dharmatech : Standard Deviation ChannelDESCRIPTION
Based on version by leojez.
Adds a 3rd standard deviation level.
Twice as fast as original version.
Refactored and simplified source code.
HOW TO USE
Load your chart
Adjust the timeframe and zoom of the chart so that the trend you're interested in is in view.
Add the indicator
Use the measuring tool to measure the number of bars from the start of the trend to the latest candle.
Open settings for the indicator.
Set the length value to the number of bars that you noted.
الانحراف المعياري (التقلّب)
Advanced Weighted Residual Arbitrage AnalyzerThe Advanced Weighted Residual Arbitrage Analyzer is a sophisticated tool designed for traders aiming to exploit price deviations between various asset pairs. By examining the differences in normalized price relations and their weighted residuals, this indicator provides insights into potential arbitrage opportunities in the market.
Key Features:
Multiple Relation Analysis: Analyze up to five different asset relations simultaneously, offering a comprehensive view of potential arbitrage setups.
Normalization Functions: Choose from a variety of normalization techniques like SMA, EMA, WMA, and HMA to ensure accurate comparisons between different price series.
Dynamic Weighting: Residuals are weighted based on their correlation, ensuring that stronger correlations have a more pronounced impact on the analysis. Weighting can be adjusted using several functions including square, sigmoid, and logistic.
Regression Flexibility: Incorporate linear, polynomial, or robust regression to calculate residuals, tailoring the analysis to different market conditions.
Customizable Display: Decide which plots to display for clarity and focus, including normalized relations, weighted residuals, and the difference between the screen relation and the average weighted residual.
Usage Guidelines:
Configure the asset pairs you wish to analyze using the Symbol Relations group in the settings.
Adjust the normalization, volatility, regression, and weighting functions based on your preference and the specific characteristics of the asset pairs.
Monitor the weighted residuals for deviations from the mean. Larger deviations suggest stronger arbitrage opportunities.
Use the difference plot (between the screen relation and average weighted residual) as a quick visual cue for potential trade setups. When this plot deviates significantly from zero, it indicates a possible arbitrage opportunity.
Regularly update and adjust the parameters to account for changing market conditions and ensure the most accurate analysis.
In the Advanced Weighted Residual Arbitrage Analyzer , the value set in Alert Threshold plays a crucial role in delineating a normalized band. This band serves as a guide to identify significant deviations and potential trading opportunities.
When we observe the plots of the green line and the purple line, the Alert Threshold provides a boundary for these plots. The following points explain the significance:
Breach of the Band: When either the green or purple line crosses above or below the Alert Threshold , it indicates a significant deviation from the mean. This breach can be interpreted as a potential trading signal, suggesting a possible arbitrage opportunity.
Convergence to the Mean: If the green line converges with the purple line , it denotes that the price relation has reverted to its mean. This convergence typically suggests that the arbitrage opportunity has been exhausted, and the market dynamics are returning to equilibrium.
Trade Execution: A trader can consider entering a trade when the lines breach the Alert Threshold . The return of the green line to align closely with the purple line can be seen as a signal to exit the trade, capitalizing on the reversion to the mean.
By monitoring these plots in conjunction with the Alert Threshold , traders can gain insights into market imbalances and exploit potential arbitrage opportunities. The convergence and divergence of these lines, relative to the normalized band, serve as valuable visual cues for trade initiation and termination.
When you're analyzing relations between two symbols (for instance, BINANCE:SANDUSDT/BINANCE:NEARUSDT ), you're essentially looking at the price relationship between the two underlying assets. This relationship provides insights into potential imbalances between the assets, which arbitrage traders can exploit.
Breach of the Lower Band: If the purple line touches or crosses below the lower Alert Threshold , it indicates that the first symbol (in our example, SANDUSDT ) is undervalued relative to the second symbol ( NEARUSDT ). In practical terms:
Action: You would consider buying the first symbol ( SANDUSDT ) and selling the second symbol ( NEARUSDT ).
Rationale: The expectation is that the price of the first symbol will rise, or the price of the second symbol will fall, or both, thereby converging back to their historical mean relationship.
Breach of the Upper Band: Conversely, if the difference plot touches or crosses above the upper Alert Threshold , it suggests that the first symbol is overvalued compared to the second. This implies:
Action: You'd consider selling the first symbol ( SANDUSDT ) and buying the second symbol ( NEARUSDT ).
Rationale: The anticipation here is that the price of the first symbol will decrease, or the price of the second will increase, or both, bringing the relationship back to its historical average.
Convergence to the Mean: As mentioned earlier, when the green line aligns closely with the purple line, it's an indication that the assets have returned to their typical price relationship. This serves as a signal for traders to consider closing out their positions, locking in the gains from the arbitrage opportunity.
It's important to note that when you're trading based on symbol relations, you're essentially betting on the relative performance of the two assets. This strategy, often referred to as "pairs trading," seeks to capitalize on price imbalances between related financial instruments. By taking opposing positions in the two symbols, traders aim to profit from the eventual reversion of the price difference to the mean.
VolatilityIndicatorsLibrary "VolatilityIndicators"
This is a library of Volatility Indicators .
It aims to facilitate the grouping of this category of indicators, and also offer the customized supply of
the parameters and sources, not being restricted to just the closing price.
@Thanks and credits:
1. Dynamic Zones: Leo Zamansky, Ph.D., and David Stendahl
2. Deviation: Karl Pearson (code by TradingView)
3. Variance: Ronald Fisher (code by TradingView)
4. Z-score: Veronique Valcu (code by HPotter)
5. Standard deviation: Ronald Fisher (code by TradingView)
6. ATR (Average True Range): J. Welles Wilder (code by TradingView)
7. ATRP (Average True Range Percent): millerrh
8. Historical Volatility: HPotter
9. Min-Max Scale Normalization: gorx1
10. Mean Normalization: gorx1
11. Standardization: gorx1
12. Scaling to unit length: gorx1
13. LS Volatility Index: Alexandre Wolwacz (Stormer), Fabrício Lorenz, Fábio Figueiredo (Vlad) (code by me)
14. Bollinger Bands: John Bollinger (code by TradingView)
15. Bollinger Bands %: John Bollinger (code by TradingView)
16. Bollinger Bands Width: John Bollinger (code by TradingView)
dev(source, length, anotherSource)
Deviation. Measure the difference between a source in relation to another source
Parameters:
source (float)
length (simple int) : (int) Sequential period to calculate the deviation
anotherSource (float) : (float) Source to compare
Returns: (float) Bollinger Bands Width
variance(src, mean, length, biased, degreesOfFreedom)
Variance. A statistical measurement of the spread between numbers in a data set. More specifically,
variance measures how far each number in the set is from the mean (average), and thus from every other number in the set.
