# Economic Calendar & Fed Watch - FOMC, CPI, NFP, Macro Events (`michael_b/economic-calendar-fed-watch`) Actor Forward-looking macro economic events with consensus, prior, and surprise: CPI, NFP, GDP, FOMC decisions, Fed speeches, central bank meetings. Sources: Finviz (US), Federal Reserve (FOMC + speeches), Investing.com (international + past actuals). Built for AI agents and MCP pipelines. - **URL**: https://apify.com/michael\_b/economic-calendar-fed-watch.md - **Developed by:** [Michal Búci](https://apify.com/michael_b) (community) - **Categories:** AI, Agents, News - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $5.00 / 1,000 results 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 ## Economic Calendar & Fed Watch Forward-looking macro economic events for AI agents — **CPI, NFP, GDP, FOMC decisions, Fed speeches, central bank meetings** — with consensus, prior, and a precomputed surprise verdict. Designed primarily as an MCP tool for AI agents doing portfolio analysis, pre-trade risk checks, and macro briefings. --- ### Quick example A row for the May 2026 Non Farm Payrolls release looks like: ```json { "scheduledAt": "2026-05-08T12:30:00Z", "country": "US", "importance": "high", "eventName": "Non Farm Payrolls", "eventType": "release", "consensus": 60.0, "actual": null, "prior": 178.0, "unit": "thousands", "surpriseDirection": null, "surpriseVsConsensus": null, "surpriseVsPrior": null, "...": "..." } ```` When the release prints, `actual` fills in and surprise fields auto-populate. *** ### Output fields Fields are ordered most-useful-first. Sparse fields (only populated for specific event types) are noted. #### Primary (always populated) | Field | What it means | |---|---| | `scheduledAt` | When the event releases / starts. ISO 8601 UTC, always ends in `Z`. | | `country` | ISO 3166-1 alpha-2 country code. v1 is mostly `US`; international with Investing.com enabled. | | `importance` | Normalized impact: `low`, `medium`, `high`. Filter to `high` to cut macro noise. See **How importance is set** below for source-specific details. | | `eventName` | Source-published name. e.g. `Non Farm Payrolls`, `Fed Williams Speech`, `FOMC Minutes`. | | `eventType` | `release` / `meeting` / `statement` / `press_conference` / `minutes` / `speech` / `testimony` / `auction` / `beige_book` / `other`. | #### Headline values (the "what happened / what's expected") | Field | What it means | |---|---| | `consensus` | **What economists/analysts think the actual will be**, parsed numeric. The market's expectation. Null when no consensus is published or the event isn't numeric (speeches, meeting dates). | | `actual` | **The released value**, parsed numeric. Null for upcoming events that haven't released yet. | | `prior` | **Same indicator from the previous reporting period** (last month / last quarter). Helps gauge trend. | | `unit` | Unit of those numeric values: `percent`, `thousands`, `millions`, `billions`, `index`, `count`, `currency_usd`. Without this an LLM can't tell whether 60 is "lots" or "barely anything". | | `surpriseDirection` | **Quick verdict**: `beat` = bullish vs expectation; `miss` = bearish; `inline` = within tolerance. Accounts for inverse indicators (higher unemployment = `miss`, not `beat`). Null when actual or consensus is missing. | | `surpriseVsConsensus` | `actual − consensus`, same unit. The standard surprise metric used to price-in macro risk. | | `surpriseVsPrior` | `actual − prior`, same unit. Useful for trend/narrative ("is the indicator turning?"). | **How to read consensus / actual / prior in plain English:** - **Consensus** = "what the market expects to happen" - **Actual** = "what actually got released" - **Prior** = "what the same indicator was last time" - **Surprise** = `actual − consensus`. Positive surprise ≠ always good — it depends on the indicator. `surpriseDirection` gives you the macro-meaningful answer. #### Secondary numeric / standardization | Field | What it means | |---|---| | `forecast` | Alternate forecast where the source publishes one (e.g. TradingEconomics on Finviz). Often null. When present and different from `consensus`, it's a second opinion. | | `eventNameStandard` | Controlled-vocabulary identifier for cross-source matching: `NFP`, `CPI_YOY`, `FOMC_DECISION`, `FED_SPEECH`, etc. Use this to filter or join across data sources. Unmapped events use `UNKNOWN_` prefix. | | `importanceScore` | Importance as integer `1` (low) / `2` (medium) / `3` (high). For sortability. | | `isHigherPositive` | Direction encoding: `1` = higher actual is bullish for the subject (GDP, jobs); `-1` = bearish (unemployment, inflation, jobless claims); `0` = neutral. Drives `surpriseDirection`. | | `period` | Human-readable period the data covers: `April 2026`, `Q1 2026`. | | `referencePeriodEnd` | ISO date when the reporting period ends. Useful