Distance between EMA 50-100/100-150This script calculates and plots the percentage difference between the 50-period, 100-period, and 150-period Exponential Moving Averages (EMA) on a TradingView chart. The aim is to provide a clear visual representation of the market's momentum by analyzing the distance between key EMAs over time.
Key features of this script:
1. EMA Calculation : The script computes the EMA values for 50, 100, and 150 periods and calculates the percentage difference between EMA 50 and 100, and between EMA 100 and 150.
2. Custom Threshold : Users can adjust a threshold percentage to highlight significant divergences between the EMAs. A default threshold is set to 0.1%.
3. Visual Alerts : When the percentage difference exceeds the threshold, a visual marker appears on the chart:
Green Circles for bullish momentum (positive divergence),
Red Circles for bearish momentum (negative divergence),
Diamonds to indicate the first occurrence of new bullish or bearish signals, allowing users to catch fresh market trends.
4. Dynamic Plotting : The script plots two lines representing the percentage difference for each EMA pair, offering a quick and intuitive way to monitor trends.
Ideal for traders looking to gauge market direction using the relationship between multiple EMAs, this script simplifies analysis by focusing on key moving average interactions.
Distance
Swing DistanceHello fellas,
This simple indicator helps to visualize the distance between swings. It consists of two lines, the highest and the lowest line, which show the highest and lowest value of the set lookback, respectively. Additionally, it plots labels with the distance (in %) between the highest and the lowest line when there is a change in either the highest or the lowest value.
Use Case:
This tool helps you get a feel for which trades you might want to take and which timeframe you might want to use.
Side Note: This indicator is not intended to be used as a signal emitter or filter!
Best regards,
simwai
Vwap Z-Score with Signals [UAlgo]The "VWAP Z-Score with Signals " is a technical analysis tool designed to help traders identify potential buy and sell signals based on the Volume Weighted Average Price (VWAP) and its Z-Score. This indicator calculates the VWAP Z-Score to show how far the current price deviates from the VWAP in terms of standard deviations. It highlights overbought and oversold conditions with visual signals, aiding in the identification of potential market reversals. The tool is customizable, allowing users to adjust parameters for their specific trading needs.
🔶 Features
VWAP Z-Score Calculation: Measures the deviation of the current price from the VWAP using standard deviations.
Customizable Parameters: Allows users to set the length of the VWAP Z-Score calculation and define thresholds for overbought and oversold levels.
Reversal Signals: Provides visual signals when the Z-Score crosses the specified thresholds, indicating potential buy or sell opportunities.
🔶 Usage
Extreme Z-Score values (both positive and negative) highlight significant deviations from the VWAP, useful for identifying potential reversal points.
The indicator provides visual signals when the Z-Score crosses predefined thresholds:
A buy signal (🔼) appears when the Z-Score crosses above the lower threshold, suggesting the price may be oversold and a potential upward reversal.
A sell signal (🔽) appears when the Z-Score crosses below the upper threshold, suggesting the price may be overbought and a potential downward reversal.
These signals can help you identify potential entry and exit points in your trading strategy.
🔶 Disclaimer
The "VWAP Z-Score with Signals " indicator is designed for educational purposes and to assist traders in their technical analysis. It does not guarantee profitable trades and should not be considered as financial advice.
Users should conduct their own research and use this indicator in conjunction with other tools and strategies.
Trading involves significant risk, and it is possible to lose more than your initial investment.
Triple EMA Distance IndicatorTriple EMA Distance Indicator
The Triple EMA Distance indicator comprises two sets of triple exponential moving averages (EMAs). One set uses the same smoothing length for all EMAs, while the other set doubles the length for the last EMA. This indicator provides visual cues based on the relationship between these EMAs and candlestick patterns.
Blue Condition:
Indicates when the fast EMA is above the slow EMA.
The distance between the two EMAs is increasing.
Candlesticks and EMAs are colored light blue.
Orange Condition:
Activates when the fast EMA is below the slow EMA.
The distance between the two EMAs is increasing.
Candlesticks and EMAs are colored orange.
Beige Condition:
Occurs when the fast EMA is below the slow EMA.
The distance between the two EMAs is decreasing.
Candlesticks and EMAs are colored beige.
Light Blue Condition:
Represents when the fast EMA is above the slow EMA.
The distance between the two EMAs is decreasing.
Candlesticks and EMAs are colored light blue.
