👁️🔥 Insider Cluster Buy Detector — 3+ Insiders Same Stock
Pricing
from $250.00 / 1,000 insider cluster records
👁️🔥 Insider Cluster Buy Detector — 3+ Insiders Same Stock
Detects CLUSTERS of insider buying — 3+ insiders (CEO/CFO/Directors/10%-owners) buying the same stock inside a 30/60/90-day window. Cluster signals outperform single-insider trades by 4-7% (Lakonishok-Lee). Pay-per-cluster. Bloomberg / TipRanks / OpenInsider Pro alternative.
Pricing
from $250.00 / 1,000 insider cluster records
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👁️🔥 Insider Cluster Buy Detector — 3+ Insiders Buying the Same Stock in 30 Days
The hedge-fund-grade insider-signal actor. Returns CLUSTERS of insider buying — events where 3+ distinct insiders (CEO, CFO, Directors, 10%-owners) buy the same stock inside a rolling 30/60/90-day window. Cluster signals are the highest-conviction insider trades on the planet — Lakonishok-Lee (2001) and Cohen-Malloy-Pomorski (2012) both documented that clusters of 3+ insiders outperform single-insider signals by 4-7% annually.
This is not another raw Form 4 firehose. Single insider buys are noisy — Directors get gifted shares, exercise options, accept comp grants, rebalance for divorce. A single CFO buy can be a personal-finance event. But when 3+ executives independently buy in the same 30-day window? That is a coordinated signal — they all see the same upside-asymmetry in their internal numbers and they are voting with their personal capital.
What you get (per cluster record)
Every dataset row is a fully-aggregated cluster — one row per stock, NOT one row per insider:
| Field | Meaning |
|---|---|
symbol, company_name, sector | Ticker, issuer name, OpenInsider industry classification |
cluster_start_date / cluster_end_date | The first and last insider-buy trade dates inside the window |
insider_count | Distinct insiders (≥ min_cluster_size) |
insider_names | All insider names — list, deduped |
insider_roles | All roles — list (CEO, CFO, Director, COO, 10% Owner, etc.) |
total_shares_bought | Sum of shares across the cluster |
total_value_usd | Sum of trade values in USD |
current_stock_price | Last reference price (most recent cluster trade price) |
stock_move_since_cluster_start_pct | Spot vs first-cluster-trade reference, percent |
is_all_buy | true iff every trade in window is a P-Purchase, no sells/exercises mixed |
cluster_strength_score | Composite — insider_count × role_weight_sum × log(total_value_usd). Higher = stronger conviction. CEO=5, CFO=4, COO/Pres=3, Director=2, 10%-Owner=4, Other=1. |
top_insider | Highest-role buyer in the cluster (the "anchor") |
data_source | openinsider.com cluster-buys + per-symbol drilldown |
Why clusters > single insider signals
Academic finance has been on this for two decades:
- Lakonishok & Lee (2001) — Are Insider Trades Informative? Found cluster buys outperform by 4.8% in the year following.
- Cohen, Malloy & Pomorski (2012) — Decoding Inside Information. Distinguished "opportunistic" trades (cluster signals) from "routine" ones (calendar buys). Opportunistic outperformed the market by 8.2% annually.
- Jeng, Metrick & Zeckhauser (2003) — Documented that single Director buys carry almost no predictive content. The signal lives in the cluster.
Translation: a single 10K-share CEO buy is noise. Three insiders piling in over 30 days is alpha.
Inputs
min_cluster_size— default 3 (academic threshold). 2 catches CEO+CFO duos. 5+ catches mega-clusters (whole-board buys).date_range—last_30d(canonical),last_60d(quarterly cycle),last_90d(cross-quarter accumulation).min_value_usd— default 25000. Filters out trivial gifts/exercises.exclude_sells— default true. Pure-buy clusters only — historically outperform mixed clusters by 2-3%.tickers— optional watchlist filter. Leave empty for full-universe scan.limit— max cluster records (default 25; tune by usage).include_industry— adds sector classification to each cluster.
Data source & method
Primary: OpenInsider (openinsider.com/latest-cluster-buys) — already pre-aggregates Form 4 filings into cluster format with the Ins (insider count) column. We pull the cluster page, drill into each stock via the per-symbol screener (/screener?s=TICKER&fd=N), extract every insider's name + role + trade date + share count + price, then build the composite cluster record.
Validation: SEC EDGAR Form 4 full-text (efts.sec.gov/LATEST/search-index?forms=4) — used as a cross-check when OpenInsider cluster data is stale or a ticker isn't covered. EDGAR is the authoritative source; OpenInsider is the aggregator on top.
Anti-bot risk: LOW on both — OpenInsider serves static HTML and welcomes scrapers. SEC EDGAR explicitly publishes scraping guidance and only asks for a descriptive User-Agent.
Cluster window: rolling — cluster_end_date - cluster_start_date ≤ date_range. If insiders bought on 5 separate days inside the window, the cluster insider_count = 5. We dedupe by insider name to avoid double-counting the same person twice in the window.
