Polymarket API: Your First Python Script to Fetch Live Odds
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How to Fetch Live Polymarket Odds Using Python Script (API Guide)
Polymarket is a live financial market where probabilities are priced in real time by traders putting money on the line. Building your prediction market on top of Polymarket means you are pulling “odds” in the traditional sense; instead, it’s about reading market sentiment, liquidity, and price discovery as they evolve.
For operators, CTOs, and product teams exploring prediction markets, understanding how Polymarket exposes its data and how to work with it correctly is the first real technical checkpoint.
This guide walks through how Polymarket exposes live market data, how implied probabilities are derived, and how to fetch them using a simple Python script. Also, find out how to move from a basic API call to a production-ready implementation using the right architecture.
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How Polymarket Works and What Does Polymarket API Mean?
Polymarket operates on a simple structure where events have outcomes, and each outcome has tokens, represented in the form of real money. Understand that;
- Each market represents a real-world event.
- Each event has multiple outcomes.
- Each outcome is represented by a token whose price reflects the market’s perceived probability.
Unlike sportsbook platforms, prediction markets don’t have fixed odds. The prices here are formed through liquidity-based price discovery, which moves up or down as traders buy and sell outcome tokens. The decisions traders take are based on their beliefs, information, and risk appetite.
Polymarket provides access to multiple technical interfaces; however, not all of them are unified into a single, developer-friendly API layer. On an infrastructure level, Polymarket provides;
- CLOB API – Used for placing and managing trades via wallet-based authentication.
- Gamma REST API – Read-only access to markets, prices, and liquidity.
- WebSocket feeds – Internal real-time updates, primarily for trading systems.
- On-chain contracts & subgraphs – For settlement and historical data.
What You Can and Cannot Do via the Polymarket API?
| What Polymarket API support? | What This Means |
|---|---|
| Fetch active markets | Market data can be queried via GraphQL. |
| Fetch outcome prices | Prices reflect implied probabilities, not odds. |
| Track volume & liquidity | Useful for filtering low-signal markets. |
| Access timestamps & metadata | Market status and activity can be monitored. |
| What Polymarket API Does Not Support? | What This Means |
|---|---|
| Place trades | Requires wallet interaction and smart contract calls. |
| Execute orders via API | No trading endpoints exist. |
| Use official SDKs | Polymarket does not provide or maintain SDKs. |
| Rely on guaranteed uptime | Public endpoints are not production-grade. |
Polymarket API Access and What We Can Actually Use to Build a Prediction Market Platform
Polymarket exposes its data and functionality across multiple technical interfaces where each layer serves a different purpose, maturity level, and usage pattern. Understanding these interfaces is critical for building anything beyond a proof-of-concept.
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Public GraphQL Endpoints – Core Data Feed
- Active and historical markets
- Outcome lists per market
- Token prices (implied probabilities)
- Volume, liquidity, timestamps, and metadata
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CLOB API — Trading Interfaces for Market Makers
Polymarket uses a Central Limit Order Book (CLOB) with REST-like endpoints, designed for the following purposes.
- Placing limits and market orders.
- Cancelling orders.
- Fetching order status and trade fills.
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WebSocket & Real-Time Feeds
- Often tied to internal services rather than public endpoints.
- Designed to support trading latency requirements.
- Not uniformly documented or guaranteed as a public API.
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Smart Contracts & On-Chain Data
- Data ingestion pipelines
- Reliability engineering
- Rate control and caching
- On-chain + off-chain consistency
- Order execution layers
At the center of most integrations is Polymarket’s GraphQL service. This is the most accessible and practical way to get structured market data, as it provides;
This API layer and the information it extracts are used to populate dashboards and for monitoring. It’s used for analytical and reporting purposes, market discovery, frontend event display, and backend data ingestion.
Live feeds exist, but they don’t operate through the open WebSockets as you would generally expect from traditional financial APIs. Polymarket’s real-time data streams are:
The fourth API layer is where all markets go live on the Polygon blockchain. Here, smart contracts govern three things, issuing outcome tokens, making liquidity adjustments, and trades & settlements.
Most production platforms use indexed on-chain events to complement API data, ensuring accuracy and auditability.
It’s clear that integrating Polymarket data and functionality is not a one-to-one API plug-in. It demands making changes at the architectural level, including;
Your First Python Script to Fetch Live Polymarket Odds
To fetch Polymarket odds using Python, we need to build a system that understands how market data is retrieved, how odds are derived, and what developers must and must not do when working with Polymarket data.
