# FRED Economic Data Fetcher — Time Series & Indicators (`scrapeworks/fred-economic-data`) Actor Fetch economic time-series data from FRED (Federal Reserve Economic Data, St. Louis Fed) in bulk. Pass a list of FRED series IDs like GDP, UNRATE or CPIAUCSL and get clean date/value rows — with optional date range, units transforms and frequency aggregation. No API key required. - **URL**: https://apify.com/scrapeworks/fred-economic-data.md - **Developed by:** [Nicolas van Arkens](https://apify.com/scrapeworks) (community) - **Categories:** Business, Developer tools, Other - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $1.00 / 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 ## FRED Economic Data Fetcher Pull economic time-series straight from **FRED — Federal Reserve Economic Data** (St. Louis Fed) as clean, flat JSON. Pass a **list of FRED series IDs** — GDP, unemployment, CPI, interest rates, and 800,000+ more — and get one tidy `date` + `value` row per observation. Optional date range, units transformations (percent change, year-over-year, etc.) and frequency aggregation. **No API key required.** ### What you get One record per observation, ready for spreadsheets, dashboards or models: | Field | Description | |-------|-------------| | `series_id` | The FRED series ID (e.g. `UNRATE`). | | `date` | Observation date, `YYYY-MM-DD`. | | `value` | The numeric value (or `null` for gaps/holidays). | | `units_transform` | Transformation applied (`lin`, `pch`, `pc1`, …). | | `series_title` | Human-readable series name — *with a FRED API key.* | | `native_units` | The series' real units, e.g. "Percent" — *with a key.* | | `native_frequency` | e.g. "Monthly", "Quarterly" — *with a key.* | | `seasonal_adjustment` | e.g. "Seasonally Adjusted" — *with a key.* | | `last_updated` | When FRED last revised the series — *with a key.* | ### Input Everything is **bulk by default** — request as many series as you like in one run. | Field | Type | Description | |-------|------|-------------| | `seriesIds` | array | One or more FRED series IDs, e.g. `["GDP", "UNRATE", "CPIAUCSL"]`. | | `observationStart` / `observationEnd` | string | Optional `YYYY-MM-DD` date range. | | `units` | enum | Transformation: `lin` (levels), `chg`, `ch1`, `pch`, `pc1`, `pca`, `log`. | | `frequency` | enum | Aggregate to Daily/Weekly/Monthly/Quarterly/Annual (or keep native). | | `aggregationMethod` | enum | When aggregating: `avg`, `sum`, or `eop` (end of period). | | `sortOrder` | enum | Oldest-first or newest-first. | | `apiKey` | string | Optional free FRED key — unlocks series metadata on every row. | | `maxResultsPerSeries` | integer | Cap on observations per series. | #### Input example ```json { "seriesIds": ["GDP", "UNRATE", "CPIAUCSL"], "observationStart": "2015-01-01", "units": "lin", "sortOrder": "asc", "maxResultsPerSeries": 5000 } ```` #### Popular series IDs - `GDP` — Gross Domestic Product - `UNRATE` — Unemployment Rate - `CPIAUCSL` — Consumer Price Index (CPI) - `FEDFUNDS` — Federal Funds Effective Rate - `DGS10` — 10-Year Treasury Constant Maturity Rate - `MORTGAGE30US` — 30-Year Fixed Mortgage Rate - `PAYEMS` — All Employees, Total Nonfarm (payrolls) - `T10Y2Y` — 10-Year minus 2-Year Treasury Spread ### Output ```json { "series_id": "UNRATE", "series_title": null, "date": "2024-01-01", "value": 3.7, "units_transform": "lin", "native_units": null, "native_frequency": null, "seasonal_adjustment": null, "last_updated": null, "ok": true } ``` With a FRED API key the metadata fields (`series_title`, `native_units`, `native_frequency`, `seasonal_adjustment`, `last_updated`) are populated too. ### Use cases - **Dashboards & reporting** — feed live macro indicators into Sheets, Power BI, or a web app. - **Quant & research** — pull deep history for many series in one run for backtests and models. - **Newsletters & content** — grab the latest CPI, jobs, or rates numbers on a schedule. ### FAQ **Do I need a FRED API key?