# 📅 Earnings Calendar — Upcoming Earnings + EPS Estimates (`nexgendata/earnings-calendar`) Actor Track upcoming earnings releases with consensus EPS estimates, revenue estimates, prior-period surprises, fiscal period, and market cap. Daily and weekly views for earnings traders, sell-side analysts, options vol desks, IR teams. Bloomberg earnings calendar alternative — pay-per-result. - **URL**: https://apify.com/nexgendata/earnings-calendar.md - **Developed by:** [NexGenData](https://apify.com/nexgendata) (community) - **Categories:** Business - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $50.00 / 1,000 earnings records This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## 📅 Earnings Calendar — Upcoming Earnings + EPS Estimates Stop paying $24,000 a year for a Bloomberg seat just to see who reports tomorrow. This actor returns a clean, structured **earnings calendar** for US-listed companies with consensus EPS estimates, fiscal periods, prior-year comparables, market caps, and (optionally) the most recent reported surprise for each name. Pay only for the records you pull — no annual contracts, no Refinitiv enterprise license, no Estimize floor. If you trade earnings, sit on a sell-side desk, run an options vol book, or work in IR — this is the data you actually use. Cron it nightly, pin it to your dashboard, or stream the upcoming week into your backtest harness. --- ### What you get per record Every dataset item is one upcoming (or just-reported) earnings event: | Field | Example | Description | |---|---|---| | `symbol` | `NVDA` | US listing ticker | | `company_name` | `NVIDIA Corporation` | Full issuer name | | `sector` | `Technology` | GICS-style sector (inferred when not provided) | | `earnings_date` | `2026-05-14` | ISO date of the scheduled release | | `time_of_day` | `AMC` | BMO (before market open) / AMC (after market close) / null when not supplied | | `fiscal_period` | `Q1 2026` | Calendar-anchored fiscal quarter being reported | | `fiscal_quarter_ending_raw` | `Mar/2026` | The raw period label from the data source | | `consensus_eps_estimate` | `0.92` | Wall Street consensus EPS in USD | | `consensus_revenue_estimate_usd` | `null` | Revenue consensus (null in free tier) | | `n_analysts` | `38` | Number of analysts contributing to the consensus | | `prior_period_eps` | `0.61` | Year-ago comparable EPS | | `eps_growth_yoy_pct` | `50.82` | (consensus / prior_year - 1) × 100 | | `market_cap_usd` | `2150000000000` | Raw USD market cap snapshot | | `prior_year_report_date` | `5/14/2025` | The year-ago report date for context | | `last_eps_actual` | `1.85` | Most recently reported EPS (hydration) | | `last_eps_consensus` | `1.73` | Consensus for that quarter (hydration) | | `last_eps_surprise_pct` | `6.94` | Surprise % of the most recent report (hydration) | | `last_quarter_period` | `Mar 2026` | Period of the most recent report | | `last_reported_date` | `4/30/2026` | Date of the most recent report | | `actual_eps` | `2.01` | Populated when the event is in the past | | `eps_surprise_pct` | `4.69` | Surprise on the event row when reported | | `nasdaq_url` | `https://www.nasdaq.com/...` | Issuer earnings page | | `stockanalysis_url` | `https://stockanalysis.com/...` | Issuer fundamentals page | | `data_source` | `nasdaq_earnings_calendar` | Provenance | --- ### Input parameters - **limit** — max records to return. `0` returns everything in the date window. - **date_range** — `today` / `this_week` / `next_week` / `this_month` / `all` (45-day forward window). - **min_market_cap_billion** — minimum market cap in $B. Use `10` for large-cap only, `50` for mega-cap, `0` for everything. - **sector** — filter to a single GICS sector or `all`. - **with_estimates_only** — exclude names without a published consensus EPS estimate. Sell-side typically `true`; IR teams often `false`. - **hydrate_surprises** — make one extra API call per symbol to attach last-reported surprise data. On by default. Turn off for the fastest pull. --- ### Sample input ```json { "limit": 200, "date_range": "this_week", "min_market_cap_billion": 5, "sector": "Technology", "with_estimates_only": true, "hydrate_surprises": true } ```` That request pulls the current trading week's tech earnings calendar, $5B+ market cap, only names with analyst coverage, with the prior-quarter surprise stamped on each row. *** ### Who pays for this - **Earnings traders** — pre-event