with limited permissions but MAY call other Actors - exactly what a proxy needs, not obvious from the docs.
- **Memory limits**: builds share an 8 GB pool on the default plan; push Actors sequentially or builds block each other.
## Try it
The endpoint is `https://datasignalslab--datasignals-mcp.apify.actor/mcp` - add it to Claude Desktop, Claude Code or Cursor as a remote MCP server with an Apify token, and ask something like: *"Check insider buying clusters from the past week, and for the top company also check recent 8-K events and congress trades."* The agent chains three tools and answers with scored, source-linked data.
All tools are documented on the [MCP server page](/datasignals-mcp.html) and the [Apify listing](https://apify.com/datasignalslab/datasignals-mcp?fpr=wnlxst). You can also discover and connect the server through [Smithery](https://smithery.ai/servers/datasignalslab/datasignals-lab) and the official [MCP Registry](https://registry.modelcontextprotocol.io).
*Financial tools return data for research, screening and monitoring - not investment advice.*
---
# How to Track Insider Buying Clusters From SEC Form 4 (Without a $90/Month Subscription)
URL: https://datasignalslab.com/blog/track-insider-buying-clusters-sec-form4/
Corporate insiders - officers, directors, 10%+ owners - must report every trade in their own stock to the SEC within two business days, on Form 4. Thousands of these filings arrive every week. Almost all of them are noise: option exercises, tax sales, automatic plan trades, tiny purchases.
The pattern researchers and practitioners actually care about is the **cluster**: two or more insiders at the same company buying on the open market within a short window. One director buying $50K could be anything. A CFO, a CEO and two directors buying the same week is a statement.
## Why clusters beat single filings
- **Independent conviction.** Multiple insiders deciding to buy with their own money at the same time is a stronger prior than one.
- **Open-market purchases only.** Clusters built from real purchases (transaction code P) exclude option exercises and grants, which dominate raw Form 4 volume and mean nothing.
- **Seniority weighting.** A CFO's purchase carries different information than a junior officer's. Scoring by role separates them.
## What a scored cluster looks like
A real example from the [Form 4 cluster scanner](/sec-form4-insider-buying.html): four insiders at one small-cap, including the CFO, bought a combined $1.17M of stock in the same window - cluster score 71.7 out of 100, with every underlying filing linked back to SEC EDGAR so you can read each transaction yourself.
The score blends the number of distinct insiders, the combined dollar value and the seniority of the buyers. The output is a ranked shortlist instead of a firehose.
## The honest caveat
Insider buying clusters are a research lead, not a money machine. Published research (Finance Research Letters, 2024) finds that naive Form 4 signals do not survive as a standalone trading strategy after costs. What the data is genuinely good for: screening, due-diligence timing, and as one input among several - knowing which names deserve a closer look this week.
## Ways to get the data
- **Dashboard subscriptions**: Fintel's Gold tier runs about $89/month; InsiderScore is enterprise-priced. Good if you browse daily.
- **Raw EDGAR**: free, but you parse XML inside daily index files yourself and build the clustering, deduplication and scoring.
- **Pay-per-result feed**: the [SEC Form 4 Insider Trading Cluster Scanner](https://apify.com/datasignalslab/sec-form4-insider-buying-clusters?fpr=wnlxst) does the parsing, clustering and scoring and returns JSON at $0.20 per cluster signal. It pairs naturally with the [Form 144 selling monitor](/sec-form144-insider-selling.html) - planned sales on one side, executed buying clusters on the other (see our guide: [what is Form 144?](/what-is-sec-form-144.html)). AI agents can call both through the free [MCP server](/datasignals-mcp.html).
*Data for research, screening and monitoring - not investment advice. Historical patterns do not guarantee future results.*
---
# How to Get US Congress Stock-Trading Data From the Official Source
URL: https://datasignalslab.com/blog/congress-trading-data-official-source/
Since the STOCK Act (2012), members of the US Congress must disclose their personal stock trades within 45 days. Sites like Capitol Trades and QuiverQuant turned "Pelosi trades" into a retail phenomenon. But where does that data actually come from, and what does it take to use it programmatically?
## The official sources
- **House of Representatives**: the House Clerk publishes Periodic Transaction Reports (PTRs) as PDFs on disclosures-clerk.house.gov. Newer reports are e-filed and machine-readable; older ones are scanned paper.
