ER-Adaptive ATR Limit Channels w/ States [Loxx]As simple as it gets, channels based on high, low and ATR distances, Shows possible short term support / resistance or can be used as a take profit/stop-loss in some trading systems. It does this by comparing high/low values of price to multiplied by a multiple of ATR to determine when the trend changes. States are included to change the sensitivity to trend changes. 1 is very sensitive, 3 is least sensitive.
This uses Loxx's Expanded Source Types. You can read about them here:
What is ER 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
Efficiencyratio
STD-Filtered, Adaptive Exponential Hull Moving Average [Loxx]STD-Filtered, Adaptive Exponential Hull Moving Average is a Kaufman Efficiency Ratio Adaptive Hull Moving Average that uses EMA instead of WMA for its computation. I've also added standard deviation stepping to further smooth the signal. Using EMA instead of WMA turns the Hull into what's called the AEHMA. You can read more about the EHMA here: eceweb1.rutgers.edu
What is the traditional Hull Moving Average?
The Hull Moving Average (HMA) attempts to minimize the lag of a traditional moving average while retaining the smoothness of the moving average line. Developed by Alan Hull in 2005, this indicator makes use of weighted moving averages to prioritize more recent values and greatly reduce lag. The resulting average is more responsive and well-suited for identifying entry points.
What is Kaufman's Efficiency Ratio?
The Efficiency Ratio (ER) was first presented by Perry Kaufman in his 1995 book ‘Smarter Trading‘. It is calculated by dividing the price change over a period by the absolute sum of the price movements that occurred to achieve that change. The resulting ratio ranges between 0 and 1 with higher values representing a more efficient or trending market.
The value of the ER ranges between 0 and 1. It has the value of 1 when prices move in the same direction for the full time over which the indicator is calculated, e.g. n bars period. It has a value of 0 when prices are unchanged over the n periods. When prices move in wide swings within the interval, the sum of the denominator becomes very large compared to the numerator and ER approaches zero.
Some uses for ER:
A qualifier for a trend following trade; a trend is considered “persistent” only when RE is above a certain value, e.g. 0.3 or 0.4 .
A filter to screen out choppy stocks/markets, where breakouts are frequently “fakeouts”.
In an adaptive trading system, helping to determine whether to apply a trend following algorithm or a mean reversion algorithm.
It is used in the calculation of Kaufman’s Adaptive Moving Average (KAMA).
How to calculate the Hull Adaptive Moving Average (HAMA)
Find Signal to Noise ratio (SNR)
Normalize SNR from 0 to 1
Calculate adaptive alphas
Apply EMAs
Included
Bar coloring
Signals
Alerts
Loxx's Expanded Source Types
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
ER-Adaptive ATR [Loxx]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
You can use this indicator the same way you'd use the standard ATR.
Pips-Stepped, Adaptive-ER DSEMA w/ DSL [Loxx]Pips-Stepped, Adaptive-ER DSEMA w/ DSL is an Efficiency-Ratio-Adaptive, Double-Smoothed EMA with Pips Stepping and Discontinued Signal Lines. This combination reduces noise and improves signal quality.
What is Double Smoothed Exponential Moving Average (DSEMA) ?
The Double Smoothed Exponential Moving Average is a lot less laggy compared to a traditional EMA . It's also considered a leading indicator compared to the EMA , and is best utilized whenever smoothness and speed of reaction to market changes are required.
What is the efficiency ratio?
In statistical terms, the Efficiency Ratio tells us the fractal efficiency of price changes. ER fluctuates between 1 and 0, but these extremes are the exception, not the norm. ER would be 1 if prices moved up 10 consecutive periods or down 10 consecutive periods. ER would be zero if price is unchanged over the 10 periods.
