Hurst Future Lines of Demarcation StrategyJ. M. Hurst introduced a concept in technical analysis known as the Future Line of Demarcation (FLD), which serves as a forward-looking tool by incorporating a simple yet profound line into future projections on a financial chart. Specifically, the FLD is constructed by offsetting the price half a cycle ahead into the future on the time axis, relative to the Hurst Cycle of interest. For instance, in the context of a 40 Day Cycle, the FLD would be represented by shifting the current price data 20 days forward on the chart, offering an idea of future price movement anticipations.
The utility of FLDs extends into three critical areas of insight, which form the backbone of the FLD Trading Strategy:
A price crossing the FLD signifies the confirmation of either a peak or trough formation, indicating pivotal moments in price action.
Such crossings also help determine precise price targets for the upcoming peak or trough, aligned with the cycle of examination.
Additionally, the occurrence of a peak in the FLD itself signals a probable zone where the price might experience a trough, helping to anticipate of future price movements.
These insights by Hurst in his "Cycles Trading Course" during the 1970s, are instrumental for traders aiming to determine entry and exit points, and to forecast potential price movements within the market.
To use the FLD Trading Strategy, for example when focusing on the 40 Day Cycle, a trader should primarily concentrate on the interplay between three Hurst Cycles:
The 20 Day FLD (Signal) - Half the length of the Trade Cycle
The 40 Day FLD (Trade) - The Cycle you want to trade
The 80 Day FLD (Trend) - Twice the length of the Trade Cycle
Traders can gauge trend or consolidation by watching for two critical patterns:
Cascading patterns, characterized by several FLDs running parallel with a consistent separation, typically emerge during pronounced market trends, indicating strong directional momentum.
Consolidation patterns, on the other hand, occur when multiple FLDs intersect and navigate within the same price bandwidth, often reversing direction to traverse this range multiple times. This tangled scenario results in the formation of Pause Zones, areas where price momentum is likely to temporarily stall or where the emergence of a significant trend might be delayed.
This simple FLD indicator provides 3 FLDs with optional source input and smoothing, A-through-H FLD interaction background, adjustable “Close the Trade” triggers, and a simple strategy for backtesting it all.
The A-through-H FLD interactions are a framework designed to classify the different types of price movements as they intersect with or diverge from the Future Line of Demarcation (FLD). Each interaction (designated A through H by color) represents a specific phase or characteristic within the cycle, and understanding these can help traders anticipate future price movements and make informed decisions.
The adjustable “Close the Trade” triggers are for setting the crossover/under that determines the trade exits. The options include: Price, Signal FLD, Trade FLD, or Trend FLD. For example, a trader may want to exit trades only when price finally crosses the Trade FLD line.
Shoutouts & Credits for all the raw code, helpful information, ideas & collaboration, conversations together, introductions, indicator feedback, and genuine/selfless help:
🏆 @TerryPascoe
🏅 @Hpotter
👏 @parisboy
Hurst
Hurst ALMA Channels With Signals [UAlgo]
In the pursuit of identifying potential market pivots, a single measurement of Average True Range (ATR), may not provide sufficient information on its own, lacking directional insights. However, by employing a Moving Average (MA), specifically the Arnaud Legoux MA with Hurst C. calculation applied, a potential trading range can be visualized, taking recent volatility into account.
The underlying assumption is that if volatility remains relatively stable and the price extends beyond this ATR-derived range, there is a high probability of a reversion to the mean. At this point, it is postulated that available buying or selling pressure is depleted, prompting a pivot back to the mean.
To enhance the analysis, multiple MAs of different lengths are plotted. While individual MAs alone may not convey substantial information, observing reversions to the mean between MAs of varying lengths becomes insightful. Shorter MAs may oscillate above or below longer MAs, returning to the mean and creating crossover patterns.
The key innovation lies in combining these two concepts. By utilizing three different length MAs with corresponding ATR lengths, a dynamic system is established. The smallest band fluctuates within the medium band, and the medium band oscillates within the large band, creating approximate short, medium, and long trading ranges relative to the MAs.
