Information Coefficient

IC is the correlation between a predicted signal and realized returns. It is the primary metric for judging whether a signal has predictive power before building a full backtest.

The Problem

Reporting a single IC value (or worse, the best IC from many trials) tells you almost nothing. IC varies over time and across market regimes. A signal with mean IC = 0.04 and std = 0.02 (IC_IR = 2.0) is far more valuable than one with mean IC = 0.08 and std = 0.10 (IC_IR = 0.8). Without time-series statistics and proper standard errors, you cannot distinguish a real signal from noise.

The Pattern

WRONG

from scipy.stats import spearmanr

# Single pooled IC - hides time variation, inflates significance
ic, pval = spearmanr(all_predictions.flatten(), all_returns.flatten())
print(f"IC = {ic:.4f}, p = {pval:.4f}")