Scatterplot — Indicators and Strategies

Statistical Trend Analysis (Scatterplot) [BigBeluga]Statistical Trend Analysis (Scatterplot) provides a unique perspective on market dynamics by combining the statistical concept of z-scores with scatterplot visualization to assess price momentum and potential trend shifts. 🧿 What is Z-Score? Definition: A z-score is a statistical measure that quantifies how far a data point is from the mean, expressed in terms of standard deviations. In this Indicator: A high positive z-score indicates the price is significantly above the average. A low negative z-score indicates the price is significantly below the average. The indicator also calculates the rate of change of the z-score, helping identify momentum shifts in the market. 🧿 Key Features: Scatterplot Visualization: Displays data points of z-score and its change across four quadrants. Quadrants help interpret market conditions: Upper Right (Strong Bullish Momentum): Most data points here signal an ongoing uptrend. Upper Left (Weakening Momentum): Data points here may indicate a potential market shift or ranging market. Lower Left (Strong Bearish Momentum): Indicates a dominant downtrend. Lower Right (Trend Shift to Bullish/Ranging): Suggests weakening bearish momentum or an emerging uptrend. Color-Coded Candles: Candles are dynamically colored based on the z-score, providing a visual cue about the price's deviation from the mean. Z-Score Time Series: A line plot of z-scores over time shows price deviation trends. A gray histogram displays the rate of change of the z-score, highlighting momentum shifts. 🧿 Usage: Use the scatterplot and quadrant gauges to understand the current market momentum and potential shifts. Monitor the z-score line plot to identify overbought/oversold conditions. Utilize the gray histogram to detect momentum reversals and trend strength. This tool is ideal for traders who rely on statistical insights to confirm trends, detect potential reversals, and assess market momentum visually and quantitatively.

Ehlers Loops [BigBeluga]The Ehlers Loops indicator is based on the concepts developed by John F. Ehlers, which provide a visual representation of the relationship between price and volume dynamics. This tool helps traders predict future market movements by observing how price and volume data interact within four distinct quadrants of the loop, each representing different combinations of price and volume directions. The unique structure of this indicator provides insights into the strength and direction of market trends, offering a clearer perspective on price behavior relative to volume. 🔵 KEY FEATURES & USAGE ● Four Price-Volume Quadrants: The Ehlers Loops chart consists of four quadrants: +Price & +Volume (top-right) – Typically indicates a bullish continuation in the market. -Price & +Volume (bottom-right) – Generally shows a bearish continuation. +Price & -Volume (top-left) – Typically indicates an exhaustion of demand with a potential reversal. -Price & -Volume (bottom-left) – Indicates exhaustion of supply and near trend reversal. By watching how symbols move through these quadrants over time, traders can assess shifts in momentum and volume flow. ● Price and Volume Scaling in Standard Deviations: Both price and volume data are individually filtered using HighPass and SuperSmoother filters, which transform them into band-limited signals with zero mean. This scaling allows traders to view data in terms of its deviation from the average, making it easier to spot abnormal movements or trends in both price and volume. ● Loops Trajectories with Tails: The loops draw a trail of price and volume dynamics over time, allowing traders to observe historical price-volume interactions and predict future movements based on the curvature and direction of the rotation. ● Price & Volume Histograms: On the right side of the chart, histograms for each symbol provide a summary of the most recent price and volume values. These histograms allow traders to easily compare the strength and direction of multiple assets and evaluate market conditions at a glance. ● Flexible Symbol Display & Customization: Traders can select up to five different symbols to be displayed within the Ehlers Loops. The settings also allow customization of symbol size, colors, and visibility of the histograms. Additionally, traders can adjust the LPPeriod and HPPeriod to change the smoothness and lag of the loops, with a shorter LPPeriod offering more responsiveness and a longer HPPeriod emphasizing longer-term trends. 🔵 USAGE 🔵 SETTINGS Low pass Period: default is 10 to obtain minimum lag with just a little smoothing. High pass Period: default is 125 (half of the year if Daily timeframe) to capture the longer term moves. 🔵 CONCLUSION The Ehlers Loops indicator offers a visually rich and highly customizable way to observe price and volume dynamics across multiple assets. By using band-limited signals and scaling data into standard deviations, traders gain a powerful tool for identifying market trends and predicting future movements. Whether you're tracking short-term fluctuations or long-term trends, Ehlers Loops can help you stay ahead of the market by offering key insights into the relationship between price and volume.

