Accurate Bollinger Bands mcbw_ [True Volatility Distribution]The Bollinger Bands have become a very important technical tool for discretionary and algorithmic traders alike over the last decades. It was designed to give traders an edge on the markets by setting probabilistic values to different levels of volatility. However, some of the assumptions that go into its calculations make it unusable for traders who want to get a correct understanding of the volatility that the bands are trying to be used for. Let's go through what the Bollinger Bands are said to show, how their calculations work, the problems in the calculations, and how the current indicator I am presenting today fixes these.
--> If you just want to know how the settings work then skip straight to the end or click on the little (i) symbol next to the values in the indicator settings window when its on your chart <--
--------------------------- What Are Bollinger Bands ---------------------------
The Bollinger Bands were formed in the 1980's, a time when many retail traders interacted with their symbols via physically printed charts and computer memory for personal computer memory was measured in Kb (about a factor of 1 million smaller than today). Bollinger Bands are designed to help a trader or algorithm see the likelihood of price expanding outside of its typical range, the further the lines are from the current price implies the less often they will get hit. With a hands on understanding many strategies use these levels for designated levels of breakout trades or to assist in defining price ranges.
--------------------------- How Bollinger Bands Work ---------------------------
The calculations that go into Bollinger Bands are rather simple. There is a moving average that centers the indicator and an equidistant top band and bottom band are drawn at a fixed width away. The moving average is just a typical moving average (or common variant) that tracks the price action, while the distance to the top and bottom bands is a direct function of recent price volatility. The way that the distance to the bands is calculated is inspired by formulas from statistics. The standard deviation is taken from the candles that go into the moving average and then this is multiplied by a user defined value to set the bands position, I will call this value 'the multiple'. When discussing Bollinger Bands, that trading community at large normally discusses 'the multiple' as a multiplier of the standard deviation as it applies to a normal distribution (gaußian probability). On a normal distribution the number of standard deviations away (which trades directly use as 'the multiple') you are directly corresponds to how likely/unlikely something is to happen:
1 standard deviation equals 68.3%, meaning that the price should stay inside the 1 standard deviation 68.3% of the time and be outside of it 31.7% of the time;
2 standard deviation equals 95.5%, meaning that the price should stay inside the 2 standard deviation 95.5% of the time and be outside of it 4.5% of the time;
3 standard deviation equals 99.7%, meaning that the price should stay inside the 3 standard deviation 99.7% of the time and be outside of it 0.3% of the time.
Therefore when traders set 'the multiple' to 2, they interpret this as meaning that price will not reach there 95.5% of the time.
---------------- The Problem With The Math of Bollinger Bands ----------------
In and of themselves the Bollinger Bands are a great tool, but they have become misconstrued with some incorrect sense of statistical meaning, when they should really just be taken at face value without any further interpretation or implication.
In order to explain this it is going to get a bit technical so I will give a little math background and try to simplify things. First let's review some statistics topics (distributions, percentiles, standard deviations) and then with that understanding explore the incorrect logic of how Bollinger Bands have been interpreted/employed.
---------------- Quick Stats Review ----------------
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(If you are comfortable with statistics feel free to skip ahead to the next section)
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-------- I: Probability distributions --------
When you have a lot of data it is helpful to see how many times different results appear in your dataset. To visualize this people use "histograms", which just shows how many times each element appears in the dataset by stacking each of the same elements on top of each other to form a graph. You may be familiar with the bell curve (also called the "normal distribution", which we will be calling it by). The normal distribution histogram looks like a big hump around zero and then drops off super quickly the further you get from it. This shape (the bell curve) is very nice because it has a lot of very nifty mathematical properties and seems to show up in nature all the time. Since it pops up in so many places, society has developed many different shortcuts related to it that speed up all kinds of calculations, including the shortcut that 1 standard deviation = 68.3%, 2 standard deviations = 95.5%, and 3 standard deviations = 99.7% (these only apply to the normal distribution). Despite how handy the normal distribution is and all the shortcuts we have for it are, and how much it shows up in the natural world, there is nothing that forces your specific dataset to look like it. In fact, your data can actually have any possible shape. As we will explore later, economic and financial datasets *rarely* follow the normal distribution.