TimeSeriesRecurrencePlotLibrary "TimeSeriesRecurrencePlot"
In descriptive statistics and chaos theory, a recurrence plot (RP) is a plot showing, for each moment i i in time, the times at which the state of a dynamical system returns to the previous state at `i`, i.e., when the phase space trajectory visits roughly the same area in the phase space as at time `j`.
```
A recurrence plot (RP) is a graphical representation used in the analysis of time series data and dynamical systems. It visualizes recurring states or events over time by transforming the original time series into a binary matrix, where each element represents whether two consecutive points are above or below a specified threshold. The resulting Recurrence Plot Matrix reveals patterns, structures, and correlations within the data while providing insights into underlying mechanisms of complex systems.
```
~starling7b
___
Reference:
en.wikipedia.org
github.com
github.com
github.com
github.com
juliadynamics.github.io
distance_matrix(series1, series2, max_freq, norm)
Generate distance matrix between two series.
Parameters:
series1 (float) : Source series 1.
series2 (float) : Source series 2.
max_freq (int) : Maximum frequency to inpect or the size of the generated matrix.
norm (string) : Norm of the distance metric, default=`euclidean`, options=`euclidean`, `manhattan`, `max`.
Returns: Matrix with distance values.
method normalize_distance(M)
Normalizes a matrix within its Min-Max range.
Namespace types: matrix
Parameters:
M (matrix) : Source matrix.
Returns: Normalized matrix.
method threshold(M, threshold)
Updates the matrix with the condition `M(i,j) > threshold ? 1 : 0`.
Namespace types: matrix
Parameters:
M (matrix) : Source matrix.
threshold (float)
Returns: Cross matrix.
rolling_window(a, b, sample_size)
An experimental alternative method to plot a recurrence_plot.
Parameters:
a (array) : Array with data.
b (array) : Array with data.
sample_size (int)
Returns: Recurrence_plot matrix.
SimilarityMeasuresLibrary "SimilarityMeasures"
Similarity measures are statistical methods used to quantify the distance between different data sets
or strings. There are various types of similarity measures, including those that compare:
- data points (SSD, Euclidean, Manhattan, Minkowski, Chebyshev, Correlation, Cosine, Camberra, MAE, MSE, Lorentzian, Intersection, Penrose Shape, Meehl),
- strings (Edit(Levenshtein), Lee, Hamming, Jaro),
- probability distributions (Mahalanobis, Fidelity, Bhattacharyya, Hellinger),
- sets (Kumar Hassebrook, Jaccard, Sorensen, Chi Square).
---
These measures are used in various fields such as data analysis, machine learning, and pattern recognition. They
help to compare and analyze similarities and differences between different data sets or strings, which
can be useful for making predictions, classifications, and decisions.
---
References:
en.wikipedia.org
cran.r-project.org
numerics.mathdotnet.com
github.com
github.com
github.com
Encyclopedia of Distances, doi.org
ssd(p, q)
Sum of squared difference for N dimensions.
Parameters:
p (float ) : `array` Vector with first numeric distribution.
q (float ) : `array` Vector with second numeric distribution.
Returns: Measure of distance that calculates the squared euclidean distance.
euclidean(p, q)
Euclidean distance for N dimensions.
Parameters:
p (float ) : `array` Vector with first numeric distribution.
q (float ) : `array` Vector with second numeric distribution.
Returns: Measure of distance that calculates the straight-line (or Euclidean).
manhattan(p, q)
Manhattan distance for N dimensions.
Parameters:
p (float ) : `array` Vector with first numeric distribution.
q (float ) : `array` Vector with second numeric distribution.
Returns: Measure of absolute differences between both points.
minkowski(p, q, p_value)
Minkowsky Distance for N dimensions.
Parameters:
p (float ) : `array` Vector with first numeric distribution.
q (float ) : `array` Vector with second numeric distribution.
p_value (float) : `float` P value, default=1.0(1: manhatan, 2: euclidean), does not support chebychev.
Returns: Measure of similarity in the normed vector space.
chebyshev(p, q)
Chebyshev distance for N dimensions.
Parameters:
p (float ) : `array` Vector with first numeric distribution.
q (float ) : `array` Vector with second numeric distribution.
Returns: Measure of maximum absolute difference.
correlation(p, q)
Correlation distance for N dimensions.
Parameters:
p (float ) : `array` Vector with first numeric distribution.
q (float ) : `array` Vector with second numeric distribution.