Relative Strength Scatter Plot [LuxAlgo]The Relative Strength Scatter Plot indicator is a tool that shows the historical performance of various user-selected securities against a selected benchmark. This tool is inspired by Relative Rotation Graphs®. Relative Rotation Graphs® is a registered trademark of JOOS Holdings B.V. This script is neither endorsed, nor sponsored, nor affiliated with them. 🔶 USAGE This tool depicts a simple scatter plot using the relative strength ratio as the X-axis and its momentum as the Y-axis of the user-selected symbols against the selected benchmark. The graph is divided into four quadrants, and the interpretation of the graph is done depending on where a point is situated on the graph: A point in the green quadrant would indicate that the security is leading the benchmark in strength, with positive strength momentum. A point in the yellow quadrant would indicate that the security is leading the benchmark in strength, with negative strength momentum. A point in the blue quadrant would indicate that the security is lagging behind the benchmark in strength, with positive strength momentum. A point in the red quadrant would indicate that the security is lagging behind the benchmark in strength, with negative strength momentum. The trail of each symbol allows the user to see the evolution of the relative strength momentum relative to the relative strength ratio. The length of the trail can be controlled by the "Trail Length" setting. 🔶 DETAILS Our relative strength ratio estimate is first obtained from the relative strength between the symbol of interest and the benchmark, the result is then smoothed using a linearly weighted moving average (wma). This result is then normalized with a wma of the smoothed relative strength, this ratio is again smoothed with the wma and multiplied by 100. The relative strength momentum estimate is obtained from the ratio between the previously estimated RS-Ratio and its wma, this ratio is then multiplied by 100. 🔶 SETTINGS Calculation Window: Calculation window of the RS-Ratio and RS-Momentum metrics. Symbols: Symbols used for the computation of the graph, each settings line allows us to determine whether the symbol is to be displayed on the graph as well as its color. Benchmark: Benchmark symbol used for the computation of the graph. Indices are commonly used as a benchmark. 🔹 Graph Settings Trail Length: Number of past data points to display on the graph for each symbol. Resolution: Controls the horizontal length of the graph.

Volume and Price Z-Score [Multi-Asset] - By Leviathan This script offers in-depth Z-Score analytics on price and volume for 200 symbols. Utilizing visualizations such as scatter plots, histograms, and heatmaps, it enables traders to uncover potential trade opportunities, discern market dynamics, pinpoint outliers, delve into the relationship between price and volume, and much more. A Z-Score is a statistical measurement indicating the number of standard deviations a data point deviates from the dataset's mean. Essentially, it provides insight into a value's relative position within a group of values (mean). - A Z-Score of zero means the data point is exactly at the mean. - A positive Z-Score indicates the data point is above the mean. - A negative Z-Score indicates the data point is below the mean. For instance, a Z-Score of 1 indicates that the data point is 1 standard deviation above the mean, while a Z-Score of -1 indicates that the data point is 1 standard deviation below the mean. In simple terms, the more extreme the Z-Score of a data point, the more “unusual” it is within a larger context. If data is normally distributed, the following properties can be observed: - About 68% of the data will lie within ±1 standard deviation (z-score between -1 and 1). - About 95% will lie within ±2 standard deviations (z-score between -2 and 2). - About 99.7% will lie within ±3 standard deviations (z-score between -3 and 3). Datasets like price and volume (in this context) are most often not normally distributed. While the interpretation in terms of percentage of data lying within certain ranges of z-scores (like the ones mentioned above) won't hold, the z-score can still be a useful measure of how "unusual" a data point is relative to the mean. The aim of this indicator is to offer a unique way of screening the market for trading opportunities by conveniently visualizing where current volume and price activity stands in relation to the average. It also offers features to observe the convergent/divergent relationships between asset’s price movement and volume, observe a single symbol’s activity compared to the wider market activity and much more. Here is an overview of a few important settings. Z-SCORE TYPE ◽️ Z-Score Type: Current Z-Score Calculates the z-score by comparing current bar’s price and volume data to the mean (moving average with any custom length, default is 20 bars). This indicates how much the current bar’s price and volume data deviates from the average over the specified period. A positive z-score suggests that the current bar's price or volume is above the mean of the last 20 bars (or the custom length set by the user), while a negative z-score means it's below that mean. Example: Consider an asset whose current price and volume both show deviations from their 20-bar averages. If the price's Z-Score is +1.5 and the volume's Z-Score is +2.0, it means the asset's price is 1.5 standard deviations above its average, and its trading volume is 2 standard deviations above its average. This might suggest a significant upward move with strong trading activity. ◽️ Z-Score Type: Average Z-Score Calculates the custom-length average of symbol's