-------- II: Percentiles --------
After you have made the histogram of your dataset you have built the "probability distribution" of your own dataset that is specific to all the data you have collected. There is a whole complicated framework for how to accurately calculate percentiles but we will dramatically simplify it for our use. The 'percentile' in our case is just the number of data points we are away from the "middle" of the data set (normally just 0). Lets say I took the difference of the daily close of a symbol for the last two weeks, green candles would be positive and red would be negative. In this example my dataset of day by day closing price difference is:
week 1:
week 2:
sorting all of these value into a single dataset I have:
I can separate the positive and negative returns and explore their distributions separately:
negative return distribution =
positive return distribution =
Taking the 25th% percentile of these would just be taking the value that is 25% towards the end of the end of these returns. Or akin the 100%th percentile would just be taking the vale that is 100% at the end of those:
negative return distribution (50%) = -5
positive return distribution (50%) = +4
negative return distribution (100%) = -10
positive return distribution (100%) = +20
Or instead of separating the positive and negative returns we can also look at all of the differences in the daily close as just pure price movement and not account for the direction, in this case we would pool all of the data together by ignoring the negative signs of the negative reruns
combined return distribution =
In this case the 50%th and 100%th percentile of the combined return distribution would be:
combined return distribution (50%) = 4
combined return distribution (100%) = 10
Sometimes taking the positive and negative distributions separately is better than pooling them into a combined distribution for some purposes. Other times the combined distribution is better.
Most financial data has very different distributions for negative returns and positive returns. This is encapsulated in sayings like "Price takes the stairs up and the elevator down".
-------- III: Standard Deviation --------
The formula for the standard deviation (refereed to here by its shorthand 'STDEV') can be intimidating, but going through each of its elements will illuminate what it does. The formula for STDEV is equal to:
square root ( (sum ) / N )
Going back the the dataset that you might have, the variables in the formula above are:
'mean' is the average of your entire dataset
'x' is just representative of a single point in your dataset (one point at a time)
'N' is the total number of things in your dataset.
Going back to the STDEV formula above we can see how each part of it works. Starting with the '(x - mean)' part. What this does is it takes every single point of the dataset and measure how far away it is from the mean of the entire dataset. Taking this value to the power of two: '(x - mean) ^ 2', means that points that are very far away from the dataset mean get 'penalized' twice as much. Points that are very close to the dataset mean are not impacted as much. In practice, this would mean that if your dataset had a bunch of values that were in a wide range but always stayed in that range, this value ('(x - mean) ^ 2') would end up being small. On the other hand, if your dataset was full of the exact same number, but had a couple outliers very far away, this would have a much larger value since the square par of '(x - mean) ^ 2' make them grow massive. Now including the sum part of 'sum ', this just adds up all the of the squared distanced from the dataset mean. Then this is divided by the number of values in the dataset ('N'), and then the square root of that value is taken.
There is nothing inherently special or definitive about the STDEV formula, it is just a tool with extremely widespread use and adoption. As we saw here, all the STDEV formula is really doing is measuring the intensity of the outliers.
--------------------------- Flaws of Bollinger Bands ---------------------------
The largest problem with Bollinger Bands is the assumption that price has a normal distribution. This is assumption is massively incorrect for many reasons that I will try to encapsulate into two points:
Price return do not follow a normal distribution, every single symbol on every single timeframe has is own unique distribution that is specific to only itself. Therefore all the tools, shortcuts, and ideas that we use for normal distributions do not apply to price returns, and since they do not apply here they should not be used. A more general approach is needed that allows each specific symbol on every specific timeframe to be treated uniquely.
The distributions of price returns on the positive and negative side are almost never the same. A more general approach is needed that allows positive and negative returns to be calculated separately.
In addition to the issues of the normal distribution assumption, the standard deviation formula (as shown above in the quick stats review) is essentially just a tame measurement of outliers (a more aggressive form of outlier measurement might be taking the differences to the power of 3 rather than 2). Despite this being a bit of a philosophical question, does the measurement of outlier intensity as defined by the STDEV formula really measure what we want to know as traders when we're experiencing volatility? Or would adjustments to that formula better reflect what we *experience* as volatility when we are actively trading? This is an open ended question that I will leave here, but I wanted to pose this question because it is a key part of what how the Bollinger Bands work that we all assume as a given.
Circling back on the normal distribution assumption, the standard deviation formula used in the calculation of the bands only encompasses the deviation of the candles that go into the moving average and have no knowledge of the historical price action. Therefore the level of the bands may not really reflect how the price action behaves over a longer period of time.
