Standard Deviation Buy Sell Signals [UOI]The "Standard Deviation Buy Sell Signals" which is a Mean and VWAP Deviation Super Pack that includes many additional features is an advanced technical analysis tool designed to assist traders in making well-informed decisions in the financial markets. It incorporates various functions and calculations to provide a comprehensive analysis of price movements, trends, and potential trading opportunities in different timeframes. The Super Pack combines elements of volume-weighted average price (VWAP), mean calculation on multiple time frames, standard deviation signals and bands, overbought and oversold signals, measures of central tendency, and multiple time frame calculations of mean reversion. A truly unique indicator.
Here is the details of the supper pack and what is included:
1. VWAP (Volume-Weighted Average Price): The Mean and VWAP Deviation Super Pack includes VWAP, which calculates the average price of a security weighted by its trading volume. This helps traders identify the average price at which a significant amount of trading activity has occurred and can serve as a reference point for determining whether the current price is overvalued or undervalued.
2. Standard Deviation Signals and Bands: The Super Pack incorporates standard deviation signals and bands to measure the volatility of price movements. By calculating the standard deviation of price data, it identifies price levels that deviate significantly from the average, indicating potential overbought or oversold conditions. The standard deviation bands provide visual boundaries that help traders assess the likelihood of a price reversal or continuation. The bands are hidden to avoid too many lines but you can enable them in the setting. See image below:
3. Overbought and Oversold Signals: Using the standard deviation calculations, the Mean and VWAP Deviation Super Pack generates overbought and oversold signals. These signals indicate when a security's price has moved to an extreme level, suggesting a potential reversal or correction in the near future. Traders can use these signals to time their entries or exits in the market. You can change the RSI number in the setting to get more or less signals.
4. Measures of Central Tendency: The Super Pack incorporates measures of central tendency, such as the mean, median, or mode, to provide a sense of the average or typical price behavior. These measures help traders identify the prevailing trend or price direction and assess the likelihood of a trend continuation or reversal. This provide reassurance of whether price is too far from center in multiple time frames.
5. Multiple Time Frame Calculation of Mean Reversion: The Mean and VWAP Deviation Super Pack employs multiple time frame calculations to identify mean reversion opportunities. It compares the current price with the historical average price over different time periods, allowing traders to identify situations where the price has deviated significantly from its mean and is likely to revert back to its average value. This can be useful for swing trading or short-term trading strategies.
By combining these various functions, the Mean and VWAP Deviation Super Pack provides traders with a comprehensive analysis of price dynamics, trend strength, potential reversals, and mean reversion opportunities. It aids in making more informed trading decisions and improving overall trading performance.
Why is this super pack indicator an essential trading strategy for every trader:
Standard deviation and mean reversion are valuable tools for traders, especially when the market is in a ranging phase. A ranging market is characterized by price movements that oscillate between defined support and resistance levels, with no clear trend in either direction. In such market conditions, standard deviation and mean reversion strategies can be particularly effective. Here's why:
1. Standard Deviation: Standard deviation is a statistical measure that quantifies the volatility or dispersion of price data around its average. In a ranging market, where prices tend to fluctuate within a certain range, standard deviation can help identify overbought and oversold levels. When the price reaches the upper end of the range, the standard deviation bands widen, indicating higher volatility and a potential selling opportunity. Conversely, when the price reaches the lower end of the range, the bands narrow, suggesting lower volatility and a potential buying opportunity. Traders can use these signals to anticipate price reversals and take advantage of the predictable nature of ranging markets.
2. Mean Reversion: Mean reversion is a concept that suggests prices tend to move back toward their average or mean over time. In a ranging market, where prices repeatedly move between support and resistance levels, mean reversion strategies can be highly effective. By identifying when the price has deviated significantly from its mean, traders can anticipate a potential reversal back toward the average. When the price reaches extreme levels, indicating overbought or oversold conditions, traders can enter positions in the opposite direction, expecting the price to revert to its mean. Mean reversion strategies can be implemented using various indicators, including Bollinger Bands, moving averages, or standard deviation bands.
3. Range Boundaries: In a ranging market, the upper and lower boundaries of the price range serve as reliable reference points for traders. Standard deviation and mean reversion strategies capitalize on the repetitive nature of price movements within these boundaries. Traders can set their entry and exit points based on the standard deviation bands or mean reversion signals to take advantage of price reversals near the range boundaries. By properly identifying and reacting to these levels, traders can profit from the price oscillations within the range.
