Quote Carry-Forward
Quote carry-forward is the practice of reusing the most recent valid bid or ask quote when no newer quote is available for a time interval or snapshot.
In market data systems, quotes do not always arrive at every timestamp or interval. Some markets update frequently, while others may have long periods without quote changes. Quote carry-forward fills these gaps by carrying the most recent valid quote forward into later timestamps, snapshots, or analytics windows.
This does not mean a new quote event occurred. It means the last observed quote remains the current known state until it is replaced, invalidated, or cleared. Carry-forward logic is commonly used when building best bid and offer views, quote snapshots, time-aligned datasets, sparse flat files, and historical analytics.
Correct handling is important because carried-forward quotes can affect spread calculations, mid-price estimates, liquidity analysis, and quote availability metrics. Systems should clearly distinguish between a quote that was newly published at a timestamp and a quote that was carried forward from an earlier event.
Why It Matters
Quote carry-forward matters because quote data is often sparse or event-driven rather than emitted on a fixed schedule. Without carry-forward logic, time-based datasets may appear to have missing bid or ask values even though the last known market state was still valid.
For trading systems, analytics platforms, and historical data pipelines, carry-forward rules help create consistent time-aligned datasets. They also help users understand when a quote value represents a fresh update versus the most recent known state.
How does quote carry-forward work?
Quote carry-forward works by storing the latest valid quote and applying it to subsequent time intervals until a newer quote arrives. For example, if the best bid and ask are updated at 10:00:01 and no new quote is received until 10:00:07, a one-second snapshot dataset may carry the 10:00:01 quote into the 10:00:02 through 10:00:06 rows.
The exact rules depend on the dataset. Some systems may carry forward both bid and ask independently. Others may apply maximum age limits, invalidate quotes after trading halts, or reset quote state at session boundaries.
What is the difference between quote carry-forward and a new quote?
A new quote is an actual market data event received from an exchange, venue, or data provider. A carried-forward quote is a derived representation of the latest known quote state reused in a later time bucket or snapshot.
This distinction is important because carried-forward values should not be counted as new quote messages. They represent state continuity, not additional market activity.
Why is quote carry-forward used in sparse datasets?
Sparse datasets may store only changes rather than every repeated state. Quote carry-forward allows users to reconstruct a complete time-aligned view from sparse quote updates.
For example, flat files may contain quote changes at irregular times. When users need fixed-interval snapshots, carry-forward logic can fill each interval with the latest known quote so spreads, mid-prices, and best bid/ask states can be analyzed consistently.
Real-World Example
Suppose a crypto exchange publishes a best bid of 50,000 and a best ask of 50,001 at 12:00:00. No quote updates occur for the next five seconds. A one-second analytics table may show the same bid and ask for 12:00:01, 12:00:02, 12:00:03, 12:00:04, and 12:00:05.
Those repeated values are carried forward from the original quote event. They help users see the best known market state at each second, but they should not be interpreted as five new quote updates.
Quote Carry-Forward and CoinAPI Market Data API
CoinAPI Market Data API provides access to quote and order book data that can be used to build current market views, historical analytics, and time-aligned datasets. When working with sparse quote data or flat files, users may apply quote carry-forward logic to maintain the latest known bid and ask across intervals.
This is especially useful for analytics workflows that require consistent timestamps across trades, quotes, candles, and order book data. Understanding carry-forward behavior helps users interpret whether a quote value came from a fresh market event or from the latest known quote state.