Returns: Measure of maximum absolute difference.
cosine(p, q)
Cosine distance between provided vectors.
Parameters:
p (float ) : `array` 1D Vector.
q (float ) : `array` 1D Vector.
Returns: The Cosine distance between vectors `p` and `q`.
---
angiogenesis.dkfz.de
camberra(p, q)
Camberra distance for N dimensions.
Parameters:
p (float ) : `array` Vector with first numeric distribution.
q (float ) : `array` Vector with second numeric distribution.
Returns: Weighted measure of absolute differences between both points.
mae(p, q)
Mean absolute error is a normalized version of the sum of absolute difference (manhattan).
Parameters:
p (float ) : `array` Vector with first numeric distribution.
q (float ) : `array` Vector with second numeric distribution.
Returns: Mean absolute error of vectors `p` and `q`.
mse(p, q)
Mean squared error is a normalized version of the sum of squared difference.
Parameters:
p (float ) : `array` Vector with first numeric distribution.
q (float ) : `array` Vector with second numeric distribution.
Returns: Mean squared error of vectors `p` and `q`.
lorentzian(p, q)
Lorentzian distance between provided vectors.
Parameters:
p (float ) : `array` Vector with first numeric distribution.
q (float ) : `array` Vector with second numeric distribution.
Returns: Lorentzian distance of vectors `p` and `q`.
---
angiogenesis.dkfz.de
intersection(p, q)
Intersection distance between provided vectors.
Parameters:
p (float ) : `array` Vector with first numeric distribution.
q (float ) : `array` Vector with second numeric distribution.
Returns: Intersection distance of vectors `p` and `q`.
---
angiogenesis.dkfz.de
penrose(p, q)
Penrose Shape distance between provided vectors.
Parameters:
p (float ) : `array` Vector with first numeric distribution.
q (float ) : `array` Vector with second numeric distribution.
Returns: Penrose shape distance of vectors `p` and `q`.
---
angiogenesis.dkfz.de
meehl(p, q)
Meehl distance between provided vectors.
Parameters:
p (float ) : `array` Vector with first numeric distribution.
q (float ) : `array` Vector with second numeric distribution.
Returns: Meehl distance of vectors `p` and `q`.
---
angiogenesis.dkfz.de
edit(x, y)
Edit (aka Levenshtein) distance for indexed strings.
Parameters:
x (int ) : `array` Indexed array.
y (int ) : `array` Indexed array.
Returns: Number of deletions, insertions, or substitutions required to transform source string into target string.
---
generated description:
The Edit distance is a measure of similarity used to compare two strings. It is defined as the minimum number of
operations (insertions, deletions, or substitutions) required to transform one string into another. The operations
are performed on the characters of the strings, and the cost of each operation depends on the specific algorithm
used.
The Edit distance is widely used in various applications such as spell checking, text similarity, and machine
translation. It can also be used for other purposes like finding the closest match between two strings or
identifying the common prefixes or suffixes between them.
---
github.com
www.red-gate.com
planetcalc.com
lee(x, y, dsize)
Distance between two indexed strings of equal length.
Parameters:
x (int ) : `array` Indexed array.
y (int ) : `array` Indexed array.
dsize (int) : `int` Dictionary size.
Returns: Distance between two strings by accounting for dictionary size.
---
www.johndcook.com
hamming(x, y)
Distance between two indexed strings of equal length.
Parameters:
x (int ) : `array` Indexed array.
y (int ) : `array` Indexed array.
Returns: Length of different components on both sequences.
---
en.wikipedia.org
jaro(x, y)
Distance between two indexed strings.
Parameters:
x (int ) : `array` Indexed array.
y (int ) : `array` Indexed array.
Returns: Measure of two strings' similarity: the higher the value, the more similar the strings are.
The score is normalized such that `0` equates to no similarities and `1` is an exact match.
---
rosettacode.org
mahalanobis(p, q, VI)
Mahalanobis distance between two vectors with population inverse covariance matrix.
Parameters:
p (float ) : `array` 1D Vector.
q (float ) : `array` 1D Vector.
VI (matrix) : `matrix` Inverse of the covariance matrix.
Returns: The mahalanobis distance between vectors `p` and `q`.
---
people.revoledu.com
stat.ethz.ch
docs.scipy.org
fidelity(p, q)
Fidelity distance between provided vectors.
Parameters:
p (float ) : `array` 1D Vector.
q (float ) : `array` 1D Vector.