Variance is often depicted by this symbol: σ2. It is used by both analysts and traders to determine volatility and market security.
Parameters:
src (float) : (float) Source to calculate variance
mean (float) : (float) Mean (Moving average)
length (simple int) : (int) The sequential period to calcule the variance (number of values in data set)
biased (simple bool) : (bool) Defines the type of standard deviation. If true, uses biased sample variance (n),
degreesOfFreedom (simple int) : (int) Degrees of freedom. The number of values in the final calculation of a statistic that are free to vary.
Default value is n-1, where n here is length. Only applies when biased parameter is defined as true.
Returns: (float) Standard deviation
stDev(src, length, mean, biased, degreesOfFreedom)
Measure the Standard deviation from a source in relation to it's moving average.
In this implementation, you pass the average as a parameter, allowing a more personalized calculation.
Parameters:
src (float) : (float) Source to calculate standard deviation
length (simple int) : (int) The sequential period to calcule the standard deviation
mean (float) : (float) Moving average.
biased (simple bool) : (bool) Defines the type of standard deviation. If true, uses biased sample variance (n),
else uses unbiased sample variance (n-1 or another value, as long as it is in the range between 1 and n-1), where n=length.
degreesOfFreedom (simple int) : (int) Degrees of freedom. The number of values in the final calculation of a statistic that are free to vary.
Default value is n-1, where n here is length.
Returns: (float) Standard deviation
zscore(src, mean, length, biased, degreesOfFreedom)
Z-Score. A z-score is a statistical measurement that indicates how many standard deviations a data point is from
the mean of a data set. It is also known as a standard score. The formula for calculating a z-score is (x - μ) / σ,
where x is the individual data point, μ is the mean of the data set, and σ is the standard deviation of the data set.
Z-scores are useful in identifying outliers or extreme values in a data set. A positive z-score indicates that the
data point is above the mean, while a negative z-score indicates that the data point is below the mean. A z-score of
0 indicates that the data point is equal to the mean.
Z-scores are often used in hypothesis testing and determining confidence intervals. They can also be used to compare
data sets with different units or scales, as the z-score standardizes the data. Overall, z-scores provide a way to
measure the relative position of a data point in a data
Parameters:
src (float) : (float) Source to calculate z-score
mean (float) : (float) Moving average.
length (simple int) : (int) The sequential period to calcule the standard deviation
biased (simple bool) : (bool) Defines the type of standard deviation. If true, uses biased sample variance (n),
else uses unbiased sample variance (n-1 or another value, as long as it is in the range between 1 and n-1), where n=length.
degreesOfFreedom (simple int) : (int) Degrees of freedom. The number of values in the final calculation of a statistic that are free to vary.
Default value is n-1, where n here is length.
Returns: (float) Z-score
atr(source, length)
ATR: Average True Range. Customized version with source parameter.
Parameters:
source (float) : (float) Source
length (simple int) : (int) Length (number of bars back)
Returns: (float) ATR
atrp(length, sourceP)
ATRP (Average True Range Percent)
Parameters:
length (simple int) : (int) Length (number of bars back) for ATR
sourceP (float) : (float) Source for calculating percentage relativity
Returns: (float) ATRP
atrp(source, length, sourceP)
ATRP (Average True Range Percent). Customized version with source parameter.
Parameters:
source (float) : (float) Source for ATR
length (simple int) : (int) Length (number of bars back) for ATR
sourceP (float) : (float) Source for calculating percentage relativity
Returns: (float) ATRP
historicalVolatility(lengthATR, lengthHist)
Historical Volatility
Parameters:
lengthATR (simple int) : (int) Length (number of bars back) for ATR
lengthHist (simple int) : (int) Length (number of bars back) for Historical Volatility
Returns: (float) Historical Volatility
historicalVolatility(source, lengthATR, lengthHist)
Historical Volatility
Parameters:
source (float) : (float) Source for ATR
lengthATR (simple int) : (int) Length (number of bars back) for ATR
lengthHist (simple int) : (int) Length (number of bars back) for Historical Volatility
Returns: (float) Historical Volatility
minMaxNormalization(src, numbars)
Min-Max Scale Normalization. Maximum and minimum values are taken from the sequential range of
numbars bars back, where numbars is a number defined by the user.
Parameters:
src (float) : (float) Source to normalize
numbars (simple int) : (int) Numbers of sequential bars back to seek for lowest and hightest values.
Returns: (float) Normalized value
minMaxNormalization(src, numbars, minimumLimit, maximumLimit)
Min-Max Scale Normalization. Maximum and minimum values are taken from the sequential range of
numbars bars back, where numbars is a number defined by the user.
In this implementation, the user explicitly provides the desired minimum (min) and maximum (max) values for the scale,
rather than using the minimum and maximum values from the data.
Parameters:
src (float) : (float) Source to normalize
numbars (simple int) : (int) Numbers of sequential bars back to seek for lowest and hightest values.
minimumLimit (simple float) : (float) Minimum value to scale
maximumLimit (simple float) : (float) Maximum value to scale
Returns: (float) Normalized value
meanNormalization(src, numbars, mean)
Mean Normalization
Parameters:
src (float) : (float) Source to normalize
numbars (simple int) : (int) Numbers of sequential bars back to seek for lowest and hightest values.
mean (float) : (float) Mean of source
Returns: (float) Normalized value
standardization(src, mean, stDev)
Standardization (Z-score Normalization). How "outside the mean" values relate to the standard deviation (ratio between first and second)
Parameters:
src (float) : (float) Source to normalize
mean (float) : (float) Mean of source
stDev (float) : (float) Standard Deviation
Returns: (float) Normalized value
scalingToUnitLength(src, numbars)
Scaling to unit length
Parameters:
src (float) : (float) Source to normalize
numbars (simple int) : (int) Numbers of sequential bars back to seek for lowest and hightest values.
Returns: (float) Normalized value
lsVolatilityIndex(movingAverage, sourceHvol, lengthATR, lengthHist, lenNormal, lowerLimit, upperLimit)
LS Volatility Index. Measures the volatility of price in relation to an average.
