# Polymarket Multi-Outcome Arbitrage Scanner (`seralifatih/pm-arbitrage`) Actor Detects mathematical arbitrage on Polymarket multi-outcome events (election winners, championship finishers, top-K markets). Scans every event, classifies its structure, computes fee-adjusted basket returns, verifies per-leg liquidity. JSON output, no API keys needed. - **URL**: https://apify.com/seralifatih/pm-arbitrage.md - **Developed by:** [Fatih ฤฐlhan](https://apify.com/seralifatih) (community) - **Categories:** Developer tools, AI, Automation - **Stats:** 2 total users, 1 monthly users, 0.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $250.00 / 1,000 arbitrage opportunities 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 ## ๐ŸŽฏ Polymarket Multi-Outcome Arbitrage Scanner > **No noise, just alpha.** Concrete arithmetic arbitrage on Polymarket's multi-outcome events. [![Run on Apify](https://apify.com/actor-badge?actor=seralifatih/pm-arbitrage)](https://apify.com/seralifatih/pm-arbitrage) [![Python 3.11](https://img.shields.io/badge/python-3.11-blue.svg)](https://www.python.org/) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) A signal engine that scans every Polymarket event with mutually-exclusive outcomes โ€” election winners, championship finishers, "who will be the X" markets โ€” and detects when the **YES prices fail to sum to their mathematical expected value**. That's free arbitrage. Every output is a complete basket trade with the math worked out, fees subtracted, and per-leg liquidity verified against live order books. --- ### โœจ Key Features - **Real arbitrage, not vibes** โ€” every signal is rigorous arithmetic. ฮฃ YES across N mutually-exclusive outcomes must equal a known constant (1.0 for winner-take-all, K for top-K). Deviations are quantifiable edge. - **Two arb directions** โ€” `buy_yes_basket` (when ฮฃ YES < expected) and `buy_no_basket` (when ฮฃ YES > expected). The scanner picks the profitable side automatically. - **Conservative event classifier** โ€” three structures qualify (winner-take-all, top-K) and the scanner explicitly rejects cumulative "by date" events, price ladders, and prop bets. No false signal from non-exclusive outcomes. - **Fee-adjusted returns** โ€” every opportunity reports `gross_return_pct`, `fees_pct` (Polymarket's 4% round-trip), and the headline `net_return_pct` you actually capture. - **Real per-leg liquidity tests** โ€” walks each child market's CLOB order book at a configurable position size; flags `all_legs_fillable: true/false/null` per opportunity. - **Confidence-scored signals** โ€” every result tagged with a 0โ€“100 `signal_score` weighing return, fillability, leg count, and time-to-resolution. - **Public-read only** โ€” no API keys required. Runs on Apify free tier. - **Async-first pipeline** โ€” concurrent event fetch + bounded-concurrency CLOB probes; full scan in 30โ€“90 seconds. --- ### ๐Ÿ“Š Sample Output Each opportunity is a complete basket trade with the math worked out: ```json { "id": "lebanon-parliamentary-election-winner-buy_yes_basket", "event_title": "Lebanon Parliamentary Election Winner", "event_url": "https://polymarket.com/event/lebanon-parliamentary-election-winner", "resolution_date": "2026-05-31", "event_type": "winner_take_all", "expected_sum_yes": 1.0, "arb_type": "buy_yes_basket", "leg_count": 21, "sum_yes_price": 0.789, "deviation_from_one": -0.211, "fees_pct": 4.0, "gross_return_pct": 26.74, "net_return_pct": 22.74, "legs": [ { "market_id": "0x7fc8...", "question": "Will Lebanese Forces win the most seats in the 2026 Lebanese parliamentary election?", "outcome_label": "Lebanese Forces (LF)", "side": "YES", "price": 0.06, "market_url": "https://polymarket.com/event/will-lebanese-forces-win-the-most-seats...", "liquidity_usd": 19892, "fillable": true } ], "liquidity": { "tested_usd_per_leg": 100, "all_legs_fillable": true, "fillable_leg_count": 21, "total_leg_count": 21 }, "signal_score": 85, "signal_label": "Pure arbitrage", "notes": null } ```` A `ScanSummary` record is also written