Heat profileA trader once told me that top wicks equals sell interest and bottom wicks equals buy interest. If that's true then this indicator tries to organize and visualize this idea.
It uses transparent boxes to give the impression of a heat map. Due to limitations of my own skill and possibly pinescript it is not possible to render it in a useful manner using different colors that depicts buy and sell interests respectively. This means it works more like a volume profile in that it mixes the buy and sell interest together in the heat map. This can still be helpful because it help traders focus their attention on areas other than the current price candle.
In my limited time of using it, it seems like on the large timeframes the highlighted areas is where the price wants to go, and on small time frames the darkest areas is where the price wants to go. But i will leave it up to any user to spot and use their own patterns with the indicator.
Last but not least, the indicator only uses the last 50 candles, which can be too little on a small timeframe. Unfortunately the way i have done it this limitation is hardcoded in the script due to how pinescript works, by editing the code you can increase it. (Put max_boxes_count = x after overlay = true. Maximum number is 500)
Hope you enjoy. Have a nice day.
HEAT
Boxes_PlotIn the world of data visualization, heatmaps are an invaluable tool for understanding complex datasets. They use color gradients to represent the values of individual data points, allowing users to quickly identify patterns, trends, and outliers in their data. In this post, we will delve into the history of heatmaps, and then discuss how its implemented.
The "Boxes_Plot" library is a powerful and versatile tool for visualizing multiple indicators on a trading chart using colored boxes, commonly known as heatmaps. These heatmaps provide a user-friendly and efficient method for analyzing the performance and trends of various indicators simultaneously. The library can be customized to display multiple charts, adjust the number of rows, and set the appropriate offset for proper spacing. This allows traders to gain insights into the market and make informed decisions.
Heatmaps with cells are interesting and useful for several reasons. Firstly, they allow for the visualization of large datasets in a compact and organized manner. This is especially beneficial when working with multiple indicators, as it enables traders to easily compare and contrast their performance. Secondly, heatmaps provide a clear and intuitive representation of the data, making it easier for traders to identify trends and patterns. Finally, heatmaps offer a visually appealing way to present complex information, which can help to engage and maintain the interest of traders.
History of Heatmaps
The concept of heatmaps can be traced back to the 19th century when French cartographer and sociologist Charles Joseph Minard used color gradients to visualize statistical data. He is well-known for his 1869 map, which depicted Napoleon's disastrous Russian campaign of 1812 using a color gradient to represent the dwindling size of Napoleon's army.
In the 20th century, heatmaps gained popularity in the fields of biology and genetics, where they were used to visualize gene expression data. In the early 2000s, heatmaps found their way into the world of finance, where they are now used to display stock market data, such as price, volume, and performance.
The boxes_plot function in the library expects a normalized value from 0 to 100 as input. Normalizing the data ensures that all values are on a consistent scale, making it easier to compare different indicators. The function also allows for easy customization, enabling users to adjust the number of rows displayed, the size of the boxes, and the offset for proper spacing.
One of the key features of the library is its ability to automatically scale the chart to the screen. This ensures that the heatmap remains clear and visible, regardless of the size or resolution of the user's monitor. This functionality is essential for traders who may be using various devices and screen sizes, as it enables them to easily access and interpret the heatmap without needing to make manual adjustments.
In order to create a heatmap using the boxes_plot function, users need to supply several parameters:
1. Source: An array of floating-point values representing the indicator values to display.
2. Name: An array of strings representing the names of the indicators.
3. Boxes_per_row: The number of boxes to display per row.
4. Offset (optional): An integer to offset the boxes horizontally (default: 0).
5. Scale (optional): A floating-point value to scale the size of the boxes (default: 1).
The library also includes a gradient function (grad) that is used to generate the colors for the heatmap. This function is responsible for determining the appropriate color based on the value of the indicator, with higher values typically represented by warmer colors such as red and lower values by cooler colors such as blue.
Implementing Heatmaps as a Pine Script Library
In this section, we'll explore how to create a Pine Script library that can be used to generate heatmaps for various indicators on the TradingView platform. The library utilizes colored boxes to represent the values of multiple indicators, making it simple to visualize complex data.
We'll now go over the key components of the code:
grad(src) function: This function takes an integer input 'src' and returns a color based on a predefined color gradient. The gradient ranges from dark blue (#1500FF) for low values to dark red (#FF0000) for high values.
boxes_plot() function: This is the main function of the library, and it takes the following parameters:
source: an array of floating-point values representing the indicator values to display
name: an array of strings representing the names of the indicators
boxes_per_row: the number of boxes to display per row
offset (optional): an integer to offset the boxes horizontally (default: 0)
scale (optional): a floating-point value to scale the size of the boxes (default: 1)
The function first calculates the screen size and unit size based on the visible chart area. Then, it creates an array of box objects representing each data point. Each box is assigned a color based on the value of the data point using the grad() function. The boxes are then plotted on the chart using the box.new() function.
