# 🔮 Polymarket Prediction Markets — Odds, Outcomes, Volume (`nexgendata/polymarket-prediction-markets-tracker`) Actor Track Polymarket prediction markets: outcomes and outcome prices/odds, volume, liquidity, and end date. For traders, researchers, and media tracking event probabilities. - **URL**: https://apify.com/nexgendata/polymarket-prediction-markets-tracker.md - **Developed by:** [NexGenData](https://apify.com/nexgendata) (community) - **Categories:** Business - **Stats:** 11 total users, 0 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $20.00 / 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 ## 🔮 Polymarket Prediction Markets Tracker — Odds, Outcomes, Volume **Pay-per-result Polymarket data — $0.10 per market record. No web-socket plumbing, no Gamma/CLOB API wrangling, no rate-limit babysitting. Just the live odds, outcomes, traded volume and liquidity of the world's largest prediction-market venue, delivered as flat JSON.** Polymarket is the deepest real-money prediction market on the planet — billions of dollars of cumulative volume across elections, macro, crypto, sports, geopolitics, and pop culture. The odds it prints are widely treated by journalists, traders, and researchers as a real-time probability estimate that often beats polls and pundits. But getting that data out cleanly is annoying: the public surface is split across the Gamma markets API and the CLOB order-book API, outcome prices arrive as stringified JSON arrays, volume and liquidity live on different objects, and you have to paginate and filter yourself. This actor collapses all of that into one call. Point it at a keyword (or pull the whole active board), set a minimum-volume floor to cut the noise, and get back a clean record per market: the exact question, the outcome labels, the live outcome prices (your implied probabilities), the cumulative traded volume, the on-book liquidity, and the market's end date. It is purpose-built for event-driven traders building probability dashboards, journalists who want a defensible "the market says 63%" number, and quant researchers backtesting market-implied probabilities against realized outcomes. ### Why use this The hard part of Polymarket data is not the HTTP request — it is everything around it. Outcome prices come back as `"[\"0.63\", \"0.37\"]"` strings that you have to parse and align to the outcome labels. Volume and liquidity are reported on different endpoints. Closed, resolved, and zero-volume "junk" markets flood the active board unless you filter them out. And if you poll naively you get throttled. This actor handles all of it. You get parsed, label-aligned outcome prices, volume and liquidity merged onto a single row, an `activeOnly` switch to drop closed markets, and a `minVolume` floor so a market that has only ever traded \$4 never pollutes your dashboard. Because billing is pay-per-result, a tightly-filtered query that returns 12 relevant markets costs you for 12 records — not for the thousands of dead markets you filtered away. You also do not need a Polymarket account, an Ethereum wallet, or any on-chain tooling to read the data. The actor reads Polymarket's public market data the same way the website does. ### What you get Every record represents one Polymarket market and carries exactly these fields: - `question` — the exact market question as displayed on Polymarket (e.g. "Will the Fed cut rates in September 2026?") - `outcomes` — the array of outcome labels (typically `["Yes", "No"]`, but multi-outcome markets carry their full label set) - `outcomePrices` — the live price per outcome, aligned index-for-index with `outcomes`. On Polymarket a price of `0.63` means the market is pricing that outcome at a 63% implied probability and the share trades at \$0.63 - `volume` — cumulative traded volume on the market in USD - `liquidity` — current on-book liquidity in USD (depth available to trade against right now) - `endDate` — the market's scheduled resolution / end date Records are flat JSON, ready to drop into Snowflake, BigQuery, Postgres, a pandas DataFrame, or a pgvector store without remapping. ### Use cases - **Probability dashboards** — Pull every active market above a volume floor on a 5-minute schedule and render a live "what the market thinks" board for elections, macro events, or crypto milestones. The `outcomePrices` field is your implied-probability series straight out of the box. - **Newsroom sourcing** — When a reporter needs a defensible number ("prediction markets put the odds at 63%"), filter by the event keyword, sort by `volume` to find the most-traded market on that question, and cite its `outcomePrices`. Deeper volume = a more credible market estimate. - **Event-driven trading research** — Backtest Polymarket-implied probabilities against realized outcomes. Snapshot `outcomePrices` over time, join to the eventual resolution, and measure the market's calibration and your edge. - **Cross-venue arbitrage scouting** — Run this alongside the Kalshi tracker, align markets on the same real-world event, and surface spreads where Polymarket and Kalshi disagree on the same outcome's probability. - **Macro & rates monitoring** — Track Fed-decision, CPI, and recession markets with a `questionContains` keyword and a `minVolume` floor, and pipe the implied-probability moves