Included:
Bar coloring
Signals
Alerts
EMA and FEMA Signal/ DSL smoothing
Loxx's Expanded Source Types
Efficiency-Ratio-Adaptive EMA [Loxx]Efficiency ratio was invented by Perry Kaufman as a measure of volatility and as a way of making some calculations adaptive. In his adaptive moving average he uses 3 periods for calculation which makes it a bit "cryptic" and, by all means, not so simple to use. This version is simplifying the whole thing without an intention to clone the KAMA indicator--but with the intention to use the efficiency ratio for adapting the average calculations and to use only two parameters for that:
period
price
Included:
Bar coloring
Adaptive Oscillator constructor [lastguru]Adaptive Oscillators use the same principle as Adaptive Moving Averages. This is an experiment to separate length generation from oscillators, offering multiple alternatives to be combined. Some of the combinations are widely known, some are not. Note that all Oscillators here are normalized to -1..1 range. This indicator is based on my previously published public libraries and also serve as a usage demonstration for them. I will try to expand the collection (suggestions are welcome), however it is not meant as an encyclopaedic resource , so you are encouraged to experiment yourself: by looking on the source code of this indicator, I am sure you will see how trivial it is to use the provided libraries and expand them with your own ideas and combinations. I give no recommendation on what settings to use, but if you find some useful setting, combination or application ideas (or bugs in my code), I would be happy to read about them in the comments section.
The indicator works in three stages: Prefiltering, Length Adaptation and Oscillators.
Prefiltering is a fast smoothing to get rid of high-frequency (2, 3 or 4 bar) noise.
Adaptation algorithms are roughly subdivided in two categories: classic Length Adaptations and Cycle Estimators (they are also implemented in separate libraries), all are selected in Adaptation dropdown. Length Adaptation used in the Adaptive Moving Averages and the Adaptive Oscillators try to follow price movements and accelerate/decelerate accordingly (usually quite rapidly with a huge range). Cycle Estimators, on the other hand, try to measure the cycle period of the current market, which does not reflect price movement or the rate of change (the rate of change may also differ depending on the cycle phase, but the cycle period itself usually changes slowly).
Chande (Price) - based on Chande's Dynamic Momentum Index (CDMI or DYMOI), which is dynamic RSI with this length
Chande (Volume) - a variant of Chande's algorithm, where volume is used instead of price
VIDYA - based on VIDYA algorithm. The period oscillates from the Lower Bound up (slow)
VIDYA-RS - based on Vitali Apirine's modification of VIDYA algorithm (he calls it Relative Strength Moving Average). The period oscillates from the Upper Bound down (fast)
Kaufman Efficiency Scaling - based on Efficiency Ratio calculation originally used in KAMA
Deviation Scaling - based on DSSS by John F. Ehlers
Median Average - based on Median Average Adaptive Filter by John F. Ehlers
Fractal Adaptation - based on FRAMA by John F. Ehlers
MESA MAMA Alpha - based on MESA Adaptive Moving Average by John F. Ehlers
MESA MAMA Cycle - based on MESA Adaptive Moving Average by John F. Ehlers , but unlike Alpha calculation, this adaptation estimates cycle period
Pearson Autocorrelation* - based on Pearson Autocorrelation Periodogram by John F. Ehlers
DFT Cycle* - based on Discrete Fourier Transform Spectrum estimator by John F. Ehlers
Phase Accumulation* - based on Dominant Cycle from Phase Accumulation by John F. Ehlers
Length Adaptation usually take two parameters: Bound From (lower bound) and To (upper bound). These are the limits for Adaptation values. Note that the Cycle Estimators marked with asterisks(*) are very computationally intensive, so the bounds should not be set much higher than 50, otherwise you may receive a timeout error (also, it does not seem to be a useful thing to do, but you may correct me if I'm wrong).
The Cycle Estimators marked with asterisks(*) also have 3 checkboxes: HP (Highpass Filter), SS (Super Smoother) and HW (Hann Window). These enable or disable their internal prefilters, which are recommended by their author - John F. Ehlers . I do not know, which combination works best, so you can experiment.
Chande's Adaptations also have 3 additional parameters: SD Length (lookback length of Standard deviation), Smooth (smoothing length of Standard deviation) and Power ( exponent of the length adaptation - lower is smaller variation). These are internal tweaks for the calculation.
Oscillators section offer you a choice of Oscillator algorithms:
Stochastic - Stochastic
Super Smooth Stochastic - Super Smooth Stochastic (part of MESA Stochastic) by John F. Ehlers
CMO - Chande Momentum Oscillator
RSI - Relative Strength Index
Volume-scaled RSI - my own version of RSI. It scales price movements by the proportion of RMS of volume
Momentum RSI - RSI of price momentum
Rocket RSI - inspired by RocketRSI by John F. Ehlers (not an exact implementation)
MFI - Money Flow Index
LRSI - Laguerre RSI by John F. Ehlers
LRSI with Fractal Energy - a combo oscillator that uses Fractal Energy to tune LRSI gamma
Fractal Energy - Fractal Energy or Choppiness Index by E. W. Dreiss
Efficiency ratio - based on Kaufman Adaptive Moving Average calculation
DMI - Directional Movement Index (only ADX is drawn)
Fast DMI - same as DMI, but without secondary smoothing
If no Adaptation is selected (None option), you can set Length directly. If an Adaptation is selected, then Cycle multiplier can be set.
