Effective Volume (ADV) v3Effective Volume (ADV) v3: Enhanced Accumulation/Distribution Analysis Tool
This indicator is an updated version of the original script by cI8DH, now upgraded to Pine Script v5 with added functionality, including the Volume Multiple feature. The tool is designed for analyzing Accumulation/Distribution (A/D) volume, referred to here as "Effective Volume," which represents the volume impact in alignment with price direction, providing insights into bullish or bearish trends through volume.
Accumulation/Distribution Volume Analysis : The script calculates and visualizes Effective Volume (ADV), helping traders assess volume strength in relation to price action. By factoring in bullish or bearish alignment, Effective Volume highlights points where volume strongly supports price movements.
Volume Multiple Feature for Volume Multiplication : The Volume Multiple setting (default value 2) allows you to set a multiplier to identify bars where Effective Volume exceeds the previous bar’s volume by a specified factor. This feature aids in pinpointing significant shifts in volume intensity, often associated with potential trend changes.
Customizable Aggregation Types : Users can choose from three volume aggregation types:
Simple - Standard SMA (Simple Moving Average) for averaging Effective Volume
Smoothed - RMA (Recursive Moving Average) for a less volatile, smoother line
Cumulative - Accumulated Effective Volume for ongoing trend analysis
Volume Divisor : The “Divide Vol by” setting (default 1 million) scales down the Effective Volume value for easier readability. This allows Effective Volume data to be aligned with the scale of the price chart.
Visualization Elements
Effective Volume Columns : The Effective Volume bar plot changes color based on volume direction:
Green Bars : Bullish Effective Volume (volume aligns with price movement upwards)
Red Bars : Bearish Effective Volume (volume aligns with price movement downwards)
Moving Average Lines :
Volume Moving Average - A gray line representing the moving average of total volume.
A/D Moving Average - A blue line showing the moving average of Accumulation/Distribution (A/D) Effective Volume.
High ADV Indicator : A “^” symbol appears on bars where the Effective Volume meets or exceeds the Volume Multiple threshold, highlighting bars with significant volume increase.
How to Use
Analyze Accumulation/Distribution Trends : Use Effective Volume to observe if bullish or bearish volume aligns with price direction, offering insights into the strength and sustainability of trends.
Identify Volume Multipliers with Volume Multiple : Adjust Volume Multiple to track when Effective Volume has notably increased, signaling potential shifts or strengthening trends.
Adjust Volume Display : Use the volume divisor setting to scale Effective Volume for clarity, especially when viewing alongside price data on higher timeframes.
With customizable parameters, this script provides a flexible, enhanced perspective on Effective Volume for traders analyzing volume-based trends and reversals.
Outlier
Outlier changes alertAn indicator that calculates click (price change), percentage change, and Z-score changes while displaying outliers based on defined ranges.
Outlier Detection:
Mark outliers (for price, percentage, Z-score) based on user-defined thresholds. For example, any price movement exceeding a certain Z-score or percentage change could be marked as an outlier and displayed on chart.
Indicator Overview:
1. Click (Price Change):
Calculate the absolute price change from one period to another (e.g., from the current closing price to the previous closing price).
2. Percentage Change:
Calculate the percentage price change over a specific period, showing how much the price has changed in relative terms compared to the previous price.
3. Z-Score:
Compute the Z-score to standardize the price change relative to its historical average and standard deviation. The Z-score helps in detecting whether a price movement is an outlier or falls within a normal range of volatility.
Band Pass Normalized Suite (BPNS)Outlier-Free Normalization and Band Pass Filtering
We present a technique for normalizing and filtering a given time series, source, in order to improve its stationarity and enhance its features. The technique includes two stages: outlier-free normalization and band pass filtering.
Outlier-Free Normalization:
In order to normalize source and reduce the impact of outliers, we first smooth the time series using an exponential moving average with a smoothing factor of alpha. The smoothed time series is then normalized by subtracting the minimum value within a given lookback period, dev_lookback, and dividing the result by the range (maximum - minimum) within the same lookback period. Outliers are detected and excluded from the normalization process by identifying values that are more than outlier_level standard deviations away from the exponentially smoothed average.
Band Pass Filtering:
After normalization, the time series is passed through a band pass filter to remove low and high frequency components. The specifics of the band pass filter implementation are not provided.