z-score. Think of it as a smoothed version of the Current Z-Score. Instead of just looking at the z-score calculated on the latest bar, it considers the average behavior over the last few bars. By doing this, it helps reduce sudden jumps and gives a clearer, steadier view of the market. Example: Instead of a single bar, imagine the average price and volume of an asset over the last 5 bars. If the price's 5-bar average Z-Score is +1.0 and the volume's is +1.5, it tells us that, over these recent bars, both the price and volume have been consistently above their longer-term averages, indicating sustained increase. ◽️ Z-Score Type: Relative Z-Score Calculates a relative z-score by comparing symbol’s current bar z-score to the mean (average z-score of all symbols in the group). This is essentially a z-score of a z-score, and it helps in understanding how a particular symbol's activity stands out not just in its own historical context, but also in relation to the broader set of symbols being analyzed. In other words, while the primary z-score tells you how unusual a bar's activity is for that specific symbol, the relative z-score informs you how that "unusualness" ranks when compared to the entire group's deviations. This can be particularly useful in identifying symbols that are outliers even among outliers, indicating exceptionally unique behaviors or opportunities. Example: If one asset's price Z-Score is +2.5 and volume Z-Score is +3.0, but the group's average Z-Scores are +0.5 for price and +1.0 for volume, this asset’s Relative Z-Score would be high and therefore stand out. This means that asset's price and volume activities are notably high, not just by its own standards, but also when compared to other symbols in the group. DISPLAY TYPE ◽️ Display Type: Scatter Plot The Scatter Plot is a visual tool designed to represent values for two variables, in this case the Z-Scores of price and volume for multiple symbols. Each symbol has it's own dot with x and y coordinates: X-Axis: Represents the Z-Score of price. A symbol further to the right indicates a higher positive deviation in its price from its average, while a symbol to the left indicates a negative deviation. Y-Axis: Represents the Z-Score of volume. A symbol positioned higher up on the plot suggests a higher positive deviation in its trading volume from its average, while one lower down indicates a negative deviation. Here are some guideline insights of plot positioning: - Top-Right Quadrant (High Volume-High Price): Symbols in this quadrant indicate a scenario where both the trading volume and price are higher than their respective mean. - Top-Left Quadrant (High Volume-Low Price): Symbols here reflect high trading volumes but prices lower than the mean. - Bottom-Left Quadrant (Low Volume-Low Price): Assets in this quadrant have both low trading volume and price compared to their mean. - Bottom-Right Quadrant (Low Volume-High Price): Symbols positioned here have prices that are higher than their mean, but the trading volume is low compared to the mean. The plot also integrates a set of concentric squares which serve as visual guides: - 1st Square (1SD): Encapsulates symbols that have Z-Scores within ±1 standard deviation for both price and volume. Symbols within this square are typically considered to be displaying normal behavior or within expected range. - 2nd Square (2SD): Encapsulates those with Z-Scores within ±2 standard deviations. Symbols within this boundary, but outside the 1 SD square, indicate a moderate deviation from the norm. - 3rd Square (3SD): Represents symbols with Z-Scores within ±3 standard deviations. Any symbol outside this square is deemed to be a significant outlier, exhibiting extreme behavior in terms of either its price, its volume, or both. By assessing the position of symbols relative to these squares, traders can swiftly identify which assets are behaving typically and which are showing unusual activity. This visualization simplifies the process of spotting potential outliers or unique trading opportunities within the market. The farther a symbol is from the center, the more it deviates from its typical behavior. ◽️ Display Type: Columns In this visualization, z-scores are represented using columns, where each symbol is presented horizontally. Each symbol has two distinct nodes: - Left Node: Represents the z-score of volume. - Right Node: Represents the z-score of price. The height of these nodes can vary along the y-axis between -4 and 4, based on the z-score value: - Large Positive Columns: Signify a high or positive z-score, indicating that the price or volume is significantly above its average. - Large Negative Columns: Represent a low or negative z-score, suggesting that the price or volume is considerably below its average. - Short Columns Near 0: Indicate that the price or volume is close to its mean, showcasing minimal deviation. This columnar representation provides a clear, intuitive view of how each symbol's price and volume deviate from their respective averages. ◽️ Display Type: Circles In this visualization style, z-scores are depicted using circles. Each symbol is horizontally aligned and represented by: - Solid Circle: Represents the z-score of price. - Transparent Circle: Represents the z-score of volume. The vertical position of these circles on the y-axis ranges between -4 and 4, reflecting the z-score value: - Circles Near the Top: Indicate a high or positive z-score, suggesting the price or volume is well above its average. - Circles Near the Bottom: Represent a low or negative z-score, pointing to the price or volume being notably below its average. - Circles Around the Midline (0): Highlight that the price or volume is