Parameters:
movingAverage (float) : (float) A moving average
sourceHvol (float) : (float) Source for calculating the historical volatility
lengthATR (simple int) : (float) Length for calculating the ATR (Average True Range)
lengthHist (simple int) : (float) Length for calculating the historical volatility
lenNormal (simple int) : (float) Length for normalization
lowerLimit (simple int)
upperLimit (simple int)
Returns: (float) LS Volatility Index
lsVolatilityIndex(sourcePrice, movingAverage, sourceHvol, lengthATR, lengthHist, lenNormal, lowerLimit, upperLimit)
LS Volatility Index. Measures the volatility of price in relation to an average.
Parameters:
sourcePrice (float) : (float) Source for measure the distance
movingAverage (float) : (float) A moving average
sourceHvol (float) : (float) Source for calculating the historical volatility
lengthATR (simple int) : (float) Length for calculating the ATR (Average True Range)
lengthHist (simple int) : (float) Length for calculating the historical volatility
lenNormal (simple int)
lowerLimit (simple int)
upperLimit (simple int)
Returns: (float) LS Volatility Index
bollingerBands(src, length, mult, basis)
Bollinger Bands. A Bollinger Band is a technical analysis tool defined by a set of lines plotted
two standard deviations (positively and negatively) away from a simple moving average (SMA) of the security's price,
but can be adjusted to user preferences. In this version you can pass a customized basis (moving average), not only SMA.
Parameters:
src (float) : (float) Source to calculate standard deviation used in Bollinger Bands
length (simple int) : (int) The time period to be used in calculating the standard deviation
mult (simple float) : (float) Multiplier used in standard deviation. Basically, the upper/lower bands are standard deviation multiplied by this.
basis (float) : (float) Basis of Bollinger Bands (a moving average)
Returns: (float) A tuple of Bollinger Bands, where index 1=basis; 2=basis+dev; 3=basis-dev; and dev=multiplier*stdev
bollingerBands(src, length, aMult, basis)
Bollinger Bands. A Bollinger Band is a technical analysis tool defined by a set of lines plotted
two standard deviations (positively and negatively) away from a simple moving average (SMA) of the security's price,
but can be adjusted to user preferences. In this version you can pass a customized basis (moving average), not only SMA.
Also, various multipliers can be passed, thus getting more bands (instead of just 2).
Parameters:
src (float) : (float) Source to calculate standard deviation used in Bollinger Bands
length (simple int) : (int) The time period to be used in calculating the standard deviation
aMult (float ) : (float ) An array of multiplies used in standard deviation. Basically, the upper/lower bands are standard deviation multiplied by this.
This array of multipliers permit the use of various bands, not only 2.
basis (float) : (float) Basis of Bollinger Bands (a moving average)
Returns: (float ) An array of Bollinger Bands, where:
index 1=basis; 2=basis+dev1; 3=basis-dev1; 4=basis+dev2, 5=basis-dev2, 6=basis+dev2, 7=basis-dev2, Nup=basis+devN, Nlow=basis-devN
and dev1, dev2, devN are ```multiplier N * stdev```
bollingerBandsB(src, length, mult, basis)
Bollinger Bands %B - or Percent Bandwidth (%B).
Quantify or display where price (or another source) is in relation to the bands.
%B can be useful in identifying trends and trading signals.
Calculation:
%B = (Current Price - Lower Band) / (Upper Band - Lower Band)
Parameters:
src (float) : (float) Source to calculate standard deviation used in Bollinger Bands
length (simple int) : (int) The time period to be used in calculating the standard deviation
mult (simple float) : (float) Multiplier used in standard deviation
basis (float) : (float) Basis of Bollinger Bands (a moving average)
Returns: (float) Bollinger Bands %B
bollingerBandsB(src, length, aMult, basis)
Bollinger Bands %B - or Percent Bandwidth (%B).
Quantify or display where price (or another source) is in relation to the bands.
%B can be useful in identifying trends and trading signals.
Calculation
%B = (Current Price - Lower Band) / (Upper Band - Lower Band)
Parameters:
src (float) : (float) Source to calculate standard deviation used in Bollinger Bands
length (simple int) : (int) The time period to be used in calculating the standard deviation
aMult (float ) : (float ) Array of multiplier used in standard deviation. Basically, the upper/lower bands are standard deviation multiplied by this.
This array of multipliers permit the use of various bands, not only 2.
basis (float) : (float) Basis of Bollinger Bands (a moving average)
Returns: (float ) An array of Bollinger Bands %B. The number of results in this array is equal the numbers of multipliers passed via parameter.
bollingerBandsW(src, length, mult, basis)
Bollinger Bands Width. Serve as a way to quantitatively measure the width between the Upper and Lower Bands
Calculation:
Bollinger Bands Width = (Upper Band - Lower Band) / Middle Band
Parameters:
src (float) : (float) Source to calculate standard deviation used in Bollinger Bands
length (simple int) : (int) Sequential period to calculate the standard deviation
mult (simple float) : (float) Multiplier used in standard deviation
basis (float) : (float) Basis of Bollinger Bands (a moving average)
Returns: (float) Bollinger Bands Width
bollingerBandsW(src, length, aMult, basis)
Bollinger Bands Width. Serve as a way to quantitatively measure the width between the Upper and Lower Bands
Calculation
Bollinger Bands Width = (Upper Band - Lower Band) / Middle Band
Parameters:
src (float) : (float) Source to calculate standard deviation used in Bollinger Bands
length (simple int) : (int) Sequential period to calculate the standard deviation
aMult (float ) : (float ) Array of multiplier used in standard deviation. Basically, the upper/lower bands are standard deviation multiplied by this.
This array of multipliers permit the use of various bands, not only 2.
basis (float) : (float) Basis of Bollinger Bands (a moving average)
Returns: (float ) An array of Bollinger Bands Width. The number of results in this array is equal the numbers of multipliers passed via parameter.
dinamicZone(source, sampleLength, pcntAbove, pcntBelow)
Get Dynamic Zones
Parameters:
source (float) : (float) Source
sampleLength (simple int) : (int) Sample Length
pcntAbove (simple float) : (float) Calculates the top of the dynamic zone, considering that the maximum values are above x% of the sample
pcntBelow (simple float) : (float) Calculates the bottom of the dynamic zone, considering that the minimum values are below x% of the sample
Returns: A tuple with 3 series of values: (1) Upper Line of Dynamic Zone;
(2) Lower Line of Dynamic Zone; (3) Center of Dynamic Zone (x = 50%)
Examples:
Range Projections [TFO]The purpose of this indicator is to see how often price reached certain standard deviations from a selected time range. The inspiration for this was to study ICT (Inner Circle Trader) concepts regarding the Central Bank Dealer’s Range (CBDR), which is 2:00 pm - 8:00 pm New York local time according to ICT Core Content. However, the idea and data collection could certainly be applied to any range of time.