Returns: The Bhattacharyya Coefficient between vectors `p` and `q`.
---
en.wikipedia.org
bhattacharyya(p, q)
Bhattacharyya distance between provided vectors.
Parameters:
p (float ) : `array` 1D Vector.
q (float ) : `array` 1D Vector.
Returns: The Bhattacharyya distance between vectors `p` and `q`.
---
en.wikipedia.org
hellinger(p, q)
Hellinger distance between provided vectors.
Parameters:
p (float ) : `array` 1D Vector.
q (float ) : `array` 1D Vector.
Returns: The hellinger distance between vectors `p` and `q`.
---
en.wikipedia.org
jamesmccaffrey.wordpress.com
kumar_hassebrook(p, q)
Kumar Hassebrook distance between provided vectors.
Parameters:
p (float ) : `array` 1D Vector.
q (float ) : `array` 1D Vector.
Returns: The Kumar Hassebrook distance between vectors `p` and `q`.
---
github.com
jaccard(p, q)
Jaccard distance between provided vectors.
Parameters:
p (float ) : `array` 1D Vector.
q (float ) : `array` 1D Vector.
Returns: The Jaccard distance between vectors `p` and `q`.
---
github.com
sorensen(p, q)
Sorensen distance between provided vectors.
Parameters:
p (float ) : `array` 1D Vector.
q (float ) : `array` 1D Vector.
Returns: The Sorensen distance between vectors `p` and `q`.
---
people.revoledu.com
chi_square(p, q, eps)
Chi Square distance between provided vectors.
Parameters:
p (float ) : `array` 1D Vector.
q (float ) : `array` 1D Vector.
eps (float)
Returns: The Chi Square distance between vectors `p` and `q`.
---
uw.pressbooks.pub
stats.stackexchange.com
www.itl.nist.gov
kulczynsky(p, q, eps)
Kulczynsky distance between provided vectors.
Parameters:
p (float ) : `array` 1D Vector.
q (float ) : `array` 1D Vector.
eps (float)
Returns: The Kulczynsky distance between vectors `p` and `q`.
---
github.com
Stochastic Distance Indicator [CC]The Stochastic Distance Indicator was created by Vitali Apirine (Stocks and Commodities Jun 2023 pgs 16-21), and this is a new method that measures the absolute distance between a price and its highest and lowest values over a long period. It uses the stochastic formula to create an oscillator using this distance value and smooths the value. Obviously, there is a lag in signals due to the lookback periods, but it does a good job of staying above the midline when the stock is in a strong uptrend and vice versa. Of course, I'm open to suggestions, but I'm deciding to create buy and sell signals based on comparing the unsmoothed and smoothed values. Buy when the line turns green and sell when it turns red.
Let me know if there are any other indicators you would like to see me publish!
distance_ratioLibrary "distance_ratio"
Collection of types and functions that can be used for the calculation of the ratio of a distance
from a barrier price using several methods. Methods supported are percentagewise (PERC), atr-based (ATR), fixed
profit (PROF), tick-based (TICKS), risk reward ratio (RR) and local extrema (LOC).
This library is meant to replace my previously published "distance_percentile" library since it offers a more intuitive interface by using the method syntax.
SpreadTrade - Auto-Cointegration (ps5)Decsription: Auto-Cointegration-Based Pair Trading Strategy (revised version)
To review, there are three popular styles of Pair trading: distance-based pair trading, correlation-based pair trading and cointegration-based pair trading. Typically, they require preliminary statistical estimation of the viability of the corresponding strategy.
Basically a pair trade strategy boils down to shorting the outperforming instrument and going long on the underperforming instrument whenever the temporary correlation weakens which means one instrument is going up and another is going down. Apart from the typical cointegration strategy which employs two cointegrated instruments, this script uses just one instrument, in base timeframe and in lagged timeframe, actually making it an auto-cointegration, or better still, an auto-correlation strategy.
Notice that each moving average function may require different Threshold settings.The orange cross symbol indicates the exit points. To filter out the signals use higher values for the LongWindow and the Threshold parameters. Also pay attention that in some cases with some moving averages the color of the signals has to be inverted.
EMAs DistancesThis indicator shows 4 configurable EMAs and the distances (values and percentages) to the last price of the stock, etf or index.
Distance From Moving AverageThis indicator shows the distance between the current price and the Moving Average price.
