2.14 Taming Indicator Tails with Sigmoid Transforms
A wrong-scale sigmoid destroys the signal. Center on training median; scale α so IQR lands in the linear region (α≈1.2 for tanh). On SPX Range/Close, α=1.2 lifts AUC 0.503 → 0.519.
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2.13 Why Indicator Histograms Matter
The histogram is the primary diagnostic. Scalars (R/IQR, RE, MI, TCR) confuse shapes needing different fixes. Identical scalars hide light-tail, heavy-tail, or bimodal — only bimodal needs a split.
2.12 CMMA: A Better Momentum Primitive Than Price-minus-MA Alone
CMMA fixes Close − MA(k) with log prices, prev-bar MA, ATR with current bar, √(k+1) divisor. On SPX, test/train std ratio drops 5.25 → 1.03; MI lifts 1.4 → 2.0. Pool-compatible.
2.11 Why ATR Normalization Is More Than a Volatility Trick
ATR captures within-bar and between-bar movement in the instrument's own units. On SPX 20d momentum, 2020/2017 std ratio drops 5.0 raw → 1.05 ATR-normalized, no MI loss. Structural, not heuristic.
2.10 How to Build Stationary Indicators from Non-Stationary Prices
Six transforms turn non-stationary prices into stationary indicators, log returns to forced centering. Pick the lightest passing ADF, coverage, rolling-variance. ATR-norm 20d momentum wins on SPX.
2.9 The Case Against Raw Price Indicators
Raw price is non-stationary in mean, non-stationary in variance, and incomparable across instruments. A model trained on SPX from 1990 to 2010 sees 71% of the 2010 to 2026 test rows outside its training support. The in-sample AUC of 0.582 collapses to 0.498 live.
2.8 Why You Should Test Long and Short Thresholds Separately
Long-side and short-side threshold scans on the same indicator are two hypotheses, not one. Equity drift, return skew, and conditional-distribution asymmetry break the mirror.
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2.7 How to Test Indicator Thresholds Without Fooling Yourself
scanning 41 RSI thresholds and reporting the best one inflates the naive p-value by an order of magnitude. The right test shuffles the target, re-runs the full threshold scan thousands of times, and compares the observed best statistic to the distribution of best statistics from noise.
2.6 Why Predictive Power Often Lives in the Tails
R/IQR detects stretched distributions but says nothing about whether the stretch carries the signal. On market data the stretch usually carries it. The Tail Concentration Ratio splits per-decile mutual information and tells you whether the tails are noise to squash or signal to preserve.
2.5 Range/IQR: A Simple Test for Indicator Tail Problems
R/IQR is the ratio of total range to interquartile range. The denominator is anchored to the body of the distribution. The numerator follows the tails. The ratio is the only honest tail measurement on data where the standard deviation is already contaminated by the tails it is supposed to describe.
2.4 Relative Entropy as an Indicator Quality Score
Relative entropy as a quality score is the cheapest single-number test for whether an indicator uses the range it lives on. The catch: a well-shaped histogram of pure noise scores as well as a well-shaped histogram of signal.