Trend Day IndentificationVolatility is cyclical, after a large move up or down the market typically "ranges" during the next session. Directional order flow that enters the market during this subsequent session tends not to persist, this non-persistency of transactions leads to a non-trend day which is when I trade intraday reversionary strategies.
This script finds trend days in BTC with the purpose of:
1) counting trend day frequency
2) predicting range contraction for the next 1-2 days so I can run intraday reversion strategies
Trend down is defined as daily bar opening within X% of high and closing within X% of low
Trend up is defined as daily bar opening within X% of low and closing within X% of high
default parameters are:
1) open range extreme = 15% (open is within 15% of high or low)
2) close range extreme = 15% (close is within 15% of high or low)
There is also an atr filter that checks that the trend day has a larger range than the previous 4 bars this is to make sure we find true range expansion vs recent ranges.
Notes:
If a trend day occurs after a prolonged sideways contraction it can signal a breakout - this is less common but is an exception to the rule. These types of occurrences can lead to the persistency of order flow and result in extended directional daily runs.
If a trend day occurs close to 20 days high or low (stopping just short OR pushing slightly through) then wait an additional day before trading intraday reversion strategies.
Meanreversion
Mean Shift Pivot ClusteringCore Concepts
According to Jeff Greenblatt in his book "Breakthrough Strategies for Predicting Any Market", Fibonacci and Lucas sequences are observed repeated in the bar counts from local pivot highs/lows. They occur from high to high, low to high, high to low, or low to high. Essentially, this phenomenon is observed repeatedly from any pivot points on any time frame. Greenblatt combines this observation with Elliott Waves to predict the price and time reversals. However, I am no Elliottician so it was not easy for me to use this in a practical manner. I decided to only use the bar count projections and ignore the price. I projected a subset of Fibonacci and Lucas sequences along with the Fibonacci ratios from each pivot point. As expected, a projection from each pivot point resulted in a large set of plotted data and looks like a huge gong show of lines. Surprisingly, I did notice clusters and have observed those clusters to be fairly accurate.
Fibonacci Sequence: 1, 2, 3, 5, 8, 13, 21, 34...
Lucas Sequence: 2, 1, 3, 4, 7, 11, 18, 29, 47...
Fibonacci Ratios (converted to whole numbers): 23, 38, 50, 61, 78, 127, 161...
Light Bulb Moment
My eyes may suck at grouping the lines together but what about clustering algorithms? I chose to use a gimped version of Mean Shift because it doesn't require me to know in advance how many lines to expect like K-Means. Mean shift is computationally expensive and with Pinescript's 500ms timeout, I had to make due without the KDE. In other words, I skipped the weighting part but I may try to incorporate it in the future. The code is from Harrison Kinsley . He's a fantastic teacher!
Usage
Search Radius: how far apart should the bars be before they are excluded from the cluster? Try to stick with a figure between 1-5. Too large a figure will give meaningless results.
Pivot Offset: looks left and right X number of bars for a pivot. Same setting as the default TradingView pivot high/low script.
Show Lines Back: show historical predicted lines. (These can change)
Use this script in conjunction with Fibonacci price retracement/extension levels and/or other support/resistance levels. If it's no where near a support/resistance and there's a projected time pivot coming up, it's probably a fake out.
Notes
Re-painting is intended. When a new pivot is found, it will project out the Fib/Lucas sequences so the algorithm will run again with additional information.
The script is for informational and educational purposes only.
Do not use this indicator by itself to trade!
Channel of linear regression of rate of change from the mean The indicator calculates the difference between the closing price and the average as a percentage and after that it calculates the average linear regression and then draws it in the form of a channel.
Preferably use it on 30 min or 15 min or 1 Hour or 2H time frames .
Exiting outside the upper or lower channel limits represents high price inflation, and returning inside the channel means the possibility of the price rising or falling for the average or the other limit of the channel.
Channel lines may represent places of support and resistance.
