# 👁️🔥 Insider Cluster Buy Detector — 3+ Insiders Same Stock (`nexgendata/insider-cluster-detector`) Actor 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. - **URL**: https://apify.com/nexgendata/insider-cluster-detector.md - **Developed by:** [NexGenData](https://apify.com/nexgendata) (community) - **Categories:** Business - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $250.00 / 1,000 insider cluster records This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## 👁️🔥 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_score` desc, work the top 5 as research priorities for Monday. * **Earnings-week scan** — bump `min_cluster_size` to 4-5, scan `last_60d` to 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=2` to 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](https://apify.com/nexgendata/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](https://apify.com/nexgendata/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](https://apify.com/nexgendata/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](https://apify.com/nexgendata/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](https://apify.com/nexgendata/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](https://apify.com/nexgendata/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. #### Apify affiliate program — free credits + 30% off Sign up to Apify via our referral link and you'll get: - **Free starter credits** to test this actor and the rest of our 220+ actor fleet - **30% off platform fees** for the life of your account **[Browse the full NexGenData catalog and sign up here](https://apify.com/nexgendata?fpr=2ayu9b)** — same Apify, same actors, just cheaper for you. *Built and maintained by NexGenData.* # Actor input Schema ## `min_cluster_size` (type: `integer`): Minimum number of DISTINCT insiders that must have bought the same stock inside the rolling window for a cluster to qualify. 3 is the academic-research threshold (Lakonishok-Lee 2001, Cohen-Malloy-Pomorski 2012) — clusters of 3+ insiders outperform single-insider signals by 4-7% annually. 2 catches early-stage signals (CEO+CFO duos). 5+ is the rare mega-cluster (whole board piling in) — historically the strongest reversal signal at distressed names. Anything under 3 is NOT a cluster, just two friends. ## `date_range` (type: `string`): How far back to scan for clustered insider buying. last_30d is the canonical academic-research window — insiders rarely act independently on the same thesis if their decisions are spaced more than a month apart. last_60d catches slower-developing clusters (quarterly board cycles). last_90d catches the rare cross-quarter accumulation pattern at deep-value names. Shorter windows = fresher signal but fewer hits. Longer windows = more hits but lower per-cluster strength. ## `min_value_usd` (type: `integer`): Skip clusters where the total combined dollar value of all insider buys is below this threshold. Filters out trivial token buys (1-2K share Director gifts/exercises). Common thresholds: 25000 (OpenInsider default — material-trade screen), 100000 (mid-cap conviction floor), 500000 (large-cap whale-magnet), 1000000 (only mega-clusters). Note: very high thresholds drastically reduce hit count — most quality cluster signals run 50K-300K combined. ## `exclude_sells` (type: `boolean`): If true, only return clusters where EVERY insider transaction in the window was a buy (no sells, no exercises, no dispositions mixed in). The is_all_buy=true flag isolates the highest-signal pattern — when no insider is taking money off the table while 3+ are putting it in. Setting to false lets through mixed-direction clusters (e.g., 3 buys + 1 sell — still bullish-leaning but diluted). Pure-buy clusters historically outperform mixed clusters by 2-3% in the 6-month window. ## `limit` (type: `integer`): Maximum number of cluster records to push to the dataset (one row per stock — already aggregated, so a stock with 8 insiders piling in is ONE record not eight). Typical hedge-fund-analyst usage: 10-50 clusters per refresh — that's the full universe of actionable smart-money signals on any given day. Backtesters pull 200+. Each cluster record is a pay-per-result charge of $0.25. ## `tickers` (type: `array`): Optional list of stock tickers to restrict the cluster scan to. Useful when you already track a watchlist and only want cluster alerts for those names. Leave empty to scan the full universe of US-listed stocks with recent insider activity. Examples: ['AAPL', 'MSFT', 'NVDA'] or ['MYGN', 'BABA', 'INTC']. Case-insensitive, max 100 tickers. ## `include_industry` (type: `boolean`): If true, include the OpenInsider-supplied industry classification (e.g., 'Pharmaceutical Preparations', 'Semiconductors', 'Commercial Banks') as the sector field on each cluster record. Used by quant funds to group cluster signals by sector — a cluster wave across multiple regional banks is a different signal than one isolated bank cluster. Set to false to skip and save a tiny amount of bandwidth. ## Actor input object example ```json { "min_cluster_size": 3, "date_range": "last_30d", "min_value_usd": 25000, "exclude_sells": true, "limit": 5, "tickers": [], "include_industry": true } ```` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "min_cluster_size": 3, "date_range": "last_30d", "min_value_usd": 25000, "exclude_sells": true, "limit": 5, "tickers": [], "include_industry": true }; // Run the Actor and wait for it to finish const run = await client.actor("nexgendata/insider-cluster-detector").