Estimate dominant cycle length using Hilbert Transform.
Calculate phase angle of dominant cycle from Hilbert Transform.
Generate in-phase and quadrature components via Hilbert Transform.
Create sine and lead sine waves from Hilbert Transform analysis.
Identify whether data is in trending or cycling state via Hilbert Transform.
Compute difference between current value and value n periods ago in column.
Calculate percent change between current and prior value over specified periods.
Compute rolling product of column values over the window.
Shift values in column by specified number of periods.
Return rolling sum of values in column over specified window.
Compute one-day percent change of a triple-smoothed EMA.
Subtract slow MA from fast MA to show momentum histogram.
Determine time since highest high and lowest low over lookback period.
Calculate difference between Aroon up and Aroon down values.
Measure trend strength by comparing directional movement indicators.
Smooth ADX by averaging current and prior ADX values.
Compute momentum by comparing sum of gains to sum of losses.
Evaluate deviation from average price to identify overbought/oversold.
Measure directional movement strength without smoothing.
Calculate rate of change between current price and price n periods ago.
Measure buying and selling pressure by comparing positive and negative money flow.
Compute difference of fast and slow EMAs plus signal line and histogram.
Extended MACD allowing custom MA types for fast, slow, and signal lines.
Fixed MACD with preset fast=12 and slow=26 EMAs plus signal line.
Compute momentum oscillator that compares average gains to average losses.
Compute %K and %D lines based on recent high/low range.
Fast stochastic oscillator using only %K and %D without slowing.
Calculate stochastic of RSI values to highlight overbought/oversold RSI.
Combine short-, medium-, and long-term buying pressure into one oscillator.
Momentum indicator comparing current close to highest high over period.
Compute upper, middle, and lower bands based on moving average and standard deviation.
Compute EMA with reduced lag by applying EMA twice.
Calculate weighted moving average that gives more weight to recent data.
Generate smoothed trendline using Hilbert Transform.
Adaptive moving average that adjusts smoothing based on volatility.
Adaptive moving average with dual lines (MAMA and FAMA) based on market cycles.
Return midpoint of the highest high and lowest low over the period.
Calculate average of high and low prices over the lookback period.
Determine stop-and-reverse points based on price trends.
Extended SAR with separate acceleration factors for long and short trends.
Calculate the arithmetic mean of values over a fixed window.
Apply two-stage moving average to create a triangular weighting effect.
Combine three EMAs to reduce lag without sacrificing smoothness.
Compute highly smoothed EMA using multiple passes and a volume factor.
Calculate average where recent values carry greater weight linearly.
Return the average of high, low, and close prices.
Calculate the midpoint between high and low prices.
Compute the average of high, low, and close prices.
Calculate weighted close price as (high + low + close + close) / 4.
Calculate market beta of asset returns over the specified period.
Calculate rolling kurtosis of column values over specified window.
Fit a linear regression over the past values to forecast the next point.
Compute angle of the regression line from fitted linear regression.
Return intercept value from linear regression over the lookback period.
Calculate slope of the best-fit line from linear regression.
Compute rolling maximum of column values over the window.
Return rolling average of values in column over specified window.
Return rolling median of values in column over specified window.
Calculate rolling minimum of column values over the window.
Return rolling autocorrelation of column values over specified window.
Return rolling quantile value for column over the window at specified q.
Compute rolling skewness of column over the window.
Compute rolling standard deviation of column over the window.
Calculate rolling standard error of the mean for column over the window.
Predict next value using linear regression over the past period.
Compute rolling variance of column values over the window.
Measure volatility by averaging true range over the given period.
Express ATR as a percentage of closing price.
Calculate the greatest of high-low, high-previous close, and low-previous close.
Accumulate volume flow based on where price closes within its range.
Compute difference between fast and slow EMAs of the A/D line.
Track cumulative volume by adding or subtracting volume based on price direction.