# Prediction Market Resolution Evidence Checker (`bigdavidson/prediction-market-resolution-evidence`) Actor Paste Kalshi weather market URLs or tickers and get the live NWS/METAR observation that resolves them: running daily high or low at the exact settlement station, implied outcome, and a confidence grade. Hold-or-close evidence for $0.05 per market checked. - **URL**: https://apify.com/bigdavidson/prediction-market-resolution-evidence.md - **Developed by:** [Jack Sheward](https://apify.com/bigdavidson) (community) - **Categories:** Developer tools, Other - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $50.00 / 1,000 market checks This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. 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 ## Prediction Market Resolution Evidence Checker Paste **prediction market** URLs or tickers — get back the **live underlying observable that resolves each market**, with source link, implied outcome, and a confidence grade. For a Kalshi NYC daily-high bracket, that means: the running daily high at Central Park (KNYC) **right now**, from the same official observation chain the market settles against — so you can decide whether to **hold or close the position** before settlement. v0.1 covers **weather (temperature) markets**: full observation evidence for all Kalshi KXHIGH (daily high) and KXLOW (daily low) series across 20 US cities, each mapped to the exact settlement station — including the gotchas (Chicago = Midway, Dallas = DFW, Houston = Hobby). Polymarket weather URLs are accepted and return live market data, but observation evidence for them is not yet supported (see Limitations). No login, no API keys — everything comes from public, unauthenticated APIs (Kalshi market data, Polymarket gamma, aviationweather.gov, api.weather.gov). ### What you get One record per market checked: | Field | Example | Notes | |---|---|---| | `market_url` | input URL/ticker | `null` for auto-discovered markets | | `ticker` | `KXHIGHNY-26JUN11-B92.5` | market identifier (Kalshi ticker / Polymarket slug) | | `event_ticker` | `KXHIGHNY-26JUN11` | groups the brackets of one city-day | | `platform` | `kalshi` | `kalshi` or `polymarket` | | `market_title` | `Will the high temp in NYC be 92-93° …` | | | `city`, `station_id` | `NYC`, `KNYC` | the exact station the market settles on | | `series_type` | `KXHIGH` | `KXHIGH` (daily high) or `KXLOW` (daily low) | | `strike_type`, `floor_strike`, `cap_strike` | `between`, `92`, `93` | bracket bounds in °F | | `bracket_label` | `92° to 93°` | human-readable bracket | | `resolution_date` | `2026-06-11` | the station-local calendar day being measured | | `market_status` | `active` | `active` / `finalized` etc. straight from the platform | | `current_yes_bid`, `current_yes_ask`, `last_price` | `0.06`, `0.07`, `0.06` | USD per contract | | `underlying_observable` | object | **the evidence** — see below | | `market_implied_outcome` | `no` | `yes` / `no` / `uncertain` — how the bracket would settle if the running value were final | | `resolution_confidence` | `high` | `high` / `medium` / `low` (`unsupported` for Polymarket) | | `notes` | array | settlement results, staleness flags, hold/close direction hints | The `underlying_observable` object: ```json { "source": "aviationweather.gov", "metric": "daily_high_f", "value_f": 90.0, "latest_obs_f": 90.0, "as_of_utc": "2026-06-11T22:00:00Z", "obs_count": 19, "station_local_day": "2026-06-11", "url": "https://aviationweather.gov/api/data/metar?ids=KNYC&format=json&hours=20" } ```` `value_f` is the **running daily high (or low)** computed over the station's *local* calendar day — observations before local midnight are excluded, exactly the windowing the market resolves on. `url` is the public source query so every number is auditable. Example full record (live output, lightly trimmed): ```json { "market_url": "KXHIGHNY-26JUN11-B92.5", "ticker": "KXHIGHNY-26JUN11-B92.5", "event_ticker": "KXHIGHNY-26JUN11", "platform": "kalshi", "market_title": "Will the high temp in NYC be 92-93° on Jun 11, 2026?", "city": "NYC", "station_id": "KNYC", "series_type": "KXHIGH", "strike_type": "between", "floor_strike": 92, "cap_strike": 93, "bracket_label": "92° to 