Nasdaq VXN Volatility Warning IndicatorToday I am sharing with the community a volatility indicator that uses the Nasdaq VXN Volatility Index to help you or your algorithms avoid black swan events. This is a similar the indicator I published last week that uses the SP500 VIX, but this indicator uses the Nasdaq VXN and can help inform strategies on the Nasdaq index or Nasdaq derivative instruments.
Variance is most commonly used in statistics to derive standard deviation (with its square root). It does have another practical application, and that is to identify outliers in a sample of data. Variance is defined as the squared difference between a value and its mean. Calculating that squared difference means that the farther away the value is from the mean, the more the variance will grow (exponentially). This exponential difference makes outliers in the variance data more apparent.
Why does this matter?
There are assets or indices that exist in the stock market that might make us adjust our trading strategy if they are behaving in an unusual way. In some instances, we can use variance to identify that behavior and inform our strategy.
Is that really possible?
Let’s look at the relationship between VXN and the Nasdaq100 as an example. If you trade a Nasdaq index with a mean reversion strategy or algorithm, you know that they typically do best in times of volatility . These strategies essentially attempt to “call bottom” on a pullback. Their downside is that sometimes a pullback turns into a regime change, or a black swan event. The other downside is that there is no logical tight stop that actually increases their performance, so when they lose they tend to lose big.
So that begs the question, how might one quantitatively identify if this dip could turn into a regime change or black swan event?
The Nasdaq Volatility Index ( VXN ) uses options data to identify, on a large scale, what investors overall expect the market to do in the near future. The Volatility Index spikes in times of uncertainty and when investors expect the market to go down. However, during a black swan event, historically the VXN has spiked a lot harder. We can use variance here to identify if a spike in the VXN exceeds our threshold for a normal market pullback, and potentially avoid entering trades for a period of time (I.e. maybe we don’t buy that dip).
Does this actually work?
In backtesting, this cut the drawdown of my index reversion strategies in half. It also cuts out some good trades (because high investor fear isn’t always indicative of a regime change or black swan event). But, I’ll happily lose out on some good trades in exchange for half the drawdown. Lets look at some examples of periods of time that trades could have been avoided using this strategy/indicator:
Example 1 – With the Volatility Warning Indicator, the mean reversion strategy could have avoided repeatedly buying this pullback that led to this asset losing over 75% of its value:
Example 2 - June 2018 to June 2019 - With the Volatility Warning Indicator, the drawdown during this period reduces from 22% to 11%, and the overall returns increase from -8% to +3%
How do you use this indicator?
This indicator determines the variance of VXN against a long term mean. If the variance of the VXN spikes over an input threshold, the indicator goes up. The indicator will remain up for a defined period of bars/time after the variance returns below the threshold. I have included default values I’ve found to be significant for a short-term mean-reversion strategy, but your inputs might depend on your risk tolerance and strategy time-horizon. The default values are for 1hr VXN data/charts. It will pull in variance data for the VXN regardless of which chart the indicator is applied to.
Disclaimer: Open-source scripts I publish in the community are largely meant to spark ideas or be used as building blocks for part of a more robust trade management strategy. If you would like to implement a version of any script, I would recommend making significant additions/modifications to the strategy & risk management functions. If you don’t know how to program in Pine, then hire a Pine-coder. We can help!
S&P500 VIX Volatility Warning IndicatorToday I am sharing with the community a volatility indicator that can help you or your algorithms avoid black swan events. Variance is most commonly used in statistics to derive standard deviation (with its square root). It does have another practical application, and that is to identify outliers in a sample of data. Variance in statistics is defined as the squared difference between a value and its mean. Calculating that squared difference means that the farther away the value is from the mean, the more the variance will grow (exponentially). This exponential difference makes outliers in the variance data more apparent.
Why does this matter?
There are assets or indices that exist in the stock market that might make us adjust our trading strategy if they are behaving in an unusual way. In some instances, we can use variance to identify that behavior and inform our strategy.
Is that really possible?
