Adaptive Exponential Moving AverageThis indicator was originally developed by Vitali Apirine (Stocks & Commodities V. 37:5 (April, 2019): Adaptive Exponential Moving Average ).
This is his second modification of Kaufman Moving Average. In essence, the idea remains the same as in the previous ( ): the smoothing constant is calculated as a special ratio between the current price and highest/lowest prices for a given period.
In combination with EMA you can obtain a trading system based on double crossovers:
Long , when AEMA crosses up EMA
Short , when AEMA crosses down EMA
Source code on request
Adaptive
Autonomous Recursive Moving AverageIntroduction
People often ask me what is my best indicators, i can't really respond to this question with a straight answer but i would say you to check this indicator. The Autonomous Recursive Moving Average (ARMA) is an adaptive moving average that try to minimize the sum of squares thanks to a ternary operator, this choice can seem surprising since most of the adaptive moving averages adapt to a smoothing variable thanks to exponential averaging, but there are lot of downsides to this method, i really wanted to have a flat filter during flat markets and this is what i achieved.
The Indicator
length control the amount of smoothing during trending periods, gamma is the trend sensitivity threshold, higher values of gamma will make an overall flat filter, adjust gamma to skip ranging markets.
gamma = 2, we can adjust to 3 while preserving smoothing reactivity with trading periods.
gamma = 3
low length and higher gamma create more boxy result, the filter add overshoots directly in the output, its unfortunate.
The Zero-Lag option can reduce the lag as well as getting additional flat results without changing gamma.
Conclusion
The indicator need work, but i can't leave without publishing it, the overshoots are a big problems, changing sma for another stable filter can help. I hope you find an use to it, i really like this indicator.
Thanks for reading
Jurik Adaptive Moving AverageThis is Jurik Research's original moving average and a predecessor of the well-known Jurik Moving Average (JMA). It was developed by Mark Jurik in 1994. The purpose was the same: to create the best noise reduction filter.
The algorithms of JAMA and JMA have big differences. JAMA is less responsive than JMA - sometimes it makes it better than JMA but closely depends on the objective assigned to it.
On the screenshots you can see how they behave together with different period settings.
The red line is JAMA, the purple line is JMA .
Period: 7
BTCUSD, D
AAPL, D
Period: 14
BTCUSD, D
AAPL, D
Period: 50
BTCUSD, D
AAPL, D
Reference: www.jurikres.com
Ratio OCHL Averager - An Alternative to VWAPIntroduction
I had the idea to make this indicator thanks to @dpanday with the support of @Coppermine and @Reika. Vwap is a non parametric indicator based on volume used by lot of traders and institutions, its non parametric particularity makes it great because it don't need to go through parameter optimization. Today i present a similar indicator called Ratio OCHL Averager based on exponential averaging by using the ratio of open-close to high-low range by using monthly high/low.
The Indicator
The indicator can more recursive by checking the "recursive" option, this allow to use the indicator output instead of the open price for the calculation of the ratio of open-close to high-low range. The result is a more reactive estimation,
The indicator reactivity change based on the time frame you are in, using higher time frame result in a more reactive indicator, however it is way less reactive than the vwap, this is a personal choice since i wanted this indicator to be smooth even with high time frames, if you want to change that you use another resolution for H and L in line 5,6.
Conclusion
I presented an alternative to vwap based on the Ratio OCHL indicator. I hope you like it and thanks for reading !
Thanks to Coppermine and Reika for the support during the creation of the indicator
Volume Adaptive BandsIntroduction
I have been asked by @Coppermine and @Verbena to make bands that use volume to provide adaptive results. My first approach was to use exponential averaging, in order to do so i needed to quantify volume movement using rescaling with the objective to make the bands go away from each others when there is low volume, this approach is efficient and can work on any time frame, however i decided at the end to use another method which rely on recursive weighting, cleaner but more parametric. Those bands aim to highlight great breakouts point to go with the trend.
The Indicator
length control the period of the moving averages used in the script, however low length's don't necessarily provide indications for shorter terms breakouts as shown here :
As i said the bands are close to each others when there is high volume and away when there is low volumes.
