EMA Bollinger Bands with customized std dev and moving averageTo use EMA with band you need to set input parameter named as "TypeOfMa" to 1.
If you set TypeOfMa = 1 then it will use EMA average for Bollinger bands.
If you set TypeOfMa = 0 then it will use MA average for Bollinger bands.
חפש סקריפטים עבור "bands"
Floating Bands of the Argentine Peso (Sebastian.Waisgold)
The BCRA ( Central Bank of the Argentine Republic ) announced that as of Monday, April 15, 2025, the Argentine Peso (USDARS) will float within a system of divergent exchange rate bands.
The upper band was set at ARS 1400 per USD on 15/04/2025, with a +1% monthly adjustment distributed daily, rising by a fraction each day.
The lower band was set at ARS 1000 per USD on 15/04/2025, with a –1% monthly adjustment distributed daily, falling by a fraction each day.
This indicator is crucial for anyone trading USDARS, since the BCRA will only intervene in these situations:
- Selling : if the Peso depreciates against the USD above the upper band .
- Buying : if the Peso appreciates against the USD below the lower band .
Therefore, this indicator can be used as follows:
- If USDARS is above the upper band , it is “expensive” and you may sell .
- If USDARS is below the lower band , it is “cheap” and you may buy .
It can also be applied to other assets such as:
- USDTARS
- Dollar Cable / CCL (Contado con Liquidación) , derived from the BCBA:YPFD / NYSE:YPF ratio.
A mid band —exactly halfway between the upper and lower bands—has also been added.
Once added, the indicator should look like this:
In the following image you can see:
- Upper Floating Band
- Lower Floating Band
- Mid Floating Band
User Configuration
By double-clicking any line you can adjust:
- Start day (Dia de incio), month (Mes de inicio), and year (Año de inicio)
- Initial upper band value (Valor inicial banda superior)
- Initial lower band value (Valor inicial banda inferior)
- Monthly rate Tasa mensual %)
It is recommended not to modify these settings for the Argentine Peso, as they reflect the BCRA’s official framework. However, you may customize them—and the line colors—for other assets or currencies implementing a similar band scheme.
M2 GLI SD BandsHighly customizable M2 Global Liquidity Index with adaptive standard deviation bands.
The SD bands incorporate data from M2 with varying lags to capture M2's full impact on the price of Bitcoin spread across multiple weeks.
EMAs are used for smoothing. Offset, smoothing, and other features are customizable.
Swing BandsThis indicator is a result of experimentation with price action of candle high and lows for quantifying reversals and trend continuation.
The band area shows trend reversal incoming and possible chop.
Middle line is the trend reversal price level. Candle colors change if the close price is above or below the middle line.
Long and short positions can be taken when above or below the bands.
Trend continuations are in effect when price retraces into the bands and breaks above or below in the same direction of the trend.
StDev BandsThis is a "bands"-type indicator. It was developed out of my Sharpe Ratio indicator . It uses the standard deviation of returns as basis for drawing the bands. I'm going to update this indicator as the other indicator evolves. Please be sure you know how to calculate Sharpe Ratio and check out the Sharpe Ratio indicator as well. This will help you understand the purpose of this indicator a bit more.
As a very short introduction. Many investors use the standard deviation of returns as risk measurement . I admit the defaults of this indicator aren't perfect. Normally investors use the standard deviation over a 1 year period. Traditional finance uses 265 days, and because crypto never sleeps, we could use 365. I defaulted it to 20.
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 :)
Standard Deviation BandsStandard Deviation Bands
คำอธิบายอินดิเคเตอร์:
อินดิเคเตอร์ SD Bands (Standard Deviation Bands) เป็นเครื่องมือวิเคราะห์ทางเทคนิคที่ออกแบบมาเพื่อวัดความผันผวนของราคาและระบุโอกาสในการเทรดที่อาจเกิดขึ้น อินดิเคเตอร์นี้จะแสดงผลเป็นเส้นขอบ 2 เส้นบนกราฟราคาโดยตรง โดยอ้างอิงจากค่าเฉลี่ยเคลื่อนที่ (Moving Average) และค่าส่วนเบี่ยงเบนมาตรฐาน (Standard Deviation)
* เส้นบน (Upper Band): แสดงระดับที่ราคาเคลื่อนไหวสูงกว่าค่าเฉลี่ย
* เส้นล่าง (Lower Band): แสดงระดับที่ราคาเคลื่อนไหวต่ำกว่าค่าเฉลี่ย
ความกว้างของช่องระหว่างเส้นทั้งสองบ่งบอกถึงระดับความผันผวนของตลาดในปัจจุบัน
วิธีการใช้งานอย่างละเอียด:
คุณสามารถนำอินดิเคเตอร์ SD Bands ไปประยุกต์ใช้ได้หลายวิธีเพื่อประกอบการตัดสินใจ ดังนี้:
1. การใช้เป็นแนวรับ-แนวต้านแบบไดนามิก (Dynamic Support & Resistance)
* แนวรับ: เมื่อราคาวิ่งลงมาแตะหรือเข้าใกล้เส้นล่าง (เส้นสีน้ำเงิน) เส้นนี้อาจทำหน้าที่เป็นแนวรับชั่วคราวและมีโอกาสที่ราคาจะเด้งกลับขึ้นไปหาเส้นกลาง
* แนวต้าน: เมื่อราคาวิ่งขึ้นไปแตะหรือเข้าใกล้เส้นบน (เส้นสีแดง) เส้นนี้อาจทำหน้าที่เป็นแนวต้านชั่วคราวและมีโอกาสที่ราคาจะย่อตัวลงมา
2. การวัดความผันผวนและสัญญาณ Breakout
* ช่วงตลาดสงบ (Low Volatility): เมื่อเส้น SD ทั้งสองเส้นบีบตัวเข้าหากันเป็นช่องที่แคบมาก (คล้ายกับ Bollinger Squeeze) แสดงว่าตลาดมีความผันผวนต่ำมาก ซึ่งมักจะเป็นสัญญาณว่ากำลังจะเกิดการเคลื่อนไหวครั้งใหญ่ (Breakout)
