Coding K’s Volatility Bands in TradingView
How to Code & Use the K’s Volatility Bands in Pine Script
The great Bollinger Bands are one of the first things we must learn when analyzing time series. This is because of their sound statistical reasoning, their wide adoption across market participants, and their success when deployed in trading strategies. We can also see other variations that resemble the Bands in an attempt to enhance it. This article will show how to code K’s volatility bands using Pine Script (TradingView’s coding language).
If you are interested by trend following indicators and strategies then my book could interest you. It features advanced trend-following indicators and strategies with a GitHub page dedicated to the continuously updated code. Also, this book features the original colors after having optimized for printing costs. If you feel that this interests you, feel free to visit the below Amazon link, or if you prefer to buy the PDF version, you could contact me on LinkedIn.
Trend Following Strategies in Python: How to Use Indicators to Follow the Trend.
Amazon.com: Trend Following Strategies in Python: How to Use Indicators to Follow the Trend.: 9798756939620: Kaabar…www.amazon.com
K’s Volatility Bands
As stated previously, the volatility bands family contains many indicators which have been discussed in previous articles such as:
The Bollinger bands: These are the pioneers which are predominantly used. They are based on a simple moving average and a standard deviation envelope around the market price.
The augmented Bollinger bands: They try to enhance the previous calculation by including the highs and lows in the calculation and an exponential moving average to take into account more recent data.
The Keltner channel: The channel is calculated using the average true range indicator, a volatility proxy that replaces the standard deviation measure used in the previous two bands.
The VAMA bands: Based on the volatility-adjusted moving average, the VAMA band gives a sizeable weight to volatility so that risk is accounted for. They are the same as the Bollinger bands but simply use the volatility-adjusted moving average instead of the simple one.
The fractal adaptive volatility bands: Also a simple bands overlay indicator based on another type of moving averages called the fractal adaptive moving average.
The aim now is to present another indicator which shares some common traits with the augmented Bollinger bands but takes it to a slightly higher level.
We must first start by calculating a rolling measure based on the average between the highest high and the lowest low in the last specified lookback window.
This will give us a type of moving average that tracks the market price. The specificity here is that when the market does not make higher highs nor lower lows, the line will be flat. A flat line can also be thought of as a magnet of the price as the ranging property could hint to a further sideways movement.
The K’s volatility bands assume the worst with volatility and thus will take the maximum volatility for a given lookback period. Unlike the Bollinger bands which will take the latest volatility calculation every single step of time, K’s volatility bands will suppose that we must be protected by the maximum of volatility for that period which will give us from time to time stable support and resistance levels. The formula for the bands can be written down as follows. We can add a multiplier to the formula if we wish.
Coding K’s Volatility Bands in Pine Script
We want to create and plot the K’s volatility bands on the GBPCHF values loaded on the TradingView platform. Note that you must create an account to be able to view the charts, the good news is that it is free. Locate the Chart button on the home screen and then choose any asset you would like to calculate the indicator on. Now, on the bottom of the screen, locate Pine Editor and warm up your fingers to do some coding.
The first step is to specify the version of Pine Script. In our case it is 5 which has some minor changes from the fourth version.
//@version = 5The next step is to specify the name of the indicator by using the following syntax. We must also set the overlay argument to true because we want to chart the bands in the same panel as the price. Setting it to false will put the bands in another panel which makes it difficult to analyze.
indicator("K's Volatility Bands", overlay = true)We can now set the default lookback period values and the multiplier using the input() function.
lookback = input(defval = 13, title = 'Lookback')
multiplier = input(defval = 2, title = 'Multiplier')Now, we apply the median function using the ta.highest() and ta.lowest() functions which output the highest or lowest values given an array of values and a moving window which basically the lookback period.
median = (ta.highest(high, lookback) + ta.lowest(low, lookback)) / 2The maximum volatility is calculated using the standard deviation function ta.stdev() inside a ta.highest() function so that it outputs the desired result.
maximum_volatility = ta.highest(ta.stdev(close, lookback), lookback)Finally, the bands can be easily calculated following this below syntax.
upper_band = median + (multiplier * maximum_volatility)
lower_band = median - (multiplier * maximum_volatility)Let us plot the indicator and visualize the chart.
plot(upper_band, color = color.red)
plot(median, color = color.blue)
plot(lower_band, color = color.green)
The full code can be found below.
// This source code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
// © Sofien-Kaabar//@version=5
indicator("K's Volatility Bands", overlay = true)
lookback = input(defval = 13, title = 'Lookback')
multiplier = input(defval = 2, title = 'Multiplier')median = (ta.highest(high, lookback) + ta.lowest(low, lookback)) / 2maximum_volatility = ta.highest(ta.stdev(close, lookback), lookback)upper_band = median + (multiplier * maximum_volatility)
lower_band = median - (multiplier * maximum_volatility)plot(upper_band, color = color.red)
plot(median, color = color.blue)
plot(lower_band, color = color.green)If you want to see how to create all sorts of algorithms yourself, feel free to check out Lumiwealth. From algorithmic trading to blockchain and machine learning, they have hands-on detailed courses that I highly recommend.
Learn Algorithmic Trading with Python Lumiwealth
Learn how to create your own trading algorithms for stocks, options, crypto and more from the experts at Lumiwealth. Click to learn more
Summary
To sum up, what I am trying to do is to simply contribute to the world of objective technical analysis which is promoting more transparent techniques and strategies that need to be back-tested before being implemented. This way, technical analysis will get rid of the bad reputation of being subjective and scientifically unfounded.
I recommend you always follow the the below steps whenever you come across a trading technique or strategy:
Have a critical mindset and get rid of any emotions.
Back-test it using real life simulation and conditions.
If you find potential, try optimizing it and running a forward test.
Always include transaction costs and any slippage simulation in your tests.
Always include risk management and position sizing in your tests.
Finally, even after making sure of the above, stay careful and monitor the strategy because market dynamics may shift and make the strategy unprofitable.