for joining to date-indexed data. | | `currency` | ISO 4217 currency code associated with the country. | #### Speech-specific (only populated for speech / testimony rows) | Field | What it means | |---|---| | `speakerName` | Speaker's name, e.g. `Christopher J. Waller`. | | `speakerRole` | Speaker's title: `Chair`, `Vice Chair for Supervision`, `Governor`, `FOMC Member`. | | `speechTopic` | Subject the speaker is addressing, when published. | | `speechVenue` | Physical / online venue. | | `transcriptUrl` | URL to the prepared remarks / transcript (when published). Inline text is intentionally NOT included — saves tokens. | | `livestreamUrl` | URL to a livestream when scheduled (FOMC press conferences, some Fed speeches). | #### Provenance / debug (always present, useful for audits) | Field | What it means | |---|---| | `rawConsensus` / `rawActual` / `rawPrior` | Source-formatted strings *before* parsing — e.g. `"60K"`, `"$7.5B"`, `"2.4%"`. Useful for fidelity / debugging if a parse looks wrong. | | `sourceName` | `Finviz` / `FederalReserve` / `Investing`. | | `sourceUrl` | Canonical URL of the calendar landing page (not per-event). | | `eventUrl` | Best available per-event detail page URL. Investing.com 100%, Finviz ~97%, Fed ~3-10%. When two sources both cover the same event, the URL with the richer detail page wins (so a Fed-source row can carry an Investing URL through dedup). | | `sourceEventId` | The source's own internal event identifier (Finviz `calendarId`, Investing `event_attr_id`). Useful if you want to merge this with separate scrapes from the same source. Null for federalreserve.gov calendar.json (no stable ID). | | `eventId` | Stable cross-source dedup key: `country-YYYY-MM-DD-eventNameStandard`. Survives reruns and source merging. | | `scrapedAt` | ISO 8601 UTC timestamp this row was scraped. Lets you reason about data freshness. | *** ### MCP usage (Claude Code, Cursor, any MCP-compatible LLM) ```bash npx @apify/actors-mcp-server --tools michael_b/economic-calendar-fed-watch ``` The actor exposes its input schema as an MCP tool. Example agent prompts: - "What high-impact macro events are coming this week that could move SPY?" - "When is the next FOMC meeting?" - "Show me Fed speeches scheduled for the next 7 days." - "Did last week's CPI print beat or miss consensus?" *** ### Sources | Source | Coverage | Anti-bot | |--------|----------|----------| | Finviz `/calendar/economic` | US, current trading week, with TradingEconomics consensus | None | | Federal Reserve `calendar.json` + `ne-speeches.json` | FOMC meetings, press conferences, minutes, Beige Book, Fed speeches/testimony | None | | Investing.com `getCalendarFilteredData` AJAX | International + historical actuals (50+ countries) | curl\_cffi Chrome impersonation | ### Default behavior With **defaults** (`daysAhead=7, country=US, importance=high, includeFinviz=true, includeFed=true, includeInvesting=false`) the actor returns the next 7 days of high-impact US events plus FOMC dates and statement releases — typically 5–15 rows, well under any agent's context budget. ### Common configurations **Pre-trade risk scan (default)** — high-impact US events for the next week: ```json {} ``` **FOMC + Fed speeches scan** — next 30 days: ```json { "daysAhead": 30, "eventTypes": ["meeting", "press_conference", "minutes", "speech", "statement"], "importance": "all" } ``` **Recent surprise post-mortem** — last week's high-impact prints with actuals: ```json { "daysAhead": 0, "daysBack": 7, "importance": "high", "includeInvesting": true } ``` **International macro week** — US + EU + UK high-impact: ```json { "country": ["US", "EU", "GB"], "daysAhead": 7, "importance": "high", "includeInvesting": true } ``` **Targeted lookup** — when is the next CPI? ```json { "keyword": "CPI", "daysAhead": 45, "importance": "all", "limit": 1 } ``` *** ### How importance is set The three sources don't share an importance scheme — we normalize each into the same `low`/`medium`/`high` scale: | Source | Where importance comes from | |--------|------| | Finviz | Numeric `1`/`2`/`3` curated in their JSON. NFP, CPI, FOMC events are `3`; ISM/jobless are `2-3`; EIA, auctions, lower-tier indicators are `1`. | | Federal Reserve | We assign it ourselves — calendar.json doesn't carry an importance field. `high` for FOMC meetings/minutes/statements, press conferences, Beige Book. `medium` for everything else (speeches, testimony, conferences, statistical releases). | | Investing.com | Count of bull icons (`1`/`2`/`3`) curated by Investing. Heavily overlaps Finviz on the big releases (NFP, CPI = 3 bulls). | **Practical implication:** filtering `importance=high` returns the same NFP/CPI/FOMC universe across all three sources, but it's a heuristic alignment, not enforced. If you need a strict cross-source importance contract, filter on `eventNameStandard` (e.g. `["NFP", "CPI_YOY", "FOMC_DECISION"]`) instead. ### Limits and known gaps - **Finviz exposes only the current trading week.