Logistic Signal Calibration

What is Logistic Signal Calibration?

Logistic signal calibration is the use of logistic regression, or the logistic (sigmoid) function more generally, to turn indicator readings into a probability-style score between 0 and 1. Logistic regression models the log-odds of a binary outcome, such as price being higher after some number of bars, as a weighted sum of input features; the sigmoid maps that sum into the 0-to-1 range. Instead of a hard buy or sell flag, the output is graded: values near 1 read as confident bullish, near 0 as confident bearish, and near 0.5 as no edge.

Calibration is the stricter claim behind the score. A model is calibrated when its probabilities match observed frequencies: of all bars scored around 0.7, roughly 70 percent should resolve in the predicted direction. That property is fitted on historical data and decays as conditions drift, so the honest reading of a logistic score is ranked confidence, not a literal win probability. The weights themselves depend on feature engineering choices and on which label definition the model was trained against.

How traders use it

• As a signal filter: an underlying setup is taken only when the fitted probability clears a threshold above 0.5, so marginal signals are skipped.
• As a ranking and sizing input: higher scores get priority or larger allocations, while scores hovering near 0.5 are treated as stand-aside conditions.
• As a way to fuse features: several indicator readings are combined through learned weights into one score, a fitted alternative to hand-built sigmoid transforms or fixed-weight scoring.