Let’s look at the relationship between VIX and the S&P500 as an example. If you trade an S&P500 index with a mean reversion strategy or algorithm, you know that they typically do best in times of volatility. These strategies essentially attempt to “call bottom” on a pullback. Their downside is that sometimes a pullback turns into a regime change, or a black swan event. The other downside is that there is no logical tight stop that actually increases their performance, so when they lose they tend to lose big.
So that begs the question, how might one quantitatively identify if this dip could turn into a regime change or black swan event?
The CBOE Volatility Index (VIX) uses options data to identify, on a large scale, what investors overall expect the market to do in the near future. The Volatility Index spikes in times of uncertainty and when investors expect the market to go down. However, during a black swan event, the VIX spikes a lot harder. We can use variance here to identify if a spike in the VIX exceeds our threshold for a normal market pullback, and potentially avoid entering trades for a period of time (I.e. maybe we don’t buy that dip).
Does this actually work?
In backtesting, this cut the drawdown of my index reversion strategies in half. It also cuts out some good trades (because high investor fear isn’t always indicative of a regime change or black swan event). But, I’ll happily lose out on some good trades in exchange for half the drawdown. Lets look at some examples of periods of time that trades could have been avoided using this strategy/indicator:
Example 1 – With the Volatility Warning Indicator, the mean reversion strategy could have avoided repeatedly buying this pullback that led to SPXL losing over 75% of its value:
Example 2 - June 2018 to June 2019 - With the Volatility Warning Indicator, the drawdown during this period reduces from 22% to 11%, and the overall returns increase from -8% to +3%
How do you use this indicator?
This indicator determines the variance of the VIX against a long term mean. If the variance of the VIX spikes over an input threshold, the indicator goes up. The indicator will remain up for a defined period of bars/time after the variance returns below the threshold. I have included default values I’ve found to be significant for a short-term mean-reversion strategy, but your inputs might depend on your risk tolerance and strategy time-horizon. The default values are for 1hr VIX data. It will pull in variance data for the VIX regardless of which chart the indicator is applied to.
Disclaimer : Open-source scripts I publish in the community are largely meant to spark ideas or be used as building blocks for part of a more robust trade management strategy. If you would like to implement a version of any script, I would recommend making significant additions/modifications to the strategy & risk management functions. If you don’t know how to program in Pine, then hire a Pine-coder. We can help!
Deviation BandsThis indicator plots the 1, 2 and 3 standard deviations from the mean as bands of color (hot and cold). Useful in identifying likely points of mean reversion.
Default mean is WMA 200 but can be SMA, EMA, VWMA, and VAWMA.
Calculating the standard deviation is done by first cleaning the data of outliers (configurable).
34 EMA BandsThis is quite a simple script, just plotting a 34EMA on high's and low's of candles. Appears to work wonders though, so here it is.
There is some //'d code which I haven't finished working on, but it looks to be quite similar to Bollinger Bands, just using different math rather than standard deviations from the mean.
The bands itself is pretty self explanatory, price likes to use it as resistance when under it, it can trade inside it and it can use the upper EMA as support when in a strong upward trend.
Augmented Dickey–Fuller (ADF) mean reversion testThe augmented Dickey-Fuller test (ADF) is a statistical test for the tendency of a price series sample to mean revert .
The current price of a mean-reverting series may tell us something about the next move (as opposed, for example, to a geometric Brownian motion). Thus, the ADF test allows us to spot market inefficiencies and potentially exploit this information in a trading strategy.
Mathematically, the mean reversion property means that the price change in the next time period is proportional to the difference between the average price and the current price. The purpose of the ADF test is to check if this proportionality constant is zero. Accordingly, the ADF test statistic is defined as the estimated proportionality constant divided by the corresponding standard error.
