This indicator calculates a Bollinger Band variant using the Golden Ratio (2.618) as the standard deviation multiplier to identify extreme price extensions.

This indicator calculates a dynamic trading envelope by determining the maximum squared deviation of the price range from a simple moving average to quantify extreme volatility.

This indicator identifies high-volatility expansion zones by calculating price deviations from moving averages while filtering for market compression using a Bollinger Squeeze logic.

This indicator calculates the divergence between fast and slow exponential moving averages and overlays Bollinger Bands to identify trend exhaustion and momentum shifts.

This indicator overlays a Bollinger Band envelope onto a MACD calculation to identify volatility-adjusted trend shifts and momentum extremes.

This indicator overlays Bollinger Bands onto a MACD calculation to identify trend reversals and price momentum exhaustion through standard deviation variance.

This indicator synthesizes MACD momentum with Bollinger Band volatility bounds to visualize directional shifts and price-action extremes.

This indicator calculates a MACD signal and applies Bollinger Band envelopes to the momentum values to identify trend exhaustion and volatility extremes.

This indicator identifies trend shifts by applying Bollinger Band volatility filters to the differential momentum of Exponential Moving Averages.

This indicator overlays Bollinger Bands onto a MACD calculation to identify momentum shifts and volatility-adjusted trend reversals.

This indicator overlays a Bollinger Band volatility envelope onto the MACD momentum oscillator to identify extreme price velocity and trend exhaustion points.

This indicator calculates a Relative Strength Index and overlays Bollinger Bands onto it to identify extreme momentum exhaustion zones.

This indicator identifies volatility compression by calculating the ratio between Bollinger Band deviation and Keltner Channel width while mapping price momentum via linear regression slopes.

This indicator identifies market volatility compression by comparing Keltner Channel width against Bollinger Band standard deviation while plotting linear regression slopes to detect directional momentum shifts.

This indicator identifies trend exhaustion and breakout potential by calculating the ratio between Bollinger Band standard deviation and ATR-based volatility, filtered through a Jurik Moving Average linear regression.

This indicator identifies periods of low market volatility by detecting instances where the Bollinger Band width contracts below a specific threshold relative to the Keltner Channel.

This indicator utilizes a weighted price summation algorithm combined with an Exponential Moving Average crossover to identify trend momentum and directional shifts.

This indicator quantifies market sentiment by calculating the arithmetic distance between price extremes and a multi-period exponential moving average to visualize buying and selling pressure.

This indicator identifies potential reversal points by detecting CCI momentum shifts against moving averages while validating price action proximity to Bollinger Band boundaries and specific volatility thresholds.

This indicator quantifies bearish pressure by measuring the vertical distance between the current low price and a smoothing exponential moving average.

This indicator quantifies bearish momentum by calculating the real-time divergence between the current price floor and a smoothed exponential moving average.

This indicator quantifies bearish momentum by calculating the negative divergence between current low prices and an exponential moving average, smoothed by a secondary simple moving average.

This indicator quantifies market dominance by calculating the average distance between price extremes and an Exponential Moving Average to visualize bullish or bearish momentum shifts.

This indicator identifies potential trend reversals by monitoring price breaches of a volatility-adjusted threshold calculated from high and low extremes within a sliding window.

This indicator identifies potential trend reversals by detecting price breaches of the high-low range extremes relative to a weighted volatility buffer.

This indicator identifies trend reversals by detecting when price action breaches a specific percentage threshold of the recent high-low range, augmented by a dynamic volatility multiplier.

This script extracts strings from a CSV file into a dynamic array, organizes them lexicographically using a recursive quicksort algorithm, and exports the ordered data to a new destination file.

The logic identifies price extremes within specific hourly cycles to project linear volatility boundaries relative to the opening price.

This indicator identifies potential trend reversals by detecting specific sequences of descending highs and ascending lows combined with relative price level breakthroughs.

The indicator categorizes trading volume by correlating price range expansion and relative intensity to identify climax movements, high-churn activity, and neutral liquidity zones.

This indicator identifies price exhaustion points by correlating relative volume against price range expansion to detect significant supply and demand imbalances.

Identifies significant market activity by calculating the ratio of volume to price range to detect high-intensity churning and climax exhaustion points.

This indicator synchronizes and plots Simple Moving Average values from M5, H1, and H4 timeframes onto a single chart to visualize cross- khung trend alignment.

This indicator synchronizes and plots moving average values from the H1 timeframe onto the current chart to visualize higher-level trend dynamics.

This indicator visualizes the synchronization of Simple Moving Averages across the M5, H1, and H4 timeframes to identify high-level trend alignment.

This indicator calculates and visualizes the dynamic variance between three distinct Simple Moving Average lengths (5, 10, and 15) across various higher timeframes to identify momentum shifts.