Build a Real-Time Polymarket Order Book Monitor A trading bot should not make decisions from stale snapshots.
If you want to understand liquidity, spread, depth, or changes in market structure, you need a continuously updated view of the Polymarket order book.
This tutorial builds a lightweight Polymarket order book Python monitor using the public CLOB Market WebSocket.
Polymarket documents this channel as a real-time feed for order-book, price, and market lifecycle updates.
The implementation intentionally focuses on market data—not order execution—so it can be used as the foundation for research, dashboards, alerts, or an automated trading system.
What You'll Learn How Polymarket token IDs relate to order-book subscriptions How to connect to the CLOB Market WebSocket How to process and events How to calculate best bid, best ask, and spread How to handle reconnects and heartbeats How to detect stale market data How to turn raw WebSocket events into trading signals Architecture The important design decision is separating transport from state.
The WebSocket delivers events; your application maintains the current book.
1.
Install the Dependencies For this monitor, authentication is not required because the Market WebSocket is public.
You need a Polymarket asset ID/token ID for the outcome you want to monitor.
The Market Channel subscribes using .
For example: Do not hard-code credentials into a market-data monitor.
In this example, there are no credentials at all.
2.
Connect to the Market WebSocket The documented Market Channel endpoint is: The subscription message contains and one or more asset IDs.
A minimal monitor looks like this: The initial event contains aggregated bid and ask levels.
Polymarket also documents as an order-book price-level delta event.
3.
Don't Ignore the Heartbeat A production client should implement the documented application-level heartbeat.
Polymarket's current documentation specifies that clients send every 10 seconds and receive .
Add a dedicated heartbeat task: Then start it after connecting: This is preferable to relying blindly on the WebSocket library's protocol-level ping mechanism because the Polymarket feed specifies its own application-level heartbeat.
4.
Maintain Book State For research, repeatedly printing snapshots is not enough.
Store price levels in dictionaries: For every snapshot: A real implementation should also process every documented price-level change according to its , , and fields.
The key principle is simple: The WebSocket is the event stream.
Your local order book is the state machine.
5.
Useful Real-Time Metrics Once the book is normalized, you can calculate: Best bid Best ask Spread Midpoint Top-of-book imbalance A simple research metric is: These metrics do not constitute a profitable strategy by themselves.
They are observations that can become inputs to a broader execution or pricing model.
Production Considerations A production monitor should add: Automatic reconnection Exponential backoff Subscription restoration Heartbeat monitoring Message validation Sequence/state consistency checks Stale-data detection Structured logging Persistent event storage Graceful shutdown Do not assume that an open TCP/WebSocket connection means your market data is healthy.
Polymarket's status history has documented WebSocket incidents and CLOB maintenance events, so operational monitoring matters even when your application code is correct.
Failure Modes Connection succeeds but no data arrives Check the asset ID, subscription payload, market state, and application heartbeat.
Your book becomes inconsistent Do not treat every message as an independent snapshot.
Distinguish full events from incremental events.
Your monitor silently becomes stale Track the timestamp of the most recent valid market event and raise an alert when it exceeds your application's freshness threshold.
The code breaks after a Polymarket API change Pin and regularly review dependencies, then check the official documentat