------------ Delivering Factually Accurate Data That Traders Need------------
In light of the problems identified above, this indicator fixes all of these issue and delivers statistically correct information that discretionary and algorithmic traders can use, with truly accurate probabilities. It takes the price action of the last 2,000 candles and builds a huge dataset of distributions that you can directly select your percentiles from. It also allows you to have the positive and negative distributions calculated separately, or if you would like, you can pool all of them together in a combined distribution. In addition to this, there is a wide selection of moving averages directly available in the indicator to choose from.
Hedge funds, quant shops, algo prop firms, and advanced mechanical groups all employ the true return distributions in their work. Now you have access to the same type of data with this indicator, wherein it's doing all the lifting for you.
------------------------------ Indicator Settings ------------------------------
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---- Moving average ----
Select the type of moving average you would like and its length
---- Bands ----
The percentiles that you enter here will be pulled directly from the return distribution of the last 2,000 candles. With the typical Bollinger Bands, traders would select 2 standard deviations and incorrectly think that the levels it highlights are the 95.5% levels. Now, if you want the true 95.5% level, you can just enter 95.5 into the percentile value here. Each of the three available bands takes the true percentile you enter here.
---- Separate Positive & Negative Distributions----
If this box is checked the positive and negative distributions are treated indecently, completely separate from each other. You will see that the width of the top and bottom bands will be different for each of the percentiles you enter.
If this box is unchecked then all the negative and positive distributions are pooled together. You will notice that the width of the top and bottom bands will be the exact same.
---- Distribution Size ----
This is the number of candles that the price return is calculated over. EG: to collect the price return over the last 33 candles, the difference of price from now to 33 candles ago is calculated for the last 2,000 candles, to build a return distribution of 2000 points of price differences over 33 candles.
NEGATIVE NUMBERS(<0) == exact number of candles to include;
EG: setting this value to -20 will always collect volatility distributions of 20 candles
POSITIVE NUMBERS(>0) == number of candles to include as a multiple of the Moving Average Length value set above;
EG: if the Moving Average Length value is set to 22, setting this value to 2 will use the last 22*2 = 44 candles for the collection of volatility distributions
MORE candles being include will generally make the bands WIDER and their size will change SLOWER over time.
I wish you focus, dedication, and earnest success on your journey.
Happy trading :)
Search in scripts for "band"
HPDR Bands IndicatorThe HPDR Bands indicator is a customizable tool designed to help traders visualize dynamic price action zones. By combining historical price ranges with adaptive bands, this script provides clear insights into potential support, resistance, and midline levels. The indicator is well-suited for all trading styles, including trend-following and range-bound strategies.
Features:
Dynamic Price Bands: Calculates price zones based on historical highs and lows, blending long-term and short-term price data for responsive adaptation to current market conditions.
Probability Enhancements: Includes a probability plot derived from the relative position of the closing price within the range, adjusted for volatility to highlight potential price movement scenarios.
Fibonacci-Like Levels: Highlights key levels (100%, 95%, 88%, 78%, 61%, 50%, and 38%) for intuitive visualization of price zones, aiding in identifying high-probability trading opportunities.
Midline Visualization: Displays a midline that serves as a reference for price mean reversion or breakout analysis.
How to Use:
Trending Markets: Use the adaptive upper and lower bands to gauge potential breakout or retracement zones.
Range-Bound Markets: Identify support and resistance levels within the defined price range.
Volatility Analysis: Observe the probability plot and its sensitivity to volatility for informed decision-making.
Important Notes:
This script is not intended as investment advice. It is a tool to assist with market analysis and should be used alongside proper risk management and other trading tools.
The script is provided as-is and without warranty. Users are encouraged to backtest and validate its suitability for their specific trading needs.
Happy Trading!
If you find this script helpful, consider sharing your feedback or suggestions for improvement. Collaboration strengthens the TradingView community, and your input is always appreciated!
Concretum BandsDefinition
The Concretum Bands indicator recreates the Upper and Lower Bound of the Noise Area described in the paper "Beat the Market: An Effective Intraday Momentum Strategy for S&P500 ETF (SPY)" published by Concretum founder Zarattini, along with Barbon and Aziz, in May 2024.
Below we provide all the information required to understand how the indicator is calculated, the rationale behind it and how people can use it.
Idea Behind
The indicator aims to outline an intraday price region where the stock is expected to move without indicating any demand/supply imbalance. When the price crosses the boundaries of the Noise Area, it suggests a significant imbalance that may trigger an intraday trend.
How the Indicator is Calculated
The bands at time HH:MM are computed by taking the open price of day t and then adding/subtracting the average absolute move over the last n days from market open to minute HH:MM . The bands are also adjusted to account for overnight gaps. A volatility multiplier can be used to increase/decrease the width of the bands, similar to other well-known technical bands. The bands described in the paper were computed using a lookback period (length) of 14 days and a Volatility Multiplier of 1. Users can easily adjust these settings.