Code snippet:
bes(float source = close, float alpha = 0.7) =>
var float smoothed = na
smoothed := na(smoothed) ? source : alpha * source + (1 - alpha) * nz(smoothed )
max(source, outlier_level, dev_lookback)=>
var float max = na
src = array.new()
stdev = math.abs((source - bes(source, 0.1))/ta.stdev(source, dev_lookback))
array.push(src, stdev < outlier_level ? source : -1.7976931348623157e+308)
max := math.max(nz(max ), array.get(src, 0))
min(source, outlier_level, dev_lookback) =>
var float min = na
src = array.new()
stdev = math.abs((source - bes(source, 0.1))/ta.stdev(source, dev_lookback))
array.push(src, stdev < outlier_level ? source : 1.7976931348623157e+308)
min := math.min(nz(min ), array.get(src, 0))
min_max(src, outlier_level, dev_lookback) =>
(src - min(src, outlier_level, dev_lookback))/(max(src, outlier_level, dev_lookback) - min(src, outlier_level, dev_lookback)) * 100
To apply the outlier-free normalization and band pass filter to a given time series, source, the min_max() function can be called with the desired values for outlier_level and dev_lookback as arguments. For example:
normalized_source = min_max(source, 2, 50)
This will apply the outlier-free normalization and band pass filter to source, using an outlier_level of 2 standard deviations and a lookback period of 50 data points for both the normalization and outlier detection steps. The resulting normalized and filtered time series will be stored in normalized_source.
It is important to note that the choice of values for outlier_level and dev_lookback will have a significant impact on the resulting normalized and filtered time series. These values should be chosen carefully based on the characteristics of the input time series and the desired properties of the normalized and filtered output.
In conclusion, the outlier-free normalization and band pass filtering technique presented here provides a useful tool for preprocessing time series data and improving its stationarity and feature content. The flexibility of the method, through the choice of outlier_level and dev_lookback values, allows it to be tailored to the specific characteristics of the input time series.
Outliers Detector with N-Sigma Confidence Intervals (TG fork)Display outliers in either value change, volume or volume change that significantly deviate from the past.
This uses the standard deviation calculation and the n-sigmas statistical rule of significance, with 2-sigma (a value of 2) signifying that the observed value is stronger than 95% of past values, and 3-sigma 98.5% of past values, and so on for higher sigma values.
Outliers in price action or in volume can indicate a strong support for the move, and hence potentially more moves in the same direction in the future. Inversely, an insignificant move is less likely to be supported. And of course the stronger, the more support.
This indicator also doubles as a standard volume indicator if volume is selected as the source, but with the option of highlighting outliers.
Bars below significance can be uncolored (gray) to unclutter the visuals.
Differently to almost all other similar indicators, the background highlighting is dynamical, so that all values will be highlighted differently, not just 2-sigma or 3-sigma, but also 4-sigma, 5-sigma, etc, with a different value of transparency.
The dynamical transparency value can be calculated in two ways: either statically proportionally to the n-sigma but capped at 10-sigma, or either as a ratio relative to the highest observed sigma value over the defined lookback period (default: 300).
If you like this indicator, which is an extension of previously published indicators, please give some love to the original authors:
* tvjvzl :
* vnhilton :
This extension, authored by Tartigradia, extends tvjvzl's indi, implements vnhilton's idea of highlighting the background, and go further by adding dynamical background highlighting for any value of sigma, add support for volume and volume change (VolumeDiff) as inputs, add option to uncolor insignificant bars, allow plotting in both directions and more.
Median Absolute Deviation Filtered SMA & BBMedian Absolute Deviation (MAD) is a robust measurement of variability and more resilient against outliers and small samples.
This experiment uses MAD as a means of filtering outliers from an SMA calculation. First we construct the equivalent of a Bollinger Band, but based on the median as the basis and a multiple( k ) of MAD as the outlier cutoff.
k can be set a number of ways. As a simple multiple (3 - very conservative / 2.5 - moderately conservative / 2 - poorly conservative). Alternatively MAD can be used as an estimator of standard deviation by using a multiple of 1.4826 (SD1 - 1.4826 / SD2 - 2.9652 / SD3 - 4.4478).
Once we have a cutoff range an SMA is calculated with the outliers filtered out. Additionally a Bollinger band can be output using the filtered SMA as the basis and a multiple of the MAD instead of SD for the bands.