Key Features:
Show the distance between price and Moving Average (Read Distance Calculation for more information)
Show Historic Highs and Lows
Show Highest High and Lowest Low
Show current Highest High, current Lowest Low and current distance
Key Indicator Settings:
1. Distance Calculation
There are two ways to calculate the distance:
Spread - Calculate the difference between the price and the moving average
Percentage - Calculate the percentage change between the price and the moving average
2. Moving Average Types
There are 5 different Moving Averages:
EMA
SMA
WMA
VWMA
HMA
3. Highest High and Lowest Low
You can show or hide the Highest High and the Lowest Low plots of the series
4. Historic Highs and Lows
You can show or hide past Highs and Lows of the series
Lookback Length - Let's you adjust the frequency of local highs and lows of the series
5. Current Values
You can show or hide current value labels
EMA Price Distance TrackerThis simple indicator tracks the distance that the price is from a moving average. This can be helpful when looking for reversals based on historical informaiton.
Distance to 200 SMAThis indicator shows the relative distance (in %) from the closing price to the 200 SMA.
Distance from Vwap// How it Works \\
Measuring the distance of the close price from a higher timeframe VWAP - Volume Weighted Average Price
There is a threshold which is calculated by looking back at the previous x amount of bars and storing the highest/lowest values
If the distance from the vwap stretches above that threshold, the histogram will go green if price is above VWAP and red if its below the vwap
If the distance from the vwap reaches below the low threshold you will see the histogram flashes orange
// Settings \\
In the settings you have the ability to change what timeframe the indicator is calculated on, as well as this you can change the timeframe the VWAP is calculated on.
I always recommend using a higher timeframe vwap as they tend to me more respected
e.g on the hourly timeframe, I use the weekly VWAP, on 1 minute timeframe you may want to use 4 hour timeframe but obviously feel free to experiment
// Use Case \\
When histogram is flashing green, prices is pulling far away from the vwap, obviously you don't want to be buying a falling knife but if you have levels of confluence this can help spot reversals.
I personally wait until the first candle after its been green to get confirmation of the fall weakening. Vica versa for reds and shorts/sells.
When you see orange flashes, this shows that price has been consolidating and the price is very close to the higher time frame VWAP which could be considered a safe entry point as they tend to lead to a big move to follow
// Suggestions \\
Happy for anyone to make any suggestions on changes which could improve the script,
// Terms \\
Feel free to use the script, If you do use the script could you please just tag me as I am interested to see how people are using it. Good Luck!
High-pass filterHigh-pass filter | Pine Utilities series, ready to be used in "study-on-study" fashion |
Represents the difference between the filter and the original unfiltered data.
How to use:
1) Add a filter to your chart (in this particular case it was 4-pole Gaussian filter implemented by @everget, ty man);
2) Tap ... on your's filter status line and choose "Add study/strategy on ...", then choose High-pass filter. Alternatively, add high-pass filter directly to your chart, then High-pass filter's settings -> Basis -> choose the filter you've applied during the step 1;
3) Choose the source (op2 and hlcc4 are available as well);
4) See the difference (literally).
Peace TV
FunctionMinkowskiDistanceLibrary "FunctionMinkowskiDistance"
Method for Minkowski Distance,
The Minkowski distance or Minkowski metric is a metric in a normed vector space
which can be considered as a generalization of both the Euclidean distance and
the Manhattan distance.
It is named after the German mathematician Hermann Minkowski.
reference: en.wikipedia.org
double(point_ax, point_ay, point_bx, point_by, p_value) Minkowsky Distance for single points.
Parameters:
point_ax : float, x value of point a.
point_ay : float, y value of point a.
point_bx : float, x value of point b.
point_by : float, y value of point b.
p_value : float, p value, default=1.0(1: manhatan, 2: euclidean), does not support chebychev.
Returns: float
ndim(point_x, point_y, p_value) Minkowsky Distance for N dimensions.
Parameters:
point_x : float array, point x dimension attributes.
point_y : float array, point y dimension attributes.
p_value : float, p value, default=1.0(1: manhatan, 2: euclidean), does not support chebychev.
Returns: float
Study: Candle MA Distance (ATR)Simple script that plots price to MA distance (ATR) as a bar chart.
ATR is being used here instead of % because ATR keeps it relative to current volatility (1ATR move in either direction can have very different % for when price at 20cent and 2 dollars).
Upper/Lower band is calculated based on the average of previous peak/trough. Price approaching the band could signal overextended in the short term movement, expect price to consolidate/retrace to the MA.
