# Alternative Data Signal Aggregator (`automation-lab/alternative-data-signal-aggregator`) Actor Turn public news, hiring, and SEC filing data into one auditable company research indicator with evidence and cross-run hiring deltas. - **URL**: https://apify.com/automation-lab/alternative-data-signal-aggregator.md - **Developed by:** [Stas Persiianenko](https://apify.com/automation-lab) (community) - **Categories:** Business, News - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per event This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. 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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 ## Alternative Data Signal Aggregator Turn public news, hiring, and SEC filing data into one auditable research indicator per US-listed company. The actor joins three anonymous HTTP sources: - πŸ“° Google News RSS for headline sentiment and velocity - πŸ§‘β€πŸ’» Public Greenhouse boards for opening counts and cross-run changes - πŸ›οΈ SEC Company Facts for filing-backed revenue and gross-margin changes Instead of exporting thousands of unrelated raw records, it saves one normalized company row with a `-100..100` score, confidence, rationale, timestamps, and evidence URLs. > The score is a transparent research indicator. It is not investment advice, a price forecast, or a buy/sell instruction. ### What does Alternative Data Signal Aggregator do? For every supplied ticker and Greenhouse board mapping, the actor: 1. resolves the ticker to an SEC CIK; 2. searches recent Google News RSS headlines; 3. scores headline words with a deterministic local lexicon; 4. loads the company's current public Greenhouse openings; 5. compares job IDs with the prior named-store snapshot; 6. extracts comparable annual SEC revenue and gross profit facts; 7. calculates three component scores; 8. combines them using your normalized weights; 9. saves one evidence-linked dataset row. The first run is clearly marked as a baseline. Later runs with the same snapshot settings can report new, removed, and net job openings. ### Who is it for? #### Quant researchers Schedule a small ticker watchlist and receive consistent rows that can enter a feature store or review queue. #### Equity analysts Use evidence URLs and plain-language rationale to identify companies that need deeper primary research. #### Small funds and family offices Monitor workforce, narrative, and filing changes without maintaining three collectors and a join pipeline. #### Fintech and data-product builders Call one Actor API and deliver normalized JSON to a dashboard, alerting system, notebook, or warehouse. #### Corporate strategy teams Compare hiring direction and public narrative across peers that use Greenhouse. ### Why use this actor? - βœ… **Joined output:** one company row, not separate feeds - βœ… **Auditable:** source links, filing accession, and collection time are retained - βœ… **Repeatable:** named KVS snapshots support scheduled hiring deltas - βœ… **Transparent:** component scores, weights, confidence, and rationale are exposed - βœ… **Deterministic:** no paid AI key or opaque LLM is required - βœ… **HTTP-only:** no browser, login, proxy, or user credential is required - βœ… **Bounded:** watchlist and embedded evidence limits keep runs predictable - βœ… **Resilient:** one source failure is reported without hiding other components ### Supported companies and important limitation V1 supports US-listed companies that: - appear in the SEC public ticker mapping; - have filing facts in SEC Company Facts; and - expose a public Greenhouse job board. You must provide the Greenhouse board token explicitly. For example, the token for `https://boards.greenhouse.io/cloudflare` is `cloudflare`. The actor does not guess the board from the company name. This avoids silently attaching the wrong employer's hiring data. ### What data can I extract? | Group | Fields | |---|---| | Identity | `ticker`, `companyName`, `cik`, `asOf`, `windowDays` | | Baseline | `baseline`, hiring `deltaAvailable` | | News | article count, publishers, articles/day, count change, sentiment, component score | | Hiring | total, new, removed, net change, department mix, location mix, sample job URLs | | Fundamentals | fiscal year, filing date, accession, revenue, revenue YoY, gross margin, margin delta | | Composite | score, label, confidence, normalized weights, component scores, rationale | | Evidence | Google News RSS, Greenhouse board, SEC Company Facts, timestamps | | Diagnostics | `sourceErrors` and component-level errors | Nested component objects remain together so each dataset item is a complete research record. ### How is the signal calculated? #### News component Headline titles use a deterministic positive/negative finance-oriented word list. Average sentiment contributes most of the news score. When a previous snapshot exists, article-count