# Polymarket Events, Live Odds & AI Analysis Scraper (`logiover/polymarket-events-odds-ai-scraper`) Actor Every active Polymarket event, sports match with live scores, AI betting analysis, and Twitter signal feed. Market-level data (outcomes, prices, volumes 5m/1h/6h/24h, liquidity, spreads), sports games (teams, scores, period, status), AI rationale, X sentiment, tweet β†’ event impact map. - **URL**: https://apify.com/logiover/polymarket-events-odds-ai-scraper.md - **Developed by:** [Logiover](https://apify.com/logiover) (community) - **Categories:** Automation, News, AI - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $2.50 / 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 ## Polymarket Events, Live Odds, Sports Scores, AI Analysis & Twitter Signal Scraper **The most comprehensive Polymarket data extractor on Apify.** Scrape every active Polymarket event, every sports market with live scores, every AI-generated betting analysis, and the high-significance Twitter signal feed β€” **all in one actor**. Returns market-level data (condition IDs, outcomes, prices, volumes at 5m/1h/6h/24h/all, liquidity, spreads, end timestamps), sports game data (home/away teams, scores, periods, game status), AI analysis (betting recommendation, rationale, market context, key data, X sentiment), and Twitter activity (significance score, content, linked events / markets / probabilities). > The Bloomberg terminal of prediction markets β€” every key dataset in one Apify run. --- ### πŸš€ What this actor does Polymarket is the dominant on-chain prediction market with billions in cumulative volume. future.news is a leading Polymarket data aggregator that exposes a clean API surface. This actor turns that into four pluggable feeds: 1. **`events_rank`** β€” every active event + series with markets, prices, outcome assets. 2. **`sports`** β€” sports markets with live game data (scores, period, game status, home/away teams with logos). 3. **`twitter_activity`** β€” paginated curated Twitter feed where each tweet is **tagged with the prediction-market events it affects** (with current probability + outcome). 4. **`ai_analysis`** β€” batch AI analysis for given event slugs: betting option, rationale, market analysis, key data, X sentiment. You toggle which feeds and the actor emits everything into the dataset with a `_kind` discriminator. Use one feed for narrow runs (e.g. just `sports` for live odds) or all four for a comprehensive nightly export. --- ### πŸ’‘ Use cases - **Polymarket trading dashboards** β€” power your own UI with live event + odds data. - **Election forecasting tools** β€” pull political-event markets every minute, build a "real-time pollster" view. - **Sports betting analytics** β€” live odds + live scores in a single export. - **AI-driven trading bots** β€” feed the AI analysis output into a buy/no-buy decision pipeline. - **News + market correlation** β€” Twitter activity feed shows which tweets are moving which markets. - **Newsletter / Substack pre-game previews** β€” auto-generated from events + AI analysis. - **Election bettors aggregator** β€” Like ElectionBettingOdds.com but live and structured. - **Research datasets** β€” labelled Polymarket events Γ— tweets Γ— AI takes for ML. --- ### βš™οΈ Input configuration | Field | Type | Default | Description | |-------|------|---------|-------------| | `feeds` | `string[]` | `["events_rank","sports","twitter_activity"]` | Which feeds to fetch. | | `explodeMarkets` | `boolean` | `true` | One row per market within an event. | | `explodeAssets` | `boolean` | `false` | One row per outcome (Yes/No/team) within a market. | | `twitterMaxItems` | `integer` | `100` | Cap on tweets in `twitter_activity` (paginated). | | `minTwitterSignificance` | `integer` | `0` | Drop tweets below this significance (0-100). | | `twitterCategories` | `string[]` | `[]` | Polymarket category IDs to keep in tweet feed. | | `aiAnalysisSlugs` | `string[]` | `[]` | Event slugs to AI-analyze (auto-filled from `events_rank` when both feeds run). | | `aiAnalysisType` | `integer` | `1` | AI analysis variant. | | `minVolume24hUsd` | `number` | `0` | Drop