# FRED Federal Reserve Economic Data — 800K+ Time Series (`compute-edge/fred-economic-scraper`) Actor Extract economic time series from St. Louis Fed FRED API. 800K+ series including GDP, unemployment, CPI, treasury yields, mortgage rates, exchange rates. For macroeconomic dashboards, fintech apps, trading signals, and economic research. Free 32-char API key required. - **URL**: https://apify.com/compute-edge/fred-economic-scraper.md - **Developed by:** [Compute Edge](https://apify.com/compute-edge) (community) - **Categories:** Lead generation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $3.00 / 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 Federal Reserve Economic Data Scraper **Extract 800,000+ economic time series from the Federal Reserve's FRED database.** Fetch macroeconomic indicators like GDP, unemployment, inflation (CPI), treasury yields, housing starts, and much more. Perfect for financial dashboards, economic research, trading signal development, and fintech applications. ### What This Actor Does This Actor provides a complete interface to the FRED (Federal Reserve Economic Data) API, maintained by the Federal Reserve Bank of St. Louis. It supports four operating modes: 1. **Fetch Time Series Observations** — Retrieve historical economic data for one or more series IDs (e.g., track the 10-year Treasury yield, unemployment rate, or S&P 500 index across decades) 2. **Search Series by Text** — Find series IDs by keyword search (e.g., search for "yield curve" or "housing starts") 3. **List Category Series** — Enumerate all series within a FRED category (e.g., "Money, Banking, & Finance") 4. **Fetch Release Schedules** — Get FRED data release calendars and publication metadata #### Key Features - **800,000+ series** — Complete FRED database access - **Flexible filtering** — Date ranges, resampling frequencies (daily → annual), unit transformations (raw, % change, log) - **Pagination support** — Unlimited results (up to API limits) - **Batch processing** — Efficient extraction for large datasets - **API key protected** — Your credentials stored securely - **Error handling** — Graceful fallback for missing or inaccessible data - **Zero login required** — Free API key registration takes 30 seconds #### Popular Series IDs (Examples) | Series ID | Description | Units | |-----------|-------------|-------| | **GDP** | Real Gross Domestic Product | Billions of USD (annual) | | **UNRATE** | Civilian Unemployment Rate | % | | **CPIAUCSL** | Consumer Price Index (CPI-U) | Index, 1982-84 = 100 | | **FEDFUNDS** | Effective Federal Funds Rate | % | | **DFF** | Federal Funds Effective Rate (daily) | % | | **T10Y2Y** | 10-Year Treasury Constant Maturity minus 2-Year | % (yield curve steepness) | | **DGS10** | 10-Year Treasury Constant Maturity Rate | % | | **MORTGAGE30US** | 30-Year Fixed Rate Mortgage Average | % | | **PAYEMS** | Total Nonfarm Payroll | Thousands | | **HOUST** | Housing Starts | Thousands (annual rate) | | **SP500** | S&P 500 Index | Index level | | **VIXCLS** | CBOE Volatility Index (VIX) | Index points | | **UMCSENT** | University of Michigan Consumer Sentiment Index | Index | | **INDPRO** | Industrial Production Index | Index, 2017 = 100 | | **RSAFS** | Retail Sales (excluding automobiles) | Billions USD, annual rate | | **ICSA** | Initial Claims (weekly) | Thousands | ### Getting Started #### Step 1: Create a Free FRED API Key (30 seconds) 1. Visit https://fredaccount.stlouisfed.org/apikeys 2. Sign up (or log in) 3. Create an API key 4. Copy your 32-character key #### Step 2: Run the Actor 1. Paste your API key into the **FRED API Key** input field 2. Choose a **Mode**: - **series_observations** (default) — Fetch time-series data - **series_search** — Search by text - **category_series** — List all series in a category - **releases** — Get data release schedules 3. Configure mode-specific inputs (see below) 4. Click **Start** #### Step 3: Download Results Results appear as JSON in the **Dataset** tab. Export as CSV/JSON via the Apify UI. ### Tutorials #### Tutorial 1: Track the 10-Year Treasury Yield (DGS10) for 2024 **Goal:** Fetch daily Treasury yield data for 2024 to visualize the yield curve movements. **Input configuration:** - **Mode:** `series_observations` - **Series IDs:** `DGS10` - **Observation Start:** `2024-01-01` - **Observation End:** `2024-12-31` - **Frequency:** `d` (daily) - **Units:** `lin` (raw interest rate %) - **Max Results:** `1000` (more than enough for ~252 