positioning around the BMO / AMC clock. Filter `time_of_day` to focus on the post-close batch. - **Sell-side analysts** — coverage prep for client notes. The `n_analysts` and `prior_period_eps` fields tell you who's competing on the call. - **Options vol desks** — pricing IV crush. Combine `consensus_eps_estimate` with `last_eps_surprise_pct` to size moves. - **IR teams** — peer-window awareness. Pull your sector, see who reports the same week, and time your release to avoid clashes. - **Quant / systematic funds** — backtest fuel. Pull the 45-day forward window nightly, snapshot, accumulate. - **Fintech apps** — "what's reporting this week" widgets without a Refinitiv contract. *** ### Versus the incumbents | Source | Earnings calendar | EPS estimates | Surprise history | Market cap | Cost (US-only, retail-equivalent) | |---|---|---|---|---|---| | Bloomberg Terminal (EE / ECO) | yes | yes | yes | yes | ~$24,000 / year / seat | | FactSet | yes | yes | yes | yes | ~$12,000+ / year / seat | | Refinitiv Eikon | yes | yes | yes | yes | ~$22,000 / year / seat | | Zacks Premium | yes (web) | yes | yes | yes | $249–$549 / year | | Estimize (consensus + crowd) | yes | yes | yes | partial | $99–$2,400+ / month | | **NexGenData Earnings Calendar (this actor)** | **yes** | **yes** | **yes (opt-in hydration)** | **yes** | **$0.05 / record, no subscription** | For a single trader pulling 200 names a week, this actor runs ~$10/week — roughly **1/50th of a Zacks Premium subscription** and **1/200th of a Bloomberg seat**. *** ### Pricing — pay-per-event | Event | Price | |---|---| | Actor start | **$0.01** (charged once per run) | | Earnings record (primary event) | **$0.05** per record returned | A pull of 100 upcoming earnings = $0.01 + 100 × $0.05 = **$5.01**. A pull of 10 names for a quick smoke test = $0.51. No subscription. No minimum. No annual contract. Cron it once a day or once a year. *** ### How it works under the hood 1. The actor resolves your `date_range` to a concrete list of trading-week or calendar dates. 2. For each date, it pulls the NASDAQ public earnings-calendar JSON endpoint — the same feed that powers nasdaq.com/market-activity/earnings. 3. Rows are normalized: market cap is decoded from "$131,271,743,181" strings, EPS like "($0.59)" is parsed to negative floats, `Mar/2026` becomes `Q1 2026`, "time-pre-market" becomes BMO. 4. Year-over-year EPS growth is computed from the prior-year comparable when both values exist and the denominator isn't near-zero. 5. Sector is inferred from issuer name when the source doesn't supply one (a conservative keyword classifier; null when ambiguous). 6. If `hydrate_surprises = true`, the actor fans out one additional per-symbol call to the NASDAQ earnings-surprise endpoint and attaches the most recent reported EPS, consensus that quarter, and surprise percentage. For events whose `earnings_date` matches the last reported date within 5 days, those values also populate the `actual_eps` / `eps_surprise_pct` fields directly. 7. Dedupe is on (symbol, earnings\_date) — necessary because the same name can appear on adjacent days when the source moves a confirmed report. 8. Results sort by date ascending, then market cap descending — the biggest names of each day rise to the top. *** ### Operational notes - **No proxy needed.** The NASDAQ JSON endpoint is unauthenticated and tolerates moderate concurrency. The actor caps day-fanout at 4 and surprise-fanout at 6. - **Backfill.** Want the next quarter's earnings? Use `date_range: "all"` for 45 forward days. To go further, run the actor on a daily cron and accumulate to your own warehouse. - **Already-reported events.** When your window includes past dates (e.g. when running mid-day after the market opens), already-reported names will carry `actual_eps` and `eps_surprise_pct` if hydration is on. - **Revenue estimates.** Consensus revenue is intentionally null in this tier — the free NASDAQ feed doesn't carry it. We surface the field for forward compatibility; a premium tier may hydrate from IBES-style sources. - **Surprises by day.