Low volume period, bands will avoid to cross price
High volume, bands will be close to generate signals.
Correction Factor
Higher time frames will lower the distance between each band, this is because volume is higher during higher time frames, remember that the indicator bands are close to each others when volume is high.
1h chart eurusd.
This is why i added a correction factor, this factor can help you control the distance between each bands, when the correction factor is greater than 1 the bands will be closer to each others, this is useful for low time frames where the average volume is lower. When the time frame is high, use values between 0 and 1 to increase distance between each bands.
Correction factor = 0.2
Conclusion
I presented a new adaptive band indicator that adapt to trading volume by using recursive weighting, volume can be replaced by other indicators but you can have results going nuts, at the end its about experimentation. I hope you will find an use to it, thanks to @Coppermine and @Verbena for the request :)
Thanks for reading !
Non Parametric Adaptive Moving AverageIntroduction
Not be confused with non-parametric statistics, i define a "non-parametric" indicator as an indicator who does not have any parameter input. Such indicators can be useful since they don't need to go through parameter optimization. I present here a non parametric adaptive moving average based on exponential averaging using a modified ratio of open-close to high-low range indicator as smoothing variable.
The Indicator
The ratio of open-close to high-low range is a measurement involving calculating the ratio between the absolute close/open price difference and the range (high - low) , now the relationship between high/low and open/close price has been studied in econometrics for some time but there are no reason that the ohlc range ratio may be an indicator of volatility, however we can make the hypothesis that trending markets contain less indecision than ranging market and that indecision is measured by the high/low movements, this is an idea that i've heard various time.
Since the range is always greater than the absolute close/open difference we have a scaled smoothing variable in a range of 0/1, this allow to perform exponential averaging. The ratio of open-close to high-low range is calculated using the vwap of the close/high/low/open price in order to increase the smoothing effect. The vwap tend to smooth more with low time frames than higher ones, since the indicator use vwap for the calculation of its smoothing variable, smoothing may differ depending on the time frame you are in.
1 minute tf
1 hour tf
Conclusion
Making non parametric indicators is quite efficient, but they wont necessarily outperform classical parametric indicators. I also presented a modified version of the ratio of open-close to high-low range who can provide a smoothing variable for exponential averaging. I hope the indicator can help you in any way.
Thanks for reading !
Dynamically Adjustable FilterIntroduction
Inspired from the Kalman filter this indicator aim to provide a good result in term of smoothness and reactivity while letting the user the option to increase/decrease smoothing.
Optimality And Dynamical Adjustment
This indicator is constructed in the same manner as many adaptive moving averages by using exponential averaging with a smoothing variable, this is described by :
x= x_1 + a(y - x_1)
where y is the input price (measurements) and a is the smoothing variable, with Kalman filters a is often replaced by K or Kalman Gain , this Gain is what adjust the estimate to the measurements. In the indicator K is calculated as follow :
K = Absolute Error of the estimate/(Absolute Error of the estimate + Measurements Dispersion * length)
The error of the estimate is just the absolute difference between the measurements and the estimate, the dispersion is the measurements standard deviation and length is a parameter controlling smoothness. K adjust to price volatility and try to provide a good estimate no matter the size of length . In order to increase reactivity the price input (measurements) has been summed with the estimate error.
Now this indicator use a fraction of what a Kalman filter use for its entire calculation, therefore the covariance update has been discarded as well as the extrapolation part.
About parameters length control the filter smoothness, the lag reduction option create more reactive results.
Conclusion
You can create smoothing variables for any adaptive indicator by using the : a/(a+b) form since this operation always return values between 0 and 1 as long as a and b are positive. Hope it help !
Thanks for reading !
Trend Impulse FilterIntroduction
There is a lot of indicators similar to this one, however i think this one don't share the same calculation method and this is why i share it. This indicator aim to forecast price direction using an exponential filter architecture using highest and lowest information for the estimation of a smoothing variable. This filter is similar to the average Max-Min filter.
The Indicator
In the code a is equal to 1 when the price is greater or lower than any past price over length period, else a is equal to 0. The center parameter control the filtering degree of the output, when center is equal to 1 and a = 1 the indicator return the highest or lowest depending on market current trend, when center is superior to 1 the output will be smoother, however the reactivity of the indicator will still depend on the length parameter.