Caution: if the indicator shows the price "pulled back" to MA, the actual price could still go up (or down), just a little slower then the MA
Adjustable Settings:
- Plot as bar or line
- MA Type and Length
- ATR Length
█ Disclaimer
Past performance is not an indicator of future results.
My opinions and research are my own and do not constitute financial advice in any way whatsoever.
Nothing published by me constitutes an investment recommendation, nor should any data or Content published by me be relied upon for any investment/trading activities.
I strongly recommends that you perform your own independent research and/or speak with a qualified investment professional before making any financial decisions.
Stochastic distanceHello. I've used stochastic indicator basic formula and added distance columns of %D & %K to it. The idea of columns is from MACD indicator that have distance columns in it that rising and descending these columns shows the power of trend rising or descending. I hope it will be practical and useful.
Distance Oscillator - Support and Resistance by DGT
Prices high above the Moving Average (MA) or low below it are likely to be remedied in the future by a reverse price movement as stated in the article by Denis Alajbeg, Zoran Bubas and Dina Vasic published in International Journal of Economics, Commerce and Management
This study is the third variant that aims to present this idea, and the output of the study is presented as lines that serve as possible support and resistance levels in the future.
1st variant of the idea is presented as an centered oscillator, link to Price Distance to its MA Study , within its description you may find more about the idea and some statistical observations. Also some derivatives with MACD-X, More Than MACD and P-MACD
2nd variant of the idea are presented as colored triangle line (Volatility Colored Price/MA Line), using the same calculation methods and presented in the bottom of price chart.
Link to studies where it is included : Colored Directional Movement and Bollinger Band's Cloud , SuperTrendRange and Pivot Points vX
3rd variant (this study) as stated earlier aims to present the same idea as support and resistance levels.
Options
The users can adjust source and length of the moving average that is used as base for the distance oscillator
Signal triggering options includes length for the deviation bands, multiplier as well as smoothing of the oscillator
Line customization settings
Additionally an alert can be configured to be warned earlier to watch out for probable pullbacks or reversals
Technical details for whom interested
Calculating the price distance to the MA results in a centered oscillator lets call it Distance Oscillator (quite similar to the RSI), as shown in the blow chart
Unlike RSI, oscillations with the distance oscillator are not limited within a specific range, hence identifying overbought and oversold is not as straight forward as it is with RSI. To determine overbought and oversold levels, standard deviation of distance oscillator is calculated and bands generated with the same approach applied with Bollinger Bands.
Once we have the threshold bands then crossing those bands we may assume as important levels and draw a line, if oscillator values keeps above the threshold bands (deviation bands) the logic behind the code will update the line drawing accordingly.
To reduce noise a smoothing can be applied
Alerts :
Please note that the implementation applied here can be applied to any oscillator such as RSI, Stochastic, MFI etc even Volume (if bear candle volumes are multiplied by -1)
Disclaimer :
Trading success is all about following your trading strategy and the indicators should fit within your trading strategy, and not to be traded upon solely
The script is for informational and educational purposes only. Use of the script does not constitute professional and/or financial advice. You alone have the sole responsibility of evaluating the script output and risks associated with the use of the script. In exchange for using the script, you agree not to hold dgtrd TradingView user liable for any possible claim for damages arising from any decision you make based on use of the script
Ehlers Distance Coefficient Filter [CC]The Distance Coefficient Filter was created by John Ehlers and this is one of his more unknown moving averages. It works well because in an uptrend or downtrend it behaves more like a trailing stop system but it also deals with issues like pullbacks pretty quickly. I think it works well with the default values but let me know if you find a more profitable system. Buy when the indicator turns green and sell when it turns red.
Let me know if there are any other indicators you would like to see me publish!
[blackcat] L2 The Distance Coefficient Ehlers FilterLevel: 2
Background
John F. Ehlers introuced the Distance Coefficient Ehlers Filter in his "Rocket Science for Traders" chapter 18 on 2001.
Function
Dr. Ehlers considered the gray shading levels as distances, he had away of computing filter coefficients in terms of sharpness of the edge. White is the maximum distance in one direction from the median gray, and black is the maximum distance in the other direction. In this sense, distance is a measure of departure from the edge, taking into account the edge sharpness. Transitioning to price charts, the difference in prices can be imagined as a distance. Recalling the Pythagorean Theorem (which the length of the hypotenuse of a triangle is equal to the sum of the squares of the lengths of the other two sides), Dr Ehlers applied it to the needs and say that a generalized length at any data sample is the square root of the sum of the squares of the price difference between that price and each of the prices back for the length of the filter window. The distances squared at each data point are the coefficients of the Ehlers filter.