4. Risk Management: Standard deviation and mean reversion strategies provide traders with clear entry and exit points, allowing for effective risk management. By placing stop-loss orders beyond the range boundaries or the standard deviation bands, traders can limit their potential losses if the price continues to move against their positions. Additionally, by taking profits near the opposite range boundary or when the price reverts back to the mean, traders can secure their gains and maintain a disciplined approach to trading.
Standard deviation and mean reversion strategies offer traders a systematic approach to capitalize on ranging markets. But the cherry on top is the overbought and oversold signals:
The concept of overbought and oversold levels is widely used in technical analysis to identify potential reversals in price trends. Typically, indicators like the Relative Strength Index (RSI) are employed to determine when an asset may be overbought or oversold. However, you have developed a unique approach by incorporating an interactive variable with RSI and Average True Range (ATR) to create a distinct overbought and oversold signal. Here's why this approach stands out:
1. Divergence: Your approach introduces a divergence concept by combining RSI and ATR. Traditionally, overbought and oversold signals rely solely on RSI readings. However, by considering the interaction between RSI and ATR, you bring a new dimension to these signals. The divergence occurs when the RSI indicates overbought conditions while simultaneously ATR crosses over into bearish territory, or when the RSI signals oversold conditions along with ATR crossing over into bullish territory. This divergence adds an extra layer of confirmation to the overbought and oversold signals.
2. Reduced False Signals: The incorporation of ATR in conjunction with RSI helps filter out false signals that may occur during trending market conditions or short squeezes. Trend days or periods of increased volatility can cause RSI to remain in overbought or oversold territory for an extended period, generating numerous signals that may not be reliable. By considering the crossing of ATR into bearish or bullish territory, your approach adds a dynamic element to the signal generation process. This interactive variable helps ensure that the overbought and oversold signals are not solely based on RSI getting hot, reducing the likelihood of false signals during trending or volatile periods.
3. Improved Timing: The interaction between RSI and ATR provides a more nuanced approach to timing overbought and oversold signals. By waiting for the ATR to confirm the RSI signal, you introduce an additional condition that enhances the precision of the timing. The bearish or bullish crossover of ATR serves as a confirmation that market conditions align with the overbought or oversold signal indicated by RSI. This combined approach allows for more accurate entry or exit points, increasing the potential profitability of trades.
4. Customization and Adaptability: By creating this interactive variable with RSI and ATR, you have developed a customizable approach that can be adapted to different trading styles and preferences. Traders can adjust the sensitivity of the signals by modifying the parameters of the RSI and ATR. This flexibility allows for a personalized trading experience and enables traders to align the signals with their specific risk tolerance and market conditions.
This approach to overbought and oversold signals utilizing RSI and ATR introduces a unique perspective to technical analysis. By incorporating divergence and interactive variables, you enhance the reliability of these signals while reducing false readings. This approach provides improved timing and adaptability, making it a valuable tool for traders seeking to identify potential reversals in price trends with greater accuracy and confidence.
HOW to avoid fake signals?
When it comes to trading with standard deviation as a strategy, it's important to note that on extreme trend days, this indicator may generate false signals. This occurs because standard deviation is primarily designed to measure volatility and deviations from the mean in a range-bound market. During strong trending periods, the price tends to move in one direction with minimal deviations, rendering the standard deviation less effective.
To avoid trading based solely on standard deviation during extreme trend days, it is advisable to incorporate additional indicators that can provide insights into the stock's trend or squeeze conditions. These indicators can help determine whether the market is experiencing a strong trend or a squeeze, allowing you to avoid false signals generated by standard deviation.
By utilizing complementary indicators such as trend-following indicators (e.g., moving averages, trendlines) or volatility indicators (e.g., Bollinger Bands), you can gain a more comprehensive understanding of the market environment. These indicators can help confirm whether the stock is in a trending phase or experiencing a squeeze, helping you avoid entering trades solely based on standard deviation during these extreme trend days.
In summary, while standard deviation is a valuable tool in range-bound markets, it may produce unreliable signals on extreme trend days. By incorporating other indicators that provide insights into the stock's trend or squeeze conditions, traders can better assess the market environment and avoid false signals generated by standard deviation during these periods. This approach enhances the overall effectiveness and accuracy of trading strategies, leading to more informed and profitable decision-making.