trading days) **Expected output:** ```json [ { "seriesId": "DGS10", "seriesTitle": "10-Year Treasury Constant Maturity Rate", "date": "2024-01-02", "value": 3.85, "units": "%", "frequency": "daily", "lastUpdated": "2025-04-20T17:00:00", "popularity": 100 }, { "seriesId": "DGS10", "seriesTitle": "10-Year Treasury Constant Maturity Rate", "date": "2024-01-03", "value": 3.82, "units": "%", "frequency": "daily", "lastUpdated": "2025-04-20T17:00:00", "popularity": 100 }, ... ] ```` **Use case:** Feed this data into a charting library to visualize Treasury yield movements, or calculate volatility metrics for bond trading strategies. *** #### Tutorial 2: Search for Unemployment-Related Series **Goal:** Find all FRED series related to unemployment to build a comprehensive labor market dashboard. **Input configuration:** - **Mode:** `series_search` - **Search Text:** `unemployment rate` - **Max Results:** `100` **Expected output:** ```json [ { "seriesId": "UNRATE", "title": "Civilian Unemployment Rate", "frequency": "Monthly", "units": "%", "observationStart": "1948-01-01", "observationEnd": "2025-04-01", "popularity": 100, "notes": "The unemployment rate represents the number of unemployed persons..." }, { "seriesId": "UNRATESA", "title": "Civilian Unemployment Rate (Seasonally Adjusted)", "frequency": "Monthly", "units": "%", "observationStart": "1948-01-01", "observationEnd": "2025-04-01", "popularity": 95 }, ... ] ``` **Use case:** Identify all related series, then use their Series IDs to fetch historical observations in a follow-up run. *** #### Tutorial 3: Fetch Inflation Data with Unit Transformations **Goal:** Extract CPI data and calculate year-over-year percent change to show inflation trends. **Input configuration:** - **Mode:** `series_observations` - **Series IDs:** `CPIAUCSL` - **Observation Start:** `2020-01-01` - **Frequency:** `m` (monthly — the base frequency for CPI) - **Units:** `pch` (percent change — shows month-over-month % change) - **Max Results:** `100` **Expected output:** ```json [ { "seriesId": "CPIAUCSL", "seriesTitle": "Consumer Price Index for All Urban Consumers: All items", "date": "2025-04-01", "value": 0.41, "units": "%", "appliedUnits": "pch", "frequency": "Monthly", "lastUpdated": "2025-05-09T08:00:00", "popularity": 95 }, ... ] ``` **Note:** The `appliedUnits` field shows "pch" because we requested percent change. The `value` is now the monthly percent change instead of the index level. *** ### Input Parameters #### Mode: `series_observations` (Default) Fetch historical observations (time series data) for one or more FRED series. | Parameter | Type | Default | Description | |-----------|------|---------|-------------| | **apiKey** | string (secret) | — | Your FRED API key (32 chars, required) | | **mode** | enum | `series_observations` | Set to `series_observations` | | **seriesIds** | string | `GDP` | Comma-separated series IDs (e.g., "GDP,UNRATE,CPIAUCSL") | | **observationStart** | string (YYYY-MM-DD) | — | Earliest date (blank = oldest available) | | **observationEnd** | string (YYYY-MM-DD) | — | Latest date (blank = most recent) | | **frequency** | enum | — | Resample frequency: "" (native), "d" (daily), "w" (weekly), "bw" (biweekly), "m" (monthly), "q" (quarterly), "sa" (semi-annual), "a" (annual) | | **aggregationMethod** | enum | `avg` | When resampling: "avg" (average), "sum" (sum), "eop" (end of period) | | **units** | enum | `lin` | Transform units: "lin" (raw), "chg" (change), "pch" (% change), "pca" (compound annual %), "log" (natural log) | | **sortOrder** | enum | `desc` | Sort by date: "asc" (oldest first), "desc" (newest first) | | **maxResults** | integer | `1000` | Maximum observations to fetch (0 = unlimited, up to 100,000) | **Output fields:** - `seriesId`, `seriesTitle`, `frequency`, `frequencyShort`, `units`, `unitsShort`, `seasonalAdjustment`, `seasonalAdjustmentShort` - `lastUpdated`, `popularity`, `notes`, `observationStart`, `observationEnd` - `date` (observation date), `value` (parsed as number, null if missing) - `realtimeStart`, `realtimeEnd` (FRED revision metadata) - `appliedUnits`, `appliedFrequency` (the transformations applied) *** #### Mode: `series_search` Search for series by keyword. | Parameter | Type | Default | Description | |-----------|------|---------|-------------| | **apiKey** | string (secret) | — | Your FRED API key (required) | | **mode** | enum | — | Set to `series_search` | | **searchText** | string | — | Free-text search query (required