- **Senate**: the Senate eFD system - which sits behind an aggressive anti-bot wall and, importantly, **prohibits commercial use of the data** in its terms.
That asymmetry matters. Any commercial dataset that includes Senate trades is in murky territory. We made the deliberate choice to cover the House only, from the official Clerk feed, e-filed reports only (no OCR guessing on scans). Less coverage, clean provenance.
## What parsing actually involves
A PTR PDF contains the member's transactions: the asset name and ticker, transaction type (purchase/sale), date, and an amount **range** (disclosures use brackets like $1,001-$15,000, not exact values). Turning that into data means extracting tables from PDFs, normalizing tickers, classifying buy vs sell, and estimating sizes from the brackets - then scoring recency and size so a $500K purchase last week ranks above a $2K sale from a month ago.
## What the feed returns
The [US Congress Trading Monitor](/congress-trading-monitor.html) takes House member names and returns their recent trades parsed from the official feed: ticker, side, estimated size, date, and a recency/size score, with each record traceable to the underlying disclosure. It costs $0.20 per member analyzed - against QuiverQuant's $25/month subscription, that is the difference between paying for browsing and paying for data you actually pull. (Their dashboard is genuinely good for daily browsing; see our [comparison](/compare.html).)
For pipelines: JSON via API, webhooks on every run, or let an AI agent query it through the free [MCP server](/datasignals-mcp.html) alongside insider clusters and 8-K events - "did any House member trade a stock that also had an insider buying cluster this month?" is a three-tool agent query.
## The honest caveats
Disclosures arrive up to 45 days after the trade, amounts are ranges rather than exact figures, and academic evidence on copying congressional trades is mixed at best. This is transparency data - useful for research, journalism and screening, not a trading system.
*Data for research, screening and monitoring - not investment advice.*
---
# Biotech Catalysts: How to Monitor Clinical-Trial Readouts Automatically
URL: https://datasignalslab.com/blog/monitor-biotech-catalysts-clinical-trials/
In biotech, the calendar is the thesis. A Phase 3 readout can move a single-product company 50% in either direction overnight. Yet the primary public source for trial timing - ClinicalTrials.gov - is built for researchers and patients, not for anyone trying to answer: **"what is coming up for this company, and how much could it matter?"**
## What counts as a catalyst
Three event types on ClinicalTrials.gov are worth monitoring per sponsor:
1. **Upcoming readouts**: trials whose primary completion date is approaching. A Phase 3 with a completion date next quarter is the classic binary event.
2. **Posted results**: results sections appearing on a registered trial - data is out.
3. **Phase transitions and status changes**: a trial moving to active/completed, or being suspended/terminated (terminations are catalysts too, in the wrong direction).
Impact is not equal across trials. Phase 3 outweighs Phase 1, a lead asset outweighs the fifth indication of an approved drug, and near-term dates outweigh far ones. That is exactly what a scoring layer encodes.
## Doing it manually vs as a feed
ClinicalTrials.gov's v2 API is official, free and stable - you can query it yourself. The work is in the interpretation layer: classifying each trial event into catalyst types, weighing phase, status and proximity into a 0-100 impact score, and keeping it monitored daily.
The [Biotech Catalyst Monitor](/biotech-catalyst-monitor.html) does this per sponsor: give it company names and it returns their trials classified and impact-scored, every record linking back to the registry entry. A real example: a Moderna scan surfaced a Phase 3 with an upcoming readout at impact 100 at the top, with 10+ high-impact events behind it. It costs $0.20 per company.
It pairs with the [FDA Drug Approval and Action Monitor](/fda-drug-approval-monitor.html) - the regulatory side of the same story - and the [NIH funding monitor](/nih-research-funding-monitor.html) as the upstream R&D signal. AI agents can chain all three through the free [MCP server](/datasignals-mcp.html): "list upcoming catalysts for these five biotechs, then check their recent FDA actions."
## The honest caveats
Completion dates on ClinicalTrials.gov shift frequently - sponsors amend them - so treat dates as estimates, not commitments. Results timing does not always match the registry (companies often press-release first). And forward PDUFA dates are not in any clean public source; anyone selling "PDUFA calendars" is hand-curating. This feed deliberately covers what the official registry actually knows.
*Data for research, screening and monitoring - not investment advice. Historical patterns do not guarantee future results.*