Spider_PlotIntroduction:
Spider charts, also known as radar charts or web charts, are a powerful data visualization tool that can display multiple variables in a circular format. They are particularly useful when you want to compare different data sets or evaluate the performance of a single data set across multiple dimensions. In this blog post, we will dive into the world of spider charts, explore their benefits, and demonstrate how you can create your own spider chart using the Spider_Plot library.
Why Spider Charts are Cool:
Spider charts have a unique visual appeal that sets them apart from other chart types. They allow you to display complex data in a compact, easy-to-understand format, making them perfect for situations where you need to convey a lot of information in a limited space. Some of the key benefits of spider charts include:
Multi-dimensional analysis: Spider charts can display multiple variables at once, making them ideal for analyzing relationships between different data sets or examining a single data set across multiple dimensions.
Easy comparison: By displaying data in a circular format, spider charts make it simple to compare different data points, identify trends, and spot potential issues.
Versatility: Spider charts can be used for a wide range of applications, from business and finance to sports and health. They are particularly useful for situations where you need to analyze performance or make comparisons between different entities.
Creating Your Own Spider Chart with the Spider_Plot Library:
The Spider_Plot library is a user-friendly, easy-to-use tool that allows you to create stunning spider charts with minimal effort. To get started, you'll need to import the Spider_Plot library:
import peacefulLizard50262/Spider_Plot/1
With the library imported, you can now create your own spider chart. The first step is to normalize your data. Normalizing ensures that all data points fall within the 0 to 1 range, which is important for creating a visually balanced spider chart.
The Spider_Plot library provides the data_normalize function to help you normalize your data. This function accepts several parameters, including the normalization style ("All Time", "Range", or "Custom"), length of the range, outlier level, lookback period for standard deviation, and minimum and maximum values for the "Custom" normalization style.
Once you have normalized your data, you can create an array of your data points using the array.from function. This array will be used as input for the draw_spider_plot function, which is responsible for drawing the spider plot on your chart.
The draw_spider_plot function accepts an array of float values (the normalized data points), an array of background colors for each sector, a color for the axes, and a scaling factor.
Example Usage:
Here's an example script that demonstrates how to create a spider chart using the Spider_Plot library:
oc = data_normalize(ta.ema(math.abs(open - close), 20), "Range", 20)
// Create an array of your data points
data = array.from(tr, rsi, stoch, dev, tr, oc, tr)
// Define colors for each sector
colors = array.from(color.new(color.red, 90), color.new(color.blue, 90), color.new(color.green, 90), color.new(color.orange, 90), color.new(color.purple, 90), color.new(color.purple, 90), color.new(color.purple, 90))
// Draw the spider plot on your chart
draw_spider_plot(data, colors, color.gray, 100)
In this example, we have first normalized six different data points (rsi, source, stoch, dev, tr, and oc) using the data_normalize function. Next, we create an array of these normalized data points and define an array of colors for each sector of the spider chart. Finally, we call the draw_spider_plot function to draw the spider chart on our chart.
Conclusion:
Spider charts are a versatile and visually appealing tool for analyzing and comparing multi-dimensional data. With the Spider_Plot library, you can easily create your own spider charts and unlock valuable insights from your data. Just remember to normalize your data and create an array of data points before calling the draw_spider_plot function. Happy charting!
Library "Spider_Plot"
data_normalize(data, style, length, outlier_level, dev_lookback, min, max)
data_normalize(data, string style, int length, float outlier_level, simple int dev_lookback, float min, float max)
Parameters:
data (float) : float , A float value to normalize.
style (string) : string , The normalization style: "All Time", "Range", or "Custom".
length (int) : int , The length of the range for "Range" normalization style.
outlier_level (float) : float , The outlier level to exclude from calculations.
dev_lookback (simple int) : int , The lookback period for calculating the standard deviation.
min (float) : float , The minimum value for the "Custom" normalization style.
max (float) : float , The maximum value for the "Custom" normalization style.
Returns: array , The normalized float value.
draw_spider_plot(values, bg_colors, axes_color, scale)
draw_spider_plot(array values, array bg_colors, color axes_color, float scale)
Parameters:
values (float ) : array , An array of float values to plot in the spider plot.
bg_colors (color ) : array , An array of background colors for each sector in the spider plot.
axes_color (color) : color , The color of the axes in the spider plot. Default: color.gray
scale (float) : float , A scaling factor for the spider plot. Default: 10
Returns: void , Draws the spider plot on the chart.