The main settings of this indicator are session time, range type, and the standard deviation filter. The session time is the window of price that will be utilized for range projections. The range type can be either body or wick (on the current timeframe). The standard deviation filter is used to eliminate sessions whose ranges (from high to low) are greater than the desired/input number of standard deviations from all available session ranges.
In this example, the time range is set to 16:00 - 20:00, or the time between the New York session close and the Asia session open. Our standard deviations are set to 1, 2, 2.5, and 4. Now, by taking this session’s price range and extrapolating these extensions from the initial range, we can use these levels to see if and how price interacts with them before the next 16:00 - 20:00 session.
Furthermore, we can enable the Data Table to analyze how often price trades to these levels for the sessions that are deemed valid (determined by the standard deviation filter). This time our standard deviations are set to 1, 2, 3, and 4.
This concept can theoretically be applied to any window of time. ICT has mentioned that, in instances where the CBDR is too large, the Asia range may be used instead. We can observe that the indicator behaves the same way when we change the session to the Asia range, 20:00 - 00:00.
Ladder StDevThis indicator shows the upwards (green) and downward (red) volatility of the market. It is a standard deviation of the price but with a twist! For the upwards volatility , only the green candles are taken into account, and for the downwards only the red candles are.
Compared to my previous "Ladder ATR" indicator this a different approach to measure the the upwards and downwards volatility of the market by utilizing the standard deviation instead of the ATR. When both measure the volatility they have different "dynamics". Standard deviation increases the weight of larger values over smaller values. The ATR indicator is based on the average of absolute changes. So, if we apply the indicators on a daily chart , ATR considers intraday and between-day data, while the standard deviation calculation includes only daily returns (source price).
Inter-Exchanges Crypto Price Spread Deviation (Tartigradia)Measures the deviation of price metrics between various exchanges. It's a kind of realized volatility indicator, as the idea is that in times of high volatility (high emotions, fear, uncertainty), it's more likely that market inefficiencies will appear for the same asset between different market makers, ie, the price can temporarily differ a lot. This indicator will catch these instants of high differences between exchanges, even if they lasted only an instant (because we use high and low values).
Both standard deviation and median absolute deviation (more robust to outliers, ie, exchanges with a very different price from others won't influence the median absolute deviation, but the standard deviation yes).
Compared to other inter-exchanges spread indicators, this one offers two major features:
* The symbol automatically adapts to the symbol currently selected in user's chart. Hence, switching between tickers does not require the user to modify any option, everything is dynamically updated behind the scenes.
* It's easy to add more exchanges (requires some code editing because PineScript v5 does not allow dynamical request.security() calls).
Limitations/things to know:
* History is limited to what the ticker itself display. Ie, even if the exchanges specified in this indicator have more data than the ticker currently displayed in the user's chart, the indicator will show only a timeperiod as long as the chart.
* The indicator can manage multiple exchanges of different historical length (ie, some exchanges having more data going way earlier in the past than others), in which case they will simply be ignored from calculations when far back in the past. Hence, you should be aware that the further you go in the past, the less exchanges will have such data, and hence the less accurate the measures will be (because the deviation will be calculated from less sources than more recent bars). This is thanks to how the array.* math functions behave in case of na values, they simply skip them from calculations, contrary to math.* functions.
Fibonacci Volatility BandsFibonacci Volatility Bands are just an alternative that allows for more margin than regular Bollinger Bands. They are created based on an average of moving averages that use the Fibonacci sequence as lookback periods.
The use of the Fibonacci Volatility Bands is exactly the same as the Bollinger Bands.
TheATR™: Volatility Extremes (VolEx)Volatility is a crucial aspect of financial markets that is closely monitored by traders and investors alike. The traditional Average True Range (ATR) oscillator is a widely used technical indicator for measuring volatility in financial markets. However, there are limitations to the ATR oscillator, as it does not account for changing market conditions and may not adequately reflect extreme price movements. To address these limitations, TheATR has developed the VolEx indicator, which aims to identify extremes in the ATR oscillator by building dynamic thresholds using either a 'percentage' or 'standard deviation' based comparison with the value of the ATR.
The VolEx indicator utilizes a dynamic approach to measure volatility by considering the current level of the ATR oscillator relative to the dynamically generated thresholds. The dynamic thresholds are calculated based on the current ATR value and the chosen method of comparison (either 'percentage' or 'standard deviation'). If the ATR value exceeds the upper dynamic threshold, the market is experiencing high volatility, while a value below the lower dynamic threshold indicates low volatility.
The VolEx indicator offers several advantages over traditional volatility indicators, such as the ATR oscillator. First, it takes into account the changing market conditions and adjusts the thresholds accordingly. Second, it offers flexibility in the choice of the comparison method, allowing traders to tailor the indicator to their specific trading strategies. Finally, it provides clear signals for identifying extremes in volatility, which can be used to inform trading decisions.
In summary, the VolEx indicator developed by TheATR is a dynamic and flexible technical indicator that offers a robust approach to measuring volatility in financial markets. By utilizing dynamic thresholds and allowing for different comparison methods, the VolEx indicator provides a valuable tool for traders and investors seeking to identify extremes in market volatility..
NOTE: It is important to note that volatility, as measured by the VolEx indicator, does not provide any directional bias for the market movement. Rather, it simply indicates the degree to which the market is moving, regardless of direction. Traders and investors must use other technical or fundamental analysis tools to determine the direction of the market and make informed trading decisions based on their individual strategies and risk tolerance.
Rekt Edge Reversion BandRekt Edge Reversion band is a technical indicator that utilizes a combination of moving averages and standard deviations to determine optimal entry and exit points in the market. By comparing the current price to its moving average, the indicator identifies potential trends and determines how you can position around them by plotting buy/sell signals and two channels based on user input parameters. The user can choose between Simple Moving Average ( SMA ) or Exponential Moving Average ( EMA ) and select the moving average period, the unit of separation, the multiples of the unit, and other important parameters. The indicator's inputs can be adjusted to suit different trading styles, and it can be used on any time frame. The indicator can be used to identify potential trend reversals or breakouts (or breakdowns) when the price moves outside of the channels. The indicators potential use cases include identifying overbought or oversold conditions. With its ability to provide a clear signal on when to enter and exit a trade, this indicator is a popular tool among traders looking to make more informed and profitable trading decisions. This indicator can also be used in conjunction with other technical analysis tools to confirm or invalidate trading signals.
