Look-Ahead Bias
A form of data leakage in machine learning and quantitative finance in which information that would not have been available at the time of prediction is inadvertently used during model training or evaluation. This causes backtesting or validation results to appear artificially favorable, misrepresenting true out-of-sample model performance.
Timeline (last 90 days)
Look-ahead bias refers to using information that could only be obtained after a given point in time when computing a factor value at that point, which almost always makes backtest results better and does not trigger a code error.
103 tables on the platform use a bitemporal design, recording both the time an event occurred (valid time) and the time the information became public (record time), to combat look-ahead bias.
Look-ahead bias is when a model in a quantitative backtest uses information unavailable at time T when making a decision at time T; the solution is bitemporal modeling, allowing only queries for versions whose record time precedes the decision point.
All Facts (3)
Look-ahead bias refers to using information that could only be obtained after a given point in time when computing a factor value at that point, which almost always makes backtest results better and does not trigger a code error.
50%Unverified103 tables on the platform use a bitemporal design, recording both the time an event occurred (valid time) and the time the information became public (record time), to combat look-ahead bias.
50%UnverifiedLook-ahead bias is when a model in a quantitative backtest uses information unavailable at time T when making a decision at time T; the solution is bitemporal modeling, allowing only queries for versions whose record time precedes the decision point.
50%