* ช่วงตลาดเป็นเทรนด์ (High Volatility): เมื่อเส้น SD ขยายตัวกว้างออกอย่างรวดเร็ว พร้อมกับที่ราคาวิ่งอยู่นอกขอบ แสดงว่าตลาดเข้าสู่ช่วงเทรนด์ที่แข็งแกร่งและมีโมเมนตัมสูง
3. สัญญาณการกลับตัว (Reversal Signals)
* เมื่อราคาปิดแท่งเทียน นอกเส้น SD Bands อย่างชัดเจน (โดยเฉพาะหลังจากที่เทรนด์นั้นดำเนินมานาน) อาจเป็นสัญญาณว่าแรงซื้อ/แรงขายเริ่มอ่อนกำลังลง และมีโอกาสที่จะเกิดการกลับตัวของราคาในไม่ช้า
การตั้งค่าอินพุต (Input Parameters):
* ระยะเวลา (Length): กำหนดจำนวนแท่งเทียนที่ใช้ในการคำนวณค่าเฉลี่ยและ SD
* 20: สำหรับการวิเคราะห์ระยะสั้นถึงกลาง
* 50 หรือ 100: สำหรับการวิเคราะห์ระยะยาว
* ตัวคูณ (Multiplier): กำหนดระยะห่างของเส้น SD จากค่าเฉลี่ย
* 1.0 - 2.0: เส้นจะอยู่ใกล้ราคามากขึ้น ทำให้เกิดสัญญาณบ่อยขึ้น
* 2.0 - 3.0: เส้นจะอยู่ห่างจากราคามากขึ้น ทำให้เกิดสัญญาณที่น่าเชื่อถือมากขึ้น แต่จะเกิดไม่บ่อย
ข้อควรระวังและคำเตือน:
* อินดิเคเตอร์นี้เป็นเพียง เครื่องมือวิเคราะห์ เพื่อช่วยในการตัดสินใจ ไม่ใช่สัญญาณการซื้อขายที่ถูกต้อง 100%
* ควรใช้ร่วมกับเครื่องมืออื่นๆ เช่น RSI, MACD, หรือ Volume เพื่อยืนยันสัญญาณ
* การเทรดมีความเสี่ยงสูง ควรบริหารจัดการความเสี่ยงและตั้งจุด Stop Loss ทุกครั้ง
คุณสามารถใช้โครงสร้างนี้ในการเขียนโพสต์บน TradingView ได้เลยนะครับ ขอให้ประสบความสำเร็จกับการโพสต์อินดิเคเตอร์ของคุณครับ!
English
Standard Deviation Bands
Indicator Description:
The SD Bands (Standard Deviation Bands) indicator is a powerful technical analysis tool designed to measure price volatility and identify potential trading opportunities. The indicator displays two dynamic bands directly on the price chart, based on a moving average and a customizable standard deviation multiplier.
* Upper Band: Indicates price levels above the moving average.
* Lower Band: Indicates price levels below the moving average.
The width of the channel between these two bands provides a clear picture of current market volatility.
Detailed User Guide:
You can use SD Bands in several ways to enhance your trading decisions:
1. Dynamic Support and Resistance:
These bands can act as dynamic support and resistance levels.
* Support: When the price moves down and touches or approaches the lower band, it can act as support, offering the possibility of a rebound to the average.
* Resistance: When the price moves up and touches or approaches the upper band, it can act as resistance, offering the possibility of a rebound.
2. Volatility Measurement and Breakout Signals:
* Low Volatility (Squeeze): When the two bands converge and form a narrow channel. Indicates very low market volatility. This condition often occurs before significant price movements or breakouts.
* High Volatility (Expansion): When the bands expand and widen rapidly, it indicates that the market is entering a period of strong trending momentum with high momentum.
3. Reversal Signals:
* When the price closes significantly outside the SD Bands (especially after a long-term trend), it may signal that the current momentum has expired and a reversal may be imminent.
Input Parameters:
The indicator's parameters are fully customizable to suit your trading style:
* Length: Defines the number of bars used to calculate the moving average and standard deviation.
* 20: Suitable for short- to medium-term analysis.
* 50 or 100: Suitable for long-term trend analysis.
* Multiplier: Adjusts the sensitivity of the signal bars.
* 1.0 - 2.0: Creates narrower signal bars, leading to more frequent signals.
* 2.0 - 3.0: Creates wider signal bars, providing fewer but potentially more significant signals.
Important Warning:
* This indicator is an analytical tool only. It does not provide guaranteed buy or sell signals.
* Always use it in conjunction with other indicators (such as RSI, MACD, and Volume) for confirmation.
* Trading involves high risk. Proper risk management, including the use of stop-loss orders, is recommended.
You can use this structure for your posts on TradingView. Good luck with your indicators!
Nami Bands with Future Projection [FXSMARTLAB]The Nami Bands ( Inspired by "Nami", meaning "wave" in Japanese) are two dynamic bands around price data: an upper band and a lower band. These bands are calculated based on an Asymmetric Linear Weighted Moving Average of price and a similarly asymmetric weighted standard deviation. This weighting method emphasizes recent data without overreacting to short-term price changes, thus smoothing the bands in line with prevailing market conditions.
Advantages and Benefits of Using the Indicator
* Volatility Analysis: The bands expand and contract with market volatility, helping traders assess periods of high and low volatility. Narrow bands indicate low volatility and potential consolidation, while wide bands suggest increased volatility and potential price movement.
* Dynamic Support and Resistance Levels: By adapting to recent trends, the bands serve as dynamic support (lower band) and resistance (upper band) levels, which traders can use for entry and exit signals.
* Overbought and Oversold Conditions: When prices reach or cross the bands’ outer limits, it may signal overbought (upper band) or oversold (lower band) conditions, suggesting possible reversals or trend slowdowns.
