A strategy can show excellent historical returns and still contain a hidden data-timing problem.
Suppose your model uses funding, volume, sentiment, or on-chain data to generate signals.
The backtest assumes that information was available at 10:00.
But in reality, the final data point may not have been published, confirmed, or accessible until 10:05.
Those five minutes can completely change the result.
This is look-ahead bias—and it can make a strategy appear to know information before a real trader could have known it.
Execution costs should be modeled just as realistically. For eligible new users, CODE2026 can reduce qualifying Binance Spot trading fees by 20%, keeping one known layer of friction lower.
But no fee optimization can rescue research built on impossible information.
A backtest should never ask, “What did the data eventually show?”
It should ask:
“What could I genuinely have known at that exact moment?”
If the strategy needs tomorrow’s confirmed data to make yesterday’s decision, the edge never existed.
Suppose your model uses funding, volume, sentiment, or on-chain data to generate signals.
The backtest assumes that information was available at 10:00.
But in reality, the final data point may not have been published, confirmed, or accessible until 10:05.
Those five minutes can completely change the result.
This is look-ahead bias—and it can make a strategy appear to know information before a real trader could have known it.
Execution costs should be modeled just as realistically. For eligible new users, CODE2026 can reduce qualifying Binance Spot trading fees by 20%, keeping one known layer of friction lower.
But no fee optimization can rescue research built on impossible information.
A backtest should never ask, “What did the data eventually show?”
It should ask:
“What could I genuinely have known at that exact moment?”
If the strategy needs tomorrow’s confirmed data to make yesterday’s decision, the edge never existed.