A color option show you the trend of the market, however the generated signals are the same that can be generated from a Donchian channel.
When highest is greater than previous highest the indicator direction will move upward, else if lowest is lower than previous lowest the indicator direction will move downward. Therefore the indicator can give information on the Donchian channels direction and provide a nice filter.
Conclusions
Adapting to highest and lowest can make an indicator adapt to the essence of trend trading, the indicator i showed can be used as source for others indicator or in MA crossover strategies. If you have a strategy using Donchian channels you may be interested in using this indicator and se how it fit in your strategy. Hope you like it.
Thanks for reading !
Falling-Rising FilterIntroduction
This is a modification of an old indicator i made. This filter aim to adapt to market trend by creating a smoothing constant using highest and lowest functions. This filter is visually similar to the edge-preserving filter, this similarity can make this filter quite good for MA cross strategies.
On The Filter Code
a = nz(a ) + alpha*nz(error ) + beta*nz(error )
The first 3 terms describe a simple exponential filter where error = price - a , beta introduce the adaptive part. beta is equal to 1 when the price is greater or lower than any past price over length period, else beta is equal to alpha , someone could ask why we use two smoothing variable (alpha, beta) instead of only beta thus having :
a = nz(a ) + beta*nz(error )
well alpha make the filter converge faster to the price thus having a better estimation.
In blue the filter using only beta and in red the filter using alpha and beta with both length = 200 , the red filter converge faster to the price, if you need smoother results but less precise estimation only use beta .
Conclusion
I have presented a simple indicator using rising/falling functions to calculate an adaptive filter, this also show that when you create an exponential filter you can use more terms instead of only a = a + alpha*(price - a ) . I hope you find this indicator useful.
Thanks for reading !
♒RBCI - Range Bound Channel Index by Cryptorhythms♒ RBCI - Range Bound Channel Index by Cryptorhythms
Intro
This is my best approximation of the RBCI. Its not perfect, but it does the job well enough.
A good way to use it is to enter when the singal line (light blue) RE ENTERS the channel from below. I circled these points on the indicator in green.
Description
Information to create was gathered mostly here: www.finware.com
RBCI (Range Bound Channel Index) – is calculated by means of the channel (bandwidth) filter (CF). Channel filter simultaneously fulfills two functions:
Removes low frequent trend formed by low frequent components of the spectrum with periods, more T2= 1/fc2;
Removes high frequency noise formed by the high frequent components of the spectrum with periods, less T1= 1/fc1.
👍 Enjoying this indicator or find it useful? Please give me a like and follow! I post crypto analysis, price action strategies and free indicators regularly.
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Dynamically Adjustable Moving AverageIntroduction
The Dynamically Adjustable Moving Average (AMA) is an adaptive moving average proposed by Jacinta Chan Phooi M’ng (1) originally provided to forecast Asian Tiger's futures markets. AMA adjust to market condition in order to avoid whipsaw trades as well as entering the trending market earlier. This moving average showed better results than classical methods (SMA20, EMA20, MAC, MACD, KAMA, OptSMA) using a classical crossover/under strategy in Asian Tiger's futures from 2014 to 2015.
Dynamically Adjustable Moving Average
AMA adjust to market condition using a non-exponential method, which in itself is not common, AMA is described as follow :
1/v * sum(close,v)
where v = σ/√σ
σ is the price standard deviation.
v is defined as the Efficacy Ratio (not be confounded with the Efficiency Ratio) . As you can see v determine the moving average period, you could resume the formula in pine with sma(close,v) but in pine its not possible to use the function sma with variables for length, however you can derive sma using cumulation.
sma ≈ d/length where d = c - c_length and c = cum(close)
So a moving average can be expressed as the difference of the cumulated price by the cumulated price length period back, this difference is then divided by length. The length period of the indicator should be short since rounded version of v tend to become less variables thus providing less adaptive results.