** To slice "biggest beats this week," pull `date_range: "this_week"` and sort the result by `last_eps_surprise_pct` (for upcoming weighting) or `eps_surprise_pct` (for already-reported events). *** ### The full NexGenData market-intelligence fleet This actor is one of several pieces in a coordinated fleet — combine them to build a full Bloomberg-on-Apify stack at a fraction of the cost. - **🚀 [IPO Tracker](https://apify.com/nexgendata/ipo-tracker)** — recent + upcoming IPOs with lockup expirations, post-IPO performance, and SEC EDGAR prospectus links. Pair with the earnings calendar to catch first earnings reports of recently-IPO'd names. - **📋 [SEC Form 4 Insider Tracker](https://apify.com/nexgendata/sec-form4-insider-tracker)** — real-time insider buys and sells. Cross-reference insider activity in the 30 days before earnings — historically one of the highest-signal pre-earnings indicators. - **🚨 [SEC Form 8-K Material Events Scraper](https://apify.com/nexgendata/sec-form-8k-material-events-scraper)** — surprise filings between scheduled earnings. Companies sometimes pre-announce or guide via 8-K; this actor surfaces those. - **📊 [Finviz Stock Screener](https://apify.com/nexgendata/finviz-stock-screener)** — fundamentals and technicals screener for the full US market. Use it to build watchlists, then feed those watchlists back into the earnings calendar. - **🇨🇳 [Chinese ADRs Stock Screener](https://apify.com/nexgendata/chinese-adrs-stock-screener)** — US-listed Chinese ADRs (BABA, JD, BIDU, PDD, NIO, etc.). These names dominate certain earnings weeks and have unique fiscal calendars. - **🤖 [Finance MCP Server](https://apify.com/nexgendata/finance-mcp-server)** — the same data, exposed as a Model Context Protocol server for Claude Desktop, Cursor, and other LLM clients. Ask "what's reporting next week with a market cap over $10B" in natural language. *** ### Affiliate / referral If this actor saves you a Bloomberg seat or a Zacks Premium subscription, consider supporting NexGenData by using our affiliate link when you sign up for Apify: **https://apify.com/nexgendata?fpr=2ayu9b** Every signup helps keep the fleet maintained, the endpoints fresh, and the prices honest. # Actor input Schema ## `limit` (type: `integer`): Maximum number of earnings records to return. Set to 0 to return all matching records (typically 200+ on a busy reporting day during peak earnings season). Useful for testing smaller batches before running a full pull. ## `date_range` (type: `string`): Time window for earnings dates. 'today' = just today's reports (BMO and AMC). 'this\_week' = Monday through Friday of the current trading week — the standard 'earnings week' view. 'next\_week' = Monday through Friday of next week (forward calendar). 'this\_month' = next 30 calendar days. 'all' = a 45-day forward window covering the bulk of any earnings season. ## `min_market_cap_billion` (type: `number`): Minimum market capitalization filter in USD billions. Use 10 to filter for large-cap names ($10B+). Use 50 for mega-cap. Use 1 for mid-cap and up. Set to 0 to include all sizes — useful for biotech and small-cap earnings traders. Eliminates micro-cap noise for sell-side and options desks. ## `sector` (type: `string`): Filter by GICS-style sector. 'all' returns every sector — earnings season weeks are concentrated in Technology, Financial Services, and Healthcare. Useful for sector-rotation strategies and theme-focused vol desks (e.g. semi earnings, bank earnings, retail earnings weeks). ## `with_estimates_only` (type: `boolean`): If true, filters out names where no consensus EPS estimate is available (typically uncovered micro-caps, dual-listed ADRs without US analyst coverage, or newly-public IPOs pre-coverage initiation). Sell-side and vol desks typically want this on; IR teams often want it off for the full opportunity surface. ## `hydrate_surprises` (type: `boolean`): If true, makes one extra per-symbol API call to fetch the most-recent reported EPS, consensus, and surprise percentage (the 'last earnings reaction' field). Adds ~0.5–1s per record on full pulls but produces a much richer dataset for surprise-strategy backtests and options vol setups. ## Actor input object example ```json { "limit": 50, "date_range": "this_week", "min_market_cap_billion": 0, "sector": "all", "with_estimates_only": false, "hydrate_surprises": true } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "limit": 50, "date_range": "this_week", "min_market_cap_billion": 0, "sector": "all", "with_estimates_only": false, "hydrate_surprises": true }; // Run the Actor and wait for it to finish const run = await client.actor("nexgendata/earnings-calendar").