* Trend Confirmation and Continuation: The slope of the central moving average confirms trend direction. An upward slope generally indicates a bullish trend, while a downward slope suggests a bearish trend.
* Anticipating Breakouts and Reversals: The projected bands help identify where price movements may head, allowing traders to anticipate potential breakouts or reversals based on projected support and resistance.
Indicator Parameters
Source (src): The price data used for calculations, by default set to the average of high, low, and close (hlc3).
Length: The period over which calculations are made, defaulted to 50 periods.
Projection Length: The length for future band projection, defaulted to 20 periods.
StdDev Multiplier (mult): A multiplier for the standard deviation, defaulted to 2.0.
Internal Calculations
1. Asymmetric Linear Weighted Moving Average of Price
The indicator uses an Asymmetric Linear Weighted Moving Average (ALWMA) to calculate a central value for the price.
Asymmetric Weighting: This weighting technique assigns the highest weight to the most recent value, with weights decreasing linearly as the data points become older. This structure provides a nuanced focus on recent price trends, while still reflecting historical price levels.
2. Asymmetric Weighted Standard Deviation
The standard deviation in this indicator is also calculated using asymmetric weighting:
Purpose of Asymmetric Weighted Standard Deviation: Rather than aiming for high sensitivity to recent data, this standard deviation measure smooths out volatility by integrating weighted values across the length period, stabilizing the overall measurement of price variability.
This approach yields a balanced view of volatility, capturing broader market trends without being overly reactive to short-lived changes.
3. Upper and Lower Bands
The upper and lower bands are created by adding and subtracting the asymmetric weighted standard deviation from the asymmetric weighted average of price. This creates a dynamic envelope that adjusts to both recent price trends and the smoothed volatility measure:
These bands represent adaptable support and resistance levels that shift with recent market volatility.
Future Band Projection
The indicator provides a projection of the bands based on their current slope.
1. Calculating the Slope of the Bands
The slope for each band is derived from the difference between the current and previous values of each band.
2. Projecting the Bands into the Future
For each period into the future, up to the defined Projection Length, the bands are projected using the current slope.
This feature offers an anticipated view of where support and resistance levels may move, providing insight for future market behavior based on current trends.
PTS - Bollinger Bands with Trailing StopPTS - Bollinger Bands with Trailing Stop Strategy
Overview
The "PTS - Bollinger Bands with Trailing Stop" strategy is designed to capitalize on strong bullish market movements by combining the Bollinger Bands indicator with a dynamic trailing stop based on the Average True Range (ATR). This strategy aims to enter long positions during upward breakouts and protect profits through an adaptive exit mechanism.
Key Features
1. Bollinger Bands Indicator
Basis Moving Average Type: Choose from SMA, EMA, SMMA, WMA, or VWMA for the Bollinger Bands' basis line. Length: Adjustable period for calculating the moving average and standard deviation (default is 20). Standard Deviation Multiplier (StdDev): Determines the width of the bands (default is 2.0). Source: Select the price data used for calculations (default is the closing price).
2. Entry Conditions
The strategy enters a long position when the closing price crosses above the upper Bollinger Band , indicating strong upward momentum.
3. Exit Conditions
Trailing Stop Loss: Implemented using ATR to adjust dynamically with market volatility. ATR Length: Period for calculating ATR (default is 14). ATR Multiplier for Trailing Stop: Determines how closely the trailing stop follows the price (default is 2.0). Close Below Lower Bollinger Band: The strategy exits the long position if the closing price crosses below the lower Bollinger Band .
4. Risk Management
Commission and Slippage: Commission is set at 0.1%; slippage is set to 3. Position Sizing: Uses 100% of equity per trade (adjustable).
5. Date Range Filter
Specify the time period during which the strategy is active. Start Date: January 1, 2018. End Date: December 31, 2069.
Customizable Inputs
BB Length: Adjust the period for Bollinger Bands calculation. BB StdDev: Modify the standard deviation multiplier. Basis MA Type: Select the moving average type. Source: Choose the price data source. Offset: Shift the Bollinger Bands on the chart. ATR Length: Set the period for ATR calculation. ATR Multiplier for Trailing Stop: Adjust the trailing stop sensitivity.
Thought for 1m 7s
Description for the "PTS - Bollinger Bands with Trailing Stop" Strategy
PTS - Bollinger Bands with Trailing Stop Strategy
Overview
The "PTS - Bollinger Bands with Trailing Stop" strategy is designed to capitalize on strong bullish market movements by combining the Bollinger Bands indicator with a dynamic trailing stop based on the Average True Range (ATR). This strategy aims to enter long positions during upward breakouts and protect profits through an adaptive exit mechanism.
Key Features
1. Bollinger Bands Indicator
Basis Moving Average Type: Choose from SMA, EMA, SMMA, WMA, or VWMA for the Bollinger Bands' basis line. Length: Adjustable period for calculating the moving average and standard deviation (default is 20). Standard Deviation Multiplier (StdDev): Determines the width of the bands (default is 2.0). Source: Select the price data used for calculations (default is the closing price).
2. Entry Conditions
The strategy enters a long position when the closing price crosses above the upper Bollinger Band , indicating strong upward momentum.
3. Exit Conditions
Trailing Stop Loss: Implemented using ATR to adjust dynamically with market volatility. ATR Length: Period for calculating ATR (default is 14). ATR Multiplier for Trailing Stop: Determines how closely the trailing stop follows the price (default is 2.0). Close Below Lower Bollinger Band: The strategy exits the long position if the closing price crosses below the lower Bollinger Band .
4. Risk Management
Commission and Slippage: Commission is set at 0.1%; slippage is set to 3. Position Sizing: Uses 100% of equity per trade (adjustable).
5. Date Range Filter
Specify the time period during which the strategy is active. Start Date: January 1, 2018. End Date: December 31, 2069.
Customizable Inputs
BB Length: Adjust the period for Bollinger Bands calculation. BB StdDev: Modify the standard deviation multiplier. Basis MA Type: Select the moving average type. Source: Choose the price data source. Offset: Shift the Bollinger Bands on the chart. ATR Length: Set the period for ATR calculation. ATR Multiplier for Trailing Stop: Adjust the trailing stop sensitivity.