AMA in Forex Market
In 2014/2015 Major Forex currencies where more persistent than Asian Tiger's Futures (2) , also most traded currency pairs tend to have a strong long-term positive autocorrelation so AMA could have in theory provided good results if we only focus on the long term dependency. AMA has been tested with ASEAN-5 Currencies (3) and still showed good results, however forex is still a tricky market, also there is zero proof that switching to a long term moving average during ranging market avoid whipsaw trades (if you have a paper who prove it please pm me) .
Conclusion
An interesting indicator, however the idea behind it is far from being optimal, so far most adaptive methods tend to focus more in adapting themselves to market complexity than volatility. An interesting approach would have been to determine the validity of a signal by checking the efficacy ratio at time t . Backtesting could be a good way to see if the indicator is still performing well.
References
(1) J.C.P. M’ng, Dynamically adjustable moving average (AMA’) technical
analysis indicator to forecast Asian Tigers’ futures markets, Physica A (2018),
doi.org
(2) www.researchgate.net
(3) www.ncbi.nlm.nih.gov
ACAT Period Tracker (2000-4000 Std-Res) [acatwithcharts]This is the corresponding period length tracker for ACAT (2000-4000 Std-Res). The constraints of Pinescript make it necessary to plot a separate indicator in a separate window to actually read out the period numbers without massively skewing the chart by default.
My volatility indicators are available by subscription in several packages through SharkCharts.live - and this is planned to be the first new one ready to add. I plan to release a video explaining how to use this indicator coinciding with launch, as there's a lot to talk about. Videos on my other indicators are currently hosted on DadShark's YouTube channel.
Current pricing and subscription details will be kept up-to-date on SharkCharts.live
ACAT (2000-4000 Std-Res) [acatwithcharts]Adaptive Comprehensive Average Tracker is a 2 in 1 version of Mean Reversion MA and Compression MA. The slightly odd name is a backronym that spells "ACAT" - suffice it to say, I'm pretty proud of what these two indicators have developed into.
The best solution I've managed to find to the variable count limits in Pinescript is to split the indicator into two that cover different ranges - and that aspect also theoretically allows for some other variations.
I've had difficulty increasing the resolution much without needing a huge number of individual indicators and running into constant Pinescript timeout issues. One thing that I can do, however, is extend the range further out.
Note that "Std-Res" here for this higher range was decided mostly based on, "What do I think I can get to work for higher than 2000 about as often as the existing 600-2000 indicator?" and is semi-arbitrary. At higher period lengths, the spread between different MAs decreases so less resolution is needed, while the calculation times increase dramatically, so this script is necessarily a lot less dense than the lower lengths. Sometimes this will load much faster as a result. I'm generally finding both struggle on shorter timeframes.
Keep in mind some caveats: resolution at these lengths is lower and finding relevant period lengths this far out necessarily means you should be trading them on higher timeframes. If you see something on the 3000 period on the 4h, you're talking about events happening near W benchmark length moving averages. If you see something in these periods lengths on the D timeframe as with some parabolic bubbles/crashes, you're looking at very macro pivots and targets happening near M benchmark length moving averages.
My volatility indicators are available by subscription in several packages through SharkCharts.live - and this is planned to be the first new one ready to add. I plan to release a video explaining how to use this indicator coinciding with launch, as there's a lot to talk about. Videos on my other indicators are currently hosted on DadShark's YouTube channel.
Current pricing and subscription details will be kept up-to-date on SharkCharts.live
ACAT (600-2000 Std-Res) [acatwithcharts](This is a reupload to help standardize the names in the invite-only indicators listing, which was otherwise doomed to be forever confusing.)
Adaptive Comprehensive Average Tracker is a 2 in 1 version of Mean Reversion MA and Compression MA. The slightly odd name is a backronym that spells "ACAT" - suffice it to say, I'm pretty proud of what these two indicators have developed into.
The best solution I've managed to find to the variable count limits in Pinescript is to split the indicator into two that cover different ranges. This version covers period lengths from 600-2000 at the same resolution seen in Mean Reversion MA and Compression MA. I've then added a toggle-able feature to hide the lower indicator's plot while it's maxed out which makes them look roughly stitched together and has a nice added benefit that it require less work by the higher period script so it loads more quickly.