velocity also contributes. No article body is fetched and no claim of human-level sentiment understanding is made. #### Hiring component Current Greenhouse job IDs are compared with the previous snapshot: - new IDs become `newOpenings`; - missing old IDs become `removedOpenings`; - new minus removed becomes `netChange`. On the first run, the hiring component is a baseline with score zero. #### Fundamentals component The actor selects annual `10-K` / `FY` facts for common US-GAAP revenue concepts and `GrossProfit`. It calculates revenue year-over-year change and gross-margin percentage-point change when comparable facts exist. #### Composite Component scores are clamped to `-100..100` and combined with normalized weights. Confidence falls when headline coverage is thin, hiring has no prior snapshot, or filing metrics are unavailable. ### Input A minimal input is: ```json { "companies": [ { "ticker": "NET", "companyName": "Cloudflare", "greenhouseBoardToken": "cloudflare" } ], "lookbackDays": 7, "maxNewsArticles": 25, "maxEvidenceItems": 5, "snapshotNamespace": "my-watchlist" } ```` Available settings: | Input | Default | Purpose | |---|---:|---| | `companies` | required | 1–25 explicit company mappings | | `lookbackDays` | `7` | recent Google News window, 1–30 days | | `maxNewsArticles` | `50` | headlines scored per company, maximum 100 | | `maxEvidenceItems` | `8` | embedded headline/job evidence per source | | `weights.news` | `0.4` | relative news weight | | `weights.hiring` | `0.35` | relative hiring weight | | `weights.fundamentals` | `0.25` | relative filing-fundamentals weight | | `snapshotStoreName` | `alternative-data-signal-snapshots` | named KVS for comparisons | | `snapshotNamespace` | `default` | stable watchlist/client separation key | | `secUserAgent` | actor contact | descriptive SEC request identity | Weights can use any non-negative scale. They are normalized automatically. ### Output example ```json { "ticker": "NET", "companyName": "Cloudflare", "cik": "0001477333", "asOf": "2026-07-14T15:00:00.000Z", "windowDays": 7, "baseline": true, "news": { "articleCount": 25, "uniquePublishers": 18, "articlesPerDay": 3.57, "articleCountChange": null, "sentimentScore": 4.2, "sentimentLabel": "neutral", "componentScore": 2.94, "evidence": [] }, "hiring": { "totalOpenings": 259, "newOpenings": null, "removedOpenings": null, "netChange": null, "deltaAvailable": false, "departmentMix": {}, "locationMix": {}, "componentScore": 0, "sampleJobUrls": [] }, "fundamentals": { "latestFilingDate": "2026-02-20", "latestAccessionNumber": "...", "latestFiscalYear": 2025, "revenue": 2168000000, "revenueYoYPct": 27.8, "grossMarginPct": 75.1, "grossMarginDeltaPp": -1.2, "componentScore": 24.8 }, "composite": { "score": 7.38, "label": "neutral", "confidence": 0.83, "weights": { "news": 0.4, "hiring": 0.35, "fundamentals": 0.25 }, "componentScores": { "news": 2.94, "hiring": 0, "fundamentals": 24.8 }, "rationale": [] }, "sourceErrors": [] } ``` Values above illustrate the shape; live source data changes. ### First run and scheduled monitoring A first run stores: - current headline count; - current Greenhouse job IDs; and - collection time. It returns `baseline: true` and `hiring.deltaAvailable: false`. For comparisons: 1. keep `snapshotStoreName` unchanged; 2. keep `snapshotNamespace` unchanged; 3. keep ticker and Greenhouse token unchanged; 4. schedule the same Actor input daily or weekly. Use a unique namespace per watchlist or client. Named stores belong to the Actor account, so careless namespace reuse can merge histories for identical mappings. ### How much does it cost to monitor alternative data signals? The actor uses pay-per-event pricing: - a small one-time run-start charge; - one `company-signal` event for each dataset row produced. The formula-derived BRONZE price is `$0.0024617` per company signal, with automatic lower prices on higher Apify tiers. You are not charged per raw headline or job. The buyer output is the normalized company signal. Before a production watchlist, run one company to verify mapping and source coverage. ### How to run the actor 1. Open the Actor input page. 2. Add one to 25 company mappings. 3. Choose a stable snapshot namespace. 4. Keep the default seven-day news window or adjust it. 5. Run the Actor. 6. Review `sourceErrors`, confidence, and evidence. 7. Export JSON, CSV, Excel, or connect an integration. 