** No. The actor works out of the box using FRED's public CSV download. A free key (from fredaccount.stlouisfed.org/apikeys) simply adds series metadata to each row. **How do I find a series ID?** Search on fred.stlouisfed.org — the ID is the short code in the series page URL and title (e.g. `UNRATE`). **What about missing values?** Holidays/gaps come back as `value: null` rather than being dropped, so your time series stays aligned. ### Notes - Uses the public FRED data endpoints. Independent tool, not affiliated with or endorsed by the Federal Reserve Bank of St. Louis. - Error/"series not found" rows are returned with `ok: false` for transparency and are **never billed**. # Actor input Schema ## `seriesIds` (type: `array`): One entry per FRED series ID to fetch. Find IDs on fred.stlouisfed.org (the code shown in the series URL/title). Examples: GDP (Gross Domestic Product), UNRATE (Unemployment Rate), CPIAUCSL (Consumer Price Index), FEDFUNDS (Federal Funds Rate), DGS10 (10-Year Treasury Yield), PAYEMS (Nonfarm Payrolls), MORTGAGE30US (30-Year Mortgage Rate). Pass as many as you like — each returns its full history as date/value rows. ## `observationStart` (type: `string`): Earliest observation date to include, as YYYY-MM-DD (e.g. 2010-01-01). Leave blank for the full available history of each series. ## `observationEnd` (type: `string`): Latest observation date to include, as YYYY-MM-DD (e.g. 2024-12-31). Leave blank to include the most recent data. ## `units` (type: `string`): Optional data transformation applied to every value. 'lin' = levels (raw values, default). 'chg' = change, 'ch1' = change from a year ago, 'pch' = percent change, 'pc1' = percent change from a year ago, 'pca' = compounded annual rate of change, 'log' = natural log. ## `frequency` (type: `string`): Optionally aggregate higher-frequency data to a lower frequency (e.g. monthly to annual). Leave as 'Native' to keep each series' own frequency. ## `aggregationMethod` (type: `string`): How to combine values when aggregating to a lower frequency (only used when Frequency is set). 'avg' = average, 'sum' = total, 'eop' = end of period. ## `sortOrder` (type: `string`): Order observations by date, oldest first (ascending) or newest first (descending). ## `apiKey` (type: `string`): Optional free FRED API key from fredaccount.stlouisfed.org/apikeys. When provided, each row is enriched with series metadata (title, native units, frequency, seasonal adjustment, last-updated date). Without a key the actor still works fully using FRED's public CSV download endpoint — you just get date/value rows without the extra metadata. ## `maxResultsPerSeries` (type: `integer`): Cap on the number of observations returned for each series ID. Lower this to sample recent data; raise it to pull deep history. ## Actor input object example ```json { "seriesIds": [ "GDP", "UNRATE", "CPIAUCSL", "FEDFUNDS", "DGS10" ], "observationStart": "2015-01-01", "units": "lin", "frequency": "", "aggregationMethod": "avg", "sortOrder": "asc", "maxResultsPerSeries": 5000 } ``` # 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 = { "seriesIds": [ "GDP", "UNRATE", "CPIAUCSL" ], "observationStart": "2015-01-01", "maxResultsPerSeries": 5000 }; // Run the Actor and wait for it to finish const run = await client.actor("scrapeworks/fred-economic-data").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 = { "seriesIds": [ "GDP", "UNRATE", "CPIAUCSL", ], "observationStart": "2015-01-01", "maxResultsPerSeries": 5000, } # Run the Actor and wait for it to finish run = client.actor("scrapeworks/fred-economic-data").