스탠다드 디비에이션 (볼래틸리티)
Ladder StDevThis indicator shows the upwards (green) and downward (red) volatility of the market. It is a standard deviation of the price but with a twist! For the upwards volatility , only the green candles are taken into account, and for the downwards only the red candles are.
Compared to my previous "Ladder ATR" indicator this a different approach to measure the the upwards and downwards volatility of the market by utilizing the standard deviation instead of the ATR. When both measure the volatility they have different "dynamics". Standard deviation increases the weight of larger values over smaller values. The ATR indicator is based on the average of absolute changes. So, if we apply the indicators on a daily chart , ATR considers intraday and between-day data, while the standard deviation calculation includes only daily returns (source price).
Inter-Exchanges Crypto Price Spread Deviation (Tartigradia)Measures the deviation of price metrics between various exchanges. It's a kind of realized volatility indicator, as the idea is that in times of high volatility (high emotions, fear, uncertainty), it's more likely that market inefficiencies will appear for the same asset between different market makers, ie, the price can temporarily differ a lot. This indicator will catch these instants of high differences between exchanges, even if they lasted only an instant (because we use high and low values).
Both standard deviation and median absolute deviation (more robust to outliers, ie, exchanges with a very different price from others won't influence the median absolute deviation, but the standard deviation yes).
Compared to other inter-exchanges spread indicators, this one offers two major features:
* The symbol automatically adapts to the symbol currently selected in user's chart. Hence, switching between tickers does not require the user to modify any option, everything is dynamically updated behind the scenes.
* It's easy to add more exchanges (requires some code editing because PineScript v5 does not allow dynamical request.security() calls).
Limitations/things to know:
* History is limited to what the ticker itself display. Ie, even if the exchanges specified in this indicator have more data than the ticker currently displayed in the user's chart, the indicator will show only a timeperiod as long as the chart.
* The indicator can manage multiple exchanges of different historical length (ie, some exchanges having more data going way earlier in the past than others), in which case they will simply be ignored from calculations when far back in the past. Hence, you should be aware that the further you go in the past, the less exchanges will have such data, and hence the less accurate the measures will be (because the deviation will be calculated from less sources than more recent bars). This is thanks to how the array.* math functions behave in case of na values, they simply skip them from calculations, contrary to math.* functions.
Fibonacci Volatility BandsFibonacci Volatility Bands are just an alternative that allows for more margin than regular Bollinger Bands. They are created based on an average of moving averages that use the Fibonacci sequence as lookback periods.
The use of the Fibonacci Volatility Bands is exactly the same as the Bollinger Bands.
TheATR™: Volatility Extremes (VolEx)Volatility is a crucial aspect of financial markets that is closely monitored by traders and investors alike. The traditional Average True Range (ATR) oscillator is a widely used technical indicator for measuring volatility in financial markets. However, there are limitations to the ATR oscillator, as it does not account for changing market conditions and may not adequately reflect extreme price movements. To address these limitations, TheATR has developed the VolEx indicator, which aims to identify extremes in the ATR oscillator by building dynamic thresholds using either a 'percentage' or 'standard deviation' based comparison with the value of the ATR.
The VolEx indicator utilizes a dynamic approach to measure volatility by considering the current level of the ATR oscillator relative to the dynamically generated thresholds. The dynamic thresholds are calculated based on the current ATR value and the chosen method of comparison (either 'percentage' or 'standard deviation'). If the ATR value exceeds the upper dynamic threshold, the market is experiencing high volatility, while a value below the lower dynamic threshold indicates low volatility.
The VolEx indicator offers several advantages over traditional volatility indicators, such as the ATR oscillator. First, it takes into account the changing market conditions and adjusts the thresholds accordingly. Second, it offers flexibility in the choice of the comparison method, allowing traders to tailor the indicator to their specific trading strategies. Finally, it provides clear signals for identifying extremes in volatility, which can be used to inform trading decisions.
In summary, the VolEx indicator developed by TheATR is a dynamic and flexible technical indicator that offers a robust approach to measuring volatility in financial markets. By utilizing dynamic thresholds and allowing for different comparison methods, the VolEx indicator provides a valuable tool for traders and investors seeking to identify extremes in market volatility..
NOTE: It is important to note that volatility, as measured by the VolEx indicator, does not provide any directional bias for the market movement. Rather, it simply indicates the degree to which the market is moving, regardless of direction. Traders and investors must use other technical or fundamental analysis tools to determine the direction of the market and make informed trading decisions based on their individual strategies and risk tolerance.