8. Schedule later runs if hiring deltas are required. ### Practical analysis tips - Start with one company and confirm its Greenhouse token. - Compare confidence before ranking companies by score. - Treat small headline samples as weak evidence. - Inspect rationale and source URLs before acting on an outlier. - Use the same lookback window across peers. - Avoid interpreting a first-run hiring score as direction. - Store raw exported rows if you need your own long history. - Keep component weights stable when comparing time periods. ### Integrations and workflow patterns #### Google Sheets Send each scheduled dataset to a sheet for an analyst review queue. #### Slack or email alerts Trigger an alert only when confidence is above your threshold and score moves materially. #### Webhooks Use the run-finished webhook to pass the dataset URL to an internal pipeline. #### Snowflake, BigQuery, or PostgreSQL Load one row per ticker/run into a research feature table. #### Python notebooks Pull the dataset through the Apify API and compare component scores against your own models. #### Zapier and Make Build no-code workflows around new rows, score bands, or source errors. ### API usage with Node.js ```javascript import { ApifyClient } from 'apify-client'; const client = new ApifyClient({ token: process.env.APIFY_TOKEN }); const run = await client.actor('automation-lab/alternative-data-signal-aggregator').call({ companies: [ { ticker: 'NET', companyName: 'Cloudflare', greenhouseBoardToken: 'cloudflare' } ], snapshotNamespace: 'node-watchlist' }); const { items } = await client.dataset(run.defaultDatasetId).listItems(); console.log(items[0].composite); ``` ### API usage with Python ```python import os from apify_client import ApifyClient client = ApifyClient(os.environ['APIFY_TOKEN']) run = client.actor('automation-lab/alternative-data-signal-aggregator').call(run_input={ 'companies': [ {'ticker': 'NET', 'companyName': 'Cloudflare', 'greenhouseBoardToken': 'cloudflare'} ], 'snapshotNamespace': 'python-watchlist', }) items = client.dataset(run['defaultDatasetId']).list_items().items print(items[0]['composite']) ``` ### API usage with cURL ```bash curl -X POST \ "https://api.apify.com/v2/acts/automation-lab~alternative-data-signal-aggregator/runs?token=$APIFY_TOKEN" \ -H "Content-Type: application/json" \ -d '{ "companies":[{"ticker":"NET","companyName":"Cloudflare","greenhouseBoardToken":"cloudflare"}], "snapshotNamespace":"curl-watchlist" }' ``` Fetch results from the run's `defaultDatasetId` after it succeeds. ### Use with Apify MCP Connect the Actor to AI clients through [Apify MCP](https://mcp.apify.com/?tools=automation-lab/alternative-data-signal-aggregator). #### Claude Code ```bash claude mcp add --transport http apify \ "https://mcp.apify.com/?tools=automation-lab/alternative-data-signal-aggregator" ``` Example prompt: > Run the Alternative Data Signal Aggregator for Cloudflare using its `cloudflare` Greenhouse token. Explain each component and cite the evidence URLs. Do not give investment advice. #### Claude Desktop Add this `mcpServers` JSON to Claude Desktop's configuration: ```json { "mcpServers": { "apify": { "type": "http", "url": "https://mcp.apify.com/?tools=automation-lab/alternative-data-signal-aggregator" } } } ``` #### Cursor Open **Settings β†’ Tools & Integrations β†’ MCP**, add a remote HTTP server, and use the same `mcpServers` JSON and URL shown above. #### VS Code Open the MCP server configuration in VS Code, choose an HTTP server, and set its URL to: ```text https://mcp.apify.com/?tools=automation-lab/alternative-data-signal-aggregator ``` Example prompt: > Compare the news, hiring, and SEC components from my latest cybersecurity watchlist dataset. Flag low-confidence rows. ### Error handling The actor retries transient `429` and `5xx` responses up to three times. If one company source fails: - the failure appears in `sourceErrors`; - the relevant component contains an `error` message; - other available components still contribute a row; - confidence is reduced. Input errors, such as a missing required mapping or duplicate ticker, stop the run early with a clear message. ### Data quality and interpretation Public data has limitations: - a headline lexicon cannot understand every irony or context; - Google News coverage varies by company and day; - Greenhouse shows only roles published on that board; - a company may use several applicant-tracking systems; - SEC taxonomies and fiscal calendars vary; - amended filings can restate facts; - short windows can exaggerate change. Use confidence, component values, and evidenceβ€”not the composite alone. ### Legality and responsible use The actor accesses public Google News RSS, public Greenhouse board APIs, and official SEC data. Users are responsible for: - complying with applicable laws and source terms; - respecting SEC fair-access guidance; - using a descriptive SEC User-Agent; - avoiding misleading financial claims; - protecting exported datasets and client-specific watchlist names; - obtaining professional advice where required. This Actor does not execute trades and does not provide financial advice. ### FAQ #### Does it predict stock prices? No. It creates a transparent research indicator from three public data components. #### Does it use ChatGPT or another paid model? No. Headline scoring is deterministic and