if mode = series\_search). Examples: "unemployment rate", "inflation", "housing" | | **maxResults** | integer | `1000` | Maximum series to return (0 = unlimited, up to 1,000) | **Output fields:** - `seriesId`, `title`, `notes`, `frequency`, `frequencyShort`, `units`, `unitsShort`, `seasonalAdjustment` - `observationStart`, `observationEnd`, `lastUpdated`, `popularity`, `groupPopularity` - `realtimeStart`, `realtimeEnd` *** #### Mode: `category_series` List all series in a FRED category. | Parameter | Type | Default | Description | |-----------|------|---------|-------------| | **apiKey** | string (secret) | — | Your FRED API key (required) | | **mode** | enum | — | Set to `category_series` | | **categoryId** | integer | `32991` | FRED category ID. Examples: 32991 (Money, Banking, Finance), 9 (Population), 106 (Real estate) | | **maxResults** | integer | `1000` | Maximum series to return (0 = unlimited) | **Output fields:** Same as `series_search`. *** #### Mode: `releases` Fetch FRED data release schedules. | Parameter | Type | Default | Description | |-----------|------|---------|-------------| | **apiKey** | string (secret) | — | Your FRED API key (required) | | **mode** | enum | — | Set to `releases` | | **maxResults** | integer | `1000` | Maximum releases to return (0 = unlimited) | **Output fields:** - `releaseId`, `name`, `pressRelease` (boolean), `link`, `notes` - `realtimeStart`, `realtimeEnd` *** ### Pricing This Actor uses the **free FRED API** (no usage limits). You pay **only** for Apify compute time. - **Compute cost:** ~$0.0001-0.001 per run (depends on data volume and API latency) - **Typical cost per batch:** $0.01-0.10 for 100-1000 observations - **Bulk runs (1000+ observations):** ~$0.10-0.50 per run The FRED API itself is **completely free** — no subscriptions, no per-request charges. *** ### Output Examples #### Example 1: Single Series Observation (series\_observations mode) ```json { "seriesId": "GDP", "seriesTitle": "Real Gross Domestic Product", "frequency": "Quarterly", "frequencyShort": "q", "units": "Billions of Chained 2012 US Dollars", "unitsShort": "Bil. of Chn. 2012 $", "seasonalAdjustment": "Seasonally Adjusted", "seasonalAdjustmentShort": "SA", "lastUpdated": "2025-04-30T07:58:00", "popularity": 100, "notes": "Real GDP is the inflation adjusted value of the goods and services produced by labor and property located in the United States.", "observationStart": "1947-01-01", "observationEnd": "2025-01-01", "date": "2024-10-01", "value": 28481.0, "realtimeStart": "2024-10-01", "realtimeEnd": "2024-10-01", "appliedUnits": "lin", "appliedFrequency": null } ``` #### Example 2: Search Results (series\_search mode) ```json { "seriesId": "PAYEMS", "title": "Total Nonfarm Payroll", "notes": "The number of employees on the payroll of non-farm business establishments. Excludes proprietors and self-employed. Seasonally adjusted.", "frequency": "Monthly", "frequencyShort": "m", "units": "Thousands of Persons", "unitsShort": "Tho. of Persons", "seasonalAdjustment": "Seasonally Adjusted", "seasonalAdjustmentShort": "SA", "observationStart": "1939-01-01", "observationEnd": "2025-04-01", "lastUpdated": "2025-05-02T08:00:00", "popularity": 98, "groupPopularity": 95, "realtimeStart": "2025-05-02", "realtimeEnd": "2025-05-02" } ``` #### Example 3: Data Release (releases mode) ```json { "releaseId": 13, "name": "Real Estate Transactions by State", "pressRelease": false, "link": "https://www.redfin.com/news/data-center/", "notes": "Includes transaction counts and average prices for residential real estate.", "realtimeStart": "2025-01-15", "realtimeEnd": "2025-05-02" } ``` *** ### Troubleshooting #### "API key format invalid" error - Ensure your API key is exactly **32 alphanumeric characters** - Do not include quotes or spaces - If you lost your key, regenerate one at https://fredaccount.stlouisfed.org/apikeys #### "No records found" for a series search - Try a simpler search term (e.g., "unemployment" instead of "civilian unemployment rate") - Check FRED directly at https://fred.stlouisfed.org to verify the series exists - Some series may be discontinued or have limited data #### "Bad series ID" error - Verify the series ID exists on https://fred.stlouisfed.org/series/ - Series IDs are case-sensitive but typically uppercase (e.g., "GDP", not "gdp") - Use the search mode to discover available series IDs #### Large data requests timeout - Reduce the date range (e.g., fetch 5 years at a time instead of 