Before an Oscillator, a High Pass filter may be executed to remove cyclic components longer than the provided Highpass Length (no High Pass filter, if Highpass Length = 0). Both before and after the Oscillator a Moving Average can be applied. The following Moving Averages are included: SMA, RMA, EMA, HMA , VWMA, 2-pole Super Smoother, 3-pole Super Smoother, Filt11, Triangle Window, Hamming Window, Hann Window, Lowpass, DSSS. For more details on these Moving Averages, you can check my other Adaptive Constructor indicator:
The Oscillator output may be renormalized and postprocessed with the following Normalization algorithms:
Stochastic - Stochastic
Super Smooth Stochastic - Super Smooth Stochastic (part of MESA Stochastic) by John F. Ehlers
Inverse Fisher Transform - Inverse Fisher Transform
Noise Elimination Technology - a simplified Kendall correlation algorithm "Noise Elimination Technology" by John F. Ehlers
Except for Inverse Fisher Transform, all Normalization algorithms can have Length parameter. If it is not specified (set to 0), then the calculated Oscillator length is used.
More information on the algorithms is given in the code for the libraries used. I am also very grateful to other TradingView community members (they are also mentioned in the library code) without whom this script would not have been possible.
Efficiency RatioThe efficiency ratio (ER) is described by Perry Kaufman in his book, Trading Systems and Methods.
It works by measuring the momentum of the market, that is, the absolute change from the current price to a past price, and divides it by the volatility, which is the sum of the absolute changes of each bar. That makes this a bounded indicator, going from 0 to 100, like an oscillator. Higher values mean less noise, while lower values mean more.
Eg.: if the market moves from 10.0 to 15.0 in a directional manner, with every bar up, the ER is going to be at 100. However, if it moves up and down, and goes all over the place until finally reaching 15.0, the ER is going to be at around 20. It is very difficult for the ER to be at zero, because that would require 0 volatility, which is almost impossible to occur.
This indicator is useful when planning for trades. If you notice the ER being higher than average, you may choose to increase the position size, because that would mean that the market is directional and has less chance of a whipsaw.
Efficiency Ratio (Market Noise) by Alejandro PThis is an indicator based on the Efficiency Ratio by Perry Kaufman. Like Price Density, which we have published previously, Perry Kaufman's Efficiency Ratio is a quantifiable method of measuring market noise.
This version of the indicator includes a feature to make the values of the indicator change based on thresholds to easier visualize different market conditions. Additionally there is a directional feature which factors in the direction of the price moves.
We can use the Efficiency Ratio to set rules and only trade particular systems when noise is at an appropriate level. For example, if noise is high then we would want to avoid trend following strategies and instead trade mean-reversion strategies, and vice-versa when the opposite is true.
The Efficiency Ratio can also be used to match assets to strategies. Some assets will be naturally more noisy than others and therefore we might have a principle where we only trade those noisy assets with our mean reversion strategies and the more quiet assets with trend following strategies.
Calculation:
Efficiency Ratio = Absolute net change in close price / absolute sum of the individual close price changes
The numerator looks at the absolute close change in price. It subtracts the starting close price in the period from the final close price in the period. The denominator compares the close price of one bar to the close price of the previous bar, this is performed for each of the successive bars in the whole period and then the value is summed. The absolute price is used because there are positive and negative values because each bar may close above or below the previous bar close.
The Efficiency Ratio provides an opposite interpretation of market noise compared to Price Density. With Price Density high values = high noise and low values = low noise. With the Efficiency Ratio high values = low noise and low values = high noise.
Comparing Price Density to Perry Kaufman's Efficiency Ratio:
Similarities
Both use the sum of the individual bar moves
- Price Density - High - Low
- Efficiency Ratio - Close to Close
Differences
Price Density uses the full price range (to determine the height of the box)
Efficiency Ratio uses the net close price change over the period
The interpretation of the values is the reverse for each
Full credits to the source of the above information and interpretation.