local. #### Can it monitor companies that do not use Greenhouse? Not in v1. A public Greenhouse token is required to preserve a consistent three-source product. #### Why is hiring delta unavailable? The first run is a baseline. Rerun with the same store name, namespace, ticker, and board token. #### Why did my company return a Greenhouse 404? Check the exact token after `boards.greenhouse.io/`. Do not use a company website slug unless it matches the board URL. #### Why are revenue or margin fields null? The company's SEC taxonomy may not expose comparable annual USD facts under the supported concepts. Inspect the SEC evidence URL. #### Can I change the score weights? Yes. Provide any non-negative values; the actor normalizes them. #### Can I backtest years of signals? No. Historical backtesting is intentionally outside v1. Export scheduled rows to build your own history. #### How many companies should I run first? One. Confirm mappings and evidence, then expand up to 25 companies. ### Troubleshooting checklist If output looks wrong: 1. verify the ticker is US-listed and present in SEC mappings; 2. open the Greenhouse board URL from `evidence`; 3. inspect `sourceErrors`; 4. compare headline evidence with the selected lookback; 5. confirm snapshot namespace consistency; 6. check whether this is the baseline run; 7. review confidence before interpreting the score. ### Related scrapers Use raw component actors when you need source records instead of one normalized company signal: - [Google News Scraper](https://apify.com/automation-lab/google-news-scraper) for individual news articles - [Greenhouse Jobs Scraper](https://apify.com/automation-lab/greenhouse-jobs-scraper) for full job listings - [SEC Company Facts API Scraper](https://apify.com/automation-lab/sec-company-facts-api-scraper) for flattened XBRL facts - [Financial Statements Scraper](https://apify.com/automation-lab/financial-statements-scraper) for statement-oriented SEC output ### Changelog See the Actor changelog for user-facing releases and behavior changes. ### Support When reporting a problem, include: - the run URL; - a redacted input; - affected ticker; - Greenhouse board token; - expected versus actual component; - any `sourceErrors` value. That evidence makes source-specific diagnosis much faster. # Actor input Schema ## `companies` (type: `array`): US-listed companies to analyze. Each company needs its ticker, display name, and public Greenhouse board token (the segment after boards.greenhouse.io/). ## `lookbackDays` (type: `integer`): Count and score headlines published in this recent window. ## `maxNewsArticles` (type: `integer`): Bound the headlines used for each company score. ## `maxEvidenceItems` (type: `integer`): Maximum sample headline and job URLs retained inside each company signal. ## `weights` (type: `object`): Relative non-negative weights. Values are normalized automatically. ## `snapshotStoreName` (type: `string`): Named key-value store used to compare hiring and news counts across scheduled runs. ## `snapshotNamespace` (type: `string`): Use a stable unique namespace for each watchlist or client to prevent histories from overlapping. ## `secUserAgent` (type: `string`): Descriptive SEC request User-Agent. Include an application name and contact email for production use. ## Actor input object example ```json { "companies": [ { "ticker": "NET", "companyName": "Cloudflare", "greenhouseBoardToken": "cloudflare" } ], "lookbackDays": 7, "maxNewsArticles": 20, "maxEvidenceItems": 5, "weights": { "news": 0.4, "hiring": 0.35, "fundamentals": 0.25 }, "snapshotStoreName": "alternative-data-signal-snapshots", "snapshotNamespace": "my-watchlist", "secUserAgent": "MyResearchPipeline/1.0 research@example.com" } ``` # Actor output Schema ## `companySignals` (type: `string`): No description ## `dataset` (type: `string`): No description # 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 = { "companies": [ { "ticker": "NET", "companyName": "Cloudflare", "greenhouseBoardToken": "cloudflare" } ], "lookbackDays": 7, "maxNewsArticles": 20, "maxEvidenceItems": 5, "weights": { "news": 0.4, "hiring": 0.35, "fundamentals": 0.25 }, "snapshotStoreName": "alternative-data-signal-snapshots", "snapshotNamespace": "my-watchlist", "secUserAgent": "MyResearchPipeline/1.0 research@example.com" }; // Run the Actor and wait for it to finish const run = await client.actor("automation-lab/alternative-data-signal-aggregator").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`πŸ’Ύ 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); }); // πŸ“š Want to learn more πŸ“–? Go to β†’ 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 = { "companies": [{ "ticker": "NET", "companyName": "Cloudflare", "greenhouseBoardToken": "cloudflare", }], "lookbackDays": 7, "maxNewsArticles": 20, "maxEvidenceItems": 5, "weights": { "news": 0.4, "hiring": 0.35, "fundamentals": 0.25, }, "snapshotStoreName": "alternative-data-signal-snapshots", "snapshotNamespace": "my-watchlist", "secUserAgent": "MyResearchPipeline/1.0 research@example.com", } # Run the Actor and wait for it to finish run = client.actor("automation-lab/alternative-data-signal-aggregator").