Measurement Infrastructure

To log predictions and outcomes for asynchronous quote cycles, you log a PredictionRecord for each quote cycle and wire an OutcomeMatcher to populate the asynchronous outcomes as they arrive.

Prediction logging in high-frequency trading captures per-prediction granularity and market-state snapshots, providing the measurement foundation required to diagnose model failures and calibrate outcomes.

Asynchronous outcome reconciliation works by matching logged predictions against outcomes that arrive later, capturing the market-state conditions at prediction time to enable accurate calibration and auditing.

Yes, you can use a Parquet-backed storage backend to store and query prediction and outcome records, enabling efficient calibration-analysis workflows and signal auditing integrations.

The best way to set up measurement infrastructure before model development is to establish prediction logging and outcome tracking first, as all other model improvements depend on this measurement foundation.

You need per-prediction granularity in market-data calibration pipelines when you must condition analysis on individual quote cycles, enforce auditing, and capture market-state snapshots for accurate model evaluation.