Key Signal
Coef --> Ehlers filter coefficients array
Distance2 --> Distance array
Filt --> Ehlers filter output
Pros and Cons
100% John F. Ehlers definition translation of original work, even variable names are the same. This help readers who would like to use pine to read his book. If you had read his works, then you will be quite familiar with my code style.
Remarks
The 15th script for Blackcat1402 John F. Ehlers Week publication.
Readme
In real life, I am a prolific inventor. I have successfully applied for more than 60 international and regional patents in the past 12 years. But in the past two years or so, I have tried to transfer my creativity to the development of trading strategies. Tradingview is the ideal platform for me. I am selecting and contributing some of the hundreds of scripts to publish in Tradingview community. Welcome everyone to interact with me to discuss these interesting pine scripts.
The scripts posted are categorized into 5 levels according to my efforts or manhours put into these works.
Level 1 : interesting script snippets or distinctive improvement from classic indicators or strategy. Level 1 scripts can usually appear in more complex indicators as a function module or element.
Level 2 : composite indicator/strategy. By selecting or combining several independent or dependent functions or sub indicators in proper way, the composite script exhibits a resonance phenomenon which can filter out noise or fake trading signal to enhance trading confidence level.
Level 3 : comprehensive indicator/strategy. They are simple trading systems based on my strategies. They are commonly containing several or all of entry signal, close signal, stop loss, take profit, re-entry, risk management, and position sizing techniques. Even some interesting fundamental and mass psychological aspects are incorporated.
Level 4 : script snippets or functions that do not disclose source code. Interesting element that can reveal market laws and work as raw material for indicators and strategies. If you find Level 1~2 scripts are helpful, Level 4 is a private version that took me far more efforts to develop.
Level 5 : indicator/strategy that do not disclose source code. private version of Level 3 script with my accumulated script processing skills or a large number of custom functions. I had a private function library built in past two years. Level 5 scripts use many of them to achieve private trading strategy.
SuperTrendRange by DGTSuperTrendRange study attempts to determine the state of the market
• whether a well-established bull/bear trend is present
• whether the market is trading in a range
SuperTrendRange (STR) takes into account the volatility of the market - further details regarding volatility can be found in the description of “Volatility Bands by DGT” study
Due to its similarities to SupertTrend (ST) and Parabolic SAR (SAR), I will try to explain by stating differences between them
SuperTrendRange uses both the ATR (Average True Range) and STDEV (Standard Deviation) as part of its calculations - unlike ST and SAR where they use only ATR
Sensitivity of the indicator is adjusted using the multiplier setting of both ATR and STDEV
Additionally, unlike ST, the source of the basis of SuperTrendRange can be selected among the assets price value or its moving average
Source and Length are adjustable too
The SuperTrendRange, like Parabolic SAR indicator, appears on a chart as a series of dots, either above, below or unlike Parabolic SAR both above and below of the asset's price
A dot placed
- below the price when the market is trending upward
- above the price when it is trending downward
- both above and below when the price starts moving sideways – this is a feature that both SuperTrend and Parabolic SAR misses, where they are known to produce false signals and losing trades, whereas SuperTrendRange emphasis the zones of the ranges occurring and in most cases are considered no trade recommended zones. Please note that the range width may vary depending on how the market is volatile. It is up to the users to trade if it fits their trading strategies
Dots plotted above and below can be assumed as Support and Resistance levels
Example usages – with trading opportunities
Gold Monthly Chart
Bitcoin Daily Chart
Disclaimer:
Trading success is all about following your trading strategy and the indicators should fit within your trading strategy, and not to be traded upon solely
The script is for informational and educational purposes only. Use of the script does not constitute professional and/or financial advice. You alone have the sole responsibility of evaluating the script output and risks associated with the use of the script. In exchange for using the script, you agree not to hold dgtrd TradingView user liable for any possible claim for damages arising from any decision you make based on use of the script
Kijun-Price DistanceThis script shows optional:
- distance between close price and Kijun-Sen
- distance btween Tenkan-Sen and Kijun-Sen
- Background color of current time frame
- Background color of the next higher time frame