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 = { "limit": 50, "date_range": "this_week", "min_market_cap_billion": 0, "sector": "all", "with_estimates_only": False, "hydrate_surprises": True, } # Run the Actor and wait for it to finish run = client.actor("nexgendata/earnings-calendar").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 '{ "limit": 50, "date_range": "this_week", "min_market_cap_billion": 0, "sector": "all", "with_estimates_only": false, "hydrate_surprises": true }' | apify call nexgendata/earnings-calendar --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=nexgendata/earnings-calendar", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "📅 Earnings Calendar — Upcoming Earnings + EPS Estimates", "description": "Track upcoming earnings releases with consensus EPS estimates, revenue estimates, prior-period surprises, fiscal period, and market cap. Daily and weekly views for earnings traders, sell-side analysts, options vol desks, IR teams. Bloomberg earnings calendar alternative — pay-per-result.", "version": "0.0", "x-build-id": "ucunoxIqZm3xCRmZt" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/nexgendata~earnings-calendar/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-nexgendata-earnings-calendar", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/nexgendata~earnings-calendar/runs": { "post": { "operationId": "runs-sync-nexgendata-earnings-calendar", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/nexgendata~earnings-calendar/run-sync": { "post": { "operationId": "run-sync-nexgendata-earnings-calendar", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "limit": { "title": "Number of earnings records to return", "minimum": 0, "maximum": 2000, "type": "integer", "description": "Maximum number of earnings records to return. Set to 0 to return all matching records (typically 200+ on a busy reporting day during peak earnings season). Useful for testing smaller batches before running a full pull.", "default": 50 }, "date_range": { "title": "Date range filter", "enum": [ "today", "this_week", "next_week", "this_month", "all" ], "type": "string", "description": "Time window for earnings dates. 'today' = just today's reports (BMO and AMC). 'this_week' = Monday through Friday of the current trading week — the standard 'earnings week' view. 'next_week' = Monday through Friday of next week (forward calendar). 'this_month' = next 30 calendar days. 'all' = a 45-day forward window covering the bulk of any earnings season.", "default": "this_week" }, "min_market_cap_billion": { "title": "Minimum market cap (USD billions)", "minimum": 0, "maximum": 5000, "type": "number", "description": "Minimum market capitalization filter in USD billions. Use 10 to filter for large-cap names ($10B+). Use 50 for mega-cap. Use 1 for mid-cap and up. Set to 0 to include all sizes — useful for biotech and small-cap earnings traders. Eliminates micro-cap noise for sell-side and options desks.", "default": 0 }, "sector": { "title": "Sector filter", "enum": [ "all", "Technology", "Healthcare", "Financial Services", "Consumer Discretionary", "Consumer Staples", "Industrials", "Energy", "Real Estate", "Communication Services", "Utilities", "Materials" ], "type": "string", "description": "Filter by GICS-style sector. 'all' returns every sector — earnings season weeks are concentrated in Technology, Financial Services, and Healthcare. Useful for sector-rotation strategies and theme-focused vol desks (e.g. semi earnings, bank earnings, retail earnings weeks).", "default": "all" }, "with_estimates_only": { "title": "Only include reports with consensus EPS estimate", "type": "boolean", "description": "If true, filters out names where no consensus EPS estimate is available (typically uncovered micro-caps, dual-listed ADRs without US analyst coverage, or newly-public IPOs pre-coverage initiation). Sell-side and vol desks typically want this on; IR teams often want it off for the full opportunity surface.", "default": false }, "hydrate_surprises": { "title": "Hydrate prior-period surprise history", "type": "boolean", "description": "If true, makes one extra per-symbol API call to fetch the most-recent reported EPS, consensus, and surprise percentage (the 'last earnings reaction' field). Adds ~0.5–1s per record on full pulls but produces a much richer dataset for surprise-strategy backtests and options vol setups.", "default": true } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```