Multiple Frequency Volatility Co…: Free TradingView Indicator (Pine Script)

Add It to Your TradingView Chart

1Copy the scriptPress Copy script above, or the copy button on the code below.
2Open the Pine EditorOpen a TradingView chart and choose Pine Editor at the bottom of the screen.
3Paste over the templateCreate a new indicator, select all of the template text and paste. Press Save and give it a name.
4Add it to the chartPress Add to chart, then adjust the settings with the gear icon next to its name.

Source Code

159 lines of Pine Script v5 from pinescript/editors_picks/multiple-frequency-volatility-correlation.pine, published under MPL-2.0 (file header). Keep the author's credit and licence lines when you reuse it.

multiple-frequency-volatility-correlation.pine

// This source code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
// © RicardoSantos

//@version=5
indicator(title='Multiple Frequency Volatility Correlation', shorttitle='MFVC', overlay=false)

// description:
//  This is a complex indicator that looks to provide some insight 
//  into the correlation between volume and price volatility.
//  Rising volatility is depicted with the color green while falling
//  volatility is depicted with purple.
//  Lightness of the color is used to depict the length of the window 
//  used, darker == shorter in the 0 -> 512 window range.
//  

import RicardoSantos/ColorExtension/4 as colExt

// @function determines color and correlation level.
vc (window, smoothing) => 
    float _price = ta.stdev(close, window)
    float _vol   = ta.stdev(volume, window)
    float _cor   = ta.correlation(_price, _vol, smoothing)
    float _vol_filter = ta.rising(_price, 1) ? 100 : 300 // green : purple
    float _win_filter = 25 + (window / 512) * 40 // higher value, lighter color
    color _col   = colExt.hsl(_vol_filter, 60, _win_filter, 20)
    [_col, _cor]
//
int smoothing = input.int(10)

[c002, v002] = vc(  2, smoothing)
[c003, v003] = vc(  3, smoothing)
[c004, v004] = vc(  4, smoothing)
[c006, v006] = vc(  6, smoothing)
[c008, v008] = vc(  8, smoothing)
[c012, v012] = vc( 12, smoothing)
[c016, v016] = vc( 16, smoothing)
[c024, v024] = vc( 24, smoothing)
[c032, v032] = vc( 32, smoothing)
[c048, v048] = vc( 48, smoothing)
[c064, v064] = vc( 64, smoothing)
[c096, v096] = vc( 96, smoothing)
[c128, v128] = vc(128, smoothing)
[c192, v192] = vc(192, smoothing)
[c256, v256] = vc(256, smoothing)
[c384, v384] = vc(384, smoothing)
[c512, v512] = vc(512, smoothing)

array<float> data = array.from(
      v002, v003, v004, v006, v008, v012, v016, v024, v032, 
      v048, v064, v096, v128, v192, v256, v384, v512
      )

float min = array.min(data)
float max = array.max(data)
float avg = array.avg(data)

plot(series=v002, title='V002', color=c002, style=plot.style_circles)
plot(series=v003, title='V003', color=c003, style=plot.style_circles)
plot(series=v004, title='V004', color=c004, style=plot.style_circles)
plot(series=v006, title='V006', color=c006, style=plot.style_circles)
plot(series=v008, title='V008', color=c008, style=plot.style_circles)
plot(series=v012, title='V012', color=c012, style=plot.style_circles)
plot(series=v016, title='V016', color=c016, style=plot.style_circles)
plot(series=v024, title='V024', color=c024, style=plot.style_circles)
plot(series=v032, title='V032', color=c032, style=plot.style_circles)
plot(series=v048, title='V048', color=c048, style=plot.style_circles)
plot(series=v064, title='V064', color=c064, style=plot.style_circles)
plot(series=v096, title='V096', color=c096, style=plot.style_circles)
plot(series=v128, title='V128', color=c128, style=plot.style_circles)
plot(series=v192, title='V192', color=c192, style=plot.style_circles)
plot(series=v256, title='V256', color=c256, style=plot.style_circles)
plot(series=v384, title='V384', color=c384, style=plot.style_circles)
plot(series=v512, title='V512', color=c512, style=plot.style_circles)

plot(series=min, title='min', color=color.rgb(120, 123, 134, 40))
plot(series=max, title='max', color=color.rgb(120, 123, 134, 40))
plot(series=avg, title='avg', color=color.teal)

//
array<int> hist_values = array.new<int>(20, 0)
var array<box> hist_boxes = array.new<box>(20)
var array<box> hist_boxes1 = array.new<box>(20)

if barstate.isfirst
    for _i = 0 to 19
        array.set(hist_boxes, _i, box.new(0, 0.0, 0, 0.0))
        array.set(hist_boxes1, _i, box.new(0, 0.0, 0, 0.0, bgcolor=color.rgb(243, 159, 33, 53)))

for _e in data
    int _lvl = math.round(((1 + _e) / 2.0) * 19)
    array.set(hist_values, _lvl, array.get(hist_values, _lvl) + 1)


int __sumlength = input.int(10)