For instance, in a theoretical scenario, when the smallest band reaches the upper limit of the medium band, and simultaneously, the medium band reaches the upper limit of the large band, and the price surpasses all of them, there is a heightened probability of a market reversal.
It's important to emphasize that these observations are based on historical volatility patterns and are subject to adjustments based on specific market conditions and the chosen instrument.
The developed indicator generates three distinct signal types, each providing valuable insights into potential market pivots without disclosing specific parameters:
Large Triangles : Representing a high-probability pivot, this signal occurs when the price surpasses all bands, either at the top or bottom. It suggests an extreme point where a pivot is likely.
Medium Triangles : Indicating a notable market event, this signal emerges when the price exceeds both the small and medium bands but falls short of surpassing the large band. Additionally, the small band must have exceeded the medium band. This configuration points to a significant market move with a potential for reversion.
Small Triangles : This signal is observed when the price surpasses both the small and medium bands, yet does not breach the large band. Notably, the small band should not have exceeded the medium band. This signal type suggests a distinctive market condition where a pivot may be imminent.
These triangle signals are designed to identify key points in the market where historical patterns indicate a likelihood of reversion or significant price movement. It is crucial to note that the interpretation of these signals should be adapted to specific market conditions and instruments.
Good luck to you all !
Fib Retracement AlgoFib Retracement Algo
Description: This indicator has a series of steps that it takes before it finds possible retracement areas depending on the trend. The indicator itself is really simple to use.
How does it work?
This indicator uses the Hurst Exponent to verify whether or not the market is trending or not. It then determines the trend and then decides which retracement is possible.
If the hurst is below 0.5, then the retracement lines won't appear; they only appear in a trending market above 0.5 if you don't see the lines.
How Traders can use this indicator
Traders who trade bounces or retracements can use this indicator and enjoy the verification process that goes behind finding these retracements. This indicator can also be used to identify possible gaps on some occasions.
Examples of the indicator:
Hurst Spectral Analysis SwamiChartHaving a hard time deciding which wavelength to use for a Hurst analysis? Try a handful at once! SwamiCharts by John Ehlers offers a comprehensive way to visualize an indicator used over a range of lookback periods. The Spectral Analysis SwamiChart shows the bullish or bearish state of a spectrum of bandpasses over a user-defined range of wavelengths. The trader simply selects a bandwidth, a base wavelength, and a step/multiple to see the Spectral Analysis SwamiChart. A vertical column of green or red tends to indicate a very bullish or bearish moment in time, meaning that all bandpasses in the analyzed spectrum are in a bullish or bearish orientation simultaneously.
🏆 Shoutout to DavidF at Sigma-L for all the helpful information, conversations together, & indicator feedback.
🏅Shoutout to @HPotter for the bandpass code, and shoutout to @TerryPascoe for sharing it with me
Dominant Cycle Detection OscillatorThis is a Dominant Cycle Detection Oscillator that searches multiple ranges of wavelengths within a spectrum. Choose one of 4 different dominant cycle detection methods (MESA MAMA cycle, Pearson Autocorrelation, Discreet Fourier Transform, and Phase Accumulation) to determine the most dominant cycles and see the historical results. Straight lines can indicate a steady dominant cycle; while Wavy lines might indicate a varying dominant cycle length. The steadier the cycle, the easier it may be to predict future events in that cycle (keep the log scale in mind when considering steadiness). The presence of evenly divisible (or harmonic) cycle lengths may also indicate stronger cycles; for example, 19, 38, and 76 dominant lengths for the 2x, 4x, and 8x cycles. Practically, a trader can use these cycle outputs as the default settings for other Hurst/cycle indicators. For example, if you see dominant cycle oscillator outputs of 38 & 76 for the 4x and 8x cycle respectively, you might want to test/use defaults of 38 & 76 for the 4x & 8x lengths in the bandpass, diamond/semi-circle notation, moving average & envelope, and FLD instead of the defaults 40 & 80 for a more fine-tuned analysis.
Muting the oscillator's historical lines and overlaying the indicator on the chart can visually cue a trader to the cycle lengths without taking up extra panes. The DFT Cycle lengths with muted historical lines have been overlayed on the chart in the photo.