How the Strategy Works
1. Initialization
Calculates Bollinger Bands and ATR based on selected parameters.
2. Entry Logic
Opens a long position when the closing price exceeds the upper Bollinger Band.
3. Exit Logic
Uses a trailing stop loss based on ATR. Exits if the closing price drops below the lower Bollinger Band.
4. Date Filtering
Executes trades only within the specified date range.
Advantages
Adaptive Risk Management: Trailing stop adjusts to market volatility. Simplicity: Clear entry and exit signals. Customizable Parameters: Tailor the strategy to different assets or conditions.
Considerations
Aggressive Position Sizing: Using 100% equity per trade is high-risk. Market Conditions: Best in trending markets; may produce false signals in sideways markets. Backtesting: Always test on historical data before live trading.
Disclaimer
This strategy is intended for educational and informational purposes only. Trading involves significant risk, and past performance is not indicative of future results. Assess your financial situation and consult a financial advisor if necessary.
Usage Instructions
1. Apply the Strategy: Add it to your TradingView chart. 2. Configure Inputs: Adjust parameters to suit your style and asset. 3. Analyze Backtest Results: Use the Strategy Tester. 4. Optimize Parameters: Experiment with input values. 5. Risk Management: Evaluate position sizing and incorporate risk controls.
Final Notes
The "PTS - Bollinger Bands with Trailing Stop" strategy provides a framework to leverage momentum breakouts while managing risk through adaptive trailing stops. Customize and test thoroughly to align with your trading objectives.
Volatility Zones (VStop + Bands) — Fixed (v2)📝 What this indicator is
This script is called “Volatility Zones (VStop + Bands)”.
It is an ATR-based volatility indicator that combines dynamic volatility bands, a Volatility Stop line (VStop), and volatility spike detection into a single tool.
Unlike moving average–based indicators, this tool does not rely on averages of price direction. Instead, it measures the market’s true volatility and reacts to expansions or contractions in price ranges.
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⚙️ How it is built
The indicator uses several volatility-based components:
1. Average True Range (ATR)
o ATR is calculated over a user-defined length.
o It measures how much price typically moves in a given number of bars, making it the foundation of this indicator.
2. Volatility Bands
o Upper band = close + ATR × factor
o Lower band = close - ATR × factor
o The area between them is shaded.
o This gives traders an immediate visual sense of market volatility width — wide bands = high volatility, narrow bands = quiet market.
3. Volatility Stop (VStop)
o A stateful trailing stop based on ATR.
o It tracks the highest (or lowest) price in the current trend and places a stop offset by ATR × multiplier.
o When price crosses this stop, the indicator flips trend direction.
o This creates a dynamic stop-and-reverse mechanism that adapts to volatility.
4. Trend Zones
o When the trend is bullish, the stop is green and the chart background is shaded softly green.
o When bearish, the stop is red and the background is shaded softly red.
o This makes the market’s directional bias visually clear at all times.
5. Flip Signals (Buy/Sell Arrows)
o Whenever the VStop flips, arrows appear:
Green BUY arrows below price when the trend turns bullish.
Red SELL arrows above price when the trend turns bearish.
o These are also tied to built-in alerts for automation.
6. Volatility Spike Detection
o The script compares current ATR to its recent average.
o If ATR suddenly expands above a threshold, a small yellow “VOL” marker appears at the top of the chart.
o This highlights potential breakout phases or unusual volatility events.
7. Stop Labels
o At every trend flip, a small label appears at the bar, showing the exact stop level.
o This makes it easy to use the stop as a reference for risk management.
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📊 How it works in practice
• When price is above the VStop line, the market is considered in an uptrend.
• When price is below the VStop line, the market is in a downtrend.
• The bands expand/contract with volatility, helping traders gauge risk and position sizing.
• Flip arrows signal when trend direction changes.
• Volatility spikes warn traders that the market is entering a higher-risk phase, often before strong moves.
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🎯 How it may help traders
• Trend following → Helps traders identify whether the market is trending up or down.
• Stop placement → Provides a dynamic stop level that adjusts to volatility.
• Volatility awareness → Shaded bands and spike markers show when the market is likely to become unstable.
• Trade timing → Flip arrows and labels help identify potential entry or exit points.
• Risk management → Wide bands indicate higher risk; narrow bands suggest safer, tighter ranges.
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🌍 In what markets it is useful
Because the indicator is based purely on volatility, it works across all asset classes and timeframes:
• Stocks & ETFs → Helps identify breakouts and long-term trends.
• Forex → Very useful in spot FX where volatility shifts frequently.
• Crypto → ATR reacts strongly to high volatility, helping traders adapt stops dynamically.
• Futures & Commodities → Great for tracking trending commodities and managing risk.
Scalpers, swing traders, and position traders can all benefit by adjusting the ATR length and multipliers to suit their trading style.
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💡 Originality of this script
This is not just a mashup of existing indicators. It integrates:
• ATR-based Volatility Bands for context,
• A stateful Volatility Stop (adapted and rewritten cleanly),
• Flip arrows and labels for actionable trading signals,
• Volatility spike detection to highlight regime shifts.
The result is a comprehensive volatility-aware trading tool that goes beyond just plotting ATR or trend stops.
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🔔 Alerts
• Buy Flip → triggers when the trend changes bullish.
• Sell Flip → triggers when the trend changes bearish.
Traders can connect these alerts to automated strategies, bots, or notification systems.
Jurik Price Bands and Range Box [BigBeluga]Jurik Price Bands and Range Box
The Jurik Price Bands and Range Box - BigBeluga indicator is an advanced technical analysis tool that combines Jurik Moving Average (JMA) based price bands with a dynamic range box. This versatile indicator is designed to help traders identify trends, potential reversal points, and price ranges over a specified period.
