Concept family
Statistics
Distributions, relationships, DSP and filtering, and simulation on price series.
Statistical indicators treat price as data first and chart second. Instead of reading patterns by eye, they apply standard quantitative methods: normalizing values so different markets and timeframes compare fairly, measuring how instruments move together, fitting trends with regression, and separating signal from noise with filters borrowed from engineering.
The family runs from widely used basics such as z-scores and correlation to more specialized territory: trendiness and randomness measures like the Hurst exponent, John Ehlers' digital signal processing work on market cycles, and Monte Carlo simulation that turns assumptions into distributions of outcomes. Results shift with the lookback chosen, and markets are not stationary, so these tools are best read as estimates rather than guarantees.
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Linear Regression
The family's most implemented method: fitting a straight line through recent prices to define slope, deviation channels, and a moving baseline.
8 indicators
Correlation
Measures how closely two markets move together, the starting point for diversification checks, relative-value work, and pairs ideas.
10 indicators
Dominant Cycle Measurement
The heart of the Ehlers approach: estimating the cycle length currently dominant in price so other tools can tune themselves to it.
3 indicators
Normalization & distribution
Transforms that put any series on a comparable scale, from z-scores and percentile ranks to min-max and sigmoid mappings, plus tools that profile how returns are actually distributed and flag outliers.
Relationships
How instruments relate, to each other and to their own past: correlation, beta and alpha, autocorrelation, and the cointegration and stationarity tests behind pairs trading.
Complexity & memory
Measures of how trending, mean-reverting, or random a market currently is, from the Hurst exponent and fractal dimension to entropy, plus change-point detection that flags when that behavior shifts.
Regression & filtering
Curve fitting and smoothing methods that pull trend, cycle, and forecast structure out of noisy prices, from linear and polynomial regression to Kalman, Hodrick-Prescott, and spectral techniques.
Ehlers DSP cluster
John Ehlers' digital signal processing toolkit, which treats price as a noisy waveform, measures its dominant cycle, and adapts indicator parameters to what it finds.
Simulation
Methods that turn assumptions into ranges of outcomes: Monte Carlo price paths, resampling tests on historical data, and probability cones that frame plausible futures instead of single forecasts.