The y-axis scale for this indicator's pane (just the oscillator pane, not the chart) most likely needs to be changed to logarithmic to look normal, but it depends on the search ranges in your settings. There are instructions in the settings. In the photo, the MESA MAMA scale is set to regular (not logarithmic) which demonstrates how difficult it can be to read if not changed.
In the Spectral Analysis chapter of Hurst's book Profit Magic, he recommended doing a Fourier analysis across a spectrum of frequencies. Hurst acknowledged there were many ways to do this analysis but recommended the method described by Lanczos. Currently in this indicator, the closest thing to the method described by Lanczos is the DFT Discreet Fourier Transform method.
Shoutout to @lastguru for the dominant cycle library referenced in this code. He mentioned that he may add more methods in the future.
Hurst Spectral Analysis Oscillator"It is a true fact that any given time history of any event (including the price history of a stock) can always be considered as reproducible to any desired degree of accuracy by the process of algebraically summing a particular series of sine waves. This is intuitively evident if you start with a number of sine waves of differing frequencies, amplitudes, and phases, and then sum them up to get a new and more complex waveform." (Spectral Analysis chapter of J M Hurst's book, Profit Magic )
Background: A band-pass filter or bandpass filter is a device that passes frequencies within a certain range and rejects (attenuates) frequencies outside that range. Bandpass filters are widely used in wireless transmitters and receivers. Well-designed bandpass filters (having the optimum bandwidth) maximize the number of signal transmitters that can exist in a system while minimizing the interference or competition among signals. Outside of electronics and signal processing, other examples of the use of bandpass filters include atmospheric sciences, neuroscience, astronomy, economics, and finance.
About the indicator: This indicator will accept float/decimal length inputs to display a spectrum of 11 bandpass filters. The trader can select a single bandpass for analysis that includes future high/low predictions. The trader can also select which bandpasses contribute to a composite model of expected price action.
10 Statements to describe the 5 elements of Hurst's price-motion model:
Random events account for only 2% of the price change of the overall market and of individual issues.
National and world historical events influence the market to a negligible degree.
Foreseeable fundamental events account for about 75% of all price motion. The effect is smooth and slow changing.
Unforeseeable fundamental events influence price motion. They occur relatively seldom, but the effect can be large and must be guarded against.
Approximately 23% of all price motion is cyclic in nature and semi-predictable (basis of the "cyclic model").
Cyclicality in price motion consists of the sum of a number of (non-ideal) periodic cyclic "waves" or "fluctuations" (summation principle).
Summed cyclicality is a common factor among all stocks (commonality principle).
Cyclic component magnitude and duration fluctuate slowly with the passage of time. In the course of such fluctuations, the greater the magnitude, the longer the duration and vice-versa (variation principle).
Principle of nominality: an element of commonality from which variation is expected.
The greater the nominal duration of a cyclic component, the larger the nominal magnitude (principle of proportionality).
Shoutouts & Credits for all the raw code, helpful information, ideas & collaboration, conversations together, introductions, indicator feedback, and genuine/selfless help:
🏆 @TerryPascoe
🏅 DavidF at Sigma-L, and @HPotter
👏 @Saviolis, parisboy, and @upslidedown
Hurst Diamond Notation PivotsThis is a fairly simple indicator for diamond notation of past hi/lo pivot points, a common method in Hurst analysis. The diamonds mark the troughs/peaks of each cycle. They are offset by their lookback and thus will not 'paint' until after they happen so anticipate accordingly. Practically, traders can use the average length of past pivot periods to forecast future pivot periods in time🔮. For example, if the average/dominant number of bars in an 80-bar pivot point period/cycle is 76, then a trader might forecast that the next pivot could occur 76-ish bars after the last confirmed pivot. The numbers/labels on the y-axis display the cycle length used for pivot detection. This indicator doesn't repaint, but it has a lot of lag; Please use it for forecasting instead of entry signals. This indicator scans for new pivots in the form of a rainbow line and circle; once the hi/lo has happened and the lookback has passed then the pivot will be plotted. The rainbow color per wavelength theme seems to be authentic to Hurst (or modern Hurst software) and has been included as a default.