93°", "resolution_date": "2026-06-11", "market_status": "active", "current_yes_bid": 0.06, "current_yes_ask": 0.07, "last_price": 0.06, "underlying_observable": { "source": "aviationweather.gov", "metric": "daily_high_f", "value_f": 90.0, "latest_obs_f": 90.0, "as_of_utc": "2026-06-11T22:00:00Z", "obs_count": 19, "station_local_day": "2026-06-11", "url": "https://aviationweather.gov/api/data/metar?ids=KNYC&format=json&hours=20" }, "market_implied_outcome": "no", "resolution_confidence": "high", "notes": [ "running high below the bracket — could still rise into it" ] } ``` Export as JSON, CSV, or Excel directly from the dataset. ### Input ```json { "marketUrls": [ "https://kalshi.com/markets/kxhighny", "KXHIGHNY-26JUN12-T97", "KXHIGHNY-26JUN12", "https://polymarket.com/event/highest-temperature-in-hong-kong-on-june-11-2026" ] } ``` Or run with **no input at all** — auto-discovery checks today's open NYC daily-high event: ```json {} ``` - `marketUrls` — up to 50 entries, mix and match: - full `kalshi.com` market/event/series URLs (any path shape — the ticker is extracted from the URL), - bare Kalshi tickers: market (`KXHIGHNY-26JUN12-T97`), event (`KXHIGHNY-26JUN12`), or series (`KXHIGHNY` → resolves to today's soonest-closing open event), - `polymarket.com/event/...` or `/market/...` URLs (market data only in v0.1). - Case-insensitive. A bad entry never kills the run — it becomes a record with `notes` explaining the problem. - `autoDiscover` (default `true`) — when `marketUrls` is empty, the actor finds **today's soonest-closing open Kalshi NYC daily-high (KXHIGHNY) event** and checks every bracket in it. This is intentional: a hardcoded example URL would die the day that market settles, while auto-discovery always lands on a live market — so the default run (which the platform also uses for daily health checks) keeps working forever. - `fields` — optional list to slim the top-level output (e.g. `["market_implied_outcome"]`); `market_url`, `ticker`, `platform` are always kept. ### How outcomes and confidence are graded - `market_implied_outcome` — how the bracket would settle **if the running value were the final settled value**: `yes`, `no`, or `uncertain` when the running value is within 1°F of a strike boundary (settlement-source rounding can flip those) or the observations are stale. - `resolution_confidence` — `high`: latest obs under 90 minutes old (or the local day is already complete) with a cleanly mapped station; `medium`: obs 90 min–3 h old; `low`: stale obs, unknown station, or unparseable market. - `notes` carry the directional read for daily extremes: a running daily high can only rise, so "running high already above the bracket" means NO is locked in METAR terms, while "below the bracket" can still flip. Settled markets get the official result and settlement value in `notes`. ### Supported platforms and cities | Platform | Markets | Evidence | |---|---|---| | Kalshi | all KXHIGH/KXLOW daily temperature brackets, live or settled | full: running daily high/low at the settlement station | | Polymarket | daily temperature events/markets by URL | market data only (`resolution_confidence: "unsupported"`, `market_implied_outcome: "uncertain"`) | Kalshi settlement stations (the mapping mistakes that cost real traders money are handled): | City | KXHIGH | KXLOW | Station | |---|---|---|---| | NYC | KXHIGHNY | KXLOWTNYC | KNYC (Central Park) | | Chicago | KXHIGHCHI | KXLOWTCHI | KMDW (Midway, **not O'Hare**) | | Miami | KXHIGHMIA | KXLOWTMIA | KMIA | | Austin | KXHIGHAUS | KXLOWTAUS | KAUS | | Los Angeles | KXHIGHLAX | KXLOWTLAX | KLAX | | Denver | KXHIGHDEN | KXLOWTDEN | KDEN | | Phoenix | KXHIGHTPHX | KXLOWTPHX | KPHX | | Philadelphia | KXHIGHPHIL | KXLOWTPHIL | KPHL | | Houston | KXHIGHTHOU | KXLOWTHOU | KHOU (Hobby, **not Bush/IAH**) | | Minneapolis | KXHIGHTMIN | KXLOWTMIN | KMSP | | Oklahoma City | KXHIGHTOKC | KXLOWTOKC | KOKC | | San Francisco | KXHIGHTSFO | KXLOWTSFO | KSFO | | Washington DC | KXHIGHTDC | KXLOWTDC | KDCA | | Boston | KXHIGHTBOS | KXLOWTBOS | KBOS | | Dallas | KXHIGHTDAL | KXLOWTDAL | KDFW (**not Love Field**) | | Seattle | KXHIGHTSEA | KXLOWTSEA | KSEA | | Las Vegas | KXHIGHTLV | KXLOWTLV | KLAS | | Atlanta | KXHIGHTATL | KXLOWTATL | KATL | | San Antonio | KXHIGHTSATX | KXLOWTSATX | KSAT | | New Orleans | KXHIGHTNOLA | KXLOWTNOLA | KMSY | ### Pricing **$0.05 per market checked**, plus a small per-run start fee — no subscription. One Kalshi event (one city-day) is typically 6–14 brackets, so grading a full event costs about **$0.30–0.70** — a fraction of one mispriced contract. ### Limitations (read this before trading on the output) - **Weather markets only in v0.1.