This indicator dynamically recomputes historical price points by calculating cumulative tick volumes and resetting benchmarks based on weekly boundaries and specific tick density thresholds.

This indicator aggregates price action and volume data by resetting candle parameters once a specific threshold of tick activity is reached.

Calculates a Simple Moving Average based on price data retrieved from a dynamic external custom indicator with variable offset logic.

This indicator calculates a weighted fusion of Simple and Linear Weighted Moving Averages to generate directional momentum signals based on slope fluctuations.

This indicator transforms the Williams Percent Range into a normalized scale and overlays Bollinger Bands with a Moving Average to identify volatility extremes and trend shifts in momentum.

The algorithm dynamically adjusts price smoothing by weighting the distance between a moving average and historical volatility using a coefficient-based standard deviation filter.

This indicator utilizes a weighted multi-period price average coupled with a recursive exponential smoothing algorithm to identify extreme momentum reversals through hyperbolic tangent transformations.

This indicator utilizes a weighted moving average and a recursive second-order difference algorithm to generate a hyperbolic tangent-based cycle oscillator for identifying momentum extremes.

This indicator calculates a smoothed price cycle using a recursive mathematical formula to derive exponential momentum values that identify overbought and oversold zones.

This indicator calculates a normalized price cycle using a weighted moving average and an exponential smoothing recursive formula to identify momentum shifts and potential reversal zones.

This indicator utilizes a weighted price smoothing algorithm combined with recursive cycle decomposition and hyperbolic tangent normalization to generate directional momentum signals.

The algorithm processes a weighted price average through a recursive cycle calculation, normalizing the output via dynamic range detection and mapping it onto a hyperbolic tangent function to generate oscillation signals.

This indicator calculates the dynamic volatility of a Hull Moving Average by identifying its rate of change to detect momentum shifts and trend reversals.

This indicator identifies momentum shifts by calculating the directional velocity of a Hull Moving Average to highlight trend reversals via price slope transitions.

This indicator transforms the Williams Percent Range into a centered scale and applies a moving average to filter out noise for smoother momentum tracking.

This indicator normalizes the Williams %R oscillator to a 0-100 scale and applies a moving average filter to identify smoothed momentum trends across disparate currency pairs.

This indicator synchronizes and normalizes the Williams Percent Range of a secondary currency pair to detect overbought and oversold extremes relative to the primary chart.

This indicator transforms normalized Relative Strength Index values into a hyperbolic tangent momentum signal to identify overextended trend reversals.

This indicator synthesizes the relative strength of two distinct currency pairs by applying moving average smoothing and a hyperbolic tangent transformation to normalized RSI values.

This indicator synthesizes RVI data from two distinct currency pairs into normalized hyperbolic tangent signals to visualize relative momentum divergence.

This indicator applies a sigmoid activation function to a smoothed stochastic oscillator to normalize momentum into distinct bullish and bearish zones.

Calculates a relative sentiment score by quantifying the frequency of price closes relative to a series of multiple moving averages.

This indicator calculates a weighted momentum score by comparing a primary Linear Regression Moving Average against a multi-step array of MA periods to determine trend dominance.

The logic evaluates the relative frequency of Williams %R values exceeding a multi-step moving average to quantify momentum distribution strength.

The algorithm calculates a weighted price distance gradient and applies a secondary moving average to detect directional shifts in trend velocity.

This indicator calculates the Root Mean Square Deviation of normalized Williams Percent Range values to quantify volatility and trend stability.

This indicator quantifies the prevalence of WillRogers Price Range values relative to a multi-point moving average array to identify momentum distribution trends.

The logic transforms Williams Percent Range data into a smoothed hyperbolic tangent oscillation to identify extreme bullish and bearish momentum thresholds.

This indicator transforms Williams Percent Range data into a smoothed hyperbolic tangent oscillator to identify overextended momentum extremes and directional shifts.

The indicator calculates a weighted difference between Simple Moving Averages and Linear Weighted Moving Averages to visualize directional trend shifts via dynamic color transitions.

This indicator calculates the price spread between the highest high and lowest low over a specified rolling window to quantify historical volatility.

This script parses active and historical trade data to render visual entry and exit markers on the chart based on order types and timestamps.

This indicator maps active and historical trade executions onto the chart using trend lines and arrows while simultaneously displaying real-time account equity and floating profit/loss statistics.

An interactive overlay tool that dynamically maps active and historical trades onto the chart while calculating real-time profit margins and distance to take-profit targets.

This tool provides real-time graphical tracking of active positions by projecting horizontal price levels for entry, stop-loss, and take-profit targets directly onto the chart with live profit calculations.

A foundational multi-buffer histogram structure designed to visualize two distinct data streams simultaneously within a separate sub-window.

This indicator quantifies market momentum by calculating the smoothed percentage rate of change between consecutive simple moving averages.

This indicator identifies trend exhaustion by calculating smoothed dynamic envelopes from METRO data and generating visual signals when price breaches these boundaries in coordination with fractal structures.

This indicator calculates smoothed moving averages of the highest highs and lowest lows over a specific rolling window to identify price boundaries.

This indicator identifies price extremes by extracting maximum and minimum values from a nested Input Basis Signal, applying smoothing filters to generate volatility boundaries.

The logic identifies price position relative to the high-low range and projects smoothed upper and lower boundaries based on historical maximums and minimums.

This indicator identifies price extremes by extracting high and low values from a custom METRO source and applying moving average smoothing to establish dynamic volatility boundaries.

This indicator identifies multi-dimensional market dynamics by detecting price action signatures including breakout bars, structural pivots, and candlestick patterns.

Quantifies the spread between dual moving averages across GBPUSD, USDJPY, and GBPJPY to highlight relative price momentum and volatility shifts.

This indicator calculates a responsive price boundary by dynamically adjusting an offset based on smoothed ATR values or fixed point increments to track market structural shifts.

Quantifies market instability by normalizing the peak-to-trough spread over a rolling period into a continuous volatility trajectory.