In this script, the ADF test is applied in a rolling window with a user-defined lookback length. The calculated values of the ADF test statistic are plotted as a time series. The more negative the test statistic, the stronger the rejection of the hypothesis that there is no mean reversion. If the calculated test statistic is less than the critical value calculated at a certain confidence level (90%, 95%, or 99%), then the hypothesis of a mean reversion is accepted (strictly speaking, the opposite hypothesis is rejected).
Input parameters:
Source - The source of the time series being tested.
Length - The number of points in the rolling lookback window. The larger sample length makes the ADF test results more reliable.
Maximum lag - The maximum lag included in the test, that defines the order of an autoregressive process being implied in the model. Generally, a non-zero lag allows taking into account the serial correlation of price changes. When dealing with price data, a good starting point is lag 0 or lag 1.
Confidence level - The probability level at which the critical value of the ADF test statistic is calculated. If the test statistic is below the critical value, it is concluded that the sample of the price series is mean-reverting. Confidence level is calculated based on MacKinnon (2010) .
Show Infobox - If True, the results calculated for the last price bar are displayed in a table on the left.
More formal background:
Formally, the ADF test is a test for a unit root in an autoregressive process. The model implemented in this script involves a non-zero constant and zero time trend. The zero lag corresponds to the simple case of the AR(1) process, while higher order autoregressive processes AR(p) can be approached by setting the maximum lag of p. The null hypothesis is that there is a unit root, with the alternative that there is no unit root. The presence of unit roots in an autoregressive time series is characteristic for a non-stationary process. Thus, if there is no unit root, the time series sample can be concluded to be stationary, i.e., manifesting the mean-reverting property.
A few more comments:
It should be noted that the ADF test tells us only about the properties of the price series now and in the past. It does not directly say whether the mean-reverting behavior will retain in the future.
The ADF test results don't directly reveal the direction of the next price move. It only tells wether or not a mean-reverting trading strategy can be potentially applicable at the given moment of time.
The ADF test is related to another statistical test, the Hurst exponent. The latter is available on TradingView as implemented by balipour , QuantNomad and DonovanWall .
The ADF test statistics is a negative number. However, it can take positive values, which usually corresponds to trending markets (even though there is no statistical test for this case).
Rigorously, the hypothesis about the mean reversion is accepted at a given confidence level when the value of the test statistic is below the critical value. However, for practical trading applications, the values which are low enough - but still a bit higher than the critical one - can be still used in making decisions.
Examples:
The VIX volatility index is known to exhibit mean reversion properties (volatility spikes tend to fade out quickly). Accordingly, the statistics of the ADF test tend to stay below the critical value of 90% for long time periods.
The opposite case is presented by BTCUSD. During the same time range, the bitcoin price showed strong momentum - the moves away from the mean did not follow by the counter-move immediately, even vice versa. This is reflected by the ADF test statistic that consistently stayed above the critical value (and even above 0). Thus, using a mean reversion strategy would likely lead to losses.
Percentile Rank [racer8]The Percentile is a mathematical tool developed in the field of statistics. It determines how a value compares to a set of values.
There are many applications for this like ...
... determining your rank in your college math class
... your rank in terms of height, weight, economic status, etc.
... determining the 3-month percentile of the current stock price (which is what this indicator performs)
This indicator calculates the percentile rank for the current stock price for n periods.
For example, if the stock's current price is above 80% of the previous stock's prices over a 100-period span, then it has a percentile rank of 80.
For traders, this is extremely valuable information because it tells you if the current stock price is overbought or oversold.
If the stock's price is in the 95th percentile, then it is highly likely that it is OVERBOUGHT, and that it will revert back to the mean price.
Helplful TIP: I recommend that you set the indicator to look back over at LEAST 100 periods for accuracy!
Thanks for reading! 👍
Volume Breakout (ValueRay)Easy visuals on, if volume is way over average. Good for Mean Reverting. Higher Volume tends to higher breakout chances.
Please whisper me for for ideas how to make this better. Its a very simple script, but got some alpha. If you know how to improve, let me know and i will code it into.