** For events outside that window, enable `includeInvesting=true`. - **Speeches are excluded by default** — they're high-volume and noisy. Enable via `eventTypes: ["speech"]`. - **CME FedWatch implied probabilities are not yet included** — coming in v1.1. v1 ships FOMC meeting / press conference / minutes dates only. - **Hawkish/dovish speech lean is not inferred** — leave that to your LLM. ### Pricing This actor uses **pay-per-event**: - Per-run base fee: $0.005 - Per event returned: $0.002 A typical default call (~8 high-impact US events, next week) costs ~$0.021. # Actor input Schema ## `daysAhead` (type: `integer`): How many days into the future to include. 14 = next two weeks, enough to catch the next CPI/NFP/FOMC even late in a quiet week. Set 7 for just the upcoming trading week. ## `daysBack` (type: `integer`): How many days of past events to include. 0 = upcoming-only (default; past data is FRED's job). Set higher for surprise post-mortems and trend analysis. ## `country` (type: `array`): ISO 3166-1 alpha-2 country codes. \['US'] is the default. International coverage via Investing.com requires includeInvesting=true. ## `importance` (type: `string`): Each source assigns importance differently (Finviz curates 1-3, Investing.com curates with bull icons, Fed events default to 'high' for FOMC/minutes/statements/Beige Book and 'medium' for everything else). The three roughly agree on the big releases. What to expect: • 'High only' — only the headline-mover releases: NFP, CPI, FOMC decisions, ISM PMI, GDP, jobless claims, Michigan sentiment. Typical week: 5-15 rows. Best for AI agent context budgets. • 'Medium and above' — adds tier-2 releases: ADP, retail sales, durable goods, building permits, factory orders, Fed speeches, manufacturing PMIs. Typical week: 30-60 rows. • 'All importance levels' — adds Treasury auctions, EIA oil/gas inventory, mortgage indices, regional Fed surveys, statistical publications. Typical week: 100-200+ rows. If unsure or you need the full picture, pick 'All importance levels' and filter downstream — sources are noisy in different ways. ## `eventTypes` (type: `array`): Which event categories to include. Speeches and auctions are excluded by default to reduce noise. ## `keyword` (type: `string`): Free-text match on event name. Useful for targeted lookups: 'CPI', 'FOMC', 'Powell', 'jobless'. Case-insensitive substring match. ## `includeFinviz` (type: `boolean`): Scrape Finviz economic calendar. Best US coverage for the current week with TradingEconomics consensus baked in. Default: true. ## `includeFed` (type: `boolean`): Scrape federalreserve.gov calendar.json for FOMC meetings, press conferences, minutes release dates, Beige Book, member speeches, and testimony. Unique to this actor. ## `includeInvesting` (type: `boolean`): Scrape investing.com economic calendar. Adds international coverage and the forward calendar beyond the current week (Finviz only carries the current week, so without this the next CPI/NFP won't show until that week arrives). Required for non-US countries and Last-N surprises analysis. Default: true. ## `limit` (type: `integer`): Hard cap on total events. 0 = unlimited (default — return everything that matches the filters). Set to a positive integer (e.g. 50) to limit context for AI agents. The 10000 maximum is a safety net; even greedy 60-day scans rarely return more than 500 rows. ## Actor input object example ```json { "daysAhead": 14, "daysBack": 0, "country": [ "US" ], "importance": "high", "eventTypes": [ "release", "meeting", "statement", "press_conference", "minutes", "beige_book" ], "includeFinviz": true, "includeFed": true, "includeInvesting": true, "limit": 0 } ``` # Actor output Schema ## `results` (type: `string`): Dataset of macro events with normalized fields: eventName, eventNameStandard, eventType, country, scheduledAt (UTC), importance, consensus, actual, prior, surprise vs consensus / prior / direction, speaker info, source. # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("michael_b/economic-calendar-fed-watch").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 = {} # Run the Actor and wait for it to finish run = client.actor("michael_b/economic-calendar-fed-watch").