RedK Volume-Weighted Directional Efficiency Index (DXF)RedK Volume-Weighted Directional Efficiency Index (DXF) is a momentum indicator - that builds on Kaufman's Efficiency Ratio (ER) concept.
DXF utilizes a restricted +100/-100 oscillator to represent the "quality" of a trend, and does a good job in detecting the possibility of an upcoming trend change (in both direction and quality), improving our ability to make decisions on trade entries and exits.
Here's a quick background on Kaufman's Efficiency Ratio (ER)
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Developed by Perry Kaufman and introduced in his book “New Trading Systems and Methods”, the Efficiency Ratio reflects relative market speed to volatility. There are cases, when it is used as a filter, which helps a trader to avoid ”choppy” markets or trading ranges and to identify smoother trends.
ER is the result of dividing the net change in price movement during n-periods by the sum of all bar-to-bar price changes during the same n-periods. In case the market is trending smoother, then the ratio will be higher. In case the ratio shows readings in proximity to zero, this implies that market movement is inefficient and ”choppy”.
If the Efficiency Ratio shows a reading of +100, this means that the trading instrument is in a bull trend and trending with perfect efficiency.
If the Efficiency Ratio shows a reading of -100, this means that the trading instrument is in a bear trend and trending with perfect efficiency.
It is impossible for any instrument to have a perfect Efficiency ratio, because any movement against the major trend during the examined period of time would cause the ratio to drop.
If the Efficiency Ratio shows a reading above +30 (common setting for the "Significant Level"), this is indicative of a quality bull trend. If the ratio shows a reading below -30, this is indicative of a quality bear trend.
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Kaufman also used the ER as basis for his famous Kaufman Adaptive Moving Average (KAMA).
Read more on ER & Kama here
How is DXF different from other ER-based indicators?
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- Let's get the easy part out of the way: DXF has a "volume-weighting" option ✔
This option is OFF by default (to avoid errors with instruments with no volume data)
- once this option is applied, it provides the benefit of combining the volume effect into the calculation - those who appreciate the effect of volume on price action will hopefully find this option valuable
- The calculation of ER and how it can be "best utilized":
Let's examine the ER concept a bit closer: as a (math) concept, the (original) Efficiency Ratio (ER) takes the positive change of the price of an instrument during a certain period, and divide it by the sum of (absolute) price moves that were observed during that same period.
So, in the trader's language, we will be saying "out of a total of $20 moves (up and down) that MSFT did in the past 10 days, MSFT only made a net change of $5 up during that period" - so the "10-day ER" for MSFT in that case is 5/20 = 25% -- then we continue to observe that ongoing "10-day ER" and if it increases, we can expect that MSFT is going to establish a strong move (trend) up --- right?
the magic word here is to "observe the ongoing ER" - many of the ER based indicators just use the ER as calculated by Kaufman's original method. IMHO, these are just "point-in-time readings" - if we hope to get real insights from the ER, we need to take an average of that reading - for our "time window" we're interested in - and only then we can identify trends and patterns in the ER value as it changes during that windowss- DXF does that - and that allows a trader to say "the (weighted) 5-day average of the 10-day ER for MSFT is increasing, and that why i expect an up-trend" -- makes sense ? both the "Lookback" used to calculate the ER, and the Length of observed "window" for the Average ER are adjustable in DXF settings
Other Uses and Settings :
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- As a momentum indicator, DXF can predict an upcoming change of trend - cause that will reflect on the average ER value. There are few examples in the chart where the price move and ER trend *do not agree* - The trader can see these signs and take decisions accordingly
- DXF can help reveal best entries and exits: assume we are long-term bullish on MSFT, and we want to "buy the dip" - DXF can help reveal the time where price is recovering from extreme weakness - and that would be the ideal buy opportunities for us - exampled marked on the chart
- the Stepping & Smoothing options enable better visualization of the DXF plot. the "raw" DXF is still shown as a silver line.
- The "Significant Levels" option is available and is set to -20/+20 by default .. also adjustable in indicator settings.
- Please use DXF in combination with other trend and volume indicators, and with thorough chart / price action analysis and not in isolation to ensure you get proper signal confirmation for trades. In the chart above, you can see DXF combined with a moving average that can act as a filter and to confirm the price moves.
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As usual, feedback & comments are welcome - if you find this work useful in your trading arsenal, please share a comment - i would be more than happy to learn about that. Good luck!
