Build a Polymarket Market Scanner in Python

Trading bots should never begin by placing orders blindly. Instead, they must first answer a fundamental question: which Polymarket markets are worth looking at right now? A useful prediction market scanner continuously transforms a vast universe of prediction markets into a structured, manageable candidate set based on criteria such as market status, liquidity, volume, expiration dates, pricing, and order-book availability.

Polymarket exposes public market data without requiring authentication, providing market discovery through the Gamma API and order-book data through the CLOB API. This tutorial guides developers through building a read-only Python scanner that discovers active markets, applies quantitative filters, and outputs candidate sets for downstream strategy execution engines.

For broad market discovery, the official documentation utilizes a keyset-paginated endpoint supporting filters like closed status, liquidity ranges, volume ranges, date ranges, tags, and custom ordering. The endpoint uses opaque next_cursor and after_cursor pagination parameters with a maximum limit of 100. Once the scanner identifies target markets, it can query the CLOB API for specific order-book details such as bids, asks, and midpoint prices.

Stock photo for illustration only, not from the actual event

Setting up a minimal project in Python requires standard HTTP tooling like the httpx library. Developers can initialize their project structure with the following steps:

• Create and activate a virtual environment for the project workspace.
• Install the required HTTP client dependency using pip install httpx.
• Organize project files including scanner.py, requirements.txt, and README.md.

Because this version functions strictly as a read-only scanner, no private keys or trading credentials are required. The first core component is a market-discovery client designed to handle keyset pagination properly. Furthermore, since API responses may contain serialized JSON strings, developers must implement defensive parsing techniques to safely decode outcomes and pricing arrays into usable numeric tuples.

Metadata filtering acts as the initial funnel. If a universe contains 5,000 markets but only 100 satisfy basic liquidity and volume criteria, querying detailed order-books for all 5,000 is highly inefficient. Filtering down the candidate pool allows the system to efficiently score and prioritize markets using normalization formulas—such as combining square roots of liquidity and volume—providing strategy engines with clear directives on where to focus attention.

Source: Dev.to