At a technical level, Polymarket exposes its data through GraphQL endpoints. These endpoints act as the primary interface for retrieving market information such as active events, outcome tokens, prices, volume, and timestamps. There is no traditional REST API layer designed for public consumption, which is why most integrations rely directly on GraphQL queries.
But first, use the right tech stack;
- Python: Lightweight and widely used for data pipelines
- requests / httpx: To make GraphQL calls
- GraphQL query structure: Polymarket’s primary data interface
This approach gives developers direct access to the same data powering Polymarket’s frontend, without relying on unofficial wrappers or brittle scraping methods.
When working with Polymarket odds Python script, understand that Outcome price is not always the same as Odds. The price determined for each market event is the market-implied probability and is expressed between 0 and 1.
For instance, a price of 0.72 means 72% probability, and a price of 0.18 means 18% probability. These values can change based on the trade volume, liquidity depth, market sentiment, and size of recent orders. At the same time, if liquidity is low, prices can move sharply even on small trades. This is why reading Polymarket data without context often leads to incorrect conclusions.
Use of Polymarket API Python Script
When you query Polymarket’s GraphQL endpoint, you don’t fetch Polymarket odds using Python in a traditional sense; rather, it’s about
- Pulling active markets.
- Reading outcome token prices.
- Converting those prices into probabilities.
- Exposing market sentiment in real time.
Using Python for the Polymarket API isn’t the real challenge here. As you understand how to use Polymarket API, you also know that Python is not the limitation, but the architecture you have is. In other words, you need a robust architecture.
- Executing Trades: Polymarket wasn’t built as an API-first platform; it’s built as an on-chain market with an API layer on top. But to place trade, you need to build the wallet infrastructure, smart contract integration, and order execution layer.
To ensure all these components work well, you need to add backend services, dedicated execution engines, and web3 tooling, along with Python. - Real-time Trading: Polymarket API Python script alone cannot handle real-time trading. The reason is that Polymarket prices move fast, and polling GraphQL endpoints will also lead to delays. Polymarket API Python script does not track order lifecycle, reconcile on-chain and off-chain state, and handle race conditions.
- Production Grade Execution: Replacing a simple script in real systems, we need to build an execution layer using Node.js or a Rust-based service. TRUEiGTECH prefers Node.js for this task. Then we need wallet signer integration, transaction retry logic, and nonce management.
To achieve success in Polymarket API integration with your prediction market platform architecture, TRUEPREDiCT will;
- Design the execution layer.
- Handle on-chain interactions.
- Normalize market data.
We will make your prediction market software reliable and scalable to the point that it operates as an enterprise-grade platform.
How TRUEPREDiCT Helps You Go Beyond a Script?
By now, you must understand that to fetch Polymarket odds using Python is easy, but building a working platform with this approach is not. Most of the Polymarket API integration service providers can pull prices, maybe even visualize markets, but struggle when it comes to scaling, reliability, execution logic, or compliance.
TRUEPREDiCT fills this gap.
We focus on building production-grade prediction market systems while managing prediction market platform development cost, not demos or surface-level integrations.
- End-to-end prediction market platforms: From market creation to settlement, with architecture designed for scale.
- Polymarket-style system design: Including outcome modeling, probability logic, and market lifecycle handling.
- Custom APIs and data layers: Built to normalize market data, manage caching, and support high-read environments.
- Admin and operator dashboards: For market control, monitoring, analytics, and risk management.
- Liquidity and odds engines: Designed to handle price movement, volume weighting, and market integrity.
Use our expertise to build Polymarket-like prediction market platforms, integrate prediction markets into your existing applications, and create internal market analytics systems. We have a dedicated team of professionals to launch compliant and regulated platforms that ensure performance, security, and stability.
Teams choose TRUEPREDiCT due to our deep prediction market expertise, strong grasp of the market mechanics, and expertise in using the API-first architecture. In short, TRUEPREDICT doesn’t just help teams “connect to Polymarket.” We help them build systems that behave correctly when real users, real money, and real scale are involved.
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Conclusion
What looks like a simple API problem quickly becomes a systems challenge involving data normalization, market logic, execution layers, reliability engineering, and compliance considerations. That’s the difference between experimenting with prediction markets and actually building one.
If your goal is to move beyond scripts and prototypes, the focus has to shift from accessing data to architecting systems that can operate on it correctly. TRUEPREDICT helps teams design and build production-ready prediction market platforms from Polymarket-style architectures to custom market engines, data layers, and compliance-aware deployments.
If you’re serious about building in the prediction market space, talk to TRUEPREDiCT before you start writing production code.