to the default key-value store under `OUTPUT_SUMMARY`: ```json { "scanned_at": "2026-04-29T18:00:00Z", "total_events_scanned": 1000, "eligible_events": 38, "pure_arb_count": 7, "ev_positive_count": 11, "avg_net_return_pct": 18.4, "best_opportunity_id": "lebanon-parliamentary-election-winner-buy_yes_basket" } ``` #### Signal labels | Label | Score | Meaning | |-------|-------|---------| | `Pure arbitrage` | โ‰ฅ 80 + net return > 0 | Risk-free spread after fees | | `Strong EV+ signal` | 60โ€“79 | High-conviction edge | | `EV+ with edge` | 40โ€“59 | Mild edge โ€” position sizing matters | | `Marginal` | < 40 | Excluded by default | *** ### ๐Ÿงฎ The Math For a Polymarket event with N mutually-exclusive outcomes (e.g. "who wins the 2026 MLS Cup?", "which team finishes top 4 in Serie A?"), exactly K outcomes resolve YES. So under perfect pricing: ``` ฮฃ P(outcome_i = YES) = K ``` - **K = 1** for winner-take-all events ("X election winner", "Y nominee", "Z championship") - **K = N\_top** for top-K events ("Top 4 EPL finish", "Reach the final" with K=2, "Eurovision Top 5") When ฮฃ YES drifts from K, there's a basket trade with locked-in profit: **Buy-YES-basket arb** (ฮฃ YES < K): - Cost: ฮฃ YES dollars to buy 1 share of every YES outcome - Payout: K dollars (exactly K of them resolve YES) - Gross return: `(K - ฮฃ YES) / ฮฃ YES` **Buy-NO-basket arb** (ฮฃ YES > K): - Cost: `N - ฮฃ YES` dollars (since NO\_i = 1 - YES\_i) - Payout: `N - K` dollars (the legs that DON'T resolve YES) - Gross return: `(N - K - (N - ฮฃ YES)) / (N - ฮฃ YES) = (ฮฃ YES - K) / (N - ฮฃ YES)` The scanner subtracts a conservative 4% round-trip fee (Polymarket's 2% taker ร— open + close) to compute `net_return_pct`. Every leg is liquidity-tested against the live order book at the configured position size before being marked fillable. *** ### ๐Ÿ›ก What This Scanner Will NOT Flag The scanner uses a conservative event classifier that rejects structures where ฮฃ YES has no constraint. These look superficially like arb but are not โ€” buying their baskets has no guaranteed payout. **Rejected: cumulative "by date" events** > "Will Drake release Iceman by April / May / June?" โ€” releasing in April implies released by May too. Outcomes are nested, not exclusive. **Rejected: price/threshold ladders** > "Will Solana reach $110 / dip to $70 / reach $100 in April?" โ€” price can hit multiple thresholds. Outcomes are independent ranges. **Rejected: prop bets and multi-condition events** > "Player X: Points O/U 17.5 + Rebounds O/U 4.5 + Assists O/U 3.5" โ€” independent stat lines. > "Will Trump publicly insult someone on April 26 / 28 / 29 / 30?" โ€” independent daily events. > "What demands will Trump agree to in April?" โ€” multiple demands can co-resolve YES. If you're seeing a Polymarket event you'd expect to be arb-eligible but it's not in the output, it likely fell into one of these categories. Check `OUTPUT_SUMMARY.eligible_events` to see how many of the day's events qualified. *** ### ๐Ÿš€ How to Use #### Method 1: No-Code (Apify Console) 1. Open the actor on [Apify](https://apify.com/seralifatih/pm-arbitrage) 2. Adjust `min_signal_score` and `min_net_return_pct` for your strategy 3. Click **Start** โ€” opportunities land in the dataset, summary in `OUTPUT_SUMMARY` #### Method 2: API Integration ```bash curl -X POST "https://api.apify.com/v2/acts/seralifatih~pm-arbitrage/runs?token=YOUR_API_TOKEN" \ -H "Content-Type: application/json" \ -d '{ "min_net_return_pct": 0, "min_signal_score": 70, "min_event_liquidity_usd": 10000, "liquidity_test_amount_usd": 250, "output_limit": 25 }' ``` Fetch results: ```bash curl "https://api.apify.com/v2/datasets/DATASET_ID/items?token=YOUR_API_TOKEN&format=json" ``` #### Scheduled runs Schedule the actor every 5โ€“15 minutes during high-volatility windows (election nights, championship games, FOMC days, Eurovision, primary debates) to catch fresh basket mispricings before market makers correct them. *** ### โš™๏ธ Input Configuration ```json { "min_net_return_pct": -1.0, "min_signal_score": 