# 🇯🇵 Japan TDnet · 適時開示 東証 上場企業発表 (`nexgendata/japan-tdnet-timely-disclosures`) Actor Japan TDnet (Tokyo Stock Exchange timely-disclosure feed) — same-day issuer announcements. - **URL**: https://apify.com/nexgendata/japan-tdnet-timely-disclosures.md - **Developed by:** [NexGenData](https://apify.com/nexgendata) (community) - **Categories:** Business - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $150.00 / 1,000 disclosure 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 ## 📊 Japan TDnet — Tokyo Stock Exchange Timely Disclosure Tracker **Real-time same-day issuer disclosures from the Tokyo Stock Exchange.** TDnet (適時開示情報伝達システム / Timely Disclosure Network) is the official conduit through which every TSE Prime, Standard, and Growth listed company is mandated under Securities Listing Regulations Rule 402 to publish tender offers, earnings revisions, M&A announcements, dividend changes, share buybacks, and governance reports — within minutes of board approval. This actor turns TDnet's server-rendered HTML feed into clean structured JSON: 4-digit securities code, Japanese company name, disclosure timestamp, classified disclosure type (tender offer / earnings / M&A / governance / dividend / buyback), and the PDF link to the source document. Built for event-driven Japan-focused funds, M&A arb desks, sell-side analysts, and corporate-governance research teams who need TDnet as a same-day machine-readable feed — not a manual portal. --- ### ✅ What you get | Field | Description | |---|---| | `securities_code` | 4-digit Japanese securities code (証券コード, e.g. `7203` for Toyota Motor) | | `securities_code_internal_5digit` | TDnet's raw internal 5-digit code (`72030`) for round-trip integrity | | `company_name` | Japanese company name as it appears on TDnet | | `company_name_english_if_known` | English name if recognized (best-effort; null otherwise) | | `disclosure_date` | ISO date `YYYY-MM-DD` in Japan Standard Time (JST) | | `disclosure_time` | Time-of-day `HH:MM` JST | | `disclosure_title` | Disclosure title (Japanese, raw — the official text) | | `disclosure_title_english_if_known` | English title only when it was provided by the issuer; otherwise null | | `disclosure_type` | One of: `earnings` / `tender_offer` / `M&A` / `governance` / `dividend` / `buyback` / `material_event` / `other` | | `pdf_url` | Direct link to the issuer's official disclosure PDF | | `market_segment` | `Prime` / `Standard` / `Growth` when inferable from the TDnet listing; otherwise `Tokyo Stock Exchange` | | `exchange_code` | TDnet exchange code (`東` = Tokyo) | | `xbrl_url` | XBRL bundle URL when published alongside the disclosure (else null) | | `source_url` | Canonical TDnet index URL for the disclosure's date | | `scraped_at` | ISO-8601 UTC timestamp of extraction | Each row pushed to the dataset is one billable `disclosure-record` event. --- ### 🎯 Use cases #### Event-driven Japan equity trading Run a 06:00 JST daily sweep filtered to `disclosure_type=tender_offer` to catch every TOB (公開買付け) announcement before the market opens. M&A arb desks at Citadel, Millennium, and Tokio-based long/short shops use this exact pattern. #### M&A monitoring + activist watchlists Filter on `M&A` to catch every 合併 (merger), 株式交換 (share exchange), and 株式譲渡 (share transfer) disclosure. Cross-reference against your watchlist of activist targets to spot defensive corporate actions early. #### Corporate governance research Filter on `governance` to capture every 株主総会 (AGM/EGM) notice, コーポレートガバナンス報告書 (CG report) update, and 役員人事 (director appointment). Powers ESG funds, proxy advisors, and stewardship teams covering Japan. #### Retail / individual investor disclosure tracking Filter by `ticker_filter` to follow a single issuer's TDnet feed in machine-readable form — every disclosure, every day, classified and PDF-linked. Drop straight into a Slack alert via n8n or Zapier. --- ### 📥 Sample input ```json { "date_from": "2026-05-27", "date_to": "2026-05-29", "company_filter": "", "ticker_filter": "", "disclosure_type": "tender_offer", "max_disclosures": 50 } ```` Single-ticker daily watch: ```json { "date_from": "2026-05-29", "date_to": "2026-05-29", "ticker_filter": "7203", "disclosure_type": "all", "max_disclosures": 100 } ``` *** ### 📤 Sample output (one