low-volume events/markets. | | `onlyLive` | `boolean` | `false` | Sports: keep only `live_status: true` games. | | `useProxy` | `boolean` | `true` | | | `proxyConfiguration` | `object` | residential | | | `maxConcurrency` | `integer` | `3` | | --- ### πŸ“¦ Output rows The dataset emits four row kinds distinguished by `_kind`: #### 1. `event` (Polymarket event row β€” primary) Identity: `eventSlug`, `eventTitle`, `eventTitleTrans`, `eventDescription`, `eventIcon`, `exchange`, `tagIds`. Time: `endTimestamp`, `createdTimestamp`, `liveStartAt`, `liveStatus`, `status`. Series: `seriesSlug`, `seriesRecurrence`, `maxChance`, `maxSpread`. Resolution: `resolutionSource`, `liveStreamUrl`, `cryptoCurrentPrice`, `cryptoPriceToBeat`. Aggregate volume: `volume5m`, `volume1h`, `volume6h`, `volume24h`, `volumeAll`, `liquidity`, `marketsCount`, `totalVolume`. **When `explodeMarkets: true` (default)**, each event-market pair adds: `marketId`, `marketQuestion`, `marketIcon`, `marketStatus`, `marketEndTimestamp`, `marketGroupItem`, `marketGroupItemTrans`, `marketResolutionSource`, `marketResolvedAssetId`, `marketLiveStartAt`, `marketSportsType`, `marketLine`, `marketGroupItemThreshold`, `marketRewardsDailyRate`, `marketRewardsMinSize`, `marketRewardsMaxSpread`, `marketRewardsAmount`, `marketEnableOrderBook`, `price`, `price5m`, `price1h`, `price6h`, `price24h`, `volume5m/1h/6h/24h/All`, `liquidity`, `spread`, `createdTimestamp`. **When `explodeAssets: true`**, each outcome adds: `outcome`, `outcomeTrans`, `outcomeChance`, `outcomeBestAsk`, `outcomeBestBid`, `outcomeTokenId`. #### 2. `sports` (sports event β€” has all `event` fields plus) `gameStatus`, `gameType`, `gameElapsed`, `gameEnded`, `gameClockStartAt`, `gameClockEndAt`, `gameEndAt`, `score`, `homeTeamName`/`Logo`/`Color`/`Abbr`, `awayTeamName`/`Logo`/`Color`/`Abbr`, `thesportsMatchId`. #### 3. `series` (Polymarket series β€” meta-row for recurring events) `seriesSlug`, `seriesTitle`, `seriesVolume5m/1h/6h/24h/All`, `currentEventSlug`, `currentEventTitle`, `currentEventStatus`, `currentEventEndAt`, `currentEventLiveStartAt`, `currentEventVolume24h`, `currentEventLiquidity`. #### 4. `twitter_activity` (curated tweets affecting markets) `tweetId`, `originTweetId`, `tweetUserId`, `tweetUserHandle`, `tweetUserName`, `tweetUserAvatar`, `tweetUserVerifiedType`, `tweetOriginUserHandle`, `tweetOriginUserName`, `tweetType`, `activityType`, `tweetText`, `tweetPhotos`, `tweetVideos`, `tweetThumbnails`, `tweetTimestamp`, `tweetSignificance`, `tweetCategory`, `tweetPlatform`, `tweetEvents[]` (each with `eventSlug`, `eventTitle`, `marketTitle`, `marketId`, `volume24h`, `probability`, `outcome`), `tweetUrl`. #### 5. `ai_analysis` (AI betting analysis for an event) `eventSlug`, `aiStatus`, `aiUpdatedAt`, `aiRawContent`, `aiBettingOption`, `aiBettingRationale`, `aiContextAnalysis`, `aiKeyDataEvidence`, `aiXSentiment`. --- ### πŸ§ͺ Example outputs #### `sports` row ```json { "_kind": "sports", "eventSlug": "atp-carabel-kecmano-2026-05-15", "eventTitle": "Valencia: Camilo Ugo Carabelli vs Miomir Kecmanovic", "homeTeamName": "Camilo Ugo Carabelli", "awayTeamName": "Miomir Kecmanovic", "homeTeamLogo": "https://polymarket-upload.s3.us-east-2.amazonaws.com/country-flags/arg.png", "score": "0-1", "period": "S1", "gameStatus": "in_progress", "gameType": "tennis", "marketId": "0x9439f1eaac30c73e9780a5eebedcd9b1a7a7a6d429cd15ebba69672b6877e757", "marketQuestion": "Camilo Ugo Carabelli vs Miomir Kecmanovic β€” Moneyline", "outcome": "Camilo Ugo Carabelli", "outcomePrice": 0.385, "outcomeBestAsk": 0.39, "outcomeBestBid": 0.38, "volume24h": 26600.28, "totalVolume": 26702.56, "scrapedAt": "2026-05-15T12:00:00.000Z" } ```` #### `twitter_activity` row ```json { "_kind": "twitter_activity", "tweetId": "2055259399174619490", "tweetUserHandle": "MOSSADil", "tweetUserName": "Mossad Commentary", "tweetUserVerifiedType": "blue", "tweetText": "NEWβ€”President Trump says he may lift sanctions on Chinese companies buying Iranian oil…", "tweetTimestamp": "2026-05-15T12:10:00.293Z", "tweetSignificance": 95, "tweetCategory": ["2", "100265"], "tweetEvents": [ { "eventSlug": "trump-xi-summit-what-will-china-announce-by-may-22", "eventTitle": "Trump-Xi Summit: What will China announce by May 22?", "marketTitle": "Boeing Aircraft Purchase", "marketId": "0xecbc...", "volume24h": 25698.89, "probability": 0.5, "outcome": "Yes" } ], "tweetUrl": "https://x.com/MOSSADil/status/2055259399174619490", "scrapedAt": "2026-05-15T12:10:00.000Z" } ``` #### `ai_analysis` row ```json { "_kind": "ai_analysis", "eventSlug": "trump-xi-summit-what-will-china-announce-by-may-22", "aiStatus": "SUCCESS", "aiUpdatedAt": "2026-05-15T07:13:19.000Z", "aiBettingOption": "Boeing Aircraft Purchase: Buy No", "aiBettingRationale": "Summit concluded May 15 without official Chinese announcements on Boeing purchases…", "aiContextAnalysis": "Trump-Xi summit in Beijing aimed to stabilize trade amid Iran war, Taiwan tensions…", "aiKeyDataEvidence": "Reuters: Pre-summit, US-China eyed tariff cuts on $30B non-sensitive imports…", "aiXSentiment": "Hype around Trump securing 200 Boeing jets, soybeans, oil/LNG buys, Iran help from Xi…" } ``` *** ### 🧰 Recipes #### 1. Live odds dashboard (refresh every minute) ```json { "feeds": ["events_rank","sports"], "onlyLive": true } ``` Schedule every minute. Webhook each row β†’ dashboard. #### 2. AI-driven betting bot ```json { "feeds": ["events_rank", "ai_analysis"], "explodeMarkets": true, "minVolume24hUsd": 1000, "aiAnalysisType": 1 } ``` events\_rank auto-feeds slugs into AI analysis. Combine `aiBettingOption` with current `price` to decide trades. #### 3. Sports betting export ```json { "feeds": ["sports"], "explodeMarkets": true, "explodeAssets": true, "onlyLive": true } ``` #### 4. Politics tweet alerts (high-significance only) ```json { "feeds": ["twitter_activity"], "twitterMaxItems": 200, "minTwitterSignificance": 70, "twitterCategories": ["2", "100265"] } ``` Schedule every 5 minutes; webhook each significant tweet to Telegram. #### 5. Daily Polymarket research bundle ```json { "feeds": ["events_rank", "sports", "twitter_activity", "ai_analysis"], "twitterMaxItems": 500, "explodeMarkets": true, "explodeAssets": true } ``` Comprehensive nightly export. *** ### πŸ›‘οΈ Under the hood - **Endpoints (future.news)** β€” - `POST /events/api/rank` `{ status, order_by, limit, max_end_timestamp, ... }` - `POST /events/api/sports/list` `{ require_all_tags, list_type, live_start_at_min/max, limit, ... }` - `GET /fds/api/v1/twitter/activity/list?limit=10&cursor=...` - `POST /events/api/ai-analysis/batch` `{ exchange, slugs[], type }` - **Pagination** β€” Twitter activity uses base64-encoded cursor. - **Auto-fill** β€” AI analysis auto-fills slugs from `events_rank` when both feeds run. - **Auth** β€” none. - **Proxy** β€” Apify Residential default. - **Retries** β€” 5 attempts with exponential backoff on 403 / 429 / 5xx. *** ### ⚑ Performance | Configuration | Rows | Time | Cost | |--------------|------|------|------| | events\_rank only | 200-1000 | ~30s | <$0.01 | | events\_rank + sports + 100 tweets | 500-2000 | ~1-2 min | ~$0.04 | | All feeds, full | 1000-5000 | ~5 min | ~$0.10 | *** ### πŸ“… Recommended schedules - **Live odds feed** β€” every 1-5 minutes (`events_rank` + `sports`). - **High-significance tweet alerts** β€” every 5-15 minutes (`twitter_activity`). - **Daily AI bundle** β€” once per hour or daily (`ai_analysis`). *** ### ❓ FAQ #### What's a Polymarket "event" vs "series"? **Event** = a single resolvable market (e.g. "Will Trump announce X by May 22"). **Series** = a recurring sequence of events (e.g. "BTC Up or Down 5m" generates a new event every 5 minutes). #### Why `_kind` rows? Different kinds have different shapes β€” the discriminator lets you filter cleanly in your dataset. #### What's `tweetSignificance`? future.news's 0-100 score of how impactful a tweet is for prediction markets. 