🔵 KEY FEATURES
● Jurik Price Bands
Utilizes Jurik Moving Average for smoother, more responsive bands
//@function Calculates Jurik Moving Average
//@param src (float) Source series
//@param len (int) Length parameter
//@param ph (int) Phase parameter
//@returns (float) Jurik Moving Average value
jma(src, len, ph) =>
var float jma = na
var float e0 = 0.0
var float e1 = 0.0
var float e2 = 0.0
phaseRatio = ph < -100 ? 0.5 : ph > 100 ? 2.5 : ph / 100 + 1.5
beta = 0.45 * (len - 1) / (0.45 * (len - 1) + 2)
alpha = math.pow(beta, phaseRatio)
e0 := (1 - alpha) * src + alpha * nz(e0 )
e1 := (src - e0) * (1 - beta) + beta * nz(e1 )
e2 := (e0 + phaseRatio * e1 - nz(jma )) * math.pow(1 - alpha, 2) + math.pow(alpha, 2) * nz(e2 )
jma := e2 + nz(jma )
jma
Consists of an upper band, lower band, and a smooth price line
Bands adapt to market volatility using Jurik MA on ATR
Helps identify potential trend reversal points and overextended market conditions
● Dynamic Range Box
Displays a box representing the price range over a specified period
Calculates high, low, and mid-range prices
Option for adaptive mid-range calculation based on average price
Provides visual representation of recent price action and volatility
● Price Position Indicator
Shows current price position relative to the mid-range
Displays percentage difference from mid-range
Color-coded for quick trend identification
● Dashboard
Displays key information including current price, range high, mid, and low
Shows trend direction based on price position relative to mid-range
Provides at-a-glance market context
🔵 HOW TO USE
● Trend Identification
Use the middle of the Range Box as the primary trend reference point
Price above the middle of the Range Box indicates an uptrend
Price below the middle of the Range Box indicates a downtrend
The bar on the right shows the percentage distance of the close from the middle of the box
This percentage indicates both trend direction and strength
Refer to the dashboard for quick trend direction confirmation
● Potential Reversal Points
Upper and lower Jurik Bands can indicate potential trend reversal points
Price reaching or exceeding these bands may suggest overextended conditions
Watch for price reaction at these levels for possible trend shifts or pullbacks
Range Box high and low can serve as additional reference points for price action
● Range Analysis
Use Range Box to gauge recent price volatility and trading range
Mid-range line can act as a pivot point for short-term price movements
Percentage difference from mid-range helps quantify price position strength
🔵 CUSTOMIZATION
The Jurik Price Bands and Range Box indicator offers several customization options:
Adjust Range Box length for different timeframe analysis
Toggle between standard and adaptive mid-range calculation
Standard:
Adaptive:
Modify Jurik MA length and deviation for band calculation
Toggle visibility of Jurik Bands
By fine-tuning these settings, traders can adapt the indicator to various market conditions and personal trading strategies.
The Jurik Price Bands and Range Box indicator provides a multi-faceted approach to market analysis, combining trend identification, potential reversal point detection, and range analysis in one comprehensive tool. The use of Jurik Moving Average offers a smoother, more responsive alternative to traditional moving averages, potentially providing more accurate signals.
This indicator can be particularly useful for traders looking to understand market context quickly, identify potential reversal points, and assess current market volatility. The combination of dynamic bands, range analysis, and the informative dashboard provides traders with a rich set of data points to inform their trading decisions.
As with all technical indicators, it's recommended to use the Jurik Price Bands and Range Box in conjunction with other forms of analysis and within the context of a well-defined trading strategy. While this indicator provides valuable insights, it should be considered alongside other factors such as overall market conditions, volume, and fundamental analysis when making trading decisions.
Multi Timeframe Bull Market Support BandsMulti Timeframe Bull Market Support Bands (BMSB) Indicator
Concept and Functionality:
The Multi Timeframe Bull Market Support Bands (BMSB) indicator is a powerful tool designed to identify and visualize support levels across multiple timeframes simultaneously. The primary concept behind BMSB is to plot dynamic support bands derived from moving averages (MAs) that adapt to the prevailing bullish conditions across different timeframes. These bands act as support and resistance (S/R) levels, providing traders with critical insights into potential price bounce areas and market direction.
Key Features:
Multi Timeframe Analysis:
- The indicator plots bull market support bands for the following timeframes concurrently: Chart (with price prediction), 5 minutes (5m), 15 minutes (15m), 1 hour (1h or 60), 4 hours (4h or 240), Daily (D), 3 Days (3D), and Weekly (W).
- These bands allow traders to see how the price interacts with different support levels, potentially bouncing between them as it moves across timeframes.
Dynamic Band Visibility:
- Bands from shorter timeframes are only displayed in relevant higher timeframes:
- 5m is shown only in timeframes ≤ 15m.
- 15m is shown only in timeframes ≤ 1h.
- 1h is shown only in timeframes ≤ 4h.
- 4h is shown only in timeframes ≤ D.
- D and 3D are shown only in timeframes ≤ W.
- W is always shown.
Customizable Moving Averages:
- The period of the moving averages used to calculate the support bands can be adjusted. Any changes made will be applied across all bands to maintain consistency.
Future Band Prediction:
- If the current timeframe lacks sufficient bars to calculate a moving average, the indicator shows a blue line on the bar where the band will appear. When a new band appears on the current bar, it is highlighted in purple, allowing traders to notice the first value of the new band.
- These new bands can act as magnets, attracting price action. Knowing when a new band will appear helps traders anticipate whether the price will be drawn to the upcoming band or potentially break through it.
Benefits:
- Enhanced Market Insight: By layering support bands from multiple timeframes, traders gain a comprehensive view of market dynamics and potential bounce areas.
- Improved Decision-Making: The ability to see upcoming support bands and how the price interacts with them aids in making more informed trading decisions.
- Customization and Flexibility: Adjustable moving average periods ensure that the indicator can be tailored to fit various trading strategies and market conditions.
The Multi Timeframe Bull Market Support Bands indicator is a versatile and insightful tool for traders aiming to leverage multi-timeframe analysis to enhance their trading strategies and better understand market behavior.