Z-Score of RSIA way to see whether RSI is overbought or oversold inside its Bollinger Bands in the form of an oscillator. Z-score tells you how far the data is from the mean in terms of standard deviations. The numbers shown in the indicator are the number of standard deviations away from the average or mean. Like Bollinger Bands, if it is above the standard deviation border lines then it is considered to be outside the distribution or Bollinger Band. If it is above the upper border line then is it is overbought, if it is below the lower border line then is is oversold.
Example of how standard deviation works: 1 standard deviation contains 68% of the data, 2 SD (the default for BB) contains 95% of data, 3 SD for 99%, etc. If the data goes past that, it is considered to be outside the distribution. In statistics, data that is within the distribution is considered to be "significant".
The z-score line is colored green if it is above zero and red if it is below zero. Above zero means that the RSI is above the average line or the middle line of the Bollinger Bands, and vice-versa if it is below. A red vertical bar is shown when the the z-score is above the upper border line to show that it is overbought and a green vertical bar is shown when the z-score is below the lower border line to show that it is oversold. This is the equivalent of when the RSI is above and below its Bollinger Bands.
Efficient Trend Step Mod (v.3)This is a version 3 of my mod of the script by alexgrover - Efficient Trend Step.
The logic is based on calculation of Kaufman's efficiency ratio (ER):
ER = Direction / Volatility
where:
Direction = ABS (Close – Close )
Volatility = n ∑(ABS(Close – Close ))
n = The efficiency ratio period.
This version features volatility and volume filter and custom performance module.
Trending True RangeDisplay a smoothed true range during trending markets, thus filtering any measurement occurring during ranging markets. Whether the market is trending or ranging is determined by the position of the efficiency ratio relative to its Wilder moving average.
Settings
Resolution : resolution of the indicator
Length : period of the efficiency ratio and the Wilder moving averages used in the script
Usage
If you are not interested in volatility during ranging markets, this indicator might result useful to you. An interesting aspect is that it both measures volatility, but also determine whether the market is trending or ranging, with a zero value indicating a ranging market.
Indicator against Atr, with both length = 14, our indicator might be easier to interpret.
Note
Thx to my twitter followers for their suggestions regarding this indicator. I apologize if it's a bit short, the original code was longer and included more options, but forcing a script to be lengthy is a really bad idea, so I stayed with something less flashy but certainly more practical, "classic Grover" some might say.
Thx for reading!
Efficient Trend Step ChannelIntroduction
The efficient trend-step indicator is a trend indicator that make use of the efficiency ratio in order to adapt to the market trend strength, this indicator originally aimed to remain static during ranging states while fitting the price only when large variations occur. The trend step indicator family unlike most moving averages has a boxy appearance and could therefore not be classified as smooth, this makes it an indicator relatively uninteresting to use as input for other non-trending indicators such as oscillators.
Today a channel indicator making use of the efficient trend-step is proposed, the indicator has an upper and a lower extremity who can be used for breakout or support and resistance methodologies, however we will see that the indicator is sometimes able to return accurate support and resistance levels.
The Indicator
The indicator has the same settings has the efficient trend step indicator, length control the period of the efficiency ratio, fast control the period of the rolling standard deviation used for trending states, slow control the period of the rolling standard deviation used for ranging states, fast should be lower than slow , if both are equal then the indicator is equal to the classical trend step indicator and length does no longer affect the indicator output. Lower values of fast/slow will make the indicator more reactive to small variations thus changing direction more often.
The color changes you can see on the indicator are changed depending on the prior direction took by the indicator output, if the indicator where higher than its precedent value, then the color will be blue until the indicator is lower than its precedent value. Those colors help you have an estimate of the current trend direction.
Channel Calculation And Role
The extremities made from the efficient trend step allow for more advanced trading rules, they can act as stop/target level and can also give a rough estimate of the current market volatility, with wider extremities indicating a more volatile market.
The extremities are made directly from the dev element used by the efficient trend-step, the upper extremity is made by summing the efficient trend step with the value of dev when the efficient trend step change, the lower extremity is made the same way but the value is subtracted instead.
Is it a weird choice ? It sure is strange to see such approach, the absolute rolling average error between the price and the efficient trend step could have been a logical measure but using dev instead is more efficient and also allow for a more adaptive approach which can benefit the support and resistance methodology, the last reason is because i didn't wanted to "denature" the trend-step signature of the indicator.