Bitcoin - CME Futures Friday Close
This indicator displays the weekly Friday closing price according to the CME trading hours (Friday 4pm CT).
A horizontal line is displayed until the CME opens again on Sunday 5pm CT.
This indicator is based on the thesis, that during the weekend the Bitcoin price tends to mean reverse to the CME closing price of the prior Friday. The level can also act as support/resistance. This indicator gives a visualization of this key level for the relevant time window.
Furthermore the indicator helps to easily identify, if there is an up or down gap in the CME Bitcoin contract.
Roc Mean Reversion (ValueRay)This Indicator shows the Absolute Rate of Change in correlation to its Moving Average.
Values over 3 (gray dotted line) can savely be considered as a breakout; values over 4.5 got a high mean-reverting chance (red dotted line).
This Indicator can be used in all timeframes, however, i recommend to use it <30m, when you want search for meaningful Mean-Reverting Signals.
Please like, share and subscribe. With your love, im encouraged to write and publish more Indicators.
Percentile - Price vs FundamentalsThis is done in the same lines of below scripts
Drawdown-Price-vs-Fundamentals
Drawdown-Range
Instead of using drawdown, here we are only plotting percentile of drawdown. Also added few more fundamental stats to the indicator. Also using part of the code from Random-Color-Generator/ to automatically generate colors. This in turn uses code from @RicardoSantos for convering color based on HSL to RGB
This is how the study can be used:
Study plots percentile of price and each of the listed fundamentals based on history. History can be chose All time or particular window. If any fundamental or price is near 100 - which means it is nearer to its peak. And if something is near its bottom, it is nearer to its 0th percentile.
Price of the stock is considered undervalued based on historical levels when it is below most of the fundamentals. Price is considered overvalued based on historical levels when it is above all the fundamentals. Please note, being undervalued does not guarantee immediate mean reversion. Stocks can stay undervalued for prolonged time and can go further down. Similarly overvalued stock can stay overvalued for prolonged time before correcting itself or justifying the position. Hence, further discretion needs to be used while using this study.
Few examples:
AMZN seems to be trading in range and so are the fundamentals:
MSFT at peak along with half of the fundamentals. But, debt levels are going up along with margins reducing.
LPX is trading at 15% discount whereas most of the fundamentals are at the peak.
FLGT price seems to have gone down further whereas fundamentals look pretty healthy.
MA Visualizer™TradeChartist MA Visualizer is a Moving Average based indicator aimed to visualize price action in relation to the Moving Average in a visually engaging way.
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█ MA Visualizer Features
11 different Moving Averages to choose from the settings to visualize based on MA Visualizer Length (Default - 55 period SMA).
2 Smoothing options (default - 0, 0 uses MA length as Smoothing factor, 1 uses no Smoothing).
4 colour themes to choose from and option to adjust Visualizer Vibrance.
█ Example Charts
1. 1hr chart of OANDA:XAUUSD using 55 period WMA.
2. 15m chart of OANDA:EURUSD using 144 period Tillson T3 MA.
3. 4 hr chart of OANDA:US30USD using 55 period SMMA.
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Best Practice: Test with different settings first using Paper Trades before trading with real money
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HYE Combo Market [Indicator] (Vwap Mean Reversion+Trend Hunter)Indicator version of the strategy:
* Alerts added.
TIPS AND WARNINGS
1-) The standard settings of this combo script is designed and tested with daily timeframe. For lower timeframes, you should change the indicator settings and find the best value for yourself.
2-) Only the mean vwap line is displayed on the graph. For a detailed view, you can delete the "//" marks from the plot codes in the script code.
3-) This is an indicator for educational and experimental purposes. It cannot be considered as investment advice. You should be careful and make your own risk assessment when opening real market trades using this indicator.
HYE Mean Reversion SMAIndicator version of the strategy "HYE Mean Reversion SMA "
"Long", "Short", "Exit Long" and "Exit Short" alarms added.
Use with "Once Per Bar Close".