Click VWAP Anchored with Standard Devation BandsSimply use it by clicking on your chart on the places you find important to determine whether you entries or exits look strong or weak.
Slope NormalizerBrief:
This oscillator style indicator takes another indicator as its source and measures the change over time (the slope). It then isolates the positive slope values from the negative slope values to determine a 'normal' slope value for each.
** A 'normal' value of 1.0 is determined by the average slope plus the standard deviation of that slope.
The Scale
This indicator is not perfectly linear. The values are interpolated differently from 0.0 - 1.0 than values greater than 1.0.
From values 0.0 to 1.0 (positive or negative): it means that the value of the slope is less than 'normal' **.
Any value above 1.0 means the current slope is greater than 'normal' **.
A value of 2.0 means the value is the average plus 2x the standard deviation.
A value of 3.0 means the value is the average plus 3x the standard deviation.
A value greater than 4.0 means the value is greater than the average plus 4x the standard deviation.
Because the slope value is normalized, the meaning of these values can remain generally the same for different symbols.
Potential Usage Examples/b]
Using this in conjunction with an SMA or WMA may indicate a change in trend, or a change in trend-strength.
Any values greater than 4 indicate a very strong (and unusual) trend that may not likely be sustainable.
Any values cycling between +1.0 and -1.0 may mean indecision.
A value that is decreasing below 0.5 may predict a change in trend (slope may soon invert).
Modified Bollinger BandsThis script has been distributed for learning purposes.
A particular kind of price envelope is "Bollinger Bands" indicator. Upper and lower price range levels are determined by price envelopes. By default, Bollinger Bands are plotted in Tradingview as envelopes at a standard deviation level above and below the price's simple moving average (SMA). I attempted to modify the indicator in this version by adding several kinds of moving averages first. The key feature is that standard deviation should be modified. in Tradingview, SMA calculates the standard deviation. The allocated moving average should be used to calculate the std function when the base line is changed.
Adaptive VWAP Stdev BandsIntroduction
Heyo, here are some adaptive VWAP Standard Deviation Bands with nice colors.
I used Ehlers dominant cycle theories and ZLSMA smoothing to create this indicator.
You can choose between different algorithms to determine the dominant cycle and this will be used as reset period.
Everytime bar_index can be divided through the dominant cycle length and the result is zero VWAP resets if have chosen an adaptive mode in the settings.
The other reset event you can use is just a simple time-based event, e.g. reset every day.
Usage
I think people buy/sell when it reaches extreme zones.
Enjoy!
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Credits to:
@SandroTurriate - VWAP Stdev Bands
@blackcat1402 - Dominant Cycle Analysis
@DasanC - Dominant Cycle Analysis
@veryfid - ZLSMA
(Sry, too lazy for linking)
I took parts of their code. Ty guys for your work! Just awesome.
Beta ScreenerThis script allows you to screen up to 38 symbols for their beta. It also allows you to compare the list to not only SPY but also CRYPTO10! Features include custom time frame and custom colors.
Here is a refresher on what beta is:
Beta (β) is a measure of the volatility—or systematic risk—of a security or portfolio compared to the market as a whole (usually the S&P 500 ). Stocks with betas higher than 1.0 can be interpreted as more volatile than the S&P 500 .
Beta is used in the capital asset pricing model (CAPM), which describes the relationship between systematic risk and expected return for assets (usually stocks). CAPM is widely used as a method for pricing risky securities and for generating estimates of the expected returns of assets, considering both the risk of those assets and the cost of capital.
How Beta Works
A beta coefficient can measure the volatility of an individual stock compared to the systematic risk of the entire market. In statistical terms, beta represents the slope of the line through a regression of data points. In finance, each of these data points represents an individual stock's returns against those of the market as a whole.
Beta effectively describes the activity of a security's returns as it responds to swings in the market. A security's beta is calculated by dividing the product of the covariance of the security's returns and the market's returns by the variance of the market's returns over a specified period.
cov (a,b)/var(b)
Variety N-Tuple Moving Averages w/ Variety Stepping [Loxx]Variety N-Tuple Moving Averages w/ Variety Stepping is a moving average indicator that allows you to create 1- 30 tuple moving average types; i.e., Double-MA, Triple-MA, Quadruple-MA, Quintuple-MA, ... N-tuple-MA. This version contains 2 different moving average types. For example, using "50" as the depth will give you Quinquagintuple Moving Average. If you'd like to find the name of the moving average type you create with the depth input with this indicator, you can find a list of tuples here: Tuples extrapolated
Due to the coding required to adapt a moving average to fit into this indicator, additional moving average types will be added as they are created to fit into this unique use case. Since this is a work in process, there will be many future updates of this indicator. For now, you can choose from either EMA or RMA.
This indicator is also considered one of the top 10 forex indicators. See details here: forex-station.com
Additionally, this indicator is a computationally faster, more streamlined version of the following indicators with the addition of 6 stepping functions and 6 different bands/channels types.