The figure above represent the measurement used for making the extremities (in green).
Since the previously described measure change only when the efficient trend step change, we can conclude that such measure is representative of a relatively large variation, since the efficient trend step aim to only change when a large variations appear.
We can see that the upper extremity acted as an accurate resistance in this upper variation of AMD,
Here as well, however like other bands indicators it is safer to take into account the current trend direction, a strong uptrend will have less difficulties crossing the upper extremity, therefore it might be better to rely on the support (lower extremity) on an up-trending market (indicator in blue), and on the resistance (upper extremity) on an down-trending market (indicator in orange).
The figure above show support and resistances signals, a cross represent a false signal, while green arrows represent correct ones with their respective direction.
Conclusion
The presented indicator add more possibilities to the interpretation of the efficient trend step, the extremities can act as stop/target level, however this use has to be controlled, and the level should be in accordance to your risk/reward ratio.
Showcasing another trend-step indicator was a real pleasure. Thanks for reading :)
MAMA FAMA KAMA.. chameleon 🎵
Uses Kaufmann's Efficiency Ratio to generate adaptive inputs for Ehler's MAMA/FAMA. Alphas from the Hilbert transform are then used in place for the KAMA calculation.
Original MAMA/FAMA by everget : link
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If you find it useful please consider a tip/donation :
BTC - 3BMEXEDyWJ58eXUEALYPadbn1wwWKmf6sA
Efficient Trend Step - Spotting Trends EfficientlyIntroduction
The trend-step indicator (or auto-line) was based on volatility and aimed to spot trends in an adaptive way, however the indicator was only based on volatility and didn't gave much attention to the trend, later on i would publish an efficient version of it (efficient auto-line) based on the efficiency ratio who could adapt to the trend and eliminate potential whipsaws trades, however this approach included many settings that would require changes if the user switched markets, which reduce the utility of the indicator and make it actually super inefficient.
This is why i had to propose this indicator who remove all the flaws the efficient auto-line had without removing the core idea of it.
The Indicator
The indicator is based on recursion, when the price is superior/inferior to the indicator precedent value +/- volatility metric, then the indicator is equal to the closing price, this allow the indicator to fit the price relatively well. The volatility metric used is based on 2 standard deviations, one fast and one slow and the efficiency ratio, basically when price is trending the volatility metric will be closer to the value of the fast standard deviations, which would allow the indicator to be closer to the price, else the metric will be closer to the slow standard deviation which restrain the indicator from changing, therefore the volatility metric act as a threshold.
length control the period of the efficiency ratio, lower values of length will result in a volatility metric way closer to the fast standard deviation thus making the indicator more inclined toward making false signals.
Lower values for slow will make the indicator more reactive.
The indicator can be reactive but can also be really conservative, thus even remaining unchanged in some contrary movements of the main trend, this is called robustness and has its pro's and con's.
Conclusion
The trend-step indicators family might get to an end, or not, nonetheless they can provide precise entries and be extremely robust, which is great. Using low settings might prove to be useful to remove some noise. I hope this version find its use amongst the community. Thanks for reading !
Kaufman Adaptive Least Squares Moving AverageIntroduction
It is possible to use a wide variety of filters for the estimation of a least squares moving average, one of the them being the Kaufman adaptive moving average (KAMA) which adapt to the market trend strength, by using KAMA in an lsma we therefore allow for an adaptive low lag filter which might provide a smarter way to remove noise while preserving reactivity.
The Indicator
The lsma aim to minimize the sum of the squared residuals, paired with KAMA we obtain a great adaptive solution for smoothing while conserving reactivity. Length control the period of the efficiency ratio used in KAMA, higher values of length allow for overall smoother results. The pre-filtering option allow for even smoother results by using KAMA as input instead of the raw price.
The proposed indicator without pre-filtering in green, a simple moving average in orange, and a lsma with all of them length = 200. The proposed filter allow for fast and precise crosses with the moving average while eliminating major whipsaws.
Same setup with the pre-filtering option, the result are overall smoother.