Example Usage:
In the example provided in the source code, we use the Relative Strength Index (RSI) and the Stochastic Oscillator as the input data for the heatmap. We create two arrays, 'data_1' containing the RSI and Stochastic Oscillator values, and 'data_names_1' containing the names of the indicators. We then call the 'boxes_plot()' function with these arrays, specifying the desired number of boxes per row, offset, and scale.
Conclusion
Heatmaps are a versatile and powerful data visualization tool with a rich history, spanning multiple fields of study. By implementing a heatmap library in Pine Script, we can enhance the capabilities of the TradingView platform, making it easier for users to visualize and understand complex financial data. The provided library can be easily customized and extended to suit various use cases and can be a valuable addition to any trader's toolbox.
Library "Boxes_Plot"
boxes_plot(source, name, boxes_per_row, offset, scale)
Parameters:
source (float ) : - an array of floating-point values representing the indicator values to display
name (string ) : - an array of strings representing the names of the indicators
boxes_per_row (int) : - the number of boxes to display per row
offset (int) : - an optional integer to offset the boxes horizontally (default: 0)
scale (float) : - an optional floating-point value to scale the size of the boxes (default: 1)
Price Heat MapWhat does this chart show? Take the highest high and lowest low of 200 bars. Divide that into 20 chunks. The more time the price spends in one of those 1/20th pockets, the brighter it is lit up on the chart. Number of bars back can be modified to around 500. It starts to chug beyond that. Brightness level of heat map can be adjusted. 0.5 is default. 1 = brighter, 0 = dimmer. Use on any time frame. When price moves out of a hot zone, it can move very quickly. There's no trading strategy here, just something to help you visualize recent price action. The blue band shows the price at the center of the current "hottest" band. The yellow band is the ema (exponential moving average) of the price using the "bars back" input. --enjoy!
Stochastic RSI Heat Candles OverlayThis script is based on the Stochastic RSI indicator.
Candles are dynamically colored with a customizable RGB shading directly generated from the average Stochastic RSI current value.
By default, colors represent the following status -> green is overgought & red is oversold.
Options:
Shading colors: Choose between Red/Green, Green/Blue, and Blue/Red shadings to represent the SRSI heat.
Invert colors: Switch colors of the selected shading.
Third color: Select a value from 0 to 255 for the third color which does not vary.
Transparency: Select the transparency value.
Overlay style : Choose to color the whole candle, or only borders and wick.
Show candle side: If you choose to fully fill candles with the SRSI heat color , enable this option to plot shapes representing the candles directions.
Candle marks colors: Select bull and bear colors for direction shapes.
Overbought and Oversold situations are also represented by colored shapes at the top of the chart.
Standards Stochastic RSI parameters.
Examples:
Notes:
Do not forget to bring the script to the front of your chart to avoid being covered by default candles.
Next improvements would probably be a better coloring algorithm, however, do not hesitate to suggest any modifications in comments.
Initially, I shared a Stochastic RSI Heat Bollinger Bands indicator which gave me the idea to develop this one, you can find it on my profile.
Stochastic RSI Heat Bollinger BandsThis indicator is based on a combination of the famous Bollinger Bands and the Stochastic RSI indicator.
Bands are dynamically colored (and filled) with a RGB color composed by red and green values, directly generated from the average Stochastic RSI current value.
By default, colors represent the following status -> green zone is overgought & red zone is oversold.
A screener is also displayed on the chart, representing StochRSI K and D lines values, RSI value, as well as the current status (Overbought/Oversold) of the StochRSI and RSI indicators.
Notes:
The heat color and the screener status are both based on the average value between the K and D lines (for stochastic RSI based heat)
Bands can be disabled in options to use this indicator as a simple moving average colored by the Stochastic RSI heat color.
Options:
Various moving average types (SMA, EMA, SMMA, VWMA...)
Inversion of colors between overbought and oversold status
Enabling/disabling heat color on bands / basis line and bands background color
Show/Hide the screener
Color source can be modified to base the heat on the RSI instead of the Stochastic RSI value
Standards Bollinger bands, Stochastic and RSI parameters
Example
Feel free to suggest any improvements in comment.
Multi BB Heat Vis - SMA/EMA/Breakout - r2I don't expect to iterate any further on this script, unless any weird issue crops up.
Description and usage detailed in the comments at the top of the script. Cheers!
To repaint or not to repaint, all the relevant sources are exposed as inputs for customisation - so the choice is yours.
Cheers!
[JR] Multi Bollinger Heat Bands - EMA/Breakout optionsA little, yet hopefully useful update over my previous "Multi Bollinger Heat Bands". For those who like quick visual cue's.
In short: It's your Basic Bollinger Bands, but 3 of them, and some pointy things.
In full:
Three stacked SMA based Bollinger Bands designed just to give you a quick visual on the "heat" of movement.
Set inner band as you would expect, then set your preferred additional multiplier increments for the outer 2 bands.
Option to use EMA as alternative basis, rather than SMA.
Breakout indication shapes, which have their own multiplier (but still tied to same length/period as the BB's) so you can make them pop on their own separate "band".