STD-Stepped, Variety N-Tuple Moving Averages
STD-Stepped, Variety N-Tuple Moving Averages is the standard deviation stepped/filtered indicator of the following indicator
Last but not least, a big shoutout to @lejmer for his help in formulating a looping solution for this streamlined version. this indicator is speedy even at 50 orders deep. You can find his scripts here: www.tradingview.com
How this works
Step 1: Run factorial calculation on the depth value,
Step 2: Calculate weights of nested moving averages
factorial(depth) / (factorial(depth - k) * factorial(k); where depth is the depth and k is the weight position
Examples of coefficient outputs:
6 Depth: 6 15 20 15 6
7 Depth: 7 21 35 35 21 7
8 Depth: 8 28 56 70 56 28 8
9 Depth: 9 36 34 84 126 126 84 36 9
10 Depth: 10 45 120 210 252 210 120 45 10
11 Depth: 11 55 165 330 462 462 330 165 55 11
12 Depth: 12 66 220 495 792 924 792 495 220 66 12
13 Depth: 13 78 286 715 1287 1716 1716 1287 715 286 78 13
Step 3: Apply coefficient to each moving average
For QEMA, which is 5 depth EMA , the calculation is as follows
ema1 = ta. ema ( src , length)
ema2 = ta. ema (ema1, length)
ema3 = ta. ema (ema2, length)
ema4 = ta. ema (ema3, length)
ema5 = ta. ema (ema4, length)
In this new streamlined version, these MA calculations are packed into an array inside loop so Pine doesn't have to keep all possible series information in memory. This is handled with the following code:
temp = array.get(workarr, k + 1) + alpha * (array.get(workarr, k) - array.get(workarr, k + 1))
array.set(workarr, k + 1, temp)
After we pack the array, we apply the coefficients to derive the NTMA:
qema = 5 * ema1 - 10 * ema2 + 10 * ema3 - 5 * ema4 + ema5
Stepping calculations
First off, you can filter by both price and/or MA output. Both price and MA output can be filtered/stepped in their own way. You'll see two selectors in the input settings. Default is ATR ATR. Here's how stepping works in simple terms: if the price/MA output doesn't move by X deviations, then revert to the price/MA output one bar back.
ATR
The average true range (ATR) is a technical analysis indicator, introduced by market technician J. Welles Wilder Jr. in his book New Concepts in Technical Trading Systems, that measures market volatility by decomposing the entire range of an asset price for that period.
Standard Deviation
Standard deviation is a statistic that measures the dispersion of a dataset relative to its mean and is calculated as the square root of the variance. The standard deviation is calculated as the square root of variance by determining each data point's deviation relative to the mean. If the data points are further from the mean, there is a higher deviation within the data set; thus, the more spread out the data, the higher the standard deviation.
Adaptive Deviation
By definition, the Standard Deviation (STD, also represented by the Greek letter sigma σ or the Latin letter s) is a measure that is used to quantify the amount of variation or dispersion of a set of data values. In technical analysis we usually use it to measure the level of current volatility .
Standard Deviation is based on Simple Moving Average calculation for mean value. This version of standard deviation uses the properties of EMA to calculate what can be called a new type of deviation, and since it is based on EMA , we can call it EMA deviation. And added to that, Perry Kaufman's efficiency ratio is used to make it adaptive (since all EMA type calculations are nearly perfect for adapting).
The difference when compared to standard is significant--not just because of EMA usage, but the efficiency ratio makes it a "bit more logical" in very volatile market conditions.
See how this compares to Standard Devaition here:
Adaptive Deviation
Median Absolute Deviation
The median absolute deviation is a measure of statistical dispersion. Moreover, the MAD is a robust statistic, being more resilient to outliers in a data set than the standard deviation. In the standard deviation, the distances from the mean are squared, so large deviations are weighted more heavily, and thus outliers can heavily influence it. In the MAD, the deviations of a small number of outliers are irrelevant.
Because the MAD is a more robust estimator of scale than the sample variance or standard deviation, it works better with distributions without a mean or variance, such as the Cauchy distribution.
For this indicator, I used a manual recreation of the quantile function in Pine Script. This is so users have a full inside view into how this is calculated.
Efficiency-Ratio Adaptive ATR
Average True Range (ATR) is widely used indicator in many occasions for technical analysis . It is calculated as the RMA of true range. This version adds a "twist": it uses Perry Kaufman's Efficiency Ratio to calculate adaptive true range
See how this compares to ATR here:
ER-Adaptive ATR
Mean Absolute Deviation
The mean absolute deviation (MAD) is a measure of variability that indicates the average distance between observations and their mean. MAD uses the original units of the data, which simplifies interpretation. Larger values signify that the data points spread out further from the average. Conversely, lower values correspond to data points bunching closer to it. The mean absolute deviation is also known as the mean deviation and average absolute deviation.
This definition of the mean absolute deviation sounds similar to the standard deviation (SD). While both measure variability, they have different calculations. In recent years, some proponents of MAD have suggested that it replace the SD as the primary measure because it is a simpler concept that better fits real life.
For Pine Coders, this is equivalent of using ta.dev()
Bands/Channels
See the information above for how bands/channels are calculated. After the one of the above deviations is calculated, the channels are calculated as output +/- deviation * multiplier
Signals
Green is uptrend, red is downtrend, yellow "L" signal is Long, fuchsia "S" signal is short.
Included:
Alerts
Loxx's Expanded Source Types
Bar coloring
Signals
6 bands/channels types
6 stepping types
Related indicators
3-Pole Super Smoother w/ EMA-Deviation-Corrected Stepping
STD-Stepped Fast Cosine Transform Moving Average
ATR-Stepped PDF MA
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.
ER-Adaptive ATR, STD-Adaptive Damiani Volatmeter [Loxx]ER-Adaptive ATR, STD-Adaptive Damiani Volatmeter is a Damiani Volatmeter with both Efficiency-Ratio Adaptive ATR, used in place of ATR, and Adaptive Deviation, used in place of Standard Deviation.
What is Adaptive Deviation?
By definition, the Standard Deviation (STD, also represented by the Greek letter sigma σ or the Latin letter s) is a measure that is used to quantify the amount of variation or dispersion of a set of data values. In technical analysis we usually use it to measure the level of current volatility .
Standard Deviation is based on Simple Moving Average calculation for mean value. This version of standard deviation uses the properties of EMA to calculate what can be called a new type of deviation, and since it is based on EMA , we can call it EMA deviation. And added to that, Perry Kaufman's efficiency ratio is used to make it adaptive (since all EMA type calculations are nearly perfect for adapting).
The difference when compared to standard is significant--not just because of EMA usage, but the efficiency ratio makes it a "bit more logical" in very volatile market conditions.
The green line is the Adaptive Deviation, the white line is regular Standard Deviation. This concept will be used in future indicators to further reduce noise and adapt to price volatility .
See here for a comparison between Adaptive Deviation and Standard Deviation
What is Efficiency Ratio Adaptive ATR?
Average True Range (ATR) is widely used indicator in many occasions for technical analysis . It is calculated as the RMA of true range. This version adds a "twist": it uses Perry Kaufman's Efficiency Ratio to calculate adaptive true range
See here for a comparison between Efficiency-Ratio Adaptive ATR, and ATR.
What is the Damiani Volatmeter?
Damiani Volatmeter uses ATR and Standard deviation to tease out ticker volatility so you can better understand when it's the ideal time to trade. The idea here is that you only take trades when volatility is high so this indicator is to be coupled with various other indicators to validate the other indicator's signals. This is also useful for detecting crabbing and chopping markets.
