What It Actually Takes to Run a Polymarket Trading Bot 24/7

Running a Polymarket trading bot continuously involves more than just getting the code to work. Maintaining the bot's health during extended operation is a distinct challenge. A bot can function flawlessly in a test environment and still fail in a live setting due to various issues such as WebSocket disconnects, outdated market data, partial fills, position mismatches, execution uncertainties, failed API requests, and reaching risk limits. The core strategy might be sound, but the surrounding infrastructure must be robust.

To achieve a production-oriented Polymarket system, it's crucial to consider the entire workflow: Market Data → Strategy → Risk → Execution → Verification → Reconciliation → Monitoring → Recovery. Each layer must have a plan for handling failures. When a system is running 24/7, every component requires a strategy for what happens when something goes wrong.

The easiest way to gauge a bot's health is to check if the process is running. However, this doesn't provide a comprehensive view. The process might be alive, but the bot may be facing stale WebSocket data, incorrect positions, unknown executions, unverified market data, or outdated information. Therefore, it's better to separate process health from trading system health. A process can be running while the trading system is paused, which is a useful state for monitoring purposes.

To visualize the operational state of the trading system, it's helpful to define explicit states like HEALTHY, DEGRADED, PAUSED, RECOVERING, and FAILED. For instance, a WebSocket disconnect can move the system from HEALTHY to DEGRADED and then to PAUSED after recovery. This approach provides a clearer picture of the system's operational state compared to simply tracking socket connections.

A trading bot that depends on real-time data must anticipate connection failures. A basic connection lifecycle includes CONNECTED, DISCONNECTED, RECONNECTING, and CONNECTED. However, the critical question is whether the data is actually fresh even when the connection is reported as CONNECTED. Tracking metrics like last_event_time, last_connection_time, reconnect_count, last_disconnect, and data_age can help determine if the data is useful.

For example, if the last event arrived 9.4 seconds ago, which exceeds a maximum allowed age of 2 seconds, the system shouldn't be considered healthy. Reconnection alone doesn't guarantee recovery. If the bot reconnects but fails to process recent events, it's not in a HEALTHY state.

Understanding the nuances of a bot's recovery process is crucial. Consider a scenario where Event A occurs, followed by Event B, then a DISCONNECT, and then Events C and D before a RECONNECT. The application might never have seen Events C and D, so a simple RECONNECTED state doesn't mean the system is STATE VERIFIED. The recovery process should involve pausing, reconnecting, rebuilding or fetching the state, reconciling, verifying, checking risk, and then resuming operations. For more details, see the Polymarket WebSocket recovery guide.

Process restarts are inevitable in a production environment. The key question isn't whether the process can start but what state it should trust after restarting. Simply determining if the process starts is insufficient. A safer startup sequence involves loading persisted state, connecting to external sources, reading authoritative state, comparing it, repairing discrepancies, verifying risk, and allowing trading.

Importantly, application_started and trading_allowed are separate states. Persisting relevant state information is critical. After a restart, the bot needs a single source of truth for operational state. Different components may have varying perspectives—such as the strategy indicating BUY, the execution layer showing FILLED, the verifier noting TX_PENDING, the reconciliation layer detecting POSITION_MISMATCH, and the risk layer flagging BLOCK.

These views should not be contradictory but should work together to provide a comprehensive operational picture. The control plane can combine these perspectives to make informed decisions, such as blocking trading when there are position mismatches or unverified risks.