Lookahead Bias

The most common ML4T failure. A model uses future information during training - normalization with full-sample statistics, labels from future thresholds, standard k-fold CV on time series - producing a great backtest that fails immediately in production.

The Problem

Lookahead bias means using information that would not have been available at prediction time. It inflates backtest Sharpe and is the #1 reason strategies fail live. Common sources: full-sample normalization, future-based thresholds, train+test preprocessing, and shuffled CV on time series.

The Pattern

WRONG

from sklearn.preprocessing import StandardScaler