Hurst Exponent (Dubuc's variation method)Library "Hurst"
hurst(length, samples, hi, lo)
Estimate the Hurst Exponent using Dubuc's variation method
Parameters:
length : The length of the history window to use. Large values do not cause lag.
samples : The number of scale samples to take within the window. These samples are then used for regression. The minimum value is 2 but 3+ is recommended. Large values give more accurate results but suffer from a performance penalty.
hi : The high value of the series to analyze.
lo : The low value of the series to analyze.
The Hurst Exponent is a measure of fractal dimension, and in the context of time series it may be interpreted as indicating a mean-reverting market if the value is below 0.5 or a trending market if the value is above 0.5. A value of exactly 0.5 corresponds to a random walk.
There are many definitions of fractal dimension and many methods for its estimation. Approaches relying on calculation of an area, such as the Box Counting Method, are inappropriate for time series data, because the units of the x-axis (time) do match the units of the y-axis (price). Other approaches such as Detrended Fluctuation Analysis are useful for nonstationary time series but are not exactly equivalent to the Hurst Exponent.
This library implements Dubuc's variation method for estimating the Hurst Exponent. The technique is insensitive to x-axis units and is therefore useful for time series. It will give slightly different results to DFA, and the two methods should be compared to see which estimator fits your trading objectives best.
Original Paper:
Dubuc B, Quiniou JF, Roques-Carmes C, Tricot C. Evaluating the fractal dimension of profiles. Physical Review A. 1989;39(3):1500-1512. DOI: 10.1103/PhysRevA.39.1500
Review of various Hurst Exponent estimators for time-series data, including Dubuc's method:
www.intechopen.com
HurstExponentLibrary "HurstExponent"
Library to calculate Hurst Exponent refactored from Hurst Exponent - Detrended Fluctuation Analysis
demean(src) Calculates a series subtracted from the series mean.
Parameters:
src : The series used to calculate the difference from the mean (e.g. log returns).
Returns: The series subtracted from the series mean
cumsum(src, length) Calculates a cumulated sum from the series.
Parameters:
src : The series used to calculate the cumulative sum (e.g. demeaned log returns).
length : The length used to calculate the cumulative sum (e.g. 100).
Returns: The cumulative sum of the series as an array
aproximateLogScale(scale, length) Calculates an aproximated log scale. Used to save sample size
Parameters:
scale : The scale to aproximate.
length : The length used to aproximate the expected scale.
Returns: The aproximated log scale of the value
rootMeanSum(cumulativeSum, barId, numberOfSegments) Calculates linear trend to determine error between linear trend and cumulative sum
Parameters:
cumulativeSum : The cumulative sum array to regress.
barId : The barId for the slice
numberOfSegments : The total number of segments used for the regression calculation
Returns: The error between linear trend and cumulative sum
averageRootMeanSum(cumulativeSum, barId, length) Calculates the Root Mean Sum Measured for each block (e.g the aproximated log scale)
Parameters:
cumulativeSum : The cumulative sum array to regress and determine the average of.
barId : The barId for the slice
length : The length used for finding the average
Returns: The average root mean sum error of the cumulativeSum
criticalValues(length) Calculates the critical values for a hurst exponent for a given length
Parameters:
length : The length used for finding the average
Returns: The critical value, upper critical value and lower critical value for a hurst exponent
slope(cumulativeSum, length) Calculates the hurst exponent slope measured from root mean sum, scaled to log log plot using linear regression
Parameters:
cumulativeSum : The cumulative sum array to regress and determine the average of.
length : The length used for the hurst exponent sample size
Returns: The slope of the hurst exponent
smooth(src, length) Smooths input using advanced linear regression
Parameters:
src : The series to smooth (e.g. hurst exponent slope)
length : The length used to smooth
Returns: The src smoothed according to the given length
exponent(src, hurstLength) Wrapper function to calculate the hurst exponent slope
Parameters:
src : The series used for returns calculation (e.g. close)
hurstLength : The length used to calculate the hurst exponent (should be greater than 50)
Returns: The src smoothed according to the given length
Cyclical velocity v1This indicator is used to measure the velocity of the cycle in question.
It can be used to plot static and dynamic supports and resistances.
