Point-in-Time Metrics
Point-in-Time (PIT) metrics provide a consistent, immutable view of data as it was known at a specific moment in the past. This is critical for building robust and reliable systematic strategies, research, and backtesting. Unlike traditional mutable metrics that may change retroactively, PIT metrics preserve historical data without applying any future knowledge or corrections that emerged later.
Why Point-in-Time?
Many datasets evolve over time due to late-arriving information, retrospective corrections, or refined methodologies. If not handled carefully, this can introduce look-ahead bias, where a model or analysis unintentionally uses information that wasn't actually available at the time — leading to misleading results.
PIT metrics solve this by freezing data as it was known at the time, avoiding hindsight adjustments and ensuring results are reproducible and free from future information leaks.
📌 Recommendation: All systematic use cases (e.g., algorithmic trading, quantitative models, historical backtesting) should always rely on PIT metrics when available.
Common Reasons for Data Mutations
Even in a blockchain context, some data can change post-factum due to:
Clustering for Entity Adjustment: As more heuristics or address linkages are discovered, historical metrics may be adjusted to reflect refined entity-level data (e.g., exchange wallets).
Late-Reported Data: Off-chain or external data sources may deliver information with a delay (e.g. perpetual futures positions of a single exchange, or blockchain data that requires additional confirmations).
Data Corrections: Improvements in off-chain datasets can lead to retroactive corrections.
PIT metrics eliminate the impact of such updates by recording and serving data exactly as it appeared at that time.
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