Rekt Edge Reversion BandRekt Edge Reversion band is a technical indicator that utilizes a combination of moving averages and standard deviations to determine optimal entry and exit points in the market. By comparing the current price to its moving average, the indicator identifies potential trends and determines how you can position around them by plotting buy/sell signals and two channels based on user input parameters. The user can choose between Simple Moving Average ( SMA ) or Exponential Moving Average ( EMA ) and select the moving average period, the unit of separation, the multiples of the unit, and other important parameters. The indicator's inputs can be adjusted to suit different trading styles, and it can be used on any time frame. The indicator can be used to identify potential trend reversals or breakouts (or breakdowns) when the price moves outside of the channels. The indicators potential use cases include identifying overbought or oversold conditions. With its ability to provide a clear signal on when to enter and exit a trade, this indicator is a popular tool among traders looking to make more informed and profitable trading decisions. This indicator can also be used in conjunction with other technical analysis tools to confirm or invalidate trading signals.
Click VWAP Anchored with Standard Devation BandsSimply use it by clicking on your chart on the places you find important to determine whether you entries or exits look strong or weak.
MeanReversion by VolatilityMean reversion is a financial term for the assumption that an asset will return to its mean value.
This indicator calculate the volatility of an asset over a period of time and show the values of logRerturn, mean and standart deviations.
The default time period for volatility calculation is 252 bars at a "Daily" chart. At a "Daily" chart 252 bar means one trading-year.
See also:
MeanReversion by Logarithmic Returns
MeanReversion by Logarithmic ReturnsMean reversion is a financial term for the assumption that an asset will return to its mean value.
This indicator calculate the logarithmic returns (logReturn) of an asset over a period of time and show the values of logRerturn, mean and standart deviations.
The default time period for logReturn calculation is 252 bars at a "Daily" chart. At a "Daily" chart 252 bar means one trading-year.
See also:
MeanReversion by Volatility
Slope NormalizerBrief:
This oscillator style indicator takes another indicator as its source and measures the change over time (the slope). It then isolates the positive slope values from the negative slope values to determine a 'normal' slope value for each.
** A 'normal' value of 1.0 is determined by the average slope plus the standard deviation of that slope.
The Scale
This indicator is not perfectly linear. The values are interpolated differently from 0.0 - 1.0 than values greater than 1.0.
From values 0.0 to 1.0 (positive or negative): it means that the value of the slope is less than 'normal' **.
Any value above 1.0 means the current slope is greater than 'normal' **.
A value of 2.0 means the value is the average plus 2x the standard deviation.
A value of 3.0 means the value is the average plus 3x the standard deviation.
A value greater than 4.0 means the value is greater than the average plus 4x the standard deviation.
Because the slope value is normalized, the meaning of these values can remain generally the same for different symbols.
Potential Usage Examples/b]
Using this in conjunction with an SMA or WMA may indicate a change in trend, or a change in trend-strength.
Any values greater than 4 indicate a very strong (and unusual) trend that may not likely be sustainable.
Any values cycling between +1.0 and -1.0 may mean indecision.
A value that is decreasing below 0.5 may predict a change in trend (slope may soon invert).
VolatilityCone by ImpliedVolatilityThis volatility cone draws the implied volatility as standard deviations from a measurement date.
For best results set measurement date to high volume bars.
How to use:
1) Select VolatilityCone from Indicators
2) Click to the chart to set the measurement date
3) Determine the impliedvolatility for the measurement date of your symbol
e.g.
For S&P500 use VIX value at measurement date for implied volatility
Modified Bollinger BandsThis script has been distributed for learning purposes.
A particular kind of price envelope is "Bollinger Bands" indicator. Upper and lower price range levels are determined by price envelopes. By default, Bollinger Bands are plotted in Tradingview as envelopes at a standard deviation level above and below the price's simple moving average (SMA). I attempted to modify the indicator in this version by adding several kinds of moving averages first. The key feature is that standard deviation should be modified. in Tradingview, SMA calculates the standard deviation. The allocated moving average should be used to calculate the std function when the base line is changed.
Adaptive VWAP Stdev BandsIntroduction
Heyo, here are some adaptive VWAP Standard Deviation Bands with nice colors.
I used Ehlers dominant cycle theories and ZLSMA smoothing to create this indicator.