Shoutout to user @xinolia for the DV function used here.
Anything red means that volatility is low. Remember volatility doesn't have a direction. Anything green means volatility high despite the direction of price. The core signal line here is the green and red line that dips below two while threshold lines to "recharge". Maximum recharge happen when the core signal line shows a yellow ping. Soon after one or many yellow pings you should expect a massive upthrust of volatility . The idea here is you don't trade unless volatility is rising or green. This means that the Volatmeter has to dip into the recharge zone, recharge and then spike upward. You can also attempt to buy or sell reversals with confluence indicators when volatility is in the recharge zone, but I wouldn't recommend this. However, if you so choose to do this, then use the following indicator for confluence.
And last reminder, volatility doesn't have a direction! Red doesn't mean short, and green doesn't mean long, Red means don't trade period regardless of direction long/short, and green means trade no matter the direction long/short. This means you'll have to add an indicator that does show direction such as a mean reversion indicator like Fisher Transform or a Gaussian Filter. You can search my public scripts for various Fisher Transform and Gaussian Filter indicators.
Price-Filtered Spearman Rank Correl. w/ Floating Levels is considered the Mercedes Benz of reversal indicators
Comparison between this indicator, ER-Adaptive ATR, STD-Adaptive Damiani Volatmeter , and the regular Damiani Volatmeter . Notice that the adaptive version catches more volatility than the regular version.
How signals work
RV = Rising Volatility
VD = Volatility Dump
Plots
White line is signal
Thick red/green line is the Volatmeter line
The dotted lower lines are the zero line and minimum recharging line
Included
Bar coloring
Alerts
Signals
Related indicators
Variety Moving Average Waddah Attar Explosion (WAE)
Damiani Volatmeter
Adaptive Deviation [Loxx]Adaptive Deviation is an educational/conceptual indicator that is a new spin on the regular old standard deviation. By definition, the Standard Deviation (STD, also represented by the Greek letter sigma σ or the Latin letter s) is a measure that is used to quantify the amount of variation or dispersion of a set of data values. In technical analysis we usually use it to measure the level of current volatility.
Standard Deviation is based on Simple Moving Average calculation for mean value. This version of standard deviation uses the properties of EMA to calculate what can be called a new type of deviation, and since it is based on EMA, we can call it EMA deviation. And added to that, Perry Kaufman's efficiency ratio is used to make it adaptive (since all EMA type calculations are nearly perfect for adapting).
The difference when compared to standard is significant--not just because of EMA usage, but the efficiency ratio makes it a "bit more logical" in very volatile market conditions.
The green line is the Adaptive Deviation, the white line is regular Standard Deviation. This concept will be used in future indicators to further reduce noise and adapt to price volatility.
Included
Loxx's Expanded Source Types
STD-Filtered, ATR-Adaptive Laguerre Filter [Loxx]STD-Filtered, ATR-Adaptive Laguerre Filter is a standard Laguerre Filter that is first made ATR-adaptive and the passed through a standard deviation filter. This helps reduce noise and refine the output signal. Can apply the standard deviation filter to the price, signal, both or neither.
What is the Laguerre Filter?
The Laguerre RSI indicator created by John F. Ehlers is described in his book "Cybernetic Analysis for Stocks and Futures". The Laguerre Filter is a smoothing filter which is based on Laguerre polynomials. The filter requires the current price, three prior prices, a user defined factor called Alpha to fill its calculation. Adjusting the Alpha coefficient is used to increase or decrease its lag and it's smoothness.
Included:
Bar coloring
Signals
Alerts
Loxx's Expanded Source Types
STD-Stepped, Variety N-Tuple Moving Averages [Loxx]STD-Stepped, Variety N-Tuple Moving Averages is the standard deviation stepped/filtered indicator of the following indicator
Variety N-Tuple Moving Averages is a moving average indicator that allows you to create 1- 30 tuple moving average types; i.e., Double-MA, Triple-MA, Quadruple-MA, Quintuple-MA, ... N-tuple-MA. This version contains 5 different moving average types including T3. A list of tuples can be found here if you'd like to name the order of the moving average by depth: Tuples extrapolated
STD-Stepped, You'll notice that this is a lot of code and could normally be packed into a single loop in order to extract the N-tuple MA, however due to Pine Script limitations and processing paradigm this is not possible ... yet.
If you choose the EMA option and select a depth of 2, this is the classic DEMA ; EMA with a depth of 3 is the classic TEMA , and so on and so forth this is to help you understand how this indicator works. This version of NTMA is restricted to a maximum depth of 30 or less. Normally this indicator would include 50 depths but I've cut this down to 30 to reduce indicator load time. In the future, I'll create an updated NTMA that allows for more depth levels.
This is considered one of the top ten indicators in forex. You can read more about it here: forex-station.com
How this works
Step 1: Run factorial calculation on the depth value,
Step 2: Calculate weights of nested moving averages
factorial(nemadepth) / (factorial(nemadepth - k) * factorial(k); where nemadepth is the depth and k is the weight position
Examples of coefficient outputs:
6 Depth: 6 15 20 15 6
7 Depth: 7 21 35 35 21 7
8 Depth: 8 28 56 70 56 28 8
9 Depth: 9 36 34 84 126 126 84 36 9
10 Depth: 10 45 120 210 252 210 120 45 10
11 Depth: 11 55 165 330 462 462 330 165 55 11
12 Depth: 12 66 220 495 792 924 792 495 220 66 12
13 Depth: 13 78 286 715 1287 1716 1716 1287 715 286 78 13
Step 3: Apply coefficient to each moving average
For QEMA, which is 5 depth EMA , the caculation is as follows
ema1 = ta. ema ( src , length)
ema2 = ta. ema (ema1, length)
ema3 = ta. ema (ema2, length)
ema4 = ta. ema (ema3, length)
ema5 = ta. ema (ema4, length)
qema = 5 * ema1 - 10 * ema2 + 10 * ema3 - 5 * ema4 + ema5
Included:
Alerts
Loxx's Expanded Source Types
Bar coloring
Signals
Standard deviation stepping
Damiani Volatmeter [loxx]I wasn't going to publish this since it's one my go to private indicators, but I decided to push this out anyway. This is a variation on Damiani Volatmeter to make it easier to understand what's going on. Damiani Volatmeter uses ATR and Standard deviation to tease out ticker volatility so you can better understand when it's the ideal time to trade. The idea here is that you only take trades when volatility is high so this indicator is to be coupled with various other indicators to validate the other indicator's signals. This is also useful for detecting crabbing and chopping markets.