row) ```json { "securities_code": "2429", "securities_code_internal_5digit": "24290", "company_name": "ワールドＨＤ", "company_name_english_if_known": "World Holdings Co., Ltd.", "disclosure_date": "2026-05-29", "disclosure_time": "20:30 JST", "disclosure_title": "ｎｍｓホールディングス株式会社（証券コード：2162）に対する公開買付けの開始に関するお知らせ", "disclosure_title_english_if_known": null, "disclosure_type": "tender_offer", "pdf_url": "https://www.release.tdnet.info/inbs/140120260529555739.pdf", "market_segment": "Tokyo Stock Exchange", "exchange_code": "東", "xbrl_url": null, "source_url": "https://www.release.tdnet.info/inbs/I_list_001_20260529.html", "scraped_at": "2026-05-30T11:35:00Z" } ``` *** ### 🚀 How to use #### Apify Python client ```python from apify_client import ApifyClient client = ApifyClient("YOUR_APIFY_TOKEN") run = client.actor("nexgendata/japan-tdnet-timely-disclosures").call(run_input={ "date_from": "2026-05-27", "date_to": "2026-05-29", "disclosure_type": "tender_offer", "max_disclosures": 100, }) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item["disclosure_date"], item["securities_code"], item["company_name"], "→", item["disclosure_type"], item["pdf_url"]) ``` #### cURL ```bash curl -X POST \ "https://api.apify.com/v2/acts/nexgendata~japan-tdnet-timely-disclosures/run-sync-get-dataset-items?token=YOUR_APIFY_TOKEN" \ -H "Content-Type: application/json" \ -d '{"date_from":"2026-05-29","date_to":"2026-05-29","disclosure_type":"all","max_disclosures":50}' ``` #### MCP / agent integration This actor exposes a deterministic schema and a single high-leverage tool (`fetch_tdnet_disclosures`) — wire it into any MCP-compatible agent (Claude Desktop, Cursor, your in-house Japan research stack) and your assistant can answer questions like "any tender-offer announcements on Toyota suppliers this week?" with structured live data instead of guesswork. *** ### ⚖️ Legal & data source notes - **Data source:** [TDnet (適時開示情報伝達システム)](https://www.release.tdnet.info/inbs/I_main_00.html), operated by Tokyo Stock Exchange, Inc. (a JPX subsidiary). TDnet is the official conduit mandated under TSE Securities Listing Regulations Rule 402 — issuer announcements are public by statutory design. - **No PII:** TDnet disclosures are issuer-level corporate announcements; no natural-person personal data is extracted. - **Polite-bot identification:** every request carries `User-Agent: NexGenDataBot/1.0 (+https://thenextgennexus.com/bot; contact: hello@thenextgennexus.com)` and honors TDnet's `Cache-Control: no-cache` header. Pacing is capped at ~1 request every 3 seconds with throttled per-date sweeps to stay well within TSE's stated open-data norms. - **Retention window:** TDnet keeps the most recent ~31 days of disclosures online; older dates may return 404. For deeper historical coverage, pair with EDINET (filing-day) which retains 5+ years — see Related Actors below. - **Same-day vs filing-day:** TDnet = same-day issuer announcement (event-trader feed); EDINET = filing-day full regulatory text (compliance / fundamentals feed). They cover overlapping but distinct moments in the disclosure lifecycle. *** ### ❓ FAQ **Q: How is this different from EDINET?** A: EDINET (Electronic Disclosure for Investors' NETwork) is Japan's filing-day regulatory portal — full audited filings (有価証券報告書, 四半期報告書) submitted to the FSA. TDnet is the same-day issuer announcement portal — operated by TSE itself, where companies post material-event news (tender offers, earnings, dividends, M\&A) within minutes of board approval. Event-driven traders need TDnet; compliance / fundamentals research needs EDINET. Most institutional Japan desks use both. **Q: Why is the securities code different on TDnet (5 digits) vs Yahoo Finance Japan / Bloomberg (4 digits)?** A: TDnet uses TSE's internal 5-digit code (the standard 4-digit ticker plus a trailing `0`). The actor strips that trailing zero so `securities_code` matches what you'd put into Bloomberg as `7203 JT Equity` or Yahoo Japan as `7203.T`. The original 5-digit code is preserved in `securities_code_internal_5digit` for round-trip integrity. **Q: Why does `disclosure_title_english_if_known` come back null on most rows?