You can choose between different algorithms to determine the dominant cycle and this will be used as reset period.
Everytime bar_index can be divided through the dominant cycle length and the result is zero VWAP resets if have chosen an adaptive mode in the settings.
The other reset event you can use is just a simple time-based event, e.g. reset every day.
Usage
I think people buy/sell when it reaches extreme zones.
Enjoy!
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Credits to:
@SandroTurriate - VWAP Stdev Bands
@blackcat1402 - Dominant Cycle Analysis
@DasanC - Dominant Cycle Analysis
@veryfid - ZLSMA
(Sry, too lazy for linking)
I took parts of their code. Ty guys for your work! Just awesome.
Beta ScreenerThis script allows you to screen up to 38 symbols for their beta. It also allows you to compare the list to not only SPY but also CRYPTO10! Features include custom time frame and custom colors.
Here is a refresher on what beta is:
Beta (β) is a measure of the volatility—or systematic risk—of a security or portfolio compared to the market as a whole (usually the S&P 500 ). Stocks with betas higher than 1.0 can be interpreted as more volatile than the S&P 500 .
Beta is used in the capital asset pricing model (CAPM), which describes the relationship between systematic risk and expected return for assets (usually stocks). CAPM is widely used as a method for pricing risky securities and for generating estimates of the expected returns of assets, considering both the risk of those assets and the cost of capital.
How Beta Works
A beta coefficient can measure the volatility of an individual stock compared to the systematic risk of the entire market. In statistical terms, beta represents the slope of the line through a regression of data points. In finance, each of these data points represents an individual stock's returns against those of the market as a whole.
Beta effectively describes the activity of a security's returns as it responds to swings in the market. A security's beta is calculated by dividing the product of the covariance of the security's returns and the market's returns by the variance of the market's returns over a specified period.
cov (a,b)/var(b)
Variety N-Tuple Moving Averages w/ Variety Stepping [Loxx]Variety N-Tuple Moving Averages w/ Variety Stepping is a moving average indicator that allows you to create 1- 30 tuple moving average types; i.e., Double-MA, Triple-MA, Quadruple-MA, Quintuple-MA, ... N-tuple-MA. This version contains 2 different moving average types. For example, using "50" as the depth will give you Quinquagintuple Moving Average. If you'd like to find the name of the moving average type you create with the depth input with this indicator, you can find a list of tuples here: Tuples extrapolated
Due to the coding required to adapt a moving average to fit into this indicator, additional moving average types will be added as they are created to fit into this unique use case. Since this is a work in process, there will be many future updates of this indicator. For now, you can choose from either EMA or RMA.
This indicator is also considered one of the top 10 forex indicators. See details here: forex-station.com
Additionally, this indicator is a computationally faster, more streamlined version of the following indicators with the addition of 6 stepping functions and 6 different bands/channels types.
STD-Stepped, Variety N-Tuple Moving Averages
STD-Stepped, Variety N-Tuple Moving Averages is the standard deviation stepped/filtered indicator of the following indicator
Last but not least, a big shoutout to @lejmer for his help in formulating a looping solution for this streamlined version. this indicator is speedy even at 50 orders deep. You can find his scripts here: www.tradingview.com
How this works
Step 1: Run factorial calculation on the depth value,
Step 2: Calculate weights of nested moving averages
factorial(depth) / (factorial(depth - k) * factorial(k); where depth is the depth and k is the weight position
Examples of coefficient outputs:
6 Depth: 6 15 20 15 6
7 Depth: 7 21 35 35 21 7
8 Depth: 8 28 56 70 56 28 8
9 Depth: 9 36 34 84 126 126 84 36 9
10 Depth: 10 45 120 210 252 210 120 45 10
11 Depth: 11 55 165 330 462 462 330 165 55 11
12 Depth: 12 66 220 495 792 924 792 495 220 66 12
13 Depth: 13 78 286 715 1287 1716 1716 1287 715 286 78 13
Step 3: Apply coefficient to each moving average
For QEMA, which is 5 depth EMA , the calculation is as follows
ema1 = ta. ema ( src , length)
ema2 = ta. ema (ema1, length)
ema3 = ta. ema (ema2, length)
ema4 = ta. ema (ema3, length)
ema5 = ta. ema (ema4, length)
In this new streamlined version, these MA calculations are packed into an array inside loop so Pine doesn't have to keep all possible series information in memory. This is handled with the following code:
temp = array.get(workarr, k + 1) + alpha * (array.get(workarr, k) - array.get(workarr, k + 1))
array.set(workarr, k + 1, temp)
After we pack the array, we apply the coefficients to derive the NTMA:
qema = 5 * ema1 - 10 * ema2 + 10 * ema3 - 5 * ema4 + ema5
Stepping calculations
First off, you can filter by both price and/or MA output. Both price and MA output can be filtered/stepped in their own way. You'll see two selectors in the input settings. Default is ATR ATR. Here's how stepping works in simple terms: if the price/MA output doesn't move by X deviations, then revert to the price/MA output one bar back.