How to use the indicator
A trader may use this indicator to identify intraday moves that exceed the average move over the most recent period. A break outside the bands could be used as a signal of significant demand/supply imbalance.
Quadratic Weighted Bands"Quadratic Weighted Bands" (QWB) is designed to identify and visualize market trends and volatility using quadratic weighted filtering techniques. It works by applying quadratic weighting to a selected data source over a specified length, enhancing the sensitivity and responsiveness of the indicator to recent market movements. A major advantage of this indicator is the ability to have a longer lookback period without having too much lag. This results in a smoother output that is still very responsive. Its about twice as fast as a normal average so adjust accordingly.
The indicator is customizable, allowing users to select between the normal Quadratic Weighting (QWF) and Volume Quadratic Weighting (VQWF), choose their data source, adjust the lookback period, and modify the deviation multiplier to fit their analysis needs. Additionally, users can customize the colors of the bands and center line.
The color of the central line changes based on the direction of the trend, as well as having a neutral (ranging) color. This visual aspect makes it easier for traders to quickly see the strength and direction of the market.
Style Select: Choose between "Normal Quadratic Weighting" or "Volume Quadratic Weighting" to adapt the indicator based on volume data or standard price data.
Source: This allows for the selection of the input source for the indicator, such as HL2, ensuring the analysis is aligned with specific trading preferences.
Length: Define the lookback period for the average, with the system automatically utilizing the maximum available length if the specified range exceeds available data, ensuring it always works.
Deviation Length: Optionally adjust the lookback period for calculating deviation, enhancing the indicator's sensitivity and accuracy in identifying market volatility.
Multiplier: Fine tune the deviation multiplier to control the width of the bands, allowing traders to adjust for market volatility and personal risk tolerance.
Top Color: Customize the color of the top band, which also affects the center line's appearance. Adjusting the brightness provides visual clarity and personalization.
Bottom Color: Similarly, select the color for the bottom band, which also influences the center line. The option to adjust brightness ensures the indicator's readability and aesthetic preference.
Neutral Color: Designate a color for indicating a ranging market.
Enjoy
Bolingger Bands + Inside Bar BoxesBollinger Bands are a technical analysis tool consist of three bands—an upper, middle, and lower band—that are used to spotlight extreme short-term prices in a security. The upper band represents overbought territory, while the lower band can show you when a security is oversold. Most technicians will use Bollinger Bands® in conjunction with other analysis tools to get a better picture of the current state of a market or security.
An Inside Bar is a two-bar price action trading strategy in which the inside bar is smaller and within the high to low range of the prior bar. Inside bars show a period of consolidation in a market. They often form following a strong move in a market, as it ‘pauses’ to consolidate before making its next move. However, they can also form at market turning points and act as reversal signals from key support or resistance levels.
Multi Time Frame Composite BandsMulti Time Frame Composite Bands utilizes Fibonacci numbers (5, 8, 13, 21, 34) as period lengths for calculations. The indicator calculates a composite high line (C_high) by averaging the highest prices over Fibonacci periods, incorporating moving averages (SMA) of high prices for added refinement and smoothing. Similarly, a composite low line (C_low) is calculated by averaging the lowest prices with moving averages of low prices. The midline, obtained from the mean of C_high and C_low.
This band can function as volatility bands unlike traditional volatility indicators like Bollinger Bands , ATR bands it does not use traditional measures of volatility such standard deviation , ATR. This hugs closely to the price and during trending markets the some part of the candles stay outside the band and when the entire candle digress outside the band a price correction or reversal can be anticipated. This can be considered as a smoothed Donchian channel.
Bollinger Band ribbonThis indicator plots 9 upper and lower lines with increasing length. Lines are 0.618 upper and lower level of Bollinger band.
Bollinger Band Alert with RSI Filter IndicatorThis code is for a technical analysis indicator called Bollinger Band Alert with RSI Filter. It uses two tools: Bollinger Bands and Relative Strength Index (RSI) to identify potential trading signals in the market.
Bollinger Bands are lines plotted two standard deviations away from a simple moving average of the price of a stock or asset. They help traders determine whether prices are high or low on a relative basis.
The RSI is a momentum indicator that measures the strength of recent price changes to evaluate whether an asset is overbought or oversold.