** A: TDnet only publishes Japanese titles by default. A small subset of large-cap issuers (typically PCT-active multinationals like Toyota or Sony) post bilingual disclosures with both titles. For automated translation, pipe `disclosure_title` through DeepL or Google Cloud Translation — both handle TDnet's domain vocabulary well. **Q: How is `disclosure_type` classified?** A: A Japanese-keyword regex matches the title against ~80 known disclosure-type keywords (公開買付け → `tender_offer`, 決算短信 → `earnings`, 合併 → `M&A`, 株主総会 → `governance`, 配当 → `dividend`, 自己株式取得 → `buyback`). Multi-class titles take the highest-priority match (tender\_offer > M\&A > earnings > governance > dividend > buyback > material\_event > other). **Q: Why are weekends + Japanese exchange holidays returning zero rows?** A: TDnet only operates on TSE business days. The actor follows that calendar naturally — passing a weekend range exits cleanly with `0 results` (no error, no crash, no charge beyond `apify-actor-start`). *** ### 日本語の説明 東京証券取引所の適時開示（TDnet）から、上場企業の重要事実、決算短信、公開買付け、ガバナンス報告書を毎日収集します。証券コード、開示時刻、PDFリンク、市場区分（プライム・スタンダード・グロース）を構造化されたデータとして提供します。 主な用途： - イベント駆動型投資戦略 - M\&Aモニタリング - コーポレートガバナンス分析 - 個人投資家向け開示トラッキング 出力は全てクリーンなJSON形式で、Apify Console、n8n、Make、Zapier、MCPエコシステムと直接連携できます。日本語のタイトル（disclosure\_title）と分類されたタイプ（disclosure\_type）の両方を提供し、機械学習・全文検索・アラート配信パイプラインに最適化されています。 データ提供元は東京証券取引所のTDnet公式サイト（適時開示情報伝達システム）であり、上場規程第402条に基づく公的な開示情報です。個人情報は含まれません。約31日分の過去データに対応しています。 *** ### 🔗 Related actors Pair these public NexGenData actors with the TDnet feed for a complete Japan + APAC disclosure stack: - [**Japan EDINET Insider Filings**](https://apify.com/nexgendata/japan-edinet-insider-filings) — Japan's filing-day regulatory feed (有価証券報告書, 四半期報告書, 大量保有報告書). Pair with TDnet for full lifecycle coverage: TDnet = same-day announcement; EDINET = filing-day full text. - [**TSE Japan Stock Screener**](https://apify.com/nexgendata/tse-japan-stock-screener) — TSE Prime/Standard/Growth listed-company screener (price, market cap, sector). Cross-reference TDnet disclosures against your screened watchlist. - [**SGX Company Announcements**](https://apify.com/nexgendata/sgx-company-announcements) — Singapore Exchange's equivalent timely disclosure feed. Run both together for Japan + Singapore cross-listed coverage (Olam, Yangzijiang, etc.). - [**SEC Form 8-K Material Events Scraper**](https://apify.com/nexgendata/sec-form-8k-material-events-scraper) — US-equivalent same-day material-event disclosures. For ADR-listed Japanese issuers (Sony, Honda, Toyota), TDnet 8-K cross-reference catches translation drift early. *** ### 🏷️ About NexGenData NexGenData operates 130+ public Apify actors covering global regulatory filings, exchange data, IP intelligence, lead generation, and MCP servers for AI agents. All actors are PAY-PER-EVENT priced and work directly with the Apify Console, n8n, Make, Zapier, and the MCP ecosystem. Questions or custom Japan / APAC data requests? Email `hello@thenextgennexus.com`. # Actor input Schema ## `date_from` (type: `string`): Start of the disclosure date range, inclusive. ISO format YYYY-MM-DD, interpreted in Japan Standard Time (JST). Default is 7 days before today. TDnet retains roughly the last 31 days of disclosures online; older dates may 404. ## `date_to` (type: `string`): End of the disclosure date range, inclusive. ISO format YYYY-MM-DD, interpreted in Japan Standard Time (JST). Default is today (JST). Weekends and Japanese exchange holidays return zero rows naturally. ## `company_filter` (type: `string`): Optional substring filter against the Japanese company name as it appears on TDnet. Case-insensitive partial match. Leave blank to return every issuer on each date. Example: "トヨタ" (matches Toyota subsidiaries) or "ＨＤ" (holdings companies). ## `ticker_filter` (type: `string`): Optional 4-digit Japanese securities code (証券コード). TDnet's internal code is 5 digits — the actor strips the trailing zero so callers can pass the standard 4-digit ticker (e.g. 7203 for Toyota Motor Corp). Leave blank to return all issuers. ## `disclosure_type` (type: `string`): Filter rows to a single disclosure category. 'earnings' = 決算短信 / 業績予想; 'tender\_offer' = 公開買付け (TOB); 'M\&A' = 合併 / 株式交換 / 株式譲渡; 'governance' = 株主総会 / コーポレートガバナンス報告書 / 役員人事. 