** Sports, crypto, politics and other Kalshi/Polymarket markets are rejected with a clear note, not checked. - **Observations are evidence, not a settlement guarantee.** Kalshi settles KXHIGH/KXLOW on the official NWS **CLI climate report** for the station, which can differ from the running METAR value — typically by rounding, occasionally by 1°F or more (the CLI includes 6-hour max/min groups and corrections that hourly METARs miss). That is exactly why values within 1°F of a strike are reported as `uncertain` rather than called. - **A running daily high can still rise** (and a low can still fall) until the local day ends. A `yes` on a "between" bracket mid-afternoon can still flip; the `notes` tell you which side is locked and which is still open. - **Polymarket weather markets get market data only.** They resolve per-city on sources like Wunderground station history in whole degrees **Celsius** — a different source family that v0.1 does not map. Those records carry `resolution_confidence: "unsupported"` and an honest note instead of a misleading number. - Observation lookback is 72 hours: markets older than that return settled status without observation evidence. ### FAQ #### How do I know if my Kalshi weather market will resolve YES? Run this actor with your market URL or ticker. If the running daily high is already above a "greater" strike, YES is locked in METAR terms (a daily high cannot go back down) — the record says so in `notes`. If the value is below the strike, the market is still open to movement, and `market_implied_outcome` tells you how it would settle at this moment. #### Should I close my position before settlement? That's your call — this actor gives you the evidence: current bid/ask, the live observed value vs your strikes, whether your side is locked or still in play, and how fresh the observation is. Dead brackets (running high already past the cap) are the classic close-now case. #### What does `uncertain` mean? The running value is within 1°F of a strike boundary (settlement rounding could flip it), or the latest observation is more than 3 hours old. It deliberately refuses to call coin-flips. #### How accurate is the resolution evidence for Kalshi temperature markets? The observations come from the same station chain (ASOS/METAR via aviationweather.gov) that feeds the official NWS CLI climate report Kalshi settles on. They agree on the vast majority of city-days; when they differ it is usually rounding at a strike boundary — exactly the cases this actor reports as `uncertain` instead of calling. Treat the evidence as the strongest available real-time signal, not a settlement oracle. #### Which station does my market settle on? Every record carries `station_id` — the exact station Kalshi's CLI climate report covers, including the non-obvious ones: Chicago is Midway (KMDW), Dallas is DFW, Houston is Hobby (KHOU), NYC is Central Park (KNYC). #### Does this work for Polymarket weather markets? Partially: paste the URL and you get the live market data (brackets, prices, end date) with `resolution_confidence: "unsupported"` — v0.1 does not fetch the Celsius observation sources those markets resolve on. Full Polymarket support is the v0.2 candidate. #### What happens when a market is already settled? You get the official result and settlement value in `notes`, plus the observed daily high/low for that day when it's within the 72-hour observation window — useful for auditing how settlement compared to METAR evidence. #### Is this affiliated with Kalshi or Polymarket? Does it trade? No and no. It reads public, unauthenticated market-data and weather APIs. No account, no credentials, no orders — evidence only. ### Use with AI agents (MCP) Connect your agent to