30) - Lower `maxResults` to test smaller batches first - FRED API has a 100,000-observation limit per request #### Performance / Cost Concerns - Each series requires a separate API call to fetch metadata, then one or more calls for observations - For bulk data, prefer using observation-level frequency (e.g., "m" for monthly) over daily if possible - The Actor batches results efficiently and exits cleanly to minimize compute cost *** ### API Reference For detailed FRED API documentation, see https://fredaccount.stlouisfed.org/docs/ Common endpoints this Actor uses: - `GET /series/observations` — Fetch time-series observations - `GET /series/search` — Search series by text - `GET /category/series` — List series in a category - `GET /releases` — List data releases - `GET /series` — Get series metadata *** ### Related Actors Looking for other economic or financial data sources? - **U.S. Economic Census Data** — Census Bureau datasets - **Stock Market Data Scraper** — Real-time and historical price data - **World Bank Open Data** — Global economic indicators - **Yahoo Finance Scraper** — Stock and commodity prices *** ### Legal & Support **Disclaimer:** This Actor fetches data from the FRED API, which is maintained by the Federal Reserve Bank of St. Louis. The data is **public domain** and free to use. Always verify FRED's terms of use at https://fred.stlouisfed.org. **Support:** If you encounter issues: 1. Check the FRED API docs: https://fredaccount.stlouisfed.org/docs/ 2. Verify your API key is valid 3. Test the API directly: `curl "https://api.stlouisfed.org/fred/series?series_id=GDP&api_key=YOUR_KEY&file_type=json"` 4. Open an issue on Apify Community or contact support *** **Built with ❤️ for economists, traders, and fintech developers.** # Actor input Schema ## `apiKey` (type: `string`): Your 32-character FRED API key from https://fredaccount.stlouisfed.org/apikeys. Required. ## `mode` (type: `string`): Endpoint mode: series\_observations (fetch time-series data), series\_search (search series by text), category\_series (list all series in a category), or releases (list data release schedules). ## `seriesIds` (type: `string`): Comma-separated FRED series IDs. Examples: GDP, UNRATE, CPIAUCSL, FEDFUNDS, DFF, T10Y2Y, PAYEMS, HOUST, MORTGAGE30US, DGS10, SP500, VIXCLS, UMCSENT, INDPRO, RSAFS, ICSA. Default: GDP. (Used only in series\_observations mode.) ## `searchText` (type: `string`): Free-text search query. Examples: 'unemployment rate', 'inflation', 'treasury yield'. (Used only in series\_search mode.) ## `categoryId` (type: `integer`): FRED category numeric ID. Example: 32991 = 'Money, Banking, & Finance'. (Used only in category\_series mode.) ## `observationStart` (type: `string`): Earliest observation date in YYYY-MM-DD format. Leave blank for oldest available data. (series\_observations only.) ## `observationEnd` (type: `string`): Latest observation date in YYYY-MM-DD format. Leave blank for most recent data. (series\_observations only.) ## `frequency` (type: `string`): Resample frequency: '' (native), 'd' (daily), 'w' (weekly), 'bw' (biweekly), 'm' (monthly), 'q' (quarterly), 'sa' (semi-annual), 'a' (annual). (series\_observations only.) ## `aggregationMethod` (type: `string`): How to aggregate when resampling: 'avg' (average), 'sum' (sum), 'eop' (end of period). (series\_observations only.) ## `units` (type: `string`): Transform units: 'lin' (raw), 'chg' (change), 'ch1' (change-over-1), 'pch' (percent change), 'pc1' (percent change over 1), 'pca' (compound annual pct change), 'cch' (continuously compounded change), 'cca' (continuously compounded annual change), 'log' (natural log). (series\_observations only.) ## `sortOrder` (type: `string`): Sort observations by date: 'asc' (ascending) or 'desc' (descending). ## `maxResults` (type: `integer`): Maximum number of results to fetch. 0 = unlimited (up to API limits: 100,000 for observations, 1,000 for series searches). ## Actor input object example ```json { "mode": "series_observations", "seriesIds": "GDP", "searchText": "", "categoryId": 32991, "observationStart": "", "observationEnd": "", "frequency": "", "aggregationMethod": "avg", "units": "lin", "sortOrder": "desc", "maxResults": 1000 } ``` # Actor output Schema ## `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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("compute-edge/fred-economic-scraper").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("compute-edge/fred-economic-scraper").