Conclusion
The provided code allow for the implementation of any filter instead of KAMA, try using your own filters. Thanks for reading :)
Powered Kaufman Adaptive Moving AverageIntroduction
The ability the Kaufman adaptive moving average (KAMA) has to be flat during ranging markets and close to the price during trending markets is what make this moving average one of the most useful in technical analysis. KAMA is calculated by using exponential averaging using the efficiency ratio (ER) as smoothing variable where 1 > ER > 0 . An increasing efficiency ratio indicate a trending market. Based on one of my latest indicator (see Kaufman Adaptive Bands) i propose this modified KAMA that allow to emphasis the abilities of KAMA by powering the efficiency ratio. I also added a new option that allow for even more adaptivity.
The Indicator
The indicator is a simple KAMA of period length that use a powered ER with exponent factor .
When factor = 1 the indicator is a simple KAMA, however when factor > 1 there can be more emphasis on the flattening effect of KAMA.
You can also restrain this effect by using 1 > factor > 0
Note that when the exponent is lower than 1 and greater than 0 you are basically applying a nth square root to the value, for example pow(2,0.5) = sqrt(2) because 1/0.5 = 2, in our case :
pow(ER,factor > 1) < ER and pow(ER,1 > factor > 0) > ER
Self Powered P-KAMA
When the self powered option is checked you are basically powering ER with the reciprocal of ER as exponent, however factor does no longer change anything. This can give interesting results since the exponent depend on the market trend strength.
In orange the self powered KAMA of period length = 50 and in blue a basic powered KAMA with a factor of 3 and a period of length = 50.
Conclusion
Applying basic math to indicators is always fun and easy to do, if you have adaptive moving averages using exponential averaging try powering your smoothing variable in order to see interesting results. I hope you like this indicator. Thanks for reading !
Kaufman Adaptive BandsIntroduction
Bands are quite efficient in technical analysis, they can provide support and resistance levels, provide breakouts points, trailing stop loss/take profits positions and can show the current market volatility to the user. Most of the time bands are made from a central tendency estimator like a moving average plus/minus a volatility indicator. Therefore bands can be made out of pretty much everything thus allowing for any kind of flavors.
So i propose a band indicator made from a Kaufman adaptive moving average using an estimate of the standard deviation.
Construction
The Kaufman moving average is an exponential averager using the efficiency ratio as smoothing variable, length control the period of kama and in order to provide more smoothness a power parameter has been introduced, higher values of power will return smoother results.
The volatility indicator is made from a biased estimation of the standard deviation by using the square root of the mean of the square minus the square of the mean method, except that we use kama instead of a mean.
The bands are made by adding/subtracting this volatility indicator with kama.
How To Use
The ability of the indicator to adapt to the current market state is what makes him a great tool for avoiding major exposition during ranging market, therefore the indicator will have a greater motion during trending market, or more simply the bands will move during trending markets while staying "flat" during ranging ones. Therefore the indicator might be more suited to breakouts, even if some cases will return what where turning points, this is particularly true during ranging markets.
Of course the efficiency ratio is not an "unbiased" trend metric indicator, it can consider high volatility markets as trending markets. Its one of his downsides.
High values of power will create smoother bands.
When using a low power parameter use an higher mult. In general using a low power value will make the bands move more freely as well as making them closer to each others.
Conclusion
At least the indicator is really nice to the eyes when using high power values, its ability to adapt to the market is a great addition to other more classical bands indicators, i also introduced a volatility estimator based on kama, some might have used the following estimation : kama(abs(price - kama)) which would have created a slower result. A trailing stop might be made from it if i see request about such addition.
If you are curious here are some more images of the indicator performing on different markets. Thanks for reading !
Adaptive Trailing StopIntroduction
The ability to adapt to possible markets states is important in technical analysis, this is why making adaptive indicator might help get better results. I propose a trailing stop indicator using recursion that can adapt to the efficiency ratio. I have added alerts since it's a often requested feature.
The Indicator
Its quite classical, bands are firstly made then a trailing stop is built around them. The bands are recursive, this allow for faster calculations in general but it also allow for a faster adaptivity. An higher length or factor will make the indicator detect longer term trends, factor determine the raising power of the efficiency ratio.
When smooth is checked the trailing stop will appear smoother.
When adaptive is unchecked the indicator will still act as a trailing stop but might be more affected to ranging markets.
Set a static/trailing stop loss :
You can set your stop loss based on the indicator, a static stop loss can be set at the value of the trailing stop when you enter the market. You can also set it as trailing stop, the indicator will follow the trend thus allowing for potential profits to grow's.