The code has some input parameters that a user can change, such as length and multiplier, which are used to calculate the Bollinger Bands, and upper and lower RSI levels to define the overbought and oversold zones.
The code then uses if statements to generate alerts if certain conditions are met. The alert condition is triggered if the close price of an asset crosses above or below the upper or lower Bollinger Bands, and if the RSI is either above or below the overbought or oversold threshold levels.
Finally, the code generates plots to visualize the Bollinger Bands and displays triangles above or below the bars indicating when to enter a long or short position based on the strategy's criteria.
Weighted Bollinger Band (+ Logarithmic)ENG)
Weighted BB is more responsive to price changes than original Bollinger Bands.
the calculation formula uses a weighted method based on the current price.
Instead of using a standard deviation, I used a weighted standard deviation that weights the current price, and instead of a simple moving average, I used a weighted moving average.
Also included is a formula to log the Bollinger Bands for users who view charts on a logarithmic scale.
KOR)
원본 볼밴보다 가격변화에 대한 반응성이 높습니다.
계산식에는 현재가격에 가중을 주는 방식을 사용하였습니다.
표준편차를 사용하는 대신 저는 현재가격에 가중을 두는 가중표준편차를 사용하였고, 단순이동평균 대신 가중이동평균을 사용하였습니다.
또한 로그스케일로 차트를 보는 유저를 위해 볼린저밴드를 log화 하는 수식도 포함하였습니다.
Faytterro Bandswhat is Faytterro Bands?
it is a channel indicator like "Bollinger Bands".
what it does?
creates a channel using standard deviations and means. thus giving users an idea about the expensive and cheap zones. It uses a special weighted moving average different from standard bollinger bands, it also averages not only price but also deviations.
how it does it?
it uses this formulas:
how to use it?
its usage is the same as "bollinger band".
length represents the number of candles to be taken into account, source represents the source of those candles and stdev represents the coefficient of the standard deviation.
you can use it with other indicators:
Average Trend with Deviation BandsTL,DR: A trend indicator with deviation bands using a modified Donchian calculation
This indicator plots a trend using the average of the lowest and highest closing price and the lowest low and highest high of a given period. This is similar to Donchian channels which use an average of the lowest and highest value (of a given period). This might sound like a small change but imho it provides a better "average" when lows/highs and lowest/highest closing prices are considered in the average calculation as well.
I also added the option to show 2 deviation bands (one is deactivated by default but can be activated in the options menu). The deviation band uses the standard deviation (of the average trend) and can be used to determine if a price movement is still in a "normal" range or not. Based on my testing it is fine to use one band with a standard deviation of 1 but it is also possible to show a second band with a different deviation value if needed. The bands (and trendline) can also be used as dynamic support/resistance zones.
Trendline without deviation bands
Volume Weighted Hull Moving Average Bollinger Bands (VWHBB)Title: "Volume Weighted Hull Moving Average Bollinger Bands Indicator for TradingView"
Abstract: This script presents a TradingView indicator that displays Bollinger Bands based on the volume weighted Hull Moving Average (VEHMA) of a financial asset. The VEHMA is a technical analysis tool that combines the reduced lag of the Hull Moving Average (HMA) with volume weighting to provide a more sensitive indicator of market trends and dynamics. The Bollinger Bands are a volatility indicator that plot upper and lower bands around a moving average, which can help traders identify potential trend changes and overbought or oversold conditions. The script allows the user to customize the VEHMA length and Bollinger Band deviation parameters.
Introduction: Bollinger Bands are a popular technical analysis tool used to identify potential trend changes and overbought or oversold conditions in the market. They are constructed by plotting upper and lower bands around a moving average, with the width of the bands determined by the volatility of the asset. The VEHMA is a variant of the Hull Moving Average (HMA) that combines the reduced lag of the HMA with volume weighting to provide a more sensitive indicator of market trends and dynamics.
Methodology: The VEHMA is calculated using a weighted average of two exponential moving averages (EMAs), with the weighting based on the volume of the asset and the length of the moving average. The Bollinger Bands are calculated by plotting the VEHMA plus and minus a standard deviation of the asset's price over a specified period. The standard deviation is a measure of the volatility of the asset and helps to adjust the width of the bands based on market conditions.
Implementation: The script is implemented in TradingView's PineScript language and can be easily added to any chart on the platform. The user can customize the VEHMA length and Bollinger Band deviation parameters to suit their trading strategy. The VEHMA, Bollinger Bands, and fill colors are plotted on the chart to provide a visual representation of the indicator.
