Polymarket Trading Bot Execution Analytics: What Should You Measure After the Trade?

When a Polymarket trading bot operates normally, it might still encounter issues that aren't apparent from performance metrics. Problems like slower order execution, more partial fills, higher rejection rates, unresolved execution states, frequent position reconciliation, and longer recovery times could arise. Monitoring these aspects becomes crucial to ensure the system's health and risk management.

For an automated trading system, it's essential to monitor beyond just whether the bot traded or its profit and loss (P&L). Execution analytics helps identify problems in the execution layer, which is critical for automated trading systems. These metrics provide insights into how the execution layer is functioning, allowing you to detect anomalies and take appropriate action.

Start by measuring order latency, fill latency, fill ratio, partial-fill rate, rejection rate, execution failures, unknown execution states, and position mismatches. Among these, order latency and fill latency are particularly important. Measure the time taken for orders to move through the execution path, from the strategy decision to order creation and submission.

Analyze the distribution of these metrics rather than relying solely on averages. For example, if the 90th percentile (p95) fill latency is significantly higher than the median (p50), there might be occasional slow executions that could impact the overall system performance.

Next, track fill latency, which is the time between order submission and execution. Even if an order eventually completes successfully, a long fill latency can indicate potential issues. Track fill latency by market side, order type, strategy, and time window to identify patterns and potential problems.

The fill ratio is another essential metric. It compares the requested quantity with the actual filled quantity. For instance, if you requested 100 units and received 75, the fill ratio is 75%. This metric provides more information than simply recording "FILLED," as it accounts for requested quantity and actual execution. Partial-fill rate, on the other hand, measures how often orders are only partially executed.

This metric is useful because partial fills can lead to downstream effects, such as remaining quantity, position updates, exposure updates, and reconciliation issues.

Rejection rate is another straightforward metric, calculated by dividing the number of rejected orders by the total submitted orders. A rejection rate of 2.5% means that 2.5% of the orders did not get executed for various reasons. Tracking changes in this rate over time can help identify potential issues.

Separate execution failures into distinct categories to make them more actionable. For example, you could have ORDER_SUBMISSION_FAILURE, FILLED_PROCESSING_FAILURE, TRANSACTION_VERIFICATION_FAILURE, POSITION_RECONCILIATION_FAILURE, and RECOVERY_FAILURE. This separation helps identify the specific problem and take appropriate action.

Monitor unknown execution states, which occur when the system encounters states it can't verify immediately. Track how long these executions remain unresolved and define policies around unresolved execution states to ensure proper control and management.

Lastly, track position mismatches by comparing the system's internal position to the external state. Position mismatches can indicate discrepancies in the system's tracking of positions, which can be critical for exposure management and reconciliation.

By monitoring these metrics, you can gain valuable insights into the execution layer's health, identify potential issues, and take appropriate actions to maintain the system's stability and risk management.