95+ tweets typically move odds. #### How is AI analysis generated? future.news runs an LLM (likely Grok / GPT-class) on event description, recent news, X sentiment, and produces a structured betting recommendation. Quality varies; treat as a research input, not financial advice. #### Can I pass arbitrary event slugs to AI analysis? Yes β€” set `aiAnalysisSlugs: ["slug-1", "slug-2"]` and skip `events_rank` if you want isolated AI calls. #### How big is the events\_rank list? Typically 50-200 active events at a time, plus 20-30 active series. Sports adds another ~50-100 live/upcoming games. #### Can I get historical prices? The actor returns current snapshots + 5m / 1h / 6h / 24h aggregates. For longer history, schedule daily and stitch. #### What's `crypto_current_price` vs `crypto_price_to_beat`? For crypto-asset events, the current asset price and the resolution threshold. #### Are sports scores live? Yes β€” `score`, `period`, `gameStatus`, `gameEnded` reflect live game state. #### What's `tweetEvents[]`? For each tweet, the list of Polymarket events the tweet is tagged as affecting β€” with current `probability` and `outcome`. Critical for "this tweet just moved that market" analysis. #### What if AI analysis is missing for some slugs? The endpoint sometimes returns `null` or no entry. The actor logs and continues; status field will be empty. #### Does the actor cover non-Polymarket exchanges? Currently the AI analysis endpoint is parameterized with `exchange: "polymarket"`. future.news may expose others later. #### Can I run this in a tight loop? Yes β€” recommended `pollIterations: 1` per run, scheduled every 1-5 minutes. #### How fresh is the odds data? future.news refreshes every ~10-30 seconds. *** ### πŸ”— Related actors - **Polymarket Top Wallets & PnL Leaderboard** β€” who's winning on Polymarket. - **GMGN Trending Memecoin Scanner** β€” for the on-chain crypto side. - **GMGN Smart Money Buy Signals** β€” live crypto whale buys. - **GMGN Crypto KOL Twitter & Telegram Signal Feed** β€” crypto-specific KOL chatter. *** ### πŸ†˜ Support Bug? Want a new feed exposed? Open an issue on the actor's Apify page. # Actor input Schema ## `feeds` (type: `array`): Which feeds to fetch. `events_rank` = all active Polymarket events with markets/series. `sports` = sports markets with live scores. `twitter_activity` = curated tweet activity feed with event tags. `ai_analysis` = per-event AI betting analysis (requires event slugs). ## `explodeMarkets` (type: `boolean`): When ON, each market within an event becomes its own row (one event with 5 markets β†’ 5 rows). When OFF, markets are kept nested under the event row. ## `explodeAssets` (type: `boolean`): When ON and markets are exploded, each market outcome (Yes/No, team A/B, etc.) becomes its own row. ## `twitterMaxItems` (type: `integer`): Cap on twitter\_activity items (paginated via cursor, 10 per page). Higher = more rows but slower runs. 200 β‰ˆ 1 minute; 2000 β‰ˆ 10 minutes. ## `minTwitterSignificance` (type: `integer`): Drop tweets with significance score below this (0-100). Higher = more impactful. ## `twitterCategories` (type: `array`): Polymarket category IDs to keep in the Twitter activity feed. Empty = all. ## `aiAnalysisSlugs` (type: `array`): Polymarket event slugs to run AI analysis on (e.g. `trump-xi-summit-what-will-china-announce-by-may-22`). Required when `feeds` includes `ai_analysis`. Auto-populated from `events_rank` when omitted and that feed is enabled. ## `aiAnalysisType` (type: `integer`): AI analysis type ID. 1 is the standard betting-recommendation analysis. ## `minVolume24hUsd` (type: `number`): Drop events / markets with 24h volume below this threshold. ## `onlyLive` (type: `boolean`): Sports feed only: keep games where `live_status == true`. ## `useProxy` (type: `boolean`): Route requests through Apify Proxy. Strongly recommended ON β€” residential proxy is the most reliable way to bypass Cloudflare rate limits. ## `proxyConfiguration` (type: `object`): Apify Proxy configuration. Residential proxy group is recommended for the highest success rate against Cloudflare. ## `maxConcurrency` (type: `integer`): Maximum number of API calls running in parallel. Higher = faster runs but more chance of hitting rate limits. 