into your macro research notes. - **Sentiment & narrative tracking** — Monitor how the market re-prices a developing geopolitical or political story hour by hour, and chart the probability path as the narrative evolves. - **Liquidity screening** — Use the `liquidity` field to find markets deep enough to actually trade size in, filtering out thin markets where a single order would move the price. ### Sample output ```json { "question": "Will the Fed cut rates at the September 2026 FOMC meeting?", "outcomes": ["Yes", "No"], "outcomePrices": ["0.63", "0.37"], "volume": 4821933.12, "liquidity": 318204.55, "endDate": "2026-09-17T00:00:00Z" } ```` A multi-outcome market returns its full label set, with `outcomePrices` aligned index-for-index: ```json { "question": "Which party wins the 2026 US House majority?", "outcomes": ["Democrats", "Republicans"], "outcomePrices": ["0.54", "0.46"], "volume": 9120044.80, "liquidity": 502118.20, "endDate": "2026-11-03T00:00:00Z" } ``` ### Input parameters | Parameter | Type | Description | |---|---|---| | `questionContains` | string | Filter to markets whose question text contains this phrase (e.g. `"Fed"`, `"election"`). Omit to scan the whole board. | | `activeOnly` | boolean | When `true`, return only active, non-closed markets. Set `false` to include closed/resolved markets too. | | `minVolume` | number | Minimum cumulative traded volume (USD) a market must have to be returned. Use this to drop thin / junk markets. | | `maxResults` | integer | Maximum number of market records to return. Bounds both result size and cost. | ### How to use #### Python (apify-client) ```python from apify_client import ApifyClient client = ApifyClient("YOUR_TOKEN") run = client.actor("nexgendata/polymarket-prediction-markets-tracker").call(run_input={ "questionContains": "Fed", "activeOnly": True, "minVolume": 50000, "maxResults": 100, }) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item["question"], item["outcomePrices"], item["volume"]) ``` #### cURL ```bash curl -X POST "https://api.apify.com/v2/acts/nexgendata~polymarket-prediction-markets-tracker/run-sync-get-dataset-items?token=YOUR_TOKEN" \ -H "Content-Type: application/json" \ -d '{ "questionContains": "Fed", "activeOnly": true, "minVolume": 50000, "maxResults": 100 }' ``` Schedule it on Apify's built-in scheduler for a live feed — a 5-minute cron is ideal for trading-style dashboards, and webhooks let you fire a Slack / n8n / Make alert the moment a market crosses a probability threshold. ### Pricing This actor runs on Apify's **pay-per-event** model — you pay only for results, never for run-time: - **$0.10 per market record** — the primary event, charged once per market pushed to the dataset - **No subscription, no seat licence, no minimum commitment** #### Cost worked example - A keyword dashboard returning 25 active "Fed" markets → 25 × $0.10 = **$2.50** per run - The full active board filtered to ≥$50K volume, ~120 markets → 120 × $0.10 = **$12.00** per run - A single high-volume market lookup → **$0.10** Because you set `minVolume` and `maxResults`, you control your cost precisely before the run. If a filter returns 0 markets, you pay $0. Browse 200+ buyer-intent actors at https://apify.com/nexgendata?fpr=2ayu9b ### How this compares to the raw Polymarket APIs | Approach | What it costs you | What you get | |---|---|---| | Polymarket Gamma + CLOB APIs (DIY) | Engineering time: pagination, two-endpoint joins, string-array parsing, rate-limit handling, proxy rotation | Raw, unmerged JSON across two APIs; outcome prices as stringified arrays | | Third-party prediction-market data vendors | Custom enterprise contracts, often $1K+/mo | Aggregated dashboards, usually no clean programmatic per-market export | | **Polymarket Tracker (this actor)** | **$0.10 per market record, no subscription** | **One flat record per market — question, label-aligned outcome prices, volume, liquidity, end date — filtered and ready for ETL** | The raw Polymarket APIs are free, but "free" here means you maintain a two-endpoint client, parse stringified price arrays, merge volume and liquidity yourself, filter dead markets, and keep it all running. This actor is the "skip the plumbing" option: keyword in, clean filtered records out, pay only for what you keep. ### FAQ **Q: How fresh are the odds?** A: Each run pulls live from Polymarket's public market data, so `outcomePrices`, `volume`, and `liquidity` reflect the state of the market at run time. For trading-style use, schedule a 5-minute (or tighter) cron. **Q: How do I read `outcomePrices`?** A: It is an array aligned index-for-index with `outcomes`. For a `["Yes", "No"]` market, `outcomePrices` of `["0.63", "0.37"]` means Yes is priced at 63% implied probability (a $0.63 share) and No at 37%. Prices across outcomes sum to approximately 1.0. **Q: Can I track one specific market?** A: Yes — set `questionContains` to a distinctive phrase from the question and `maxResults` low. If multiple markets match, sort the results by `volume` to find the canonical, most-traded one. **Q: Why filter on `minVolume`?** A: Polymarket's board includes many markets that have traded only a few dollars. Those carry noisy, easily-manipulated prices. A `minVolume` floor keeps your dashboard to markets with enough real money behind them to be informative. **Q: Do I need a wallet or Polymarket account?