int s00 = int(math.sum(array.get(hist_values, 00), __sumlength))
int s01 = int(math.sum(array.get(hist_values, 01), __sumlength))
int s02 = int(math.sum(array.get(hist_values, 02), __sumlength))
int s03 = int(math.sum(array.get(hist_values, 03), __sumlength))
int s04 = int(math.sum(array.get(hist_values, 04), __sumlength))
int s05 = int(math.sum(array.get(hist_values, 05), __sumlength))
int s06 = int(math.sum(array.get(hist_values, 06), __sumlength))
int s07 = int(math.sum(array.get(hist_values, 07), __sumlength))
int s08 = int(math.sum(array.get(hist_values, 08), __sumlength))
int s09 = int(math.sum(array.get(hist_values, 09), __sumlength))
int s10 = int(math.sum(array.get(hist_values, 10), __sumlength))
int s11 = int(math.sum(array.get(hist_values, 11), __sumlength))
int s12 = int(math.sum(array.get(hist_values, 12), __sumlength))
int s13 = int(math.sum(array.get(hist_values, 13), __sumlength))
int s14 = int(math.sum(array.get(hist_values, 14), __sumlength))
int s15 = int(math.sum(array.get(hist_values, 15), __sumlength))
int s16 = int(math.sum(array.get(hist_values, 16), __sumlength))
int s17 = int(math.sum(array.get(hist_values, 17), __sumlength))
int s18 = int(math.sum(array.get(hist_values, 18), __sumlength))
int s19 = int(math.sum(array.get(hist_values, 19), __sumlength))
int stotal = s00 + s01 + s02 + s03 + s04 + s05 + s06 + s07 + s08 + s09 + s10 + s11 + s12 + s13 + s14 + s15 + s16 + s17 + s18 + s19
get_sum(int idx)=>
    int _a = switch idx
        00 => s00
        01 => s01
        02 => s02
        03 => s03
        04 => s04
        05 => s05
        06 => s06
        07 => s07
        08 => s08
        09 => s09
        10 => s10
        11 => s11
        12 => s12
        13 => s13
        14 => s14
        15 => s15
        16 => s16
        17 => s17
        18 => s18
        19 => s19
    _a
//
for _i = 0  to 19
    box _box = array.get(hist_boxes, _i)
    float _pos = -1.0 + (_i / 10.0)
    int _nhits = array.get(hist_values, _i)
    int _nhits1 = get_sum(_i)
    int _nhits1_perc = 100 * (_nhits1 / stotal)
    box.set_lefttop(    _box, 1 + bar_index         , _pos + 0.1)
    box.set_rightbottom(_box, 1 + bar_index + _nhits, _pos      )
    box.set_text(       _box, str.format('{0} ( {1}%)', str.tostring(_nhits, '0'), str.tostring(100*(_nhits / 17),'0')))

    box _box1 = array.get(hist_boxes1, _i)
    box.set_lefttop(    _box1, 1 + bar_index               , _pos + 0.1)
    box.set_rightbottom(_box1, int(1 + bar_index + _nhits1_perc), _pos      )
    box.set_text(       _box1, str.format('{0} ( {1}%)', str.tostring(_nhits1, '0'), str.tostring(_nhits1_perc,'0')))

var label_top = label.new(x=bar_index, y= 1.0, color=color.silver, style=label.style_label_lower_left, text='high synchronicity', tooltip='there is high connection between price returns and volume change.\ngreen color: rising momentum.\npurple color: falling momentum.', size=size.small)
var label_bot = label.new(x=bar_index, y=-1.0, color=color.silver, style=label.style_label_upper_left, text='low synchronicity' , tooltip='there is low connection between price returns and volume change.\ngreen color: rising momentum.\npurple color: falling momentum.' , size=size.small)
label.set_x(label_top, bar_index)
label.set_x(label_bot, bar_index)

Overview

Multiple Frequency Volatility Correlation plots individual correlation values as colored circles in a separate indicator pane alongside lines representing the minimum, maximum, and average correlations across all frequencies. Color hues indicate whether price volatility is rising or falling, while lightness corresponds to the window length. Users can adjust the correlation smoothing length parameter.

Multi-frequency correlation analysis across 17 lookback windows
Dynamic HSL color mapping for volatility directional state
Summary plots for minimum, maximum, and average correlation values
Configurable correlation smoothing input

Details

Published by

regalouisei on GitHub

Code credit

RicardoSantos, as written in the script's header

Type

Indicator, Pine Script v5

Draws

Separate pane below the chart

Markets

Any symbol

Timeframes

Any timeframe

Risk Notes

No repainting, lookahead or stacked entries were found in its code or README. That says nothing about how well it works: test it first.

File

pinescript/editors_picks/multiple-frequency-volatility-correlation.pine

Repository

github.com/regalouisei/collect-tradingview. See its other scripts in the library

Settings

The inputs you can change from the gear icon on the chart. Defaults are the author's.

Setting	Type	Default	Variable
smoothing	`int`	`10`	`smoothing`
__sumlength	`int`	`10`	`__sumlength`

Third-party code. ForexR did not write, test or endorse this script, and the summary above was generated automatically from its code and README. Indicators describe the past, and a strategy's backtest can look better than live trading, especially when a script repaints. Check it in the Strategy Tester and on a demo account before you rely on it.

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