3-5 is safe. ## Actor input object example ```json { "feeds": [ "events_rank", "sports", "twitter_activity" ], "explodeMarkets": true, "explodeAssets": true, "twitterMaxItems": 200, "minTwitterSignificance": 0, "twitterCategories": [], "aiAnalysisSlugs": [], "aiAnalysisType": 1, "minVolume24hUsd": 0, "onlyLive": false, "useProxy": true, "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] }, "maxConcurrency": 3 } ``` # Actor output Schema ## `_kind` (type: `string`): event / sports / series / twitter\_activity / ai\_analysis ## `eventSlug` (type: `string`): Polymarket event slug ## `eventTitle` (type: `string`): Event question / title ## `eventIcon` (type: `string`): Event icon URL ## `eventDescription` (type: `string`): Event description ## `seriesSlug` (type: `string`): Series slug (for recurring events) ## `seriesTitle` (type: `string`): Series title ## `marketId` (type: `string`): Polymarket condition\_id ## `marketQuestion` (type: `string`): Market question text ## `marketGroupItem` (type: `string`): Group item title (multi-outcome group) ## `outcome` (type: `string`): Outcome name (Yes/No/team) ## `outcomePrice` (type: `string`): Outcome price 0-1 ## `outcomeBestAsk` (type: `string`): Best ask ## `outcomeBestBid` (type: `string`): Best bid ## `price` (type: `string`): Market price ## `spread` (type: `string`): Spread ## `liquidity` (type: `string`): Pool liquidity ## `volume24h` (type: `string`): 24h volume ## `volumeAll` (type: `string`): All-time volume ## `endTimestamp` (type: `string`): ISO event end time ## `liveStatus` (type: `string`): Live status ## `resolutionSource` (type: `string`): Resolution source URL ## `homeTeamName` (type: `string`): Home team name (sports rows) ## `homeTeamLogo` (type: `string`): Home team logo ## `awayTeamName` (type: `string`): Away team name ## `awayTeamLogo` (type: `string`): Away team logo ## `score` (type: `string`): Live game score ## `gameStatus` (type: `string`): in\_progress / completed / etc. ## `gameType` (type: `string`): tennis / soccer / etc. ## `tweetId` (type: `string`): Tweet ID (twitter\_activity rows) ## `tweetUserHandle` (type: `string`): Tweet author handle ## `tweetUserName` (type: `string`): Tweet author display name ## `tweetText` (type: `string`): Tweet content ## `tweetSignificance` (type: `string`): future.news significance score 0-100 ## `tweetCategory` (type: `string`): Polymarket category IDs the tweet affects ## `tweetEvents` (type: `string`): Events affected by the tweet (with probability + outcome) ## `tweetTimestamp` (type: `string`): ISO tweet timestamp ## `tweetUrl` (type: `string`): Direct tweet URL ## `aiStatus` (type: `string`): AI analysis status (ai\_analysis rows) ## `aiBettingOption` (type: `string`): AI recommended betting option ## `aiBettingRationale` (type: `string`): Reasoning for the recommended bet ## `aiContextAnalysis` (type: `string`): Market context analysis ## `aiKeyDataEvidence` (type: `string`): Key data and media evidence ## `aiXSentiment` (type: `string`): X / Twitter sentiment summary ## `aiUpdatedAt` (type: `string`): AI analysis last update timestamp ## `scrapedAt` (type: `string`): Scrape timestamp # 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("logiover/polymarket-events-odds-ai-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`πŸ’Ύ 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); }); // πŸ“š Want to learn more πŸ“–? Go to β†’ 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("logiover/polymarket-events-odds-ai-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("πŸ’Ύ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # πŸ“š Want to learn more πŸ“–? Go to β†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{}' | apify call logiover/polymarket-events-odds-ai-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=logiover/polymarket-events-odds-ai-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Polymarket Events, Live Odds & AI Analysis Scraper", "description": "Every active Polymarket event, sports match with live scores, AI betting analysis, and Twitter signal feed. Market-level data (outcomes, prices, volumes 5m/1h/6h/24h, liquidity, spreads), sports games (teams, scores, period, status), AI rationale, X sentiment, tweet β†’ event impact