30, "min_event_liquidity_usd": 5000, "min_liquidity_per_leg_usd": 200, "min_legs_per_event": 3, "liquidity_test_amount_usd": 100, "max_days_to_resolution": 365, "max_events_to_scan": 1000, "output_limit": 50 } ``` | Field | Type | Default | Description | |-------|------|---------|-------------| | `min_net_return_pct` | number | `-1.0` | Minimum net return after fees. `0` = pure arbitrage only. | | `min_signal_score` | integer | `30` | Minimum 0โ€“100 signal score to include. | | `min_event_liquidity_usd` | integer | `5000` | Skip events below this cumulative liquidity. | | `min_liquidity_per_leg_usd` | integer | `200` | Each leg must have at least this liquidity (filters dead placeholders). | | `min_legs_per_event` | integer | `3` | Skip events with fewer mutually-exclusive legs than this. | | `liquidity_test_amount_usd` | integer | `100` | Per-leg position size for the depth test. | | `max_days_to_resolution` | integer | `365` | Skip events resolving beyond this horizon. | | `max_events_to_scan` | integer | `1000` | Cap on Polymarket events fetched. Higher = wider coverage, slower. | | `output_limit` | integer | `50` | Cap on opportunities returned (โ‰ค 200). | *** ### ๐Ÿงฐ Tech Stack - **Python 3.11** โ€” async-first - **httpx** โ€” concurrent Gamma + CLOB requests with bounded concurrency - **pydantic v2** โ€” strict output schema - **Apify SDK for Python** Estimated run time: 30โ€“90 seconds for the default config at 512 MB memory. *** ### โš ๏ธ Disclaimer This tool is for informational and analytical purposes only. Prediction market trading involves financial risk. Always validate signals independently before committing capital. Not financial advice. **Important caveat โ€” implicit "Other" probability:** The arithmetic identity `ฮฃ YES = K` assumes the listed outcomes cover **all** real-world possibilities. For winner-take-all events (e.g. "Alaska Governor 2026") with many candidates, Polymarket may not list every potential winner โ€” the missing probability mass on unlisted outcomes is what often keeps ฮฃ YES below 1.0 even on well-priced markets. **Top-K events** (Eurovision Top 5, EPL Top 4, Serie A Top 4) are typically more reliable because the outcome space is rigorously defined: exactly K teams finish top-K, no "other" possible. **Single-winner events with >10% deviation** deserve manual verification of outcome-space completeness before trading. The scanner reports the raw mathematical signal โ€” **you must judge whether the listed candidates exhaust the field.** *** ### ๐Ÿ”— You might also like - [**Crypto Arbitrage Scanner**](https://apify.com/seralifatih/arbitrage) โ€” CEX spot arbitrage across 8+ exchanges - [**CEX Funding Rate Arbitrage**](https://apify.com/seralifatih/cex-funding-rate-arbitrage) โ€” Perpetual funding rate basis trades # Actor input Schema ## `min_net_return_pct` (type: `number`): Minimum net basket return after fees. 0 = pure arbitrage only. Negative includes EV+ opportunities slightly below breakeven. ## `min_signal_score` (type: `integer`): 0โ€“100 confidence score required to include an opportunity. ## `min_event_liquidity_usd` (type: `integer`): Skip events whose total cumulative liquidity is below this. ## `min_liquidity_per_leg_usd` (type: `integer`): Each child market (leg) must have at least this liquidity. Filters dead placeholder candidates. ## `min_legs_per_event` (type: `integer`): Events with fewer mutually-exclusive legs than this are skipped. ## `liquidity_test_amount_usd` (type: `integer`): Per-leg position size for the order book depth test. Sets the fillable threshold. ## `max_days_to_resolution` (type: `integer`): Skip events whose latest child resolves beyond this horizon. ## `max_events_to_scan` (type: `integer`): Cap on Polymarket events fetched per run. Higher = wider coverage, slower. ## `output_limit` (type: `integer`): Cap on opportunities returned per run. ## Actor input object example ```json { "min_net_return_pct": -1, "min_signal_score": 30, "min_event_liquidity_usd": 