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SuperTrend + Relative Volume (Kernel Optimized)Introducing our new KDE Optimized Supertrend + Relative Volume Indicator! This innovative indicator combines the power of the Supertrend indicator along with Relative Volume. It utilizes the Kernel Density Estimation (KDE) to estimate the probability of a candlestick marking a significant trend break or reversal. ❓How to Interpret the KDE %: The KDE % is a crucial metric that reflects the likelihood that the current candlestick represents a true break in the SuperTrend line, supported by an increase in relative volume. It estimates the probability of a trend shift or continuation based on historical SuperTrend breaks and volume patterns: Low KDE %: A lower probability that the current break is significant. Price action is less likely to reverse, and the trend may continue. Moderate KDE - High KDE %: An increased possibility that a trend reversal or consolidation could occur. Traders should start watching for confirmation signals. 📌How Does It Work? The SuperTrend indicator uses the Average True Range (ATR) to determine the direction of the trend and identifies when the price crosses the SuperTrend line, signaling a potential trend reversal. Here's how the KDE Optimized SuperTrend Indicator works: SuperTrend Calculation: The SuperTrend indicator is calculated, and when the price breaks above (bullish) or below (bearish) the SuperTrend line, it is logged as a significant event. Relative Volume: For each break in the SuperTrend line, we calculate the relative volume (current volume vs. the average volume over a defined period). High relative volume can suggest stronger confirmation of the trend break. KDE Array Calculation: KDE is applied to the break points and relative volume data: Define the KDE options: Bandwidth, Number of Steps, and Array Range (Array Max - Array Min). Create a density range array using the defined number of steps, corresponding to potential break points. Apply a Gaussian kernel function to the break points and volume data to estimate the likelihood of the trend break being significant. KDE Value and Signal Generation: The KDE array is updated as each break occurs. The KDE % is calculated for the breakout candlestick, representing the likelihood of the trend break being significant. If the KDE value exceeds the defined activation threshold, a darker bullish or bearish arrow is plotted after bar confirmation. If the KDE value falls below the threshold, a more transparent arrow is drawn, indicating a possible but lower probability break. ⚙️Settings: SuperTrend Settings: ATR Length: The period over which the Average True Range (ATR) is calculated. Multiplier: The multiplier applied to the ATR to determine the SuperTrend threshold. KDE Settings: Bandwidth: Determines the smoothness of the KDE function and the width of the influence of each break point. Number of Bins (Steps): Defines the precision of the KDE algorithm, with higher values offering more detailed calculations. KDE Threshold %: The level at which relative volume is considered significant for confirming a break. Relative Volume Length: The number of historic candles used in calculating KDE %
Pine Script® indicator
by fluxchart
11
RSI (Kernel Optimized) | Flux Charts💎 GENERAL OVERVIEW Introducing our new KDE Optimized RSI Indicator! This indicator adds a new aspect to the well-known RSI indicator, with the help of the KDE (Kernel Density Estimation) algorithm, estimates the probability of a candlestick will be a pivot or not. For more information about the process, please check the "HOW DOES IT WORK ?" section. Features of the new KDE Optimized RSI Indicator : A New Approach To Pivot Detection Customizable KDE Algorithm Realtime RSI & KDE Dashboard Alerts For Possible Pivots Customizable Visuals ❓ HOW TO INTERPRET THE KDE % The KDE % is a critical metric that reflects how closely the current RSI aligns with the KDE (Kernel Density Estimation) array. In simple terms, it represents the likelihood that the current candlestick is forming a pivot point based on historical data patterns. a low percentage suggests a lower probability of the current candlestick being a pivot point. In these cases, price action is less likely to reverse, and existing trends may continue. At moderate levels, the possibility of a pivot increases, indicating potential trend shifts or consolidations.Traders should start monitoring closely for confirmation signals. An even higher KDE % suggests a strong likelihood that the current candlestick could form a pivot point, which could lead to a reversal or significant price movement. These points often align with overbought or oversold conditions in traditional RSI analysis, making them key moments for potential trade entry or exit. 📌 HOW DOES IT WORK ? The RSI (Relative Strength Index) is a widely used oscillator among traders. It outputs a value between 0 - 100 and gives a glimpse about the current momentum of the price action. This indicator then calculates the RSI for each candlesticks, and saves them into an array if the candlestick is a pivot. The low & high pivot RSIs' are inserted into two different arrays. Then the a KDE array is calculated for both of the low & high pivot RSI arrays. Explaining the KDE might be too much for this write-up, but for a brief explanation, here are the steps : 1. Define the necessary options for the KDE function. These are : Bandwidth & Nº Steps, Array Range (Array Max - Array Min) 2. After that, create a density range array. The array has (steps * 2 - 1) elements and they are calculated by (arrMin + i * stepCount), i being the index. 