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 '{}' | apify call michael_b/economic-calendar-fed-watch --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=michael_b/economic-calendar-fed-watch", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Economic Calendar & Fed Watch - FOMC, CPI, NFP, Macro Events", "description": "Forward-looking macro economic events with consensus, prior, and surprise: CPI, NFP, GDP, FOMC decisions, Fed speeches, central bank meetings. Sources: Finviz (US), Federal Reserve (FOMC + speeches), Investing.com (international + past actuals). Built for AI agents and MCP pipelines.", "version": "0.1", "x-build-id": "Iy9YX2dh4O63S08ZV" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/michael_b~economic-calendar-fed-watch/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-michael_b-economic-calendar-fed-watch", "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/michael_b~economic-calendar-fed-watch/runs": { "post": { "operationId": "runs-sync-michael_b-economic-calendar-fed-watch", "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/michael_b~economic-calendar-fed-watch/run-sync": { "post": { "operationId": "run-sync-michael_b-economic-calendar-fed-watch", "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": { "daysAhead": { "title": "Days ahead", "minimum": 0, "maximum": 365, "type": "integer", "description": "How many days into the future to include. 14 = next two weeks, enough to catch the next CPI/NFP/FOMC even late in a quiet week. Set 7 for just the upcoming trading week.", "default": 14 }, "daysBack": { "title": "Days back", "minimum": 0, "maximum": 365, "type": "integer", "description": "How many days of past events to include. 0 = upcoming-only (default; past data is FRED's job). Set higher for surprise post-mortems and trend analysis.", "default": 0 }, "country": { "title": "Country filter (ISO2)", "type": "array", "description": "ISO 3166-1 alpha-2 country codes. ['US'] is the default. International coverage via Investing.com requires includeInvesting=true.", "default": [ "US" ], "items": { "type": "string" } }, "importance": { "title": "Minimum importance", "enum": [ "high", "medium", "all" ], "type": "string", "description": "Each source assigns importance differently (Finviz curates 1-3, Investing.com curates with bull icons, Fed events default to 'high' for FOMC/minutes/statements/Beige Book and 'medium' for everything else). The three roughly agree on the big releases. What to expect:\n\n• 'High only' — only the headline-mover releases: NFP, CPI, FOMC decisions, ISM PMI, GDP, jobless claims, Michigan sentiment. Typical week: 5-15 rows. Best for AI agent context budgets.\n\n• 'Medium and above' — adds tier-2 releases: ADP, retail sales, durable goods, building permits, factory orders, Fed speeches, manufacturing PMIs. Typical week: 30-60 rows.\n\n• 'All importance levels' — adds Treasury auctions, EIA oil/gas inventory, mortgage indices, regional Fed surveys, statistical publications. Typical week: 100-200+ rows.\n\nIf unsure or you need the full picture, pick 'All importance levels' and filter downstream — sources are noisy in different ways.", "default": "high" }, "eventTypes": { "title": "Event types", "uniqueItems": true, "type": "array", "description": "Which event categories to include. Speeches and auctions are excluded by default to reduce noise.", "items": { "type": "string", "enum": [ "release", "meeting", "statement", "press_conference", "minutes", "speech", "testimony", "auction", "beige_book", "other" ] }, "default": [ "release", "meeting", "statement", "press_conference", "minutes", "beige_book" ] }, "keyword": { "title": "Keyword filter (optional)", "type": "string", "description": "Free-text match on event name. Useful for targeted lookups: 'CPI', 'FOMC', 'Powell', 'jobless'. Case-insensitive substring match." }, "includeFinviz": { "title": "Include Finviz source (US current week)", "type": "boolean", "description": "Scrape Finviz economic calendar. Best US coverage for the current week with TradingEconomics consensus baked in. Default: true.", "default": true }, "includeFed": { "title": "Include Federal Reserve source (FOMC + speeches)", "type": "boolean", "description": "Scrape federalreserve.gov calendar.json for FOMC meetings, press conferences, minutes release dates, Beige Book, member speeches, and testimony. Unique to this actor.", "default": true }, "includeInvesting": { "title": "Include Investing.com source (international + past actuals)", "type": "boolean", "description": "Scrape investing.com economic calendar. Adds international coverage and the forward calendar beyond the current week (Finviz only carries the current week, so without this the next CPI/NFP won't show until that week arrives). Required for non-US countries and Last-N surprises analysis. Default: true.", "default": true }, "limit": { "title": "Max events returned", "minimum": 0, "maximum": 10000, "type": "integer", "description": "Hard cap on total events. 0 = unlimited (default — return everything that matches the filters). Set to a positive integer (e.g. 50) to limit context for AI agents. The 10000 maximum is a safety net; even greedy 60-day scans rarely return more than 500 rows.", "default": 0 } } }, "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 } } } } } } } } } } ```