5000, "min_liquidity_per_leg_usd": 200, "min_legs_per_event": 3, "liquidity_test_amount_usd": 100, "max_days_to_resolution": 365, "max_events_to_scan": 1000, "output_limit": 50 } ``` # Actor output Schema ## `opportunities` (type: `string`): Dataset of detected basket arbitrage opportunities. Each item is one Polymarket event with leg-by-leg basket composition, fee-adjusted return, per-leg fillability, signal score, and event classification (winner\_take\_all or top\_k). ## `summary` (type: `string`): Single key-value record summarizing the scan: timestamp, total events scanned, eligible event count, pure arb count, EV+ count, average net return, and best opportunity ID. # 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("seralifatih/pm-arbitrage").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`๐Ÿ’พ 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); }); // ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ 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("seralifatih/pm-arbitrage").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("๐Ÿ’พ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{}' | apify call seralifatih/pm-arbitrage --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=seralifatih/pm-arbitrage", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Polymarket Multi-Outcome Arbitrage Scanner", "description": "Detects mathematical arbitrage on Polymarket multi-outcome events (election winners, championship finishers, top-K markets). Scans every event, classifies its structure, computes fee-adjusted basket returns, verifies per-leg liquidity. JSON output, no API keys needed.", "version": "0.0", "x-build-id": "Pjp4XiBDZyQFaxdlz" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/seralifatih~pm-arbitrage/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-seralifatih-pm-arbitrage", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/seralifatih~pm-arbitrage/runs": { "post": { "operationId": "runs-sync-seralifatih-pm-arbitrage", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/seralifatih~pm-arbitrage/run-sync": { "post": { "operationId": "run-sync-seralifatih-pm-arbitrage", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "min_net_return_pct": { "title": "Minimum Net Return %", "type": "number", "description": "Minimum net basket return after fees. 0 = pure arbitrage only. Negative includes EV+ opportunities slightly below breakeven.", "default": -1 }, "min_signal_score": { "title": "Minimum Signal Score", "minimum": 0, "maximum": 100, "type": "integer", "description": "0โ€“100 confidence score required to include an opportunity.", "default": 30 }, "min_event_liquidity_usd": { "title": "Minimum Event Liquidity (USD)", "type": "integer", "description": "Skip events whose total cumulative liquidity is below this.", "default": 5000 }, "min_liquidity_per_leg_usd": { "title": "Minimum Liquidity Per Leg (USD)", "type": "integer", "description": "Each child market (leg) must have at least this liquidity. Filters dead placeholder candidates.", "default": 200 }, "min_legs_per_event": { "title": "Minimum Legs Per Event", "type": "integer", "description": "Events with fewer mutually-exclusive legs than this are skipped.", "default": 3 }, "liquidity_test_amount_usd": { "title": "Liquidity Test Size (USD)", "type": "integer", "description": "Per-leg position size for the order book depth test. Sets the fillable threshold.", "default": 100 }, "max_days_to_resolution": { "title": "Max Days to Resolution", "type": "integer", "description": "Skip events whose latest child resolves beyond this horizon.", "default": 365 }, "max_events_to_scan": { "title": "Max Events to Scan", "type": "integer", "description": "Cap on Polymarket events fetched per run. Higher = wider coverage, slower.", "default": 1000 }, "output_limit": { "title": "Max Opportunities in Output", "maximum": 200, "type": "integer", "description": "Cap on opportunities returned per run.", "default": 50 } } }, "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 } } } } } } } } } } ```