For any bugs contact the creators
Cyclical IndicatorThis tool is an oscillator that detects cycles.
It is built on the basis of our centered moving averages.
It is fully configurable in design moreover it is possible to set the centering through the offset !
Offset 2, half cycle centering.
Offset 4, 1/4 cycle centering.
Recommended setting :
Offset 4
Cyclical ratio 2
Battleplan 2-Time Cycle v1This chart indicator is one of the most used cyclical analysis tools in the world!
It is possible to set offsets and scales of the battleplan.
Up to six sub-waves added together can be displayed.
With this tool, only 2-times cycles can be displayed.
For any bugs contact the creators
Hurst ExponentMy first try to implement Full Hurst Exponent.
The Hurst exponent is used as a measure of long-term memory of time series. It relates to the autocorrelations of the time series and the rate at which these decrease as the lag between pairs of values increases
The Hurst exponent is referred to as the "index of dependence" or "index of long-range dependence". It quantifies the relative tendency of a time series either to regress strongly to the mean or to cluster in a direction.
In short, depending on the value you can spot the trending / reversing market.
Values 0.5 to 1 - market trending
Values 0 to 0.5 - market tend to mean revert
Hurst Exponent is computed using Rescaled range (R/S) analysis.
I split the lookback period (N) in the number of shorter samples (for ex. N/2, N/4, N/8, etc.). Then I calculate rescaled range for each sample size.
The Hurst exponent is estimated by fitting the power law. Basically finding the slope of log(samples_size) to log(RS).
You can choose lookback and sample sizes yourself. Max 8 possible at the moment, if you want to use less use 0 in inputs.
It's pretty computational intensive, so I added an input so you can limit from what date you want it to be calculated. If you hit the time limit in PineScript - limit the history you're using for calculations.
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Disclaimer
Please remember that past performance may not be indicative of future results.
Due to various factors, including changing market conditions, the strategy may no longer perform as good as in historical backtesting.
This post and the script don’t provide any financial advice.
Simple Hurst Exponent [QuantNomad]This is a simplified version of the Hurst Exponent indicator.
In the meantime, I'm working on the full version. It's computationally intensive, so it's a challenge to squeeze it to PineScript limits. It will require some time to optimize it, so I decided to publish a simplified version for now.
The Hurst exponent is used as a measure of long-term memory of time series. It relates to the autocorrelations of the time series, and the rate at which these decrease as the lag between pairs of values increases
The Hurst exponent is referred to as the "index of dependence" or "index of long-range dependence". It quantifies the relative tendency of a time series either to regress strongly to the mean or to cluster in a direction.
In short depend on value you can spot trending / reversing market.
Values 0.5 to 1 - market trending
Values 0 to 0.5 - market tend to mean revert
####################
Disclaimer
Please remember that past performance may not be indicative of future results.
Due to various factors, including changing market conditions, the strategy may no longer perform as good as in historical backtesting.
This post and the script don’t provide any financial advice.
[blackcat] L2 Ehlers Hurst Coefficient IndicatorLevel: 2
Background
John F. Ehlers introuced Hurst Coefficient Indicator in his "Cycle Analytics for Traders" chapter 6 on 2013.
Function
The Hurst coefficient is one way to attempt to get a handle on the slope of the power density of market data. The Hurst coefficient varies between 0 and 1, and is related to the α power coefficient as H = 1 − α/2. The Hurst coefficient is more estimated than computed. Dr. Ehlers found the estimate using the fractal dimension was the most practical for shorter-term market data. The Hurst coefficient is related to the fractal dimension as H = 2 − D. Dr. Ehlers would like to make it perfectly clear that the Hurst coefficient or the fractal dimension has no direct practical application to trading not only because it is an estimate, but also because it has no predictive value. These computations only reflect the general structure of the market, and the answer you get is dependent on your assumptions. For example, the Hurst coefficient changes dramatically with the length of data used in making the estimate.
The only user input is the length of data to be used. The number can be arbitrarily large if you have sufficient data. The results are critically dependent on the input data length selected. After declaring variables, the coefficients of a 20-bar SuperSmoother filter are computed. The computations of N1, N2, and N3 are as described in the previous section. The fractal dimension is then converted to the Hurst coefficient, which is subsequently smoothed in the SuperSmoother filter.