close to its mean, with minimal deviation. ◽️ Display Type: Delta Columns There's also an option to utilize Z-Score Delta Columns. For each symbol, a single column is presented, depicting the difference between the z-score of price and the z-score of volume. The z-score delta essentially captures the disparity between how much the price and volume deviate from their respective mean: - Positive Delta: Indicates that the z-score of price is greater than the z-score of volume. This suggests that the price has deviated more from its average than the volume has from its own average. Such a scenario could point to price movements being more significant or pronounced compared to the changes in volume. - Negative Delta: Represents that the z-score of volume is higher than the z-score of price. This might mean that there are substantial volume changes, yet the price hasn't moved as dramatically. This can be indicative of potential build-up in trading interest without an equivalent impact on price. - Delta Close to 0: Means that the z-scores for price and volume are almost equal, indicating their deviations from the average are in sync. ◽️ Display Type: Z-Volume/Z-Price Heatmap This visualization offers a heatmap either for volume z-scores or price z-scores across all symbols. Here's how it's presented: Each symbol is allocated its own horizontal row. Within this row, bar-by-bar data is displayed using a color gradient to represent the z-score values. The heatmap employs a user-defined gradient scale, where a chosen "cold" color represents low z-scores and a chosen "hot" color signifies high z-scores. As the z-score increases or decreases, the colors transition smoothly along this gradient, providing an intuitive visual indication of the z-score's magnitude. - Cold Colors: Indicate values significantly below the mean (negative z-score) - Mild Colors: Represent values close to the mean, suggesting minimal deviation. - Hot Colors: Indicate values significantly above the mean (positive z-score) This heatmap format provides a rapid, visually impactful means to discern how each symbol's price or volume is behaving relative to its average. The color-coded rows allow you to quickly spot outliers. VOLUME TYPE The "Volume Type" input allows you to choose the nature of volume data that will be factored into the volume z-score calculation. The interpretation of indicator’s data changes based on this input. You can opt between: - Volume (Regular Volume): This is the classic measure of trading volume, which represents the volume traded in a given time period - bar. - OBV (On-Balance Volume): OBV is a momentum indicator that accumulates volume on up bars and subtracts it on down bars, making it a cumulative indicator that sort of measures buying and selling pressure. Interpretation Implications: - For Volume Type: Regular Volume: Positive Z-Score: Indicates that the trading volume is above its average, meaning there's unusually high trading activity . Negative Z-Score: Suggests that the trading volume is below its average, signifying unusually low trading activity. - For Volume Type: OBV: Positive Z-Score: Signifies that “buying pressure” is above its average. Negative Z-Score: Signifies that “selling pressure” is above its average. When comparing Z-Score of OBV to Z-Score of price, we can observe several scenarios. If Z-Price and Z-Volume are convergent (have similar z-scores), we can say that the directional price movement is supported by volume. If Z-Price and Z-Volume are divergent (have very different z-scores or one of them being zero), it suggests a potential misalignment between price movement and volume support, which might hint at possible reversals or weakness.

DataChart Library "DataChart" Library to plot scatterplot or heatmaps for your own set of data samples draw(this) draw contents of the chart object Parameters: this : Chart object Returns: current chart object init(this) Initialize Chart object. Parameters: this : Chart object to be initialized Returns: current chart object addSample(this, sample, trigger) Add sample data to chart using Sample object Parameters: this : Chart object sample : Sample object containing sample x and y values to be plotted trigger : Samples are added to chart only if trigger is set to true. Default value is true Returns: current chart object addSample(this, x, y, trigger) Add sample data to chart using x and y values Parameters: this : Chart object x : x value of sample data y : y value of sample data trigger : Samples are added to chart only if trigger is set to true. Default value is true Returns: current chart object addPriceSample(this, priceSampleData, config) Add price sample data - special type of sample designed to measure price displacements of events Parameters: this : Chart object priceSampleData : PriceSampleData object containing event driven displacement data of x and y config : PriceSampleConfig object containing configurations for deriving x and y from priceSampleData Returns: current chart object Sample Sample data for chart Fields: xValue : x value of the sample data yValue : y value of the sample data ChartProperties Properties of plotting chart Fields: title : Title of the chart suffix : Suffix for values. It can be used to reference 10X or 4% etc. Used only if format is not format.percent matrixSize : size of the matrix used for plotting chartType : Can be either scatterplot or heatmap. Default is scatterplot outliersStart : Indicates the percentile of data to filter out from the starting point to get rid of outliers outliersEnd : Indicates the percentile of data to filter out from the ending point to get rid of outliers. backgroundColor plotColor : color of plots on the chart. Default