[blackcat] L1 Dynamic Multi-Layer Bollinger Bands## Overview
The L1 Dynamic Multi-Layer Bollinger Bands script is a custom indicator designed to provide a comprehensive analysis of price movements using Bollinger Bands, a widely-used technical analysis tool. The Bollinger Bands consist of a middle band being a simple moving average (SMA) of the close price, and an upper and lower band that are respectively 1.382 and 2.56 times the standard deviation of the close price over a 21-day period.
## How to Use
1. **Add the Indicator to Your Chart**: Click on the "Add to Chart" button in the Pine-Script editor. This will add the L1 Dynamic Multi-Layer Bollinger Bands to your chart.
2. **Interpreting the Bands**: The Bollinger Bands are plotted on your chart. The middle band is the 21-day SMA of the close price. The upper band is 1.382 times the standard deviation above the middle band, and the lower band is 1.382 times the standard deviation below the middle band.
3. **Analyzing the Bands**: When the price moves above the upper band, it is considered an overbought condition. When the price moves below the lower band, it is considered an oversold condition. These are potential areas of support and resistance.
4. **Multi-Layer Structure**: The script also calculates two additional bands, `up2` and `loow2`, which are respectively 2.56 times the standard deviation above and below the middle band. These bands provide a more layered perspective on the volatility of the price.
5. **Color Coding**: The area between the upper and lower bands is filled with a color that indicates the direction of the price movement. Green indicates an upward trend, and red indicates a downward trend.
## Disclaimer
The information provided by this script is for educational purposes only and should not be used as the sole basis for any trading decisions. Trading involves risk, and it is possible to lose money when trading stocks and other financial instruments. Use this script at your own risk.
Kelbol Bands @shrilss The Kelbol Bands are designed to provide traders with insights into price volatility and potential trend reversal points. By combining Bollinger Bands (BB) and Keltner Channels (KC), this indicator offers a versatile approach to analyzing market dynamics.
Key Features:
- Customizable Parameters: The indicator allows traders to adjust parameters such as BB Length, BB Multiplier, KC Length, KC Multiplier, and ATR Length to suit their trading preferences and strategies.
- Timeframe Flexibility: Traders can select different timeframes for calculating Bollinger Bands and Keltner Channels independently, enhancing adaptability to various market conditions.
- Visual Representation: The indicator plots Upper, Basis (Midline), and Lower Kelbol Bands, as well as Upper, Basis, and Lower Keltner Channels and Bollinger Bands separately. This visual representation aids traders in identifying potential support and resistance levels, as well as trend direction.
- Toggle Display: Users have the option to toggle the visibility of each component individually, providing flexibility in focusing on specific aspects of price action.
Calculation Method:
- Bollinger Bands (BB) are calculated based on the selected BB Length and BB Multiplier. The upper and lower bands are derived from the simple moving average (SMA) of the price and the standard deviation of the price series.
- Keltner Channels (KC) are determined using the selected KC Length, KC Multiplier, and ATR Length. The basis (midline) of the channel is derived from the SMA of the price, while the upper and lower channels are calculated based on the average true range (ATR).
- Kelbol Bands (KBL) are a combination of Bollinger Bands and Keltner Channels. The upper, basis, and lower bands of KBL are calculated as the averages of the corresponding values of Bollinger Bands and Keltner Channels.
Donchian MA Bands [LuxAlgo]The Donchian MA Bands script is a complete trend indicator derived from the popular Donchian channel indicator as well as various customizable moving averages to estimate trend direction and build support/resistance levels & zones.
🔶 USAGE
The indicator outputs various elements, the main ones being a lower dynamic zone (blue by default), an upper dynamic zone (in orange by default), and one support and resistance level/zones (red/green by default).
A prominent lower zone is indicative of an uptrend, while a prominent upper zone is indicative of a downtrend. These zones can be used as support/resistance as well.
Support/resistance zones and levels can be used using a breakout methodology or to determine price bounced if a level was tested multiple times.
The indicator contains various modes affecting the output of the indicator, described below.
🔹 Clouds
Clouds return one upper/lower dynamic zone and look/act similarly to a trailing stop. Price over the lower zone is indicative of an uptrend, and price under the upper zone is indicative of a downtrend.
🔹 Upper Band
The upper band mode returns a dynamic zone closer to prices during an uptrend, and farther away during a downtrend.
This band can act as a support during uptrends.
🔹 Lower Band
The lower band mode returns a dynamic zone closer to prices during an uptrend, and farther away during a downtrend.
This band can act as a resistance during downtrends.
🔹 Bands
Bands return both upper and lower zones, the zones are more apparent depending on the price trend direction, with uptrends being indicated by a more visible lower zone, and downtrends being indicated by a more visible upper zone.
Breakout dots are highlighted when price breakout the indicator displayed extremities, and can be indicative of a confirmed trend reversal.
These breakouts can be more effective for trend following during trending markets. Ranging markets might return breakouts highlighting the top/bottom.
🔶 DETAILS
The core of this script is the highest / lowest mean average (MA) value for a given number of bars back ( Donchian lines).
This is repeated a few times with the obtained values.
When Bands are chosen ( Style ) this will be repeated 1 more time.
The type of mean average can be customized ( Type MA ), as well as the number of bars back ( Length ).
Depending on the choice of bands ( Style ) the script will focus on certain area's of interest.
When the option Clouds , Upper band or Lower band is chosen, an extra feature, support/resistance (S/R), will be shown.
These color-filled areas are visible when there is a difference between the 2nd and 3rd highest/lowest values.
The lines/areas can be used for stop loss, entry, exit,...
You can set the type of MA and Length separately ( Settings -> S/R ).
If you don't need this feature, simply set Type ( Settings -> S/R ) -> NONE
The shape sometimes resembles triangles, indicating a potential direction
Default the average of the highest and lowest values is plotted (Style -> Mid Donchian)
This can act as potential support/resistance or visualization of the trend, the mean average is not plotted but can be (Style -> MA)
🔹 Note
When the option Bands is chosen, an indication is plotted when the closing price breaks above the highest band or breaks below the lower band. This isn't necessarily a buy/sell signal, it is merely a signal that these lines are broken.