Shoutout to user @xinolia for the DV function used here.
Anything red means that volatility is low. Remember volatility doesn't have a direction. Anything green means volatility high despite the direction of price. The core signal line here is the green and red line that dips below two while threshold lines to "recharge". Maximum recharge happen when the core signal line shows a yellow ping. Soon after one or many yellow pings you should expect a massive upthrust of volatility. The idea here is you don't trade unless volatility is rising or green. This means that the Volatmeter has to dip into the recharge zone, recharge and then spike upward. You can also attempt to buy or sell reversals with confluence indicators when volatility is in the recharge zone, but I wouldn't recommend this. However, if you so choose to do this, then use the following indicator for confluence.
And last reminder, volatility doesn't have a direction ! Red doesn't mean short, and green doesn't mean long, Red means don't trade period regardless of direction long/short, and green means trade no matter the direction long/short. This means you'll have to add an indicator that does show direction such as a mean reversion indicator like Fisher Transform or a Gaussian Filter. You can search my public scripts for various Fisher Transform and Gaussian Filter indicators.
Price-Filtered Spearman Rank Correl. w/ Floating Levels is considered the Mercedes Benz of reversal indcators
How signals work
RV = Rising Volatility
VD = Volatility Dump
Plots
White line is signal
Thick red/green line is the Volatmeter line
The dotted lower lines are the zero line and minimum recharging line
Included
Bar coloring
Alerts
Signals
Related indicators
Variety Moving Average Waddah Attar Explosion (WAE)
STD-Filtered, N-Pole Gaussian Filter [Loxx]This is a Gaussian Filter with Standard Deviation Filtering that works for orders (poles) higher than the usual 4 poles that was originally available in Ehlers Gaussian Filter formulas. Because of that, it is a sort of generalized Gaussian filter that can calculate arbitrary (order) pole Gaussian Filter and which makes it a sort of a unique indicator. For this implementation, the practical mathematical maximum is 15 poles after which the precision of calculation is useless--the coefficients for levels above 15 poles are so high that the precision loss actually means very little. Despite this maximal precision utility, I've left the upper bound of poles open-ended so you can try poles of order 15 and above yourself. The default is set to 5 poles which is 1 pole greater than the normal maximum of 4 poles.
The purpose of the standard deviation filter is to filter out noise by and by default it will filter 1 standard deviation. Adjust this number and the filter selections (price, both, GMA, none) to reduce the signal noise.
What is Ehlers Gaussian filter?
This filter can be used for smoothing. It rejects high frequencies (fast movements) better than an EMA and has lower lag. published by John F. Ehlers in "Rocket Science For Traders".
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.
A gaussian filter with...
One Pole: f = alpha*g + (1-alpha)f
Two Poles: f = alpha*2g + 2(1-alpha)f - (1-alpha)2f
Three Poles: f = alpha*3g + 3(1-alpha)f - 3(1-alpha)2f + (1-alpha)3f
Four Poles: f = alpha*4g + 4(1-alpha)f - 6(1-alpha)2f + 4(1-alpha)3f - (1-alpha)4f
and so on...
For an equivalent number of poles the lag of a Gaussian is about half the lag of a Butterworth filters: Lag = N*P / pi^2, where,
N is the number of poles, and
P is the critical period
Special initialization of filter stages ensures proper working in scans with as few bars as possible.
From Ehlers Book: "The first objective of using smoothers is to eliminate or reduce the undesired high-frequency components in the eprice data. Therefore these smoothers are called low-pass filters, and they all work by some form of averaging. Butterworth low-pass filters can do this job, but nothing comes for free. A higher degree of filtering is necessarily accompanied by a larger amount of lag. We have come to see that is a fact of life."
References John F. Ehlers: "Rocket Science For Traders, Digital Signal Processing Applications", Chapter 15: "Infinite Impulse Response Filters"
Included
Loxx's Expanded Source Types
Signals
Alerts
Bar coloring
Related indicators
STD-Filtered, Gaussian Moving Average (GMA)
STD-Filtered, Gaussian-Kernel-Weighted Moving Average
One-Sided Gaussian Filter w/ Channels
Fisher Transform w/ Dynamic Zones
R-sqrd Adapt. Fisher Transform w/ D. Zones & Divs .
STD-Filtered, Gaussian Moving Average (GMA) [Loxx]STD-Filtered, Gaussian Moving Average (GMA) is a 1-4 pole Ehlers Gaussian Filter with standard deviation filtering. This indicator should perform similar to Ehlers Fisher Transform.
The purpose of the standard deviation filter is to filter out noise by and by default it will filter 1 standard deviation. Adjust this number and the filter selections (price, both, GMA, none) to reduce the signal noise.
What is Ehlers Gaussian filter?
This filter can be used for smoothing. It rejects high frequencies (fast movements) better than an EMA and has lower lag. published by John F. Ehlers in "Rocket Science For Traders". First implemented in Wealth-Lab by Dr René Koch.
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.
A gaussian filter with...
one pole is equivalent to an EMA filter.
two poles is equivalent to EMA(EMA())
three poles is equivalent to EMA(EMA(EMA()))
and so on...
For an equivalent number of poles the lag of a Gaussian is about half the lag of a Butterworth filters: Lag = N * P / (2 * ¶2), where,
N is the number of poles, and
P is the critical period
Special initialization of filter stages ensures proper working in scans with as few bars as possible.
From Ehlers Book: "The first objective of using smoothers is to eliminate or reduce the undesired high-frequency components in the eprice data. Therefore these smoothers are called low-pass filters, and they all work by some form of averaging. Butterworth low-pass filtters can do this job, but nothing comes for free. A higher degree of filtering is necessarily accompanied by a larger amount of lag. We have come to see that is a fact of life."
References John F. Ehlers: "Rocket Science For Traders, Digital Signal Processing Applications", Chapter 15: "Infinite Impulse Response Filters"
Included
Loxx's Expanded Source Types
Signals
Alerts
Bar coloring
Related indicators
STD-Filtered, Gaussian-Kernel-Weighted Moving Average
One-Sided Gaussian Filter w/ Channels
Fisher Transform w/ Dynamic Zones
R-sqrd Adapt. Fisher Transform w/ D. Zones & Divs.