ATR
The average true range (ATR) is a technical analysis indicator, introduced by market technician J. Welles Wilder Jr. in his book New Concepts in Technical Trading Systems, that measures market volatility by decomposing the entire range of an asset price for that period.
Standard Deviation
Standard deviation is a statistic that measures the dispersion of a dataset relative to its mean and is calculated as the square root of the variance. The standard deviation is calculated as the square root of variance by determining each data point's deviation relative to the mean. If the data points are further from the mean, there is a higher deviation within the data set; thus, the more spread out the data, the higher the standard deviation.
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.
See how this compares to Standard Devaition here:
Adaptive Deviation
Median Absolute Deviation
The median absolute deviation is a measure of statistical dispersion. Moreover, the MAD is a robust statistic, being more resilient to outliers in a data set than the standard deviation. In the standard deviation, the distances from the mean are squared, so large deviations are weighted more heavily, and thus outliers can heavily influence it. In the MAD, the deviations of a small number of outliers are irrelevant.
Because the MAD is a more robust estimator of scale than the sample variance or standard deviation, it works better with distributions without a mean or variance, such as the Cauchy distribution.
For this indicator, I used a manual recreation of the quantile function in Pine Script. This is so users have a full inside view into how this is calculated.
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 how this compares to ATR here:
ER-Adaptive ATR
Mean Absolute Deviation
The mean absolute deviation (MAD) is a measure of variability that indicates the average distance between observations and their mean. MAD uses the original units of the data, which simplifies interpretation. Larger values signify that the data points spread out further from the average. Conversely, lower values correspond to data points bunching closer to it. The mean absolute deviation is also known as the mean deviation and average absolute deviation.
This definition of the mean absolute deviation sounds similar to the standard deviation (SD). While both measure variability, they have different calculations. In recent years, some proponents of MAD have suggested that it replace the SD as the primary measure because it is a simpler concept that better fits real life.
For Pine Coders, this is equivalent of using ta.dev()
Bands/Channels
See the information above for how bands/channels are calculated. After the one of the above deviations is calculated, the channels are calculated as output +/- deviation * multiplier
Signals
Green is uptrend, red is downtrend, yellow "L" signal is Long, fuchsia "S" signal is short.
Included:
Alerts
Loxx's Expanded Source Types
Bar coloring
Signals
6 bands/channels types
6 stepping types
Related indicators
3-Pole Super Smoother w/ EMA-Deviation-Corrected Stepping
STD-Stepped Fast Cosine Transform Moving Average
ATR-Stepped PDF MA
StDev BandsThis is a "bands"-type indicator. It was developed out of my Sharpe Ratio indicator . It uses the standard deviation of returns as basis for drawing the bands. I'm going to update this indicator as the other indicator evolves. Please be sure you know how to calculate Sharpe Ratio and check out the Sharpe Ratio indicator as well. This will help you understand the purpose of this indicator a bit more.
As a very short introduction. Many investors use the standard deviation of returns as risk measurement . I admit the defaults of this indicator aren't perfect. Normally investors use the standard deviation over a 1 year period. Traditional finance uses 265 days, and because crypto never sleeps, we could use 365. I defaulted it to 20.
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
STD-Filtered, ATR-Adaptive Laguerre Filter [Loxx]STD-Filtered, ATR-Adaptive Laguerre Filter is a standard Laguerre Filter that is first made ATR-adaptive and the passed through a standard deviation filter. This helps reduce noise and refine the output signal. Can apply the standard deviation filter to the price, signal, both or neither.
What is the Laguerre Filter?