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "min_cluster_size": 3, "date_range": "last_30d", "min_value_usd": 25000, "exclude_sells": True, "limit": 5, "tickers": [], "include_industry": True, } # Run the Actor and wait for it to finish run = client.actor("nexgendata/insider-cluster-detector").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "min_cluster_size": 3, "date_range": "last_30d", "min_value_usd": 25000, "exclude_sells": true, "limit": 5, "tickers": [], "include_industry": true }' | apify call nexgendata/insider-cluster-detector --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=nexgendata/insider-cluster-detector", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "👁️🔥 Insider Cluster Buy Detector — 3+ Insiders Same Stock", "description": "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.", "version": "0.0", "x-build-id": "Mph07fk20I2QFJpaT" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/nexgendata~insider-cluster-detector/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-nexgendata-insider-cluster-detector", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/nexgendata~insider-cluster-detector/runs": { "post": { "operationId": "runs-sync-nexgendata-insider-cluster-detector", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/nexgendata~insider-cluster-detector/run-sync": { "post": { "operationId": "run-sync-nexgendata-insider-cluster-detector", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "min_cluster_size": { "title": "Minimum cluster size (insiders)", "minimum": 2, "maximum": 20, "type": "integer", "description": "Minimum number of DISTINCT insiders that must have bought the same stock inside the rolling window for a cluster to qualify. 3 is the academic-research threshold (Lakonishok-Lee 2001, Cohen-Malloy-Pomorski 2012) — clusters of 3+ insiders outperform single-insider signals by 4-7% annually. 2 catches early-stage signals (CEO+CFO duos). 5+ is the rare mega-cluster (whole board piling in) — historically the strongest reversal signal at distressed names. Anything under 3 is NOT a cluster, just two friends.", "default": 3 }, "date_range": { "title": "Cluster lookback window", "enum": [ "last_30d", "last_60d", "last_90d" ], "type": "string", "description": "How far back to scan for clustered insider buying. last_30d is the canonical academic-research window — insiders rarely act independently on the same thesis if their decisions are spaced more than a month apart. last_60d catches slower-developing clusters (quarterly board cycles). last_90d catches the rare cross-quarter accumulation pattern at deep-value names. Shorter windows = fresher signal but fewer hits. Longer windows = more hits but lower per-cluster strength.", "default": "last_30d" }, "min_value_usd": { "title": "Minimum cluster value (USD)", "minimum": 0, "maximum": 100000000, "type": "integer", "description": "Skip clusters where the total combined dollar value of all insider buys is below this threshold. Filters out trivial token buys (1-2K share Director gifts/exercises). Common thresholds: 25000 (OpenInsider default — material-trade screen), 100000 (mid-cap conviction floor), 500000 (large-cap whale-magnet), 1000000 (only mega-clusters). Note: very high thresholds drastically reduce hit count — most quality cluster signals run 50K-300K combined.", "default": 25000 }, "exclude_sells": { "title": "Pure-buy clusters only", "type": "boolean", "description": "If true, only return clusters where EVERY insider transaction in the window was a buy (no sells, no exercises, no dispositions mixed in). The is_all_buy=true flag isolates the highest-signal pattern — when no insider is taking money off the table while 3+ are putting it in. Setting to false lets through mixed-direction clusters (e.g., 3 buys + 1 sell — still bullish-leaning but diluted). Pure-buy clusters historically outperform mixed clusters by 2-3% in the 6-month window.", "default": true }, "limit": { "title": "Max cluster records to return", "minimum": 1, "maximum": 500, "type": "integer", "description": "Maximum number of cluster records to push to the dataset (one row per stock — already aggregated, so a stock with 8 insiders piling in is ONE record not eight). Typical hedge-fund-analyst usage: 10-50 clusters per refresh — that's the full universe of actionable smart-money signals on any given day. Backtesters pull 200+. Each cluster record is a pay-per-result charge of $0.25.", "default": 25 }, "tickers": { "title": "Restrict to specific tickers (optional)", "type": "array", "description": "Optional list of stock tickers to restrict the cluster scan to. Useful when you already track a watchlist and only want cluster alerts for those names. Leave empty to scan the full universe of US-listed stocks with recent insider activity. Examples: ['AAPL', 'MSFT', 'NVDA'] or ['MYGN', 'BABA', 'INTC']. Case-insensitive, max 100 tickers.", "default": [], "items": { "type": "string" } }, "include_industry": { "title": "Include industry/sector classification", "type": "boolean", "description": "If true, include the OpenInsider-supplied industry classification (e.g., 'Pharmaceutical Preparations', 'Semiconductors', 'Commercial Banks') as the sector field on each cluster record. Used by quant funds to group cluster signals by sector — a cluster wave across multiple regional banks is a different signal than one isolated bank cluster. Set to false to skip and save a tiny amount of bandwidth.", "default": true } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```