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 compute-edge/fred-economic-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=compute-edge/fred-economic-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "FRED Federal Reserve Economic Data — 800K+ Time Series", "description": "Extract economic time series from St. Louis Fed FRED API. 800K+ series including GDP, unemployment, CPI, treasury yields, mortgage rates, exchange rates. For macroeconomic dashboards, fintech apps, trading signals, and economic research. Free 32-char API key required.", "version": "0.1", "x-build-id": "exXgTwZcf4GVtBkF2" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/compute-edge~fred-economic-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-compute-edge-fred-economic-scraper", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/compute-edge~fred-economic-scraper/runs": { "post": { "operationId": "runs-sync-compute-edge-fred-economic-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/compute-edge~fred-economic-scraper/run-sync": { "post": { "operationId": "run-sync-compute-edge-fred-economic-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "apiKey": { "title": "FRED API Key", "type": "string", "description": "Your 32-character FRED API key from https://fredaccount.stlouisfed.org/apikeys. Required." }, "mode": { "title": "Mode", "enum": [ "series_observations", "series_search", "category_series", "releases" ], "type": "string", "description": "Endpoint mode: series_observations (fetch time-series data), series_search (search series by text), category_series (list all series in a category), or releases (list data release schedules).", "default": "series_observations" }, "seriesIds": { "title": "Series IDs (for series_observations)", "type": "string", "description": "Comma-separated FRED series IDs. Examples: GDP, UNRATE, CPIAUCSL, FEDFUNDS, DFF, T10Y2Y, PAYEMS, HOUST, MORTGAGE30US, DGS10, SP500, VIXCLS, UMCSENT, INDPRO, RSAFS, ICSA. Default: GDP. (Used only in series_observations mode.)", "default": "GDP" }, "searchText": { "title": "Search Text (for series_search)", "type": "string", "description": "Free-text search query. Examples: 'unemployment rate', 'inflation', 'treasury yield'. (Used only in series_search mode.)", "default": "" }, "categoryId": { "title": "Category ID (for category_series)", "type": "integer", "description": "FRED category numeric ID. Example: 32991 = 'Money, Banking, & Finance'. (Used only in category_series mode.)", "default": 32991 }, "observationStart": { "title": "Observation Start Date", "type": "string", "description": "Earliest observation date in YYYY-MM-DD format. Leave blank for oldest available data. (series_observations only.)", "default": "" }, "observationEnd": { "title": "Observation End Date", "type": "string", "description": "Latest observation date in YYYY-MM-DD format. Leave blank for most recent data. (series_observations only.)", "default": "" }, "frequency": { "title": "Resample Frequency", "enum": [ "", "d", "w", "bw", "m", "q", "sa", "a" ], "type": "string", "description": "Resample frequency: '' (native), 'd' (daily), 'w' (weekly), 'bw' (biweekly), 'm' (monthly), 'q' (quarterly), 'sa' (semi-annual), 'a' (annual). (series_observations only.)", "default": "" }, "aggregationMethod": { "title": "Aggregation Method", "enum": [ "avg", "sum", "eop" ], "type": "string", "description": "How to aggregate when resampling: 'avg' (average), 'sum' (sum), 'eop' (end of period). (series_observations only.)", "default": "avg" }, "units": { "title": "Units", "enum": [ "lin", "chg", "ch1", "pch", "pc1", "pca", "cch", "cca", "log" ], "type": "string", "description": "Transform units: 'lin' (raw), 'chg' (change), 'ch1' (change-over-1), 'pch' (percent change), 'pc1' (percent change over 1), 'pca' (compound annual pct change), 'cch' (continuously compounded change), 'cca' (continuously compounded annual change), 'log' (natural log). (series_observations only.)", "default": "lin" }, "sortOrder": { "title": "Sort Order", "enum": [ "asc", "desc" ], "type": "string", "description": "Sort observations by date: 'asc' (ascending) or 'desc' (descending).", "default": "desc" }, "maxResults": { "title": "Max Results", "minimum": 0, "maximum": 100000, "type": "integer", "description": "Maximum number of results to fetch. 0 = unlimited (up to API limits: 100,000 for observations, 1,000 for series searches).", "default": 1000 } } }, "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 } } } } } } } } } } ```