Conclusion: The VEHMA Bollinger Bands indicator is a useful tool for traders looking to identify potential trend changes and overbought or oversold conditions in the market. This script provides a convenient and customizable implementation of the indicator for use in TradingView.
Correlated ATR Bands | AdulariHow do I use it?
Never use this indicator as standalone trading signal, it should be used as confluence.
It is highly recommended to use this indicator on the 15m timeframe and above, try experimenting with the inverse feature and multipliers as well.
When the price is above the moving average this shows the bullish trend is strong.
When the price is below the moving average this shows the bearish trend is strong.
When the moving average is purple, the trend is bullish , when it is gray, the trend is bearish.
When price is above the upper band this may indicate a bearish reversal.
When price is below the lower band this may indicate a bullish reversal.
Features:
Purple line for bullish trend and gray line for bearish trend.
Custom formula combining an ATR and Hull MA to clearly indicate trend strength and direction.
Unique approach to moving averages and bands by taking the average of 2 types of MA's combined with custom ATR's, then multiplying these by correlation factors.
Bands to indicate possible trend reversals when price crosses them.
How does it work?
1 — ATR value is calculated, then the correlation between the source and ATR is calculated.
2 — Final value is calculated using the following formula:
correlation * atr + (1 - correlation) * nz(atr , atr)
3 — Moving average is calculated with the following formula:
ta.hma((1-(correlation/100*(1+weight/10)))*(ta.sma(source+value, smoothing)+ta.sma(source-value,smoothing))/2,flength)
4 — Bands calculation using multipliers.
Bollinger Bands Width and Bollinger Bands %BThis script shows both the Bollinger Band Width(BBW) and %B on the same indicator window.
Both the BBW and %B are introduced by John Bollinger(creator of Bollinger Bands) in 2010.
Default Parameter values: Length = 20, Source = Close, Mult = 2
Bollinger Bands Width (BBW): Color = (Default: Green )
- I consider stocks with "BBW >= 4" are at a volatile state and ready for price contraction, but this depends on the parameter values of your choice.
Bollinger Bands %B (%B): Color = (Default: Blue )
1. %B Above 10 = Price is Above the Upper Band
2. %B Equal to 10 = Price is at the Upper Band
3. %B Above 5 = Price is Above the Middle Line
4. %B Below 5 = Price is Below the Middle Line
5. %B Equal to 0 = Price is at the Lower Band
6. %B Below 0 = Price is Below the Lower Band
Impatient TS VWAP BandsImpatient VWAP bands are based of Traderskew's VWAP bands but are for more impatient traders.
Wicking or crossing down through the upper band indicates a good short trade entry for range-bound trading periods while wicking or crossing up through the lower band indicates a good long entry in range-bound conditions.
By default, impatience is disabled. If it is turned on, adjusting impatience determines how quickly the bands approach price: higher impatience approaches price faster. Rebound indicates how far from price the bands bounce after hitting price.
MTF VWAP & StDev BandsMulti Timeframe Volume Weighted Average Price with Standard Deviation Bands
I used the script "Koalafied VWAP D/W/M/Q/Y" by Koalafied_3 and made some changes, such as adding more standard deviation bands.
The script can display the daily, weekly, monthly, quarterly and yearly VWAP.
Standard deviation bands values can be changed (default values are 0.618, 1, 1.618, 2, 2.618, 3).
Also the previous standard deviation bands can be displayed.
SMA VWAP BANDS [qrsq]Description
This indicator is used to find support and resistance utilizing both SMA and VWAP. It can be used on lower and higher time frames to understand where price is likely to reject or bounce.
How it works
Rather than using the usual calculation for the VWAP, instead this script smooths the volume first with the SMA and then respectively calculates the smoothed multiplication of high, low and close price with the volume individually. These values are then divided by the smoothed volume to find individual VWAP's for each of the sources. The standard deviations of these are calculated, resulting in an upper, lower and middle band. It is essentially VWAP bands with some smoothed calculations in the middle.
How to use it
I like to use the bands for LTF scalping as well as HTF swings.
For scalping:
I tend to use either the 5m or 15m TF
I then set the indicator's TF to 1m
I will take a scalp based on the bands confluence with other PA methods, if price is being either supported or rejected.
For swings:
I tend to use a variety of TFs, including: 30m, 1H, 4H, D
I then set the indicator's TF to "Chart"
I will take a swing based on the bands confluence with other PA methods, if price is being either supported or rejected.
I also tend to use them on perpetual contracts as the volume seems to be more consistent and hence results in more accurate support and resistance.