Role weights (for cluster_strength_score): CEO=5, CFO=4, COO/President=3, 10%-Owner=4, Director=2, Other officer=1. Scaled by log10(1 + total_value_usd) so a $5M cluster scores higher than a $50K one even at the same insider count.
Comparison vs the legacy stack
| Bloomberg Terminal | TipRanks Premium | OpenInsider Pro | Finviz Elite | This actor | |
|---|---|---|---|---|---|
| Cost | $25K+/yr | $35/mo | $30/mo | $25/mo | $0.01 + $0.25/cluster |
| Cluster detection | ✓ (function NIM) | Partial — single insiders only | ✓ (manual screen) | Partial — calendar-only | ✓ (3+ insider window, role-weighted) |
| Cluster strength score | ✗ | ✗ | ✗ | ✗ | ✓ (composite, role × value) |
| Pure-buy filter (is_all_buy) | Manual | ✗ | Manual | Manual | ✓ (one toggle) |
| Top-insider anchor | Manual | ✗ | Manual | ✗ | ✓ |
| API / programmatic access | Bloomberg API (locked) | Limited | ✗ | ✗ | ✓ (Apify dataset/JSON/CSV) |
| Pay-as-you-go | ✗ | ✗ | ✗ | ✗ | ✓ |
| Stock move since cluster start | Manual | ✗ | ✗ | ✗ | ✓ |
| Sector classification | ✓ | ✓ | ✓ | ✓ | ✓ |
| No login / no monthly minimum | ✗ | ✗ | ✗ | ✗ | ✓ |
If you are running an insider-signal portfolio at a $5-20M hedge fund or a quant retail-research shop, your monthly cluster-signal needs are 10-50 records — that is $2.50-$12.50 here vs $35-$300 elsewhere, with full programmatic access and a much cleaner schema.
When to fire this actor
- Weekly Sunday refresh — pull 25 clusters, sort by
cluster_strength_scoredesc, work the top 5 as research priorities for Monday. - Earnings-week scan — bump
min_cluster_sizeto 4-5, scanlast_60dto catch pre-print accumulation. - Drawdown watch — when a name you own drops 20%+, fire this actor with the ticker as a one-element filter and
min_cluster_size=2to see if any insiders are stepping in. - Sector wave detection — pull last_30d at min_cluster_size=3, group by sector — multiple clusters in the same sector inside a month is the highest-conviction sector signal in finance (regional banks 2023, biotech 2024).
Cross-link — sister actors in the NexGenData fleet
This actor is one of the cluster-signal layer in a 7-actor smart-money intelligence suite. Pair with:
- sec-form4-insider-tracker — the underlying Form 4 firehose. Every individual transaction, no cluster aggregation. Use when you want the raw stream; use THIS actor when you want the signal.
- 13f-holdings-delta-tracker — quarter-over-quarter institutional position changes (NEW / INCREASED / DECREASED / EXITED across Berkshire, Tiger Global, Bridgewater, Renaissance, Citadel). Pair cluster insider signals with institutional flows for double-confirmation alpha.
- sec-form-13f-holdings-tracker — full 13F portfolio snapshots (vs the delta tracker which only emits changes). Use when you need the absolute holdings list.
- short-interest-tracker — FINRA short interest bi-weekly. Cluster insider buys with high short interest = short-squeeze setup (the classic GME/AMC pattern).
- analyst-price-targets — Wall Street consensus. Use to spot divergence: insiders piling in but Street has Sell ratings = highest-conviction asymmetric setup.
- finance-mcp-server — Claude/MCP integration layer that orchestrates this actor and the other six for natural-language hedge-fund queries.
Pricing
- $0.01 actor start fee
- $0.25 per cluster record returned
A typical scan that yields 5 clusters costs $1.26. A heavy backtester pulling 200 clusters costs $50.01.
This is premium-tier PPE pricing — the cluster signal is hedge-fund-grade alpha and the data carries direct trading value. Compare to $30/mo for OpenInsider Pro (with no API), $35/mo for TipRanks (single-insider only), or $25K/yr for Bloomberg.
Output format
Apify default dataset — pull as JSON, JSONL, CSV, Excel, RSS, or HTML. Programmatic SDK access via the Apify client (Python, Node.js, PHP, Ruby) or direct REST. Webhook-able on completion for downstream pipeline triggers.
Author / Notes
Built by NexGenData. Direct OpenInsider + SEC EDGAR data — no intermediary aggregators, no licensing layer, no monthly fees, no API key. The cluster-detection logic is open and documented above so you can verify the score weights against your own academic-finance references. PRs and feature requests welcome.
Disclaimer: insider trading data is informational. Past insider behavior does not guarantee future stock performance. Insider transactions are reported to the SEC with a 2-business-day lag and may include hedged / planned (10b5-1) trades that we cannot always distinguish from open-market buys.
About NexGenData
NexGenData publishes 220+ buyer-intent actors covering SEC filings, YC alumni, Delaware DOC, global stock screeners across 30+ exchanges, IPO calendars, IP and patent intelligence, FDA approvals, B2B lead generation, and more. Every actor is pay-per-result with no seat licensing.
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