This indicator identifies potential trend exhaustion and price pivots by monitoring price breaches against a floating trailing stop level based on previous closing behaviors.

This indicator identifies potential trend exhaustion by tracking price breaches of a trailing stop level and signaling mean reversion opportunities when momentum shifts against the preceding structural trend.

This indicator identifies potential trend shifts by tracking price breaches of dynamic stop levels and signaling momentum exhaustion through high/low histogram visual cues.

This indicator identifies potential trend reversals by calculating a dynamic stop level based on price momentum and proximity to previous structural pivots.

This indicator calculates a stochastic value based on relative price positioning and applies an extensive suite of eighteen distinct moving average algorithms to smooth the main line and signal.

This indicator applies a Jurik Moving Average (JMA) smoothing algorithm to the Stochastic Oscillator to reduce noise and identify high-probability momentum shifts.

This indicator visualizes multi-layered sentiment by mapping Stochastic Oscillator main or signal lines into a color-coded histogram based on specific threshold zones.

This indicator normalizes the position of the closing price relative to the spread between the high and low exponential moving averages.

This indicator calculates a percentage-based value representing the distance of the closing price relative to the range between the high and low Exponential Moving Averages.

The logic calculates a normalized percentage representing the distance of the exponential moving average of closing prices relative to the range between the high and low exponential moving averages.

The algorithm calculates a weighted price distribution to derive a primary trend line and applies a smoothed moving average filter to identify specific directional crossovers.

This indicator identifies structural price pivots using a dynamic depth-based fractal logic to project linear trend lines and signal high/low exhaustion points.

Identifies high-intensity price action by detecting candles whose body size exceeds a dynamic historical average while maintaining a significant proportion of the total candle range.

This indicator identifies trend exhaustion by detecting directional shifts in a Linear Weighted Moving Average and visualizes active trade states with dynamic arrow markers.

This indicator identifies potential trend shifts by detecting directional changes in the slope of a Linear Weighted Moving Average.

This indicator identifies trend exhaustion and reversal points by detecting instantaneous shifts in the slope of a Linear Weighted Moving Average.

This indicator identifies trend exhaustion by detecting divergences between the current price action and the Linear Weighted Moving Average while overlaying visual markers for active trade monitoring.

This indicator calculates a linear regression trend line and projects multiple volatility bands based on standard deviation offsets from the regression path.

This indicator aggregates and plots three concurrent layers of the Supertrend algorithm across different timeframes to synchronize short-term volatility with long-term trend direction.

The indicator calculates a weighted sum of logarithmic price returns across EURUSD, GBPUSD, USDJPY, and AUDUSD to synthesize a unified currency strength momentum signal.

This script parses historical trade data to generate a comprehensive performance report including profit factors, drawdown metrics, and sequential win/loss statistics converted into point values.

The code identifies and plots support and resistance zones on a chart based on the location of the nearest fractals, extending those zones to the current time.

The indicator identifies market structural turning points by detecting overlapping local highs and lows across two distinct lookback windows to filter high-probability trend reversals.

Identifies price reversal points by detecting local structural highs and lows within a defined rolling window.

This indicator identifies structural reversal zones by detecting price extremes over two distinct lookback periods and projecting volatility-adjusted signals based on ATR expansion.

This indicator identifies potential buy/sell signals based on highest and lowest price points over two different periods, incorporating ATR for signal placement and generating alerts upon signal creation.

This indicator identifies high-probability reversal zones by detecting concurrent price extremes across two distinct historical lookback periods relative to ATR volatility.

Identifies local structural extrema by detecting price points that exceed the highest highs and lowest lows within a defined look-back window.

This indicator identifies structural pivot points by detecting localized price extremes across two distinct temporal windows.

This indicator identifies local price extremes by detecting multi-window fractal peaks and troughs to signal potential trend reversals and liquidity exhausts.

Identifies local price peaks and troughs by locating the absolute highest high and lowest low within a rolling window offset by Average True Range volatility.

The algorithm identifies local price extremes within a dynamic lookback window to project high and low boundaries accompanied by directional entry markers.

This indicator identifies local price extremes by detecting highest highs and lowest lows within a fixed range to plot directional reversal signals.

Identifies local price extremes by detecting fractal-like peaks and troughs within a specific surrounding bar range.

This indicator identifies local price extremities by detecting the highest highs and lowest lows within a rolling neighborhood to pinpoint potential trend reversal points.

Identifies local price extremes by detecting the highest highs and lowest lows within a rolling lookback window to mark structural turning points.

Identifies structural turning points by highlighting the local maxima and minima within a rolling fractal window to map price boundaries.

Identifies significant price inflection points by detecting the highest highs and lowest lows within a rolling look-back window.

The algorithm identifies localized price extrema by locating the highest highs and lowest lows within a rolling price window to plot structural reversal points.

Identifies local price extremes by locating the highest highs and lowest lows within a rolling window to pinpoint structural reversal points.

Identifies structural pivot points by locating extreme high and low price coordinates within a defined multi-bar window.

Identifies significant structural turning points by detecting local extrema within a rolling high-low price window.

This indicator identifies significant price extremes by detecting local highs and lows across two distinct lookback windows and projects volatility-adjusted targets based on Average True Range.

This indicator identifies price extremes by detecting structural highs and lows across two distinct lookback windows and projects them using ATR-based offsets to signal trend exhaustion.

This indicator identifies structural pivot points by detecting local price extremes and plotting new support or resistance levels only when a significant price gap exceeds a predefined threshold.

This indicator identifies structural pivot points by detecting fractal price extremes and updating support or resistance levels only when a significant price gap threshold is exceeded.