CFB Adaptive MOGALEF Bands [Loxx]A Pine Script adaptation from MOGALEF Bands .
What are MOGALEF Bands?
Actual MOGALEF bands code is the final result of a lot of contributors. Syllables MO-GA-LEF are the initials of three of them.
The basic idea of bands: the markets are still in range, and trends that are moving ranges. The Mogalef bands try to estimate the current range and to project its on the future if prices move. This future estimation is often of great relevance and very useful, especialy for market profile users or pivot points users.
What is Composite Fractal Behavior ( CFB )?
All around you mechanisms adjust themselves to their environment. From simple thermostats that react to air temperature to computer chips in modern cars that respond to changes in engine temperature, r.p.m.'s, torque, and throttle position. It was only a matter of time before fast desktop computers applied the mathematics of self-adjustment to systems that trade the financial markets.
Unlike basic systems with fixed formulas, an adaptive system adjusts its own equations. For example, start with a basic channel breakout system that uses the highest closing price of the last N bars as a threshold for detecting breakouts on the up side. An adaptive and improved version of this system would adjust N according to market conditions, such as momentum, price volatility or acceleration.
Since many systems are based directly or indirectly on cycles, another useful measure of market condition is the periodic length of a price chart's dominant cycle, (DC), that cycle with the greatest influence on price action.
The utility of this new DC measure was noted by author Murray Ruggiero in the January '96 issue of Futures Magazine. In it. Mr. Ruggiero used it to adaptive adjust the value of N in a channel breakout system. He then simulated trading 15 years of D-Mark futures in order to compare its performance to a similar system that had a fixed optimal value of N. The adaptive version produced 20% more profit!
This DC index utilized the popular MESA algorithm (a formulation by John Ehlers adapted from Burg's maximum entropy algorithm, MEM). Unfortunately, the DC approach is problematic when the market has no real dominant cycle momentum, because the mathematics will produce a value whether or not one actually exists! Therefore, we developed a proprietary indicator that does not presuppose the presence of market cycles. It's called CFB (Composite Fractal Behavior) and it works well whether or not the market is cyclic.
CFB examines price action for a particular fractal pattern, categorizes them by size, and then outputs a composite fractal size index. This index is smooth, timely and accurate
Essentially, CFB reveals the length of the market's trending action time frame. Long trending activity produces a large CFB index and short choppy action produces a small index value. Investors have found many applications for CFB which involve scaling other existing technical indicators adaptively, on a bar-to-bar basis.
What is Jurik Volty used in the Juirk Filter?
One of the lesser known qualities of Juirk smoothing is that the Jurik smoothing process is adaptive. "Jurik Volty" (a sort of market volatility ) is what makes Jurik smoothing adaptive. The Jurik Volty calculation can be used as both a standalone indicator and to smooth other indicators that you wish to make adaptive.
What is the Jurik Moving Average?
Have you noticed how moving averages add some lag (delay) to your signals? ... especially when price gaps up or down in a big move, and you are waiting for your moving average to catch up? Wait no more! JMA eliminates this problem forever and gives you the best of both worlds: low lag and smooth lines.
Ideally, you would like a filtered signal to be both smooth and lag-free. Lag causes delays in your trades, and increasing lag in your indicators typically result in lower profits. In other words, late comers get what's left on the table after the feast has already begun.
Included:
-Color bars
-Fill levels
ATR BandsIn many strategies, it's quite common to use a scaled ATR to help define a stop-loss, and it's not uncommon to use it for take-profit targets as well. While it's possible to use the built-in ATR indicator and manually calculate the offset value, we felt this wasn't particularly intuitive or efficient, and could lead to the potential for miscalculations. And while there are quite a few indicators that plot ATR bands in some form or another already on TV, we could not find one that actually performed the exact way that we wanted. They all had at least one of the following gaps:
The ATR offset was not configurable (usually hard-coded to be based off the high or low, while we generally prefer to use close)
It would only print a single band (either the upper or lower), which would require the same indicator to be added twice
The ATR scaling factor was either not configurable or only stepped in whole numbers (often time fractional factors like 1.5 yield better results)
To that end, we took to making this enhanced version to meet all of the above requirements. While we were doing so, we decided to take this opportunity to also make some non-functional enhancements as well:
Updated the indicator to the most recent version of Pine
Updated the indicator definition to allow alternate (non-chart) timeframe usage
Made the input types explicitly defined to improve consistency
Updated the inputs with appropriate minimum values and step sizes where appropriate
Separated settings into logical groups
Added helptext to the indicator settings noting usage and common settings values
Explicitly titled the on-chart plots of the ATR bands so that they can more easily be identified and referenced in other indicators/scripts, as well as the Data Window
Food for thought : When looking at some of the behaviors of these ATR bands, you can see that when price first levels out, you can draw a "consolidation zone" from the first peak of the upper ATR band to the first valley of the lower ATR band that price will generally respect. Look for price to break and close outside of that zone. When that happens, price will usually (but not always) make a notable move in that direction, which can be used as either a potential trigger or as an additional confluence with other indicators/price action.