The Laguerre RSI indicator created by John F. Ehlers is described in his book "Cybernetic Analysis for Stocks and Futures". The Laguerre Filter is a smoothing filter which is based on Laguerre polynomials. The filter requires the current price, three prior prices, a user defined factor called Alpha to fill its calculation. Adjusting the Alpha coefficient is used to increase or decrease its lag and it's smoothness.
Included:
Bar coloring
Signals
Alerts
Loxx's Expanded Source Types
STD-Stepped, Variety N-Tuple Moving Averages [Loxx]STD-Stepped, Variety N-Tuple Moving Averages is the standard deviation stepped/filtered indicator of the following indicator
Variety N-Tuple Moving Averages is a moving average indicator that allows you to create 1- 30 tuple moving average types; i.e., Double-MA, Triple-MA, Quadruple-MA, Quintuple-MA, ... N-tuple-MA. This version contains 5 different moving average types including T3. A list of tuples can be found here if you'd like to name the order of the moving average by depth: Tuples extrapolated
STD-Stepped, You'll notice that this is a lot of code and could normally be packed into a single loop in order to extract the N-tuple MA, however due to Pine Script limitations and processing paradigm this is not possible ... yet.
If you choose the EMA option and select a depth of 2, this is the classic DEMA ; EMA with a depth of 3 is the classic TEMA , and so on and so forth this is to help you understand how this indicator works. This version of NTMA is restricted to a maximum depth of 30 or less. Normally this indicator would include 50 depths but I've cut this down to 30 to reduce indicator load time. In the future, I'll create an updated NTMA that allows for more depth levels.
This is considered one of the top ten indicators in forex. You can read more about it here: forex-station.com
How this works
Step 1: Run factorial calculation on the depth value,
Step 2: Calculate weights of nested moving averages
factorial(nemadepth) / (factorial(nemadepth - k) * factorial(k); where nemadepth is the depth and k is the weight position
Examples of coefficient outputs:
6 Depth: 6 15 20 15 6
7 Depth: 7 21 35 35 21 7
8 Depth: 8 28 56 70 56 28 8
9 Depth: 9 36 34 84 126 126 84 36 9
10 Depth: 10 45 120 210 252 210 120 45 10
11 Depth: 11 55 165 330 462 462 330 165 55 11
12 Depth: 12 66 220 495 792 924 792 495 220 66 12
13 Depth: 13 78 286 715 1287 1716 1716 1287 715 286 78 13
Step 3: Apply coefficient to each moving average
For QEMA, which is 5 depth EMA , the caculation is as follows
ema1 = ta. ema ( src , length)
ema2 = ta. ema (ema1, length)
ema3 = ta. ema (ema2, length)
ema4 = ta. ema (ema3, length)
ema5 = ta. ema (ema4, length)
qema = 5 * ema1 - 10 * ema2 + 10 * ema3 - 5 * ema4 + ema5
Included:
Alerts
Loxx's Expanded Source Types
Bar coloring
Signals
Standard deviation stepping
Damiani Volatmeter [loxx]I wasn't going to publish this since it's one my go to private indicators, but I decided to push this out anyway. This is a variation on Damiani Volatmeter to make it easier to understand what's going on. 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 indcators
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)
STD-Filtered, N-Pole Gaussian Filter [Loxx]This is a Gaussian Filter with Standard Deviation Filtering that works for orders (poles) higher than the usual 4 poles that was originally available in Ehlers Gaussian Filter formulas. Because of that, it is a sort of generalized Gaussian filter that can calculate arbitrary (order) pole Gaussian Filter and which makes it a sort of a unique indicator. For this implementation, the practical mathematical maximum is 15 poles after which the precision of calculation is useless--the coefficients for levels above 15 poles are so high that the precision loss actually means very little. Despite this maximal precision utility, I've left the upper bound of poles open-ended so you can try poles of order 15 and above yourself. The default is set to 5 poles which is 1 pole greater than the normal maximum of 4 poles.
The purpose of the standard deviation filter is to filter out noise by and by default it will filter 1 standard deviation. Adjust this number and the filter selections (price, both, GMA, none) to reduce the signal noise.
What is Ehlers Gaussian filter?
This filter can be used for smoothing. It rejects high frequencies (fast movements) better than an EMA and has lower lag. published by John F. Ehlers in "Rocket Science For Traders".
A Gaussian filter is one whose transfer response is described by the familiar Gaussian bell-shaped curve. In the case of low-pass filters, only the upper half of the curve describes the filter. The use of gaussian filters is a move toward achieving the