The hope is to replace several indicators that have periodic issues timing out with fewer, which should be substantially more convenient to use and on average mean dealing with fewer refreshes.
My volatility indicators are available by subscription in several packages through SharkCharts.live - and this is planned to be the first new one ready to add. I plan to release a video explaining how to use this indicator coinciding with launch, as there's a lot to talk about. Videos on my other indicators are currently hosted on DadShark's YouTube channel.
Current pricing and subscription details will be kept up-to-date on SharkCharts.live
ACAT (14-600 Std-Res) [acatwithcharts]
(This is a reupload to help standardize the names in the invite-only indicators listing, which was otherwise doomed to be forever confusing.)
Adaptive Comprehensive Average Tracker is a 2 in 1 version of Mean Reversion MA and Compression MA. The slightly odd name is a backronym that spells "ACAT" - suffice it to say, I'm pretty proud of what these two indicators have developed into.
The best solution I've managed to find to the variable count limits in Pinescript is to split the indicator into two that cover different ranges. This version covers period lengths from 14-600 at the same resolution seen in the "600 Max" versions of Mean Reversion MA and Compression MA. I've then added a toggle-able feature to hide the lower indicator's plot while it's maxed out which makes them look roughly stitched together and has a nice added benefit that it require less work by the higher period script so it loads more quickly.
The hope is to replace several indicators that have periodic issues timing out with fewer, which should be substantially more convenient to use and on average mean dealing with fewer refreshes.
My volatility indicators are available by subscription in several packages through SharkCharts.live - and this is planned to be the first new one ready to add. I plan to release a video explaining how to use this indicator coinciding with launch, as there's a lot to talk about. Videos on my other indicators are currently hosted on DadShark's YouTube channel.
Current pricing and subscription details will be kept up-to-date on SharkCharts.live
ACAT Period Tracker (14-2000 Half-Res) [acatwithcharts]This is the corresponding period length tracker for ACAT (14-2000 Half-Res). The constraints of Pinescript make it necessary to plot a separate indicator in a separate window to actually read out the period numbers without massively skewing the chart by default.
Taking some lessons from how subscribers ended up making best use of the period length trackers for my prior scripts, I've set the default line widths wide so that it pairs well on the same window as 3D WAVE-PM. Notably, you would need to use both standard range coverage of 3D WAVE-PM and the Hi-Res 605-2000 period version to pair with this higher tracking range in that setup.
My volatility indicators are available by subscription in several packages through SharkCharts.live - and this is planned to be the first new one ready to add. I plan to release a video explaining how to use this indicator coinciding with launch, as there's a lot to talk about. Videos on my other indicators are currently hosted on DadShark's YouTube channel.
Current pricing and subscription details will be kept up-to-date on SharkCharts.live
ACAT Period Tracker (14-600 Std-Res) [acatwithcharts]This is the corresponding period length tracker for ACAT (14-600 Std-Res). The constraints of Pinescript make it necessary to plot a separate indicator in a separate window to actually read out the period numbers without massively skewing the chart by default.
Taking some lessons from how subscribers ended up making best use of the period length trackers for my prior scripts, I've set the default line widths wide so that it pairs well on the same window as 3D WAVE-PM.
My volatility indicators are available by subscription in several packages through SharkCharts.live - and this is planned to be the first new one ready to add. I plan to release a video explaining how to use this indicator coinciding with launch, as there's a lot to talk about. Videos on my other indicators are currently hosted on DadShark's YouTube channel.
Current pricing and subscription details will be kept up-to-date on SharkCharts.live
ACAT Period Tracker (600-2000 Std-Res) [acatwithcharts]This is the corresponding period length tracker for ACAT (600-2000 Std-Res). The constraints of Pinescript make it necessary to plot a separate indicator in a separate window to actually read out the period numbers without massively skewing the chart by default.
Taking some lessons from how subscribers ended up making best use of the period length trackers for my prior scripts, I've set the default line widths wide so that it pairs well on the same window as 3D WAVE-PM. Notably, you would need to use the Hi-Res 605-2000 period version to pair with this higher tracking range in that setup.