Finally, while we have made what we feel are some noteworthy updates and enhancements to this indicator, and have every intention of continuing to do so as we find worthy opportunities for enhancement, credit is still due to the original author: AlexanderTeaH
Trending Bollinger Bands by SiddWolfBollinger Bands are mostly used for trend reversal. I believe they should be used for Trend Continuation and Trend Confirmation.
In this Trending Bollinger Bands script you will see two bands drawn on chart. The Upper band is suggestive of Uptrend and Lower Band is suggestive of Downtrend Market. It just provides the guidance of where the market is now and where it is headed. It is not to be used as a standalone indicator. Use this to confirm your hypothesis of Uptrend or Downtrend.
Bollinger Bands Trend
When the price crosses the moving average it is interpreted as the price is gonna continue in that direction. But most of the time it is a fake breakout. With this script you get an additional confirmation so that you know it is not a fake breakout and the price have caught the trend.
Bollinger Bands Reversal:
This indicator can also work for reversal. For example when price closes outside the outer bands, it is most likely that the trend is gonna reverse. Don't just enter the trade wait for some other confirmation as reversal trading is more complicated.
Confluence:
Confluence is the key factor for profitable trading. Don't use this indicator as standalone indicator instead combine it with other indicators and price action. Like the divergence occurring when the price is outside the bands is suggestive of trend reversal. I have created a non-delay, non-repaint indicator for finding divergence. I'd soon publish that script. Stay tuned.
Settings is the Key:
Try to play around with the settings. It is a simple yet effective indicator. Change the moving average type or length. I've found moving average RMA or WMA works better than SMA. Find the best setting that works with your setup. Set the Band Source as High/Low to make the outer bands more extreme.
Conclusion:
This is my first script but it isn't my last. I've created quite a few gems that I'm gonna publish soon. If you have any questions or suggestions feel free to comment below. I'd love to connect with you. Thank you.
multicolor Bollinger Bands (BB <-> KC)Concept:
After every low volatile phase comes a high volatile phase and after every high volatile phase comes a low volatile phase.
If the Bollinger bands are smaller then the Keltner channel (colored red), the price action is low in volatility… meaning a breakout (colored green) will happen soon.
If Bollinger band is bigger than the Keltner channel = green
If Bollinger band is smaller than the Keltner channel = red
Displaying the Keltner Channel is optional
If multicolor BB is disabled, BB color = blue (default color)
Customise colors to your liking under settings -> style
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To get alerts for all coins
1. visit » tradingview.com/crypto-screener
2. set the filter to »
Bollinger Upper Band (20) below Keltner Channels Upper Band (20)
Bollinger Lower Band (20) above Keltner Channels Lower Bands (20)
3. add your own custom filters, like: exchange, marketcap, etc…
4. choose the timeframe you want
5. enable alerts
EQ + Bandas Pro 📊 EQ + Bands Pro is an advanced indicator built on OHLC analysis. It calculates a synthetic equilibrium price and plots dynamic, robust bands that adapt to volatility while filtering outliers. The tool highlights zones of overvaluation and undervaluation, helping traders identify key imbalances, potential reversals, and trend confirmations.
Double Median ATR Bands | MisinkoMasterThe Double Median ATR Bands is a version of the SuperTrend that is designed to be smoother, more accurate while maintaining a good speed by combining the HMA smoothing technique and the median source.
How does it work?
Very simple!
1. Get user defined inputs:
=> Set them up however you want, for the result you want!
2. Calculate the Median of the source and the ATR
=> Very simple
3. Smooth the median with √length (for example if median length = 9, it would be smoothed over the length of 3 since 3x3 = 9)
4. Add ATR bands like so:
Upper = median + (atr*multiplier)
Lower = median - (atr*multiplier)
Trend Logic:
Source crossing over the upper band = uptrend
Source crossing below the lower band = downtrend
Enjoy G´s!