Users should decide on their own how they use the bands/lines/areas as entry, exit, trailing stop, stop loss, profit taking,...
🔶 SETTINGS
🔹 Bands
Style: Clouds (default), Upper band, Lower band, Bands
Type MA: choose between SMA, EMA, RMA, HullMA, WMA, VWMA (default), DEMA, TEMA, NONE (off)
Length: Length of moving average and Donchian calculations (default 20)
Colour Bands
🔹 S/R (Support/Resistance, visible with Clouds, Upper band or Lower band)
Type MA: choose between SMA, EMA, RMA, HullMA, WMA, VWMA (default), DEMA, TEMA, NONE (off)
Length: Length of moving average and Donchian calculations (default 20)
Colour S/R
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.
MTF MA Ribbon and Bands + BB, Gaussian F. and R. VWAP with StDev█ Multi Timeframe Moving Average Ribbon and Bands + Bollinger Bands, Gaussian Filter and Rolling Volume Weighted Average Price with Standard Deviation Bands
Up to 9 moving averages can be independently applied.
The length , type and timeframe of each moving average are configurable .
The lines, colors and background fill are customizable too.
This script can also display:
Moving Average Bands
Bollinger Bands
Gaussian Filter
Rolling VWAP and Standard Deviation Bands
Types of Moving Averages:
Simple Moving Average (SMA)
Exponential Moving Average (EMA)
Smoothed Moving Average (SMMA)
Weighted Moving Average (WMA)
Volume Weighted Moving Average (VWMA)
Least Squares Moving Average (LSMA)
Hull Moving Average (HMA)
Arnaud Legoux Moving Average (ALMA)
█ Moving Average
Moving Averages are price based, lagging (or reactive) indicators that display the average price of a security over a set period of time.
A Moving Average is a good way to gauge momentum as well as to confirm trends, and define areas of support and resistance.
█ Bollinger Bands
Bollinger Bands consist of a band of three lines which are plotted in relation to security prices.
The line in the middle is usually a Simple Moving Average (SMA) set to a period of 20 days (the type of trend line and period can be changed by the trader, a 20 day moving average is by far the most popular).
The SMA then serves as a base for the Upper and Lower Bands which are used as a way to measure volatility by observing the relationship between the Bands and price.
█ Gaussian Filter
Gaussian filter can be used for smoothing.
It rejects high frequencies (fast movements) better than an EMA and has lower lag.
A Gaussian filter is one whose transfer response is described by the familiar Gaussian bell-shaped curve.
In the case of low-pass filters, only the upper half of the curve describes the filter.
The use of gaussian filters is a move toward achieving the dual goal of reducing lag and reducing the lag of high-frequency components relative to the lag of lower-frequency components.
█ Rolling VWAP
The typical VWAP is designed to be used on intraday charts, as it resets at the beginning of the day.
Such VWAPs cannot be used on daily, weekly or monthly charts. Instead, this rolling VWAP uses a time period that automatically adjusts to the chart's timeframe.
You can thus use the rolling VWAP on any chart that includes volume information in its data feed.
Because the rolling VWAP uses a moving window, it does not exhibit the jumpiness of VWAP plots that reset.
Made with the help from scripts of: adam24x, VishvaP, loxx and pmk07.
Mobo BandsThis indicator is the Mobo Bands (Momentum Breakout Bands). These bands are bollinger bands that have an adjusted standard deviation. There are Buy signals when it has momentum breakouts above the bands for moves to the upside and Sell signals when it has momentum breakouts below the bands for moves to the downside. The bands simply suggest that all markets have periods of chop which we all know to be true. While the price is inside the bands it is said to be trendless. Once the breakouts happen you can take trades in the breakout direction. I like to use these to swing trade options on the hourly timeframe but the bands should work on most instruments and timeframes. I like to use it to take swings on SPY on the 1 hour chart for entries and use the Daily chart for trend confirmation.
Dual Bollinger BandsIndicator Name:
Double Bollinger Bands (2-9 & 2-20)
Description:
This indicator plots two sets of Bollinger Bands on a single chart for enhanced volatility and trend analysis:
Fast Bands (2-9 Length) – Voilet
More responsive to short-term price movements.
Useful for spotting quick reversals or scalping opportunities.
Slow Bands (2-20 Length) – Black
Smoother, trend-following bands for longer-term context.
Helps confirm broader market direction.
Both bands use the standard settings (2 deviations, SMA basis) for consistency. The transparent fills improve visual clarity while keeping the chart uncluttered.
Use Cases:
Trend Confirmation: When both bands expand together, it signals strong momentum.
Squeeze Alerts: A tight overlap suggests low volatility before potential breakouts.
Multi-Timeframe Analysis: Compare short-term vs. long-term volatility in one view.
How to Adjust:
Modify lengths (2-9 and 2-20) in the settings.
Change colors or transparency as needed.
Why Use This Script?
No Repainting – Uses standard Pine Script functions for reliability.
Customizable – Easy to tweak for different trading styles.
Clear Visuals – Color-coded bands with background fills for better readability.
Ideal For:
Swing traders, day traders, and volatility scalpers.
Combining short-term and long-term Bollinger Band strategies.
Weighted Regression Bands (Zeiierman)█ Overview
Weighted Regression Bands is a precision-engineered trend and volatility tool designed to adapt to the real market structure instead of reacting to price noise.
This indicator analyzes Weighted High/Low medians and applies user-selectable smoothing methods — including Kalman Filtering, ALMA, and custom Linear Regression — to generate a Fair Value line. Around this, it constructs dynamic standard deviation bands that adapt in real-time to market volatility.
The result is a visually clean and structurally intelligent trend framework suitable for breakout traders, mean reversion strategies, and trend-driven analysis.