** A: No. The actor reads public market data only. You never connect a wallet, sign a transaction, or place a trade — it is read-only. **Q: What about closed markets?** A: Set `activeOnly` to `false` to include closed and resolved markets, which is useful for backtesting calibration against known outcomes. Set it to `true` for live dashboards. ### Schema stability & versioning This actor follows NexGenData's **additive-only schema** contract: - New fields may be **added** at any time — they appear as new keys, defaulting to `null` for older runs. - Existing fields are **never renamed or removed** without a major-version bump and an advance changelog notice. - Field semantics (units, value sets, the index-alignment of `outcomes`↔`outcomePrices`) are **never silently changed**. You can build a production probability dashboard on this schema and trust that a Tuesday release will not break Friday's ETL job. Report anything unexpected via the actor's Apify Issues tab. ### Compliance & legal - The actor reads **public, unauthenticated** Polymarket market data the same way the website does. No wallet, credentials, or account are involved. - All requests route through Apify's compliant proxy infrastructure with polite pacing. - This actor is a **data tool, not trading or investment advice**. Prediction-market prices are estimates, not guarantees. Nothing here is a solicitation to trade. - Prediction-market access and trading are restricted in some jurisdictions (including parts of the US). You are responsible for ensuring your own use complies with your local laws and Polymarket's terms. - You are responsible for downstream-use compliance with data-protection laws (GDPR, CCPA, etc.) where applicable. ### Related NexGenData actors Part of NexGenData's **Finance & Markets** suite — pair this actor with: - [Kalshi Prediction Markets Tracker](https://apify.com/nexgendata/kalshi-prediction-markets-tracker?fpr=2ayu9b) — the CFTC-regulated US event-contract venue; align markets for cross-venue arbitrage - [Crypto Perp Funding Rates Tracker](https://apify.com/nexgendata/crypto-perp-funding-rates-tracker?fpr=2ayu9b) — perpetual-swap funding rates across major exchanges - [DeFi TVL Protocol Tracker](https://apify.com/nexgendata/defi-tvl-protocol-tracker?fpr=2ayu9b) — total-value-locked across DeFi protocols - [Stablecoin Supply & Peg Monitor](https://apify.com/nexgendata/stablecoin-supply-peg-monitor?fpr=2ayu9b) — stablecoin circulating supply and peg health - [Global Central Bank Policy Rates](https://apify.com/nexgendata/global-central-bank-policy-rates?fpr=2ayu9b) — the macro rate events that drive many prediction markets - [Finviz Stock Screener](https://apify.com/nexgendata/finviz-stock-screener?fpr=2ayu9b) — US equities filtered by sector, fundamentals, technicals Explore the full catalog of 200+ buyer-intent actors at **https://apify.com/nexgendata?fpr=2ayu9b**. # Actor input Schema ## `questionContains` (type: `string`): Filter by market question. ## `activeOnly` (type: `boolean`): Only active, non-closed markets. ## `minVolume` (type: `number`): Minimum traded volume. ## `maxResults` (type: `integer`): Maximum markets to return. ## Actor input object example ```json { "activeOnly": true, "maxResults": 500 } ``` # 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("nexgendata/polymarket-prediction-markets-tracker").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("nexgendata/polymarket-prediction-markets-tracker").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 nexgendata/polymarket-prediction-markets-tracker --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=nexgendata/polymarket-prediction-markets-tracker", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "🔮 Polymarket Prediction Markets — Odds, Outcomes, Volume", "description": "Track Polymarket prediction markets: outcomes and outcome prices/odds, volume, liquidity, and end date. For traders, researchers, and media tracking event probabilities.", "version": "0.0", "x-build-id": "jdOMTNOQMqwGVMDd1" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/nexgendata~polymarket-prediction-markets-tracker/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-nexgendata-polymarket-prediction-markets-tracker", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/nexgendata~polymarket-prediction-markets-tracker/runs": { "post": { "operationId": "runs-sync-nexgendata-polymarket-prediction-markets-tracker", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/nexgendata~polymarket-prediction-markets-tracker/run-sync": { "post": { "operationId": "run-sync-nexgendata-polymarket-prediction-markets-tracker", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "questionContains": { "title": "Question contains", "type": "string", "description": "Filter by market question." }, "activeOnly": { "title": "Active only", "type": "boolean", "description": "Only active, non-closed markets.", "default": true }, "minVolume": { "title": "Min volume", "type": "number", "description": "Minimum traded volume." }, "maxResults": { "title": "Max results", "minimum": 1, "maximum": 5000, "type": "integer", "description": "Maximum markets to return.", "default": 500 } } }, "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 } } } } } } } } } } ```