the Apify MCP server (mcp.apify.com) and it can call this actor as a tool: ask "will KXHIGHNY-26JUN12-T97 resolve YES?" and the agent passes the ticker, gets back the running daily high at the settlement station, the implied outcome, and a confidence grade. Built for hold-or-close decisions inside agentic trading workflows. ### More prediction-market data actors - [Kalshi Weather Markets Data (KXHIGH & KXLOW)](https://apify.com/bigdavidson/kalshi-weather-markets) - full bracket prices and settlement history by city and date. - [METAR & NWS Weather Station Observations API](https://apify.com/bigdavidson/metar-nws-weather-station-observations) - the raw METAR/NWS observations behind the evidence. # Actor input Schema ## `marketUrls` (type: `array`): Up to 50 kalshi.com / polymarket.com market or event URLs, or bare Kalshi tickers (KXHIGHNY-26JUN12-T97, KXHIGHNY-26JUN12, KXHIGHNY). Leave empty with Auto-discover on to check today's open NYC daily-high event. ## `autoDiscover` (type: `boolean`): When marketUrls is empty, automatically find today's soonest-closing open Kalshi NYC daily-high (KXHIGHNY) event and check every bracket in it. ## `fields` (type: `array`): Subset of top-level output fields to keep (market\_url, ticker and platform always included). Leave empty for all fields. ## Actor input object example ```json { "marketUrls": [], "autoDiscover": true, "fields": [] } ``` # 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 = { "marketUrls": [], "autoDiscover": true }; // Run the Actor and wait for it to finish const run = await client.actor("bigdavidson/prediction-market-resolution-evidence").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 = { "marketUrls": [], "autoDiscover": True, } # Run the Actor and wait for it to finish run = client.actor("bigdavidson/prediction-market-resolution-evidence").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 '{ "marketUrls": [], "autoDiscover": true }' | apify call bigdavidson/prediction-market-resolution-evidence --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=bigdavidson/prediction-market-resolution-evidence", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Prediction Market Resolution Evidence Checker", "description": "Paste Kalshi weather market URLs or tickers and get the live NWS/METAR observation that resolves them: running daily high or low at the exact settlement station, implied outcome, and a confidence grade. Hold-or-close evidence for $0.05 per market checked.", "version": "0.1", "x-build-id": "n9JmSSByXOzmCDYHR" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/bigdavidson~prediction-market-resolution-evidence/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-bigdavidson-prediction-market-resolution-evidence", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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/bigdavidson~prediction-market-resolution-evidence/runs": { "post": { "operationId": "runs-sync-bigdavidson-prediction-market-resolution-evidence", "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/bigdavidson~prediction-market-resolution-evidence/run-sync": { "post": { "operationId": "run-sync-bigdavidson-prediction-market-resolution-evidence", "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": { "marketUrls": { "title": "Market URLs or tickers", "maxItems": 50, "type": "array", "description": "Up to 50 kalshi.com / polymarket.com market or event URLs, or bare Kalshi tickers (KXHIGHNY-26JUN12-T97, KXHIGHNY-26JUN12, KXHIGHNY). Leave empty with Auto-discover on to check today's open NYC daily-high event.", "default": [], "items": { "type": "string" } }, "autoDiscover": { "title": "Auto-discover when empty", "type": "boolean", "description": "When marketUrls is empty, automatically find today's soonest-closing open Kalshi NYC daily-high (KXHIGHNY) event and check every bracket in it.", "default": true }, "fields": { "title": "Fields filter (optional)", "type": "array", "description": "Subset of top-level output fields to keep (market_url, ticker and platform always included). Leave empty for all fields.", "default": [], "items": { "type": "string" } } } }, "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 } } } } } } } } } } ```