█ How It Works
⚪ Structural High/Low Analysis
At the heart of this indicator is a custom high/low weighting system. Instead of using just the raw high or low values, it calculates a midline = (high + low) / 2, then applies one of three weighting methods to determine which price zones matter most.
Users can select the method using the “Weighted HL Method” setting:
Simple
Selects the single most dominant median (highest or lowest) in the lookback window. Ideal for fast, reactive signals.
Advanced
Ranks each bar based on a composite score: median × range × recency. This method highlights structurally meaningful bars that had both volatility and recency. A built-in Kalman filter is applied for extra stability.
Smooth
Blends multiple bars into a single weighted average using smoothed decay and range. This provides the softest and most stable structural response.
⚪ Smoothing Methods (ALMA / Linear Regression)
ALMA provides responsive, low-lag smoothing for fast trend reading.
Linear Regression projects the Fair Value forward, ideal for trend modeling.
⚪ Kalman Smoothing Filter
Before trend calculations, the indicator applies an optional Kalman-style smoothing filter. This helps:
Reduce choppy false shifts in trend,
Retain signal clarity during volatile periods,
Provide stability for long-term setups.
⚪ Deviation Bands (Dynamic Volatility Envelopes)
The indicator builds ±1, ±2, and ±3 standard deviation bands around the fair value line:
Calculated from the standard deviation of price,
Bands expand and contract based on recent volatility,
Visualizes potential overbought/oversold or trending conditions.
█ How to Use
⚪ Trend Trading & Filtering
Use the Fair Value line to identify the dominant direction.
Only trade in the direction of the slope for higher probability setups.
⚪ Volatility-Based Entries
Watch for price reaching outer bands (+2σ, +3σ) for possible exhaustion.
Mean reversion entries become higher quality when far from Fair Value.
█ Settings
Length – Lookback for Weighted HL and trend smoothing
Deviation Multiplier – Controls how wide the bands are from the fair value line
Method – Choose between ALMA or Linear Regression smoothing
Smoothing – Strength of Kalman Filter (1 = none, <1 = stronger smoothing)
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Disclaimer
The content provided in my scripts, indicators, ideas, algorithms, and systems is for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
Smooth Fibonacci BandsSmooth Fibonacci Bands
This indicator overlays adaptive Fibonacci bands on your chart, creating dynamic support and resistance zones based on price volatility. It combines a simple moving average with ATR-based Fibonacci levels to generate multiple bands that expand and contract with market conditions.
## Features
- Creates three pairs of upper and lower Fibonacci bands
- Smoothing option for cleaner, less noisy bands
- Fully customizable colors and line thickness
- Adapts automatically to changing market volatility
## Settings
Adjust the SMA and ATR lengths to match your trading timeframe. For short-term trading, try lower values; for longer-term analysis, use higher values. The Fibonacci factors determine how far each band extends from the center line - standard Fibonacci ratios (1.618, 2.618, and 4.236) are provided as defaults.
## Trading Applications
- Use band crossovers as potential entry and exit signals
- Look for price bouncing off bands as reversal opportunities
- Watch for price breaking through multiple bands as strong trend confirmation
- Identify potential support/resistance zones for placing stop losses or take profits
Fibonacci Bands combines the reliability of moving averages with the adaptability of ATR and the natural market harmony of Fibonacci ratios, offering a robust framework for both trend and range analysis.
Volatility BandsThe Volatility Bands script is a custom indicator designed to help traders visualize volatility levels in the market. It calculates dynamic bands around a central moving average, providing insights into potential support and resistance levels based on recent price action.
The script calculates multiple volatility bands (u0, u1, u2, d0, d1, d2) that adjust based on recent price movements. The outer bands (u2 and d2) represent extreme volatility levels, while the inner bands (u0, u1, d0, d1) indicate more immediate support and resistance.
Look for price reactions at the band levels. A touch of the upper bands may indicate overbought conditions, while a touch of the lower bands may indicate oversold conditions.
Central Moving Average: A smoothed moving average that adapts to price changes, providing a clear trend direction.
The script has no input parameters.
Script Functions:
erf(x): Calculates the error function for a given input x. Used in the calculation of the smoothing factor for the UMA.
uma(input): Provides a smoothed average that adapts to recent price changes, reducing lag compared to traditional moving averages.
dev(input, mu): Used to calculate the volatility bands around the central moving average.
Bollinger Bands cross %The BB strategy (Bollinger Bands strategy) on TradingView utilizes the Bollinger Bands indicator to help traders identify market volatility and potential entry points. The Bollinger Bands indicator consists of three main components:
Middle Band: This is the simple moving average (SMA), usually calculated over a 20-period. It represents the average price over a specific period.
Upper Band and Lower Band: These bands are created by adding and subtracting a multiple of the standard deviation (typically 2) from the middle band. The upper and lower bands help determine the level of price volatility.
How the BB Strategy Works:
Break above the Upper Band: When the price moves above the upper band, it might signal that the market is in an "overbought" condition. This could be a sign to consider selling, but it could also continue if the trend is strong.
Break below the Lower Band: When the price moves below the lower band, it might signal that the market is in an "oversold" condition, which could be a signal to buy if the trend is reversing.
Squeeze (Coiling): When the Bollinger Bands contract, often referred to as a "squeeze," it indicates that the market may be preparing for a strong price move. This is a critical signal in the BB strategy because the narrowing bands signify low volatility and a potential breakout in price.
Specific Strategy:
Buy when price touches the lower band and shows signs of reversal (bullish reversal): If the price touches the lower band, you might wait for a reversal signal, such as a bullish candlestick pattern or confirmation from other indicators like RSI or MACD, indicating oversold conditions.
Sell when price touches the upper band and shows signs of reversal (bearish reversal): Similarly, when the price touches the upper band, you could wait for a bearish reversal signal, such as a bearish candlestick pattern or confirmation from other indicators, and then sell.
Trend-following when bands are expanding: If the Bollinger Bands are expanding and the price continues in the same direction, it could signal a trend-following opportunity.