GMS: GW-VWAPAlright, as per usual with these, I end up adapting an existing indicator to what I want to accomplish. So this is based off the built in VWAP indicator. I added in the gummy worm to easily identify the trend, as well as the related bands to identify potential areas to either reverse position or to trim an existing one.
The middle part of the bands are the gummy worm version of VWAP. It is the VWAP using the high and another VWAP using the low. The black line is HL2 VWAP (technically 3 VWAPs).
The bands follow what I was mentioning above. So the outer most part of the bands are the high & low VWAP (with the same multiplier) and the inner bands are the HL2 VWAP.
Of course you can set whatever input source you want for these. The default is how I use it. If you want to get rid of the bar color just go to the indicator settings and un-select it at the bottom.
Source code is open so feel free to poke around.
Hope this helps,
Andre
Peak Reversal v2This is a brand new version of my Peak Reversal indicator. As with the older version, the idea behind this indicator is simple: identify potential price reversal areas, and identifying markets which are trending. In this new version I focused on improving on the old concept, but introduced a bunch of features heavily inspired by Adam Grimes' ideas from The Art and Science of Trading. (I also blatantly stole the way he colors candles outside of the bands. Sorry.)
As you can see below this indicator gives traders a plethora of tools to judge whether a market is trending, and might be mean reverting soon.
Follow me, join my group, like the script. You know the drill.
Basic functions:
You have a triplet of Keltner (ATR-based) bands in Peak Reversal. They are defined by a multiplier and an EMA, which is referred to as "the mean". There's a tight, normal, and an extreme band. The multiplier defines how far apart your bands are. By default the indicator uses 1.125, 2.25, and 3.375. The tight band is off by default, but you can turn it on in the options. The mean is also off by default. This is more a personal preference thing for me, because I happen to use a different indicator to show a couple of moving averages.
Band crosses:
Peak Reversal can indicate whenever price crosses one of the bands. This can help traders identify points where a mean reversal play could be an option. Triangles indicate these crosses. New in version 2 is the ability to choose which of the bands to use to show these crosses. If you are more of an aggressive trader, you might find it better to show tight band crosses. If you are looking for more extreme market conditions, then choose extreme. The default is "normal".
Free bars:
Indicating free bars is also a concept from the book. A "free bar" is one which stands "freely" above the bands, which means its low price is completely outside of the bands. It can be argued that a freely standing bar is an even more extreme mean deviation, than just a band cross. Traders can gain an additional advantage studying the markets this way. Free bars are not shown by default, when on, a star shape on the candles indicates free bars. Both band crosses and free bars can be shown at the same time, but there might be overlap.
Deviations:
Also based on a concept from The Art and Science of Trading, is an indication of price "deviations". You will notice that when a candle "touches" a band (high and close above band), its colored. The idea here is to show traders when a market is in motion, but also when a mean reversal might be coming next. To accomplish this, the more colors deviate, the darker the color is. The idea here is also simple, the more price deviates off the mean, the likelier it is to return to it. This uses three different shades to show these deviations. 1-2 is one shade, 3-4 another, and upwards of 5 there's only the darkest shade. I didn't make extensive studies, which color for how many candles would be appropriate to use, but I do believe it doesn't matter that much in usage. It's clear what traders gain from using this information: more deviation, the likelier a snapback becomes.
Advanced mode:
Last but not least, I decided to add an advanced mode for advanced traders. This does nothing more than flip all colors and shapes upside down. Everything that is red, becomes green. The idea is where some traders say "buy low, sell high" (standard mode), other traders might say "buy high, sell higher" (advanced mode). See for yourself, which one you like better.
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.
ema exhaustion (exa)The exa is an oscillator that combines fisher transform with distance from moving average and it is based on a theory that exhaustion can be derived from how far price is able to extend from a moving average, on average.
The fisher transform converts price into a gaussian normal distribution, also known as a bell curve {1}. A normal distribution is a type of probability distribution for a real-valued random variable {2}. Applying this method to the price of an asset can help to identify probabilities, but it will never identify certainties.