map.", "version": "1.0", "x-build-id": "XtL43LXWdgBdSikT2" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/logiover~polymarket-events-odds-ai-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-logiover-polymarket-events-odds-ai-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/logiover~polymarket-events-odds-ai-scraper/runs": { "post": { "operationId": "runs-sync-logiover-polymarket-events-odds-ai-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/logiover~polymarket-events-odds-ai-scraper/run-sync": { "post": { "operationId": "run-sync-logiover-polymarket-events-odds-ai-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "feeds": { "title": "Feeds to Scrape", "uniqueItems": true, "type": "array", "description": "Which feeds to fetch. `events_rank` = all active Polymarket events with markets/series. `sports` = sports markets with live scores. `twitter_activity` = curated tweet activity feed with event tags. `ai_analysis` = per-event AI betting analysis (requires event slugs).", "items": { "type": "string", "enum": [ "events_rank", "sports", "twitter_activity", "ai_analysis" ], "enumTitles": [ "Events / Series Rank", "Sports List + Live Scores", "Twitter Activity Feed", "AI Analysis (batch)" ] }, "default": [ "events_rank", "sports", "twitter_activity" ] }, "explodeMarkets": { "title": "Explode Markets (Events Mode)", "type": "boolean", "description": "When ON, each market within an event becomes its own row (one event with 5 markets β†’ 5 rows). When OFF, markets are kept nested under the event row.", "default": true }, "explodeAssets": { "title": "Explode Assets (Markets)", "type": "boolean", "description": "When ON and markets are exploded, each market outcome (Yes/No, team A/B, etc.) becomes its own row.", "default": true }, "twitterMaxItems": { "title": "Twitter Max Items", "minimum": 10, "type": "integer", "description": "Cap on twitter_activity items (paginated via cursor, 10 per page). Higher = more rows but slower runs. 200 β‰ˆ 1 minute; 2000 β‰ˆ 10 minutes.", "default": 200 }, "minTwitterSignificance": { "title": "Min Twitter Significance", "minimum": 0, "maximum": 100, "type": "integer", "description": "Drop tweets with significance score below this (0-100). Higher = more impactful.", "default": 0 }, "twitterCategories": { "title": "Twitter Category Filter", "type": "array", "description": "Polymarket category IDs to keep in the Twitter activity feed. Empty = all.", "default": [], "items": { "type": "string" } }, "aiAnalysisSlugs": { "title": "AI Analysis Event Slugs", "type": "array", "description": "Polymarket event slugs to run AI analysis on (e.g. `trump-xi-summit-what-will-china-announce-by-may-22`). Required when `feeds` includes `ai_analysis`. Auto-populated from `events_rank` when omitted and that feed is enabled.", "default": [], "items": { "type": "string" } }, "aiAnalysisType": { "title": "AI Analysis Type", "minimum": 1, "maximum": 10, "type": "integer", "description": "AI analysis type ID. 1 is the standard betting-recommendation analysis.", "default": 1 }, "minVolume24hUsd": { "title": "Minimum 24h Volume (USD)", "type": "number", "description": "Drop events / markets with 24h volume below this threshold.", "default": 0 }, "onlyLive": { "title": "Only Live Sports Games", "type": "boolean", "description": "Sports feed only: keep games where `live_status == true`.", "default": false }, "useProxy": { "title": "Use Apify Proxy", "type": "boolean", "description": "Route requests through Apify Proxy. Strongly recommended ON β€” residential proxy is the most reliable way to bypass Cloudflare rate limits.", "default": true }, "proxyConfiguration": { "title": "Proxy Configuration", "type": "object", "description": "Apify Proxy configuration. Residential proxy group is recommended for the highest success rate against Cloudflare.", "default": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } }, "maxConcurrency": { "title": "Max Concurrent Requests", "minimum": 1, "maximum": 10, "type": "integer", "description": "Maximum number of API calls running in parallel. Higher = faster runs but more chance of hitting rate limits. 3-5 is safe.", "default": 3 } } }, "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 } } } } } } } } } } ```