Key Signal
SmoothHurst --> Hurst Coefficient Indicator fast line
Trigger --> Hurst Coefficient Indicator slow line
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 40th 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.
Hurst Constant EnvelopeThis script follows the fundamentals described in J.M. Hurst works.
Constant Envelope is updated version of EMA with its 2 copies drawn with nominal distance from the base.
That make a constant width channels, unlike the standard envelope using the % concept.
Its width is not changed at different price levels.
Ehlers Hurst Coefficient [CC]The Hurst Coefficient was created by John Ehlers (Cycle Analytics For Traders pgs 67-68) and this is a very useful indicator to tell you if the stock is in a uptrend or downtrend. Feel free to change the length to experiment and to adjust to your needs. Buy when the indicator line is green and sell when it is red.
Let me know if there are other indicators you would like to see me publish or if you want something custom done!
Hurst ExponentThis is an aproximation on Tradingview of the Hurst Exponent.
Its quite computational expensive, so it has been simplify and sample size reduced.
If any has an idea on how to create the real Hurst Exponent here, Ill be happy to hear and help.
(2) MoTrend VS-1150A great deal has been written about trend trading, simply because it’s a profitable trading technique, that simply works. The MoTrend indicator displays trending, momentum and stiffness to the trader guiding them to potential trend trading opportunities. MoTrend also contains a very sophisticated exit strategy, allowing the trader to ride the trend to its most profitable exit point.
MoTrend was developed by determining when the Short-Term Hurst Channel(STHC) merge with the Long-Term Hurst Channel (LTHC). This event is flagged in two manors. First the STH channels blue line obscures the green or red lines of the LTH cycle channel. This triggers a change in the background color of your Cycles price chart to light green, as illustrated below, and in a change in color of the background of the MOTREND indicator to dark green on up-trends. These background colors shift to progressive shades of red when the trend is indicating down.
Because the MoTrend indicator was designed to work in concert with the Cycles indicator, the MoTrend signals are designed to progressively bleed into the Genie Cycles indicator. The two are not required to both be active on your screen at the same time, as each one is a free standing indicator working` completely independently of each other.
When the STH channel moves to the top of the LTH channel you are seeing the confirmed beginning of a positive trend. The MOTREND indicators most important asset is the ability to provide traders with a clear indications when, in all probability, the trend is coming to an end. This is accomplished by the magnetic effect of the STH channel. As long as the price range of the trades remain within the short-term channel, not exceeding the lower threshold of the bounding channel, the entire channel will remain attached to the top of the long-term channel. This magnetic effect of the short-term channel provides you with the ability to stay in your trade in the face of small, short-term reversals as long as those price changes don’t drag the STH channel lower. As soon as that occurs, your positive trend is demonstrating weakness and you should shift your trade evaluation to the stiffness histogram indicator show in the same indicator window.
The Stiffness indicator helps determine if you should continue in this trade after the Hurst cycles uncouples. The Stiffness indicator is simply counting the number of bars/days that your equity has remained above a specified moving average (MA) without penetrating that moving average. The indicator utilizes two adjustable variables, both a look-back or length for the moving average and a period of time or window that you are focused on. This is plotted as a series of columns plotted on two scales. Zero to 100 for uptrends (green columns) and 0 to -100 (red columns) for down trends. The period length provides the trader with a window of time that you want to determine if the price is penetrating the moving average you have set. The Stiffness indicator was described in the trading journal; Technical Analysis of Stocks and Commodities, by Markos Katsanos, Nov 25, 2018.
Finally, you can turn on a price line that is recalculated to become constrained within the parameters of the MoTrend indicators -100 to +100 range. This provides the trader the ability to see the relationship of price changes against the MoTrend and Stiffness indicators all in one indicator pane, window.
Access this Genie indicator for your Tradingview account, through our web site. (Links Below) This will provide you with additional educational information and reference articles, videos, input and setting options and trading strategies this indicator excels in.