'all' keeps every row and adds a classified disclosure\_type field instead. ## `max_disclosures` (type: `integer`): Maximum number of disclosure rows to return across the full date range. Each row pushed to the dataset is one billable 'disclosure-record' event. Range 1-1000. ## Actor input object example ```json { "date_from": "", "date_to": "", "company_filter": "", "ticker_filter": "", "disclosure_type": "all", "max_disclosures": 100 } ``` # 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 = { "date_from": "", "date_to": "", "company_filter": "", "ticker_filter": "", "disclosure_type": "all", "max_disclosures": 100 }; // Run the Actor and wait for it to finish const run = await client.actor("nexgendata/japan-tdnet-timely-disclosures").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 = { "date_from": "", "date_to": "", "company_filter": "", "ticker_filter": "", "disclosure_type": "all", "max_disclosures": 100, } # Run the Actor and wait for it to finish run = client.actor("nexgendata/japan-tdnet-timely-disclosures").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 '{ "date_from": "", "date_to": "", "company_filter": "", "ticker_filter": "", "disclosure_type": "all", "max_disclosures": 100 }' | apify call nexgendata/japan-tdnet-timely-disclosures --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=nexgendata/japan-tdnet-timely-disclosures", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "🇯🇵 Japan TDnet · 適時開示 東証 上場企業発表", "description": "Japan TDnet (Tokyo Stock Exchange timely-disclosure feed) — same-day issuer announcements.", "version": "0.0", "x-build-id": "XdZvN9P3KukCqOIdB" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/nexgendata~japan-tdnet-timely-disclosures/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-nexgendata-japan-tdnet-timely-disclosures", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/nexgendata~japan-tdnet-timely-disclosures/runs": { "post": { "operationId": "runs-sync-nexgendata-japan-tdnet-timely-disclosures", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/nexgendata~japan-tdnet-timely-disclosures/run-sync": { "post": { "operationId": "run-sync-nexgendata-japan-tdnet-timely-disclosures", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "date_from": { "title": "From date (ISO YYYY-MM-DD)", "type": "string", "description": "Start of the disclosure date range, inclusive. ISO format YYYY-MM-DD, interpreted in Japan Standard Time (JST). Default is 7 days before today. TDnet retains roughly the last 31 days of disclosures online; older dates may 404.", "default": "" }, "date_to": { "title": "To date (ISO YYYY-MM-DD)", "type": "string", "description": "End of the disclosure date range, inclusive. ISO format YYYY-MM-DD, interpreted in Japan Standard Time (JST). Default is today (JST). Weekends and Japanese exchange holidays return zero rows naturally.", "default": "" }, "company_filter": { "title": "Company name filter (optional)", "type": "string", "description": "Optional substring filter against the Japanese company name as it appears on TDnet. Case-insensitive partial match. Leave blank to return every issuer on each date. Example: \"トヨタ\" (matches Toyota subsidiaries) or \"ＨＤ\" (holdings companies).", "default": "" }, "ticker_filter": { "title": "Securities code filter (4-digit, optional)", "type": "string", "description": "Optional 4-digit Japanese securities code (証券コード). TDnet's internal code is 5 digits — the actor strips the trailing zero so callers can pass the standard 4-digit ticker (e.g. 7203 for Toyota Motor Corp). Leave blank to return all issuers.", "default": "" }, "disclosure_type": { "title": "Disclosure type filter", "enum": [ "all", "earnings", "tender_offer", "M&A", "governance" ], "type": "string", "description": "Filter rows to a single disclosure category. 'earnings' = 決算短信 / 業績予想; 'tender_offer' = 公開買付け (TOB); 'M&A' = 合併 / 株式交換 / 株式譲渡; 'governance' = 株主総会 / コーポレートガバナンス報告書 / 役員人事. 'all' keeps every row and adds a classified disclosure_type field instead.", "default": "all" }, "max_disclosures": { "title": "Maximum disclosures", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Maximum number of disclosure rows to return across the full date range. Each row pushed to the dataset is one billable 'disclosure-record' event. Range 1-1000.", "default": 100 } } }, "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 } } } } } } } } } } ```