is color.yellow. Only used for scatterplot type heatmapColor : color of heatmaps on the chart. Default is color.red. Only used for heatmap type borderColor : border color of the chart table. Default is color.yellow. plotSize : size of scatter plots. Default is size.large format : data representation format in tooltips. Use mintick.percent if measuring any data in terms of percent. Else, use format.mintick showCounters : display counters which shows totals on each quadrants. These are single cell tables at the corners displaying number of occurences on each quadrant. showTitle : display title at the top center. Uses the title string set in the properties counterBackground : background color of counter table cells. Default is color.teal counterTextColor : text color of counter table cells. Default is color.white counterTextSize : size of counter table cells. Default is size.large titleBackground : background color of chart title. Default is color.maroon titleTextColor : text color of the chart title. Default is color.white titleTextSize : text size of the title cell. Default is size.large addOutliersToBorder : If set, instead of removing the outliers, it will be added to the border cells. useCommonScale : Use common scale for both x and y. If not selected, different scales are calculated based on range of x and y values from samples. Default is set to false. plotchar : scatter plot character. Default is set to ascii bullet. ChartDrawing Chart drawing objects collection Fields: properties : ChartProperties object which determines the type and characteristics of chart being plotted titleTable : table containing title of the chart. mainTable : table containing plots or heatmaps. quadrantTables : Array of tables containing counters of all 4 quandrants Chart Chart type which contains all the information of chart being plotted Fields: properties : ChartProperties object which determines the type and characteristics of chart being plotted samples : Array of Sample objects collected over period of time for plotting on chart. displacements : Array containing displacement values. Both x and y values displacementX : Array containing only X displacement values. displacementY : Array containing only Y displacement values. drawing : ChartDrawing object which contains all the drawing elements PriceSampleConfig Configs used for adding specific type of samples called PriceSamples Fields: duration : impact duration for which price displacement samples are calculated. useAtrReference : Default is true. If set to true, price is measured in terms of Atr. Else is measured in terms of percentage of price. atrLength : atrLength to be used for measuring the price based on ATR. Used only if useAtrReference is set to true. PriceSampleData Special type of sample called price sample. Can be used instead of basic Sample type Fields: trigger : consider sample only if trigger is set to true. Default is true. source : Price source. Default is close highSource : High price source. Default is high lowSource : Low price source. Default is low tr : True range value. Default is ta.tr

RSI Impact Heat Map [Trendoscope]Here is a simple tool to measure and display outcome of certain RSI event over heat map. 🎲 Process 🎯Event Event can be either Crossover or Crossunder of RSI on certain value. 🎯Measuring Impact Impact of the event after N number of bars is measured in terms of highest and lowest displacement from the last close price. Impact can be collected as either number of times of ATR or percentage of price. Impact for each trigger is recorded separately and stored in array of custom type. 🎯Plotting Heat Map Heat map is displayed using pine tables. Users can select heat map size - which can vary from 10 to 90. Selecting optimal size is important in order to get right interpretation of data. Having higher number of cells can give more granular data. But, chart may not fit into the window. Having lower size means, stats are combined together to get less granular data which may not give right picture of the results. Default value for size is 50 - meaning data is displayed in 51X51 cells. Range of the heat map is adjusted automatically based on min and max value of the displacement. In order to filter out or merge extreme values, range is calculated based on certain percentile of the values. This will avoid displaying lots of empty cells which can obscure the actual impact. 🎲 Settings Settings allow users to define their event, impact duration and reference, and few display related properties. The description of these parameters are as below: 🎲 Use Cases In this script, we have taken RSI as an example to measure impact. But, we can do this for any event. This can be price crossing over/under upper/lower bollinger bands, moving average crossovers or even complex entry or exit conditions. Overall, we can use this to plot and evaluate our trade criteria. 🎲 Interpretation Q1 - If more coloured dots appear on the top right corner of the table, then the event is considered to trigger high volatility and high risk environment. Q2 - If more coloured dots appear on the top left corner, then the events are considered to trigger bearish environment. Q3 - If more coloured dots appear on the bottom left corner of the chart, then the events are considered insignificant as they neither generate higher displacement in positive or negative side. You can further alter outlier percentage to reduce the bracket and hence have higher distribution move towards Q4 - If more coloured dots appear on the bottom right corner, then the events are considered to trigger bullish environment. Will also look forward to implement this as library so that any conditions or events can be plugged into it.