Determine The Trend Direction :
You can generate buy sell signals based on the indicator position relative to the price, when the indicator is lower than the price this indicate a up trending market, when the indicator is higher than the price this indicate a down trending market. If the trailing stop move this indicate a strong current trend.
False signals with trailing stops can happen, the price might go toward the trailing stop making it generate another signal, when market is ranging and exhibiting cyclical behaviour this can affect the indicator and the user might get stuck in a series of false signals, higher length/factor values can fix that at the cost of less early signals.
Identification Of Support And Resistance
Bands during low volatility/ranging markets can return potential reversal points when crossing with the price. The indicator can also do it, even if high/low crosses are better suited to determine support and resistance levels when using a trailing stop. You can use support/resistance identification in conjonction of the current trend detected by the indicator.
Conclusion
The indicator is fully operational in fixed mode while having potential down points in adaptive mode. As you can see the code that return the bands is fully recursive and might provide a great way to create adaptive bands in the future.
I have been asked to give more detail about the indicator uses rather than the construction, i hope the showcased uses are convenient.
Note that the showcased uses can be applied to any trailing stop.
Thanks for reading.
Adaptive Autonomous Recursive Moving AverageIntroduction
Using conditions in filters is a way to make them adapt to those, i already used this methodology in one of my proposed indicators ARMA which gave a really promising adaptive filter, ARMA tried to have a flat response when dealing with ranging market while following the price when the market where trending or exhibiting volatile movements, the filter was terribly simple which is one of its plus points but its down points where clearly affecting its performance thus making it almost impractical.
Today i propose a new filter A2ARMA which aim to correct all the bad behaviours of ARMA while having a good performance on various markets thanks to the added adaptivity.
Fixes And Changes
ARMA was dealing with terribles over/under-shoots which affected its performance, adding a zero-lag option made the thing even worse, in order to fix those mistakes i first cleaned the code, then i removed the offset for src in d , this choice is optional but the filter is sometimes more accurate this way.
The major change is the use of an adaptive moving average instead of the triangular moving average that smoothed the output, this adaptive moving average is calculated using exponential averaging while using the efficiency ratio as smoothing variable, this choice surprisingly removed the majority of overshoots while adding more adaptivity to the filter.
The Indicator
The Indicator work the same way as ARMA, not reacting during flat market periods while following the price when this one is volatile or trending. length control the smoothing amount while gamma determine how the filter is affected during flat market periods, gamma = 0 is just a double smoothed adaptive moving average, higher values of gamma will filter flat markets with a certain degree.
On Intel Corp with gamma = 0, i want to filter the flat period starting at July 10, gamma = 3 will certainly help us on this task.
Hooray, the problem appear to be solved ! Lower values of gamma also produce desirable effect as shown below :
gamma = 2
So far so good, but gamma or length might have different optimal values depending on the market, also problems still exists as shown here :
Seagate is tricky, gamma at 2.4 might help
The relationship between length and gamma is somewhat complicated.
On Different Markets
While some filters will process market price the same way no matter the market they are affected, A2ARMA will change drastically depending of the market.
On AMD
On EURUSD
On BTCUSD
Comparison With ARMA
ARMA with parameters roughly matching A2RMA, overall most of the problems i wanted to fix where indeed fixed.
Conclusion
A huge thanks for the support i received during this "Blank Page" period i'am suffering, ARMA was an indicator i really wanted to further develop without giving up on the code simplicity and i think this version might provide useful results, we can also notice that the decision making is easier with this version of the indicator thanks to the added coloring (which would have been impossible with ARMA).
My work don't have license attached to it, feel free to modify and share your findings, mentioning is appreciated :)
Thanks for reading !
Bryant Adaptive Moving Average@ChartArt got my attention to this idea.
This type of moving average was originally developed by Michael R. Bryant (Adaptrade Software newsletter, April 2014). Mr. Bryant suggested a new approach, so called Variable Efficiency Ratio (VER), to obtain adaptive behaviour for the moving average. This approach is based on Perry Kaufman' idea with Efficiency Ratio (ER) which was used by Mr. Kaufman to create KAMA.
As result Mr. Bryant got a moving average with adaptive lookback period. This moving average has 3 parameters:
Initial lookback
Trend Parameter
Maximum lookback
The 2nd parameter, Trend Parameter can take any positive or negative value and determines whether the lookback length will increase or decrease with increasing ER.
Changing Trend Parameter we can obtain KAMA' behaviour
To learn more see www.adaptrade.com