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 '{ "seriesIds": [ "GDP", "UNRATE", "CPIAUCSL" ], "observationStart": "2015-01-01", "maxResultsPerSeries": 5000 }' | apify call scrapeworks/fred-economic-data --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=scrapeworks/fred-economic-data", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "FRED Economic Data Fetcher — Time Series & Indicators", "description": "Fetch economic time-series data from FRED (Federal Reserve Economic Data, St. Louis Fed) in bulk. Pass a list of FRED series IDs like GDP, UNRATE or CPIAUCSL and get clean date/value rows — with optional date range, units transforms and frequency aggregation. No API key required.", "version": "0.1", "x-build-id": "bZWIe1o0UW09jVfls" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scrapeworks~fred-economic-data/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scrapeworks-fred-economic-data", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/scrapeworks~fred-economic-data/runs": { "post": { "operationId": "runs-sync-scrapeworks-fred-economic-data", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/scrapeworks~fred-economic-data/run-sync": { "post": { "operationId": "run-sync-scrapeworks-fred-economic-data", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "seriesIds" ], "properties": { "seriesIds": { "title": "FRED series IDs", "type": "array", "description": "One entry per FRED series ID to fetch. Find IDs on fred.stlouisfed.org (the code shown in the series URL/title). Examples: GDP (Gross Domestic Product), UNRATE (Unemployment Rate), CPIAUCSL (Consumer Price Index), FEDFUNDS (Federal Funds Rate), DGS10 (10-Year Treasury Yield), PAYEMS (Nonfarm Payrolls), MORTGAGE30US (30-Year Mortgage Rate). Pass as many as you like — each returns its full history as date/value rows.", "items": { "type": "string" } }, "observationStart": { "title": "Start date (optional)", "type": "string", "description": "Earliest observation date to include, as YYYY-MM-DD (e.g. 2010-01-01). Leave blank for the full available history of each series." }, "observationEnd": { "title": "End date (optional)", "type": "string", "description": "Latest observation date to include, as YYYY-MM-DD (e.g. 2024-12-31). Leave blank to include the most recent data." }, "units": { "title": "Units transformation", "enum": [ "lin", "chg", "ch1", "pch", "pc1", "pca", "cch", "cca", "log" ], "type": "string", "description": "Optional data transformation applied to every value. 'lin' = levels (raw values, default). 'chg' = change, 'ch1' = change from a year ago, 'pch' = percent change, 'pc1' = percent change from a year ago, 'pca' = compounded annual rate of change, 'log' = natural log.", "default": "lin" }, "frequency": { "title": "Frequency aggregation", "enum": [ "", "d", "w", "bw", "m", "q", "sa", "a" ], "type": "string", "description": "Optionally aggregate higher-frequency data to a lower frequency (e.g. monthly to annual). Leave as 'Native' to keep each series' own frequency.", "default": "" }, "aggregationMethod": { "title": "Aggregation method", "enum": [ "avg", "sum", "eop" ], "type": "string", "description": "How to combine values when aggregating to a lower frequency (only used when Frequency is set). 'avg' = average, 'sum' = total, 'eop' = end of period.", "default": "avg" }, "sortOrder": { "title": "Sort order", "enum": [ "asc", "desc" ], "type": "string", "description": "Order observations by date, oldest first (ascending) or newest first (descending).", "default": "asc" }, "apiKey": { "title": "FRED API key (optional)", "type": "string", "description": "Optional free FRED API key from fredaccount.stlouisfed.org/apikeys. When provided, each row is enriched with series metadata (title, native units, frequency, seasonal adjustment, last-updated date). Without a key the actor still works fully using FRED's public CSV download endpoint — you just get date/value rows without the extra metadata." }, "maxResultsPerSeries": { "title": "Max observations per series", "minimum": 1, "maximum": 100000, "type": "integer", "description": "Cap on the number of observations returned for each series ID. Lower this to sample recent data; raise it to pull deep history.", "default": 5000 } } }, "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 } } } } } } } } } } ```