My volatility indicators are available by subscription in several packages through SharkCharts.live - and this is planned to be the first new one ready to add. I plan to release a video explaining how to use this indicator coinciding with launch, as there's a lot to talk about. Videos on my other indicators are currently hosted on DadShark's YouTube channel.
Current pricing and subscription details will be kept up-to-date on SharkCharts.live
ACAT (14-2000 Half-Res) [acatwithcharts]Adaptive Comprehensive Average Tracker is a 2 in 1 version of Mean Reversion MA and Compression MA. The slightly odd name is a backronym that spells "ACAT" - suffice it to say, I'm pretty proud of what these two indicators have developed into.
As I've looked into ways to lower variable count and optimize ACAT, I've found that decreasing the resolution in half to allow the full range that I otherwise split across two indicators is broadly more convenient to use and seems to have faster loading and far fewer timeouts. There is a significant precision tradeoff that is most acutely felt with more dramatic sawtoothing when plotting the current highest compressed period. For most practical use cases, I think people will want to make this trade-off and then if necessary to look at a compression range do a second pass with a higher resolution version of the indicator.
My volatility indicators are available by subscription in several packages through SharkCharts.live - and this is planned to be the first new one ready to add. I plan to release a video explaining how to use this indicator coinciding with launch, as there's a lot to talk about. Videos on my other indicators are currently hosted on DadShark's YouTube channel.
Current pricing and subscription details will be kept up-to-date on SharkCharts.live
Edge-Preserving FilterIntroduction
Edge-preserving smoothing is often used in image processing in order to preserve edge information while filtering the remaining signal. I introduce two concepts in this indicator, edge preservation and an adaptive cumulative average allowing for fast edge-signal transition with period increase over time. This filter have nothing to do with classic filters for image processing, those filters use kernels convolution and are most of the time in a spatial domain.
Edge Detection Method
We want to minimize smoothing when an edge is detected, so our first goal is to detect an edge. An edge will be considered as being a peak or a valley, if you recall there is one of my indicator who aim to detect peaks and valley (reference at the bottom of the post) , since this estimation return binary outputs we will use it to tell our filter when to stop filtering.
Filtering Increase By Using Multi Steps Cumulative Average
The edge detection is a binary output, using a exponential smoothing could be possible and certainly more efficient but i wanted instead to try using a cumulative average approach because it smooth more and is a bit more original to use an adaptive architecture using something else than exponential averaging. A cumulative average is defined as the sum of the price and the previous value of the cumulative average and then this result is divided by n with n = number of data points. You could say that a cumulative average is a moving average with a linear increasing period.
So lets call CMA our cumulative average and n our divisor. When an edge is detected CMA = close price and n = 1 , else n is equal to previous n+1 and the CMA act as a normal cumulative average by summing its previous values with the price and dividing the sum by n until a new edge is detected, so there is a "no filtering state" and a "filtering state" with linear period increase transition, this is why its multi-steps.
The Filter
The filter have two parameters, a length parameter and a smooth parameter, length refer to the edge detection sensitivity, small values will detect short terms edges while higher values will detect more long terms edges. Smooth is directly related to the edge detection method, high values of smooth can avoid the detection of some edges.
smooth = 200
smooth = 50
smooth = 3
Conclusion
Preserving the price edges can be useful when it come to allow for reactivity during important price points, such filter can help with moving average crossover methods or can be used as a source for other indicators making those directly dependent of the edge detection.
Rsi with a period of 200 and our filter as source, will cross triggers line when an edge is detected
Feel free to share suggestions ! Thanks for reading !
References
Peak/Valley estimator used for the detection of edges in price.
Trend tracking strategy of proprietary traders-RabbitThis is my latest strategy integration. It is a combination of trend tracking strategy and visualization trend. I believe it will bring you a clear trend discrimination and relatively reliable trading signal hints.
(Note: This strategy parameter has special parameter debugging and Optimization for BTC1h/BIANACE Heikin-ashi chart. It works best here. Other trade pairs or parameter versions of investment targets will be published specially if necessary.)