‘exa’ is an abbreviation for ema exhaustion. It can be used to identify when price is probable to revert to the mean but I prefer using it to confirm entries that are signaled following a reversion to the mean (aka buying the dip in bull markets). When price gets oversold into support, in a bull trend, then that can provide a good opportunity to enter long. However that isn’t necessarily the case when the same metrics indicate oversold conditions in a bear trend. In this situation the exa is best suited for identifying profit taking opportunities on shorts.
The default settings are a 9 lookback period and a 50 ema. By default signals will be derived from how far price is from the 50 ema relative to the probable distribution of the last 9 periods. If the exa is above 2, or below -2, then the price is in the 80th percentile of the prior 9 candles. Being outside of 3, or -3, represents the 90th percentile and 4, or -4, represents the 95th percentile.
Those ranges will never indicate a necessity of reverting to the mean, but they will indicate a higher and higher probability. I prefer to use this oscillator in combination with an indicator(s) that identifies the trend. When the oscillator reaches -2 in a bull trend then it can confirm long entry signals, whereas if it reaches +2 in a bull trend then it can be used to confirm signals to take profit.
Crossovers are especially significant because they indicate a shift in the tide. When the exa reaches 2 without crossing over then it is very much in a position to move to 3 or 4+. When it crosses above 2 then it is an indication that price is extended from the mean and exhausted.
This is certainly not a situation that implies price will revert to the mean, it simply provides confirmation.
The default settings are what I have been finding most effective personally, however that is mostly a function of the trend following tools that I use. The same principles should apply with all settings and I would encourage users to experiment with various lookback periods and emas.
{1} www.investopedia.com
{2} en.wikipedia.org
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
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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.
Coefficient of Variation - EMA and SMA StDevYet another way to try and measure volatility. An alternative to using ATR is Standard Deviation, it can be used to measure volatility or what is also known as risk. SD measures how dispersed or far away the data is from the mean. It's commonly seen in risk management formulas or portfolio diversification formulas. The problem however is that the numbers that ATR and SD give off from one equity might not be relative to others or its own past. For example, SPY can give a large number despite not being as volatile as other equities while others being compared to can have smaller volatility numbers and still be more volatile looking.
A solution I thought of is to use percentages that are relatable to different equities. I found out another name for this idea comes from statistics and is known as coefficient of variation, also known as relative standard deviation. This helps see the volatility as a percentage and not just a number that only relates to what is being seen at the moment. I put in a border line on the zero level to see where zero is at but also to edit in case there is such a thing as a percentage number that can be too high or too low for volatility to be looked at if needed. The average and standard deviation formulas can use either simple moving average or exponential moving average.
Ark Crypto HeatlineThis is the 'on chart' indicator. See also "Ark Crypto Heatband" indicator for a side-by-side BTC view, without a re-scaled line.
The crypto landscape is largely dominated by BTC and characterised by cyclical stages with varying degrees of mean reversion.
To understand what stage of the cycle we are currently experiencing, it is useful to examine to what degree the current price has extended beyond the long term average that BTC has established. This is true even when analysing other crypto assets as BTC is the dominant force in the crypto asset class.
This indicator uses the 1400 period daily SMA , which is broadly the 200 period weekly SMA. This can be configured, but historically has represented a baseline to which BTC commonly returns.
The graph plots current price in terms of multiples of this long term average. Traditionally, at multiples beyond 10, BTC is considered overextended with a higher likelihood of trending towards the mean thereafter. Colors indicate the extent of price extension.
Where the indicator is applied to non BTCUSD pairs, a smoothed conversion is applied, seeking to superimpose the BTC long period SMA onto the current chart.
The indicator specifically references BTC by default on all charts, as it is designed to use BTC as general purpose indication of where crypto as a whole currently sits. Accordingly the indicator is only to be used on crypto charts.
For best results on BTC, using BNC:BLX will give the longest historical view.