(1) Genie Cycles VS-200The Genie Cycles indicator contains two primary components. The first generates the primary turning-point Entry/Exit signals based on a hybrid algorithms that utilize multiple moving filters and oscillators, all working in concert. The second is our version of Hurst Cycles allowing the trader to view the harmonic convergence of short and long cycles.
The turning-point signals are generated by two Center of Gravity Oscillators (COG) originally developed by John Ehlers and published in Technical Analysis of Stocks and Commodities in its May 2002 issue.
COG produces a moving filter that heavily weights the most extreme and most current values in the stream of data within the window of the indicator. COG excels at determining and indicating where, within a parabolic path, tipping or turning points have occurred. Two COG indicators, each one set to a different length and different inputs are incorporated. The output of these two COG filters are them put through another Ehler’s filter, the Pass Band; July 2016 issue of TAOSAC. A pass band filter has the unique ability of removing the higher and lower frequencies from the signal, leaving behind only the core signal. Here we are taking a longer COG period of (10) days, utilizing the candles body size as it’s input and then subtracting a short period of (7) days utilizing only the close of the day. The result is an emphasis on the extreme values, i.e., the maximum apex and the minimum vertex of each parabolic swing. Finally, the Arnaud Legoux Moving Average (ALMA) is utilized as smoothing a filter to slightly shift the weighting from the COG Pass band filter, in a selective and adjustable manor to more current bars, not the most current bar. This is desirable because COG dramatically emphasizes the most current candle or bar as well as large candles and strong deviations from within the moving average.
This provides the trader with excellent responsiveness within a very smooth output signal with very few artifacts or whipsaws, producing highly reliable trading signals that indicate optimal entry and exit points with a high level of accuracy and very little lag.
The primary principals of Hurst cycles are price moves in waves that exhibit cyclic attributes based on their time scales. Genie Cycles incorporates Hurst cycles theories, but utilizes only two nested Laguerre moving filters. Laguerre moving filters have significantly less lag than traditional moving averages. These moving filters take as there inputs the highest high and the lowest lows for the two adjustable periods. The point of the indicator is to determine when a short-term swing cycle harmonizes or aligns with a long-term cycle, i.e., determining when the tops and bottoms of these cycles align.
The resulting nested channels produce natural bounding boxes. This dramatically highlights likely support and resistance levels as they often occur at prior highs or lows that this indicator is drawing. Convergence of the different cycle lengths can indicate strong trends that make excellent trading opportunities. Decoupling of the cycles indicates the end of the trend.
Hurst Cycle Channel Clone %BA %B of lazy bears Hurst Cycle Channel Clone
Remember to thank him for his great scripts.
With this you can easily see when the close is above,below or in the short or medium cycle channel.
Laguerre Filtered Hurst Cycle Channel [rumpypumpydumpy]Experiment in using a Laguerre filter on Hurst Cycle Channels.
Default settings may not be a good price fit!
Increasing the gamma value increases the degree of smoothing from the laguerre filter :
♒Hurst Cycle Channel Oscillator v1.0 by Cryptorhythms♒Hurst Cycle Channel Oscillator v1.0 by Cryptorhythms
👀 This one was not in the public library yet. Thanks to lazybear for the original Hurst Cycle Channel code, which was used to create this.
📜 Description
In the late 60's a NASA aerospace engineer J.M. Hurst published ‘The Profit Magic of Stock Transaction Timing’. Ironically, his book, by some considered the best book ever written about stock market cycles and swing trading, became available during the deepest and most extended Bear Market since the Great Depression. From 1972 on brokers couldn't give blue chip stock away in a Wall Street lunchroom. There was no market for a book by a stock market timer, and the book became a hidden treasure.
The Oscillator version of channel cycle was not on tradingview yet, so here you go, hope you all enjoy! The Hurst Channels and the Hurst Oscillator, be it combined or separate, can be implemented to uncover turning points in all time frames. Note that the Hurst Oscillator is basically just another presentation of the position of price in the Hurst Channel.
You can use it similar to an RSI looking for divergences. Also similar to a ema fast/short cross strategy when you use the signal line as entry/exit. You can also of course use the overbought/oversold zones as well.
Here is a screenshot with the example of bar coloration:
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