Statement of strategy concept:
The concept of strategy is trend tracking. The formation and continuation of trend is the product of speculation market for thousands of years. There are various strategies including CTA trend strategy, shock regression strategy, grid strategy, Martin strategy, Alpha strategy and so on. These strategies have their own merits just like different schools of Chinese knight-errant. Choose one, a master is not able to do hundreds of tricks, but to practice one trick thousands of times.
Every strategy has its own right and wrong. Trading is not violence, but a process of advancing, retreating, and making profits steadily. Therefore, the use of trend tracking strategy must overcome greed in human nature, profit and loss homology, dare to bear the shock of withdrawal in order to make a big profit when the real trend arrives. (Of course, this strategy has largely avoided filtering shocks, which will be explained later.)
Policy-building instructions:
Any trend tracking strategy can produce good results when there is a trend, so judging whether a trend strategy is good or bad depends on its withdrawal performance when it is shaking. This CTA trend tracking strategy uses Kauffman adaptive algorithm, fractal adaptive dimension, self-research algorithm and other tools, and has largely avoided filtering the signal in the shock without delay to follow the trend.
New version of the note:
The latest version adds the trend drawing of negativity, which can clearly distinguish the rising or falling or oscillating trend. However, the algorithm of strategy signal has no direct relationship with trend color. Trend color helps you to distinguish trend, and point signal helps you to refer to trade. This strategy is only a simple trading signal, risk control, warehouse management also need manual operation.
(Note: This strategy parameter has special parameter debugging and Optimization for BTC1h/BIANACE Heikin-ashi chart. It works best here. Other trade pairs or parameter versions of investment targets will be published specially if necessary.)
Good luck to all of you and a smooth deal.~
ACAT (600-2000) [acatwithcharts]Adaptive Comprehensive Average Tracker is a 2 in 1 version of Mean Reversion MA and Compression MA. The slightly odd name is a backronym that spells "ACAT" - suffice it to say, I'm pretty proud of what these two indicators have developed into.
The best solution I've managed to find to the variable count limits in Pinescript is to split the indicator into two that cover different ranges. This version covers period lengths from 600-2000. I've then added features to hide the lower indicator's plot while it's maxed out which makes them look roughly stitched together and has a nice added benefit that it require less work by the higher period script so it loads more quickly.
This is still a work in progress at the time of posting as I attempt to try to add more functionality and improve the intuitiveness of the combined menu, but assuming all goes well, I may be able to mostly deprecate the individual indicators and replace them with this combined version when I'm satisfied that it's ready to release to subscribers.
My volatility indicators are available by subscription in several packages through SharkCharts.live - and this is planned to be the first new one ready to add. I plan to release a video explaining how to use this indicator coinciding with launch, as there's a lot to talk about. Videos on my other indicators are currently hosted on DadShark's YouTube channel.
Current pricing and subscription details will be kept up-to-date on SharkCharts.live
Adaptive Comprehensive Average Tracker (ACAT) [acatwithcharts]Adaptive Comprehensive Average Tracker is a 2 in 1 version of Mean Reversion MA and Compression MA. The slightly odd name is a backronym that spells "ACAT" - suffice it to say, I'm pretty proud of what these two indicators have developed into.
At the moment, it is limited to the 600-period cap; just using the logic of the two indicators in one hits runs into the limits of Pinescript. I've got some ideas to try, but for most practical purposes, the 600 cap was generally enough and the longer lookbacks are very prone to timing out anyway.
The hope is to replace two indicators that have periodic issues timing out with one, which should be substantially more convenient to use and on average mean dealing with fewer refreshes.
This is still a work in progress at the time of posting as I attempt to try to add more functionality and improve the intuitiveness of the combined menu, but assuming all goes well, I may be able to mostly deprecate the individual indicators and replace them with this combined version when I'm satisfied that it's ready to release to subscribers.
My volatility indicators are available by subscription in several packages through SharkCharts.live - and this is planned to be the first new one ready to add. I plan to release a video explaining how to use this indicator coinciding with launch, as there's a lot to talk about. Videos on my other indicators are currently hosted on DadShark's YouTube channel.
Current pricing and subscription details will be kept up-to-date on SharkCharts.live