3. Then, define a kernel function. This indicator has 3 different kernel distribution modes : Uniform, Gaussian and Sigmoid 4. Then, define a temporary value for the current element of KDE array. 5. For each element E in the pivot RSI array, add "kernel(densityRange.get(i) - E, 1.0 / bandwidth)" to the temporary value. 6. Add 1.0 / arrSize * to the KDE array. Then the prefix sum array of the KDE array is calculated. For each candlestick, the index closest to it's RSI value in the KDE array is found using binary search. Then for the low pivot KDE calculation, the sum of KDE values from found index to max index is calculated. For the high pivot KDE, the sum of 0 to found index is used. Then if high or low KDE value is greater than the activation threshold determined in the settings, a bearish or bullish arrow is plotted after bar confirmation respectively. The arrows are drawn as long as the KDE value of current candlestick is greater than the threshold. When the KDE value is out of the threshold, a less transparent arrow is drawn, indicating a possible pivot point. 🚩 UNIQUENESS This indicator combines RSI & KDE Algorithm to get a foresight of possible pivot points. Pivot points are important entry, confirmation and exit points for traders. But to their nature, they can be only detected after more candlesticks are rendered after them. The purpose of this indicator is to alert the traders of possible pivot points using KDE algorithm right away when they are confirmed. The indicator also has a dashboard for realtime view of the current RSI & Bullish or Bearish KDE value. You can fully customize the KDE algorithm and set up alerts for pivot detection. ⚙️ SETTINGS 1. RSI Settings RSI Length -> The amount of bars taken into account for RSI calculation. Source -> The source value for RSI calculation. 2. Pivots Pivot Lengths -> Pivot lengths for both high & low pivots. For example, if this value is set to 21; 21 bars before AND 21 bars after a candlestick must be higher for a candlestick to be a low pivot. 3. KDE Activation Threshold -> This setting determines the amount of arrows shown. Higher options will result in more arrows being rendered. Kernel -> The kernel function as explained in the upper section. Bandwidth -> The bandwidth variable as explained in the upper section. The smoothness of the KDE function is tied to this setting. Nº Bins -> The Nº Steps variable as explained in the upper section. It determines the precision of the KDE algorithm.
Pine Script® indicator
by fluxchart
24
MathStatisticsKernelDensityEstimationLibrary "MathStatisticsKernelDensityEstimation" (KDE) Method for Kernel Density Estimation kde(observations, kernel, bandwidth, nsteps) Parameters: observations : float array, sample data. kernel : string, the kernel to use, default='gaussian', options='uniform', 'triangle', 'epanechnikov', 'quartic', 'triweight', 'gaussian', 'cosine', 'logistic', 'sigmoid'. bandwidth : float, bandwidth to use in kernel, default=0.5, range=(0, +inf), less will smooth the data. nsteps : int, number of steps in range of distribution, default=20, this value is connected to how many line objects you can display per script. Returns: tuple with signature: (float array, float array) draw_horizontal(distribution_x, distribution_y, distribution_lines, graph_lines, graph_labels) Draw a horizontal distribution at current location on chart. Parameters: distribution_x : float array, distribution points x value. distribution_y : float array, distribution points y value. distribution_lines : line array, array to append the distribution curve lines. graph_lines : line array, array to append the graph lines. graph_labels : label array, array to append the graph labels. Returns: void, updates arrays: distribution_lines, graph_lines, graph_labels. draw_vertical(distribution_x, distribution_y, distribution_lines, graph_lines, graph_labels) Draw a vertical distribution at current location on chart. Parameters: distribution_x : float array, distribution points x value. distribution_y : float array, distribution points y value. distribution_lines : line array, array to append the distribution curve lines. graph_lines : line array, array to append the graph lines. graph_labels : label array, array to append the graph labels. Returns: void, updates arrays: distribution_lines, graph_lines, graph_labels. style_distribution(lines, horizontal, to_histogram, line_color, line_style, linewidth) Style the distribution lines. Parameters: lines : line array, distribution lines to style. horizontal : bool, default=true, if the display is horizontal(true) or vertical(false). to_histogram : bool, default=false, if graph style should be switched to histogram. line_color : color, default=na, if defined will change the color of the lines. line_style : string, defaul=na, if defined will change the line style, options=('na', line.style_solid, line.style_dotted, line.style_dashed, line.style_arrow_right, line.style_arrow_left, line.style_arrow_both) linewidth : int, default=na, if defined will change the line width. Returns: void. style_graph(lines, lines, horizontal, line_color, line_style, linewidth) Style the graph lines and labels Parameters: lines : line array, graph lines to style. lines : labels array, graph labels to style. horizontal : bool, default=true, if the display is horizontal(true) or vertical(false). line_color : color, default=na, if defined will change the color of the lines. line_style : string, defaul=na, if defined will change the line style, options=('na', line.style_solid, line.style_dotted, line.style_dashed, line.style_arrow_right, line.style_arrow_left, line.style_arrow_both) linewidth : int, default=na, if defined will change the line width. Returns: void.
Pine Script® library
by RicardoSantos
10