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("πŸ’Ύ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # πŸ“š Want to learn more πŸ“–? Go to β†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "companies": [ { "ticker": "NET", "companyName": "Cloudflare", "greenhouseBoardToken": "cloudflare" } ], "lookbackDays": 7, "maxNewsArticles": 20, "maxEvidenceItems": 5, "weights": { "news": 0.4, "hiring": 0.35, "fundamentals": 0.25 }, "snapshotStoreName": "alternative-data-signal-snapshots", "snapshotNamespace": "my-watchlist", "secUserAgent": "MyResearchPipeline/1.0 research@example.com" }' | apify call automation-lab/alternative-data-signal-aggregator --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=automation-lab/alternative-data-signal-aggregator", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Alternative Data Signal Aggregator", "description": "Turn public news, hiring, and SEC filing data into one auditable company research indicator with evidence and cross-run hiring deltas.", "version": "0.1", "x-build-id": "jvUY32h6LFsrB2xGK" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/automation-lab~alternative-data-signal-aggregator/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-automation-lab-alternative-data-signal-aggregator", "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/automation-lab~alternative-data-signal-aggregator/runs": { "post": { "operationId": "runs-sync-automation-lab-alternative-data-signal-aggregator", "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/automation-lab~alternative-data-signal-aggregator/run-sync": { "post": { "operationId": "run-sync-automation-lab-alternative-data-signal-aggregator", "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", "required": [ "companies" ], "properties": { "companies": { "title": "🏒 Company watchlist", "minItems": 1, "maxItems": 25, "type": "array", "description": "US-listed companies to analyze. Each company needs its ticker, display name, and public Greenhouse board token (the segment after boards.greenhouse.io/).", "items": { "type": "object", "required": [ "ticker", "companyName", "greenhouseBoardToken" ], "properties": { "ticker": { "title": "Ticker", "type": "string", "description": "US stock ticker resolved through the SEC company ticker file." }, "companyName": { "title": "Company name", "type": "string", "description": "Name used in the Google News query." }, "greenhouseBoardToken": { "title": "Greenhouse board token", "type": "string", "description": "Public board token, for example cloudflare from boards.greenhouse.io/cloudflare." } } } }, "lookbackDays": { "title": "News lookback (days)", "minimum": 1, "maximum": 30, "type": "integer", "description": "Count and score headlines published in this recent window.", "default": 7 }, "maxNewsArticles": { "title": "Maximum headlines per company", "minimum": 1, "maximum": 100, "type": "integer", "description": "Bound the headlines used for each company score.", "default": 50 }, "maxEvidenceItems": { "title": "Evidence rows per source", "minimum": 1, "maximum": 20, "type": "integer", "description": "Maximum sample headline and job URLs retained inside each company signal.", "default": 8 }, "weights": { "title": "Component weights", "type": "object", "description": "Relative non-negative weights. Values are normalized automatically.", "properties": { "news": { "title": "News", "description": "Relative weight for headline sentiment and velocity.", "type": "number", "minimum": 0, "default": 0.4 }, "hiring": { "title": "Hiring", "description": "Relative weight for cross-run job-opening changes.", "type": "number", "minimum": 0, "default": 0.35 }, "fundamentals": { "title": "Fundamentals", "description": "Relative weight for filing-backed annual changes.", "type": "number", "minimum": 0, "default": 0.25 } } }, "snapshotStoreName": { "title": "Snapshot store name", "type": "string", "description": "Named key-value store used to compare hiring and news counts across scheduled runs.", "default": "alternative-data-signal-snapshots" }, "snapshotNamespace": { "title": "Snapshot namespace", "type": "string", "description": "Use a stable unique namespace for each watchlist or client to prevent histories from overlapping.", "default": "default" }, "secUserAgent": { "title": "SEC User-Agent", "type": "string", "description": "Descriptive SEC request User-Agent. Include an application name and contact email for production use.", "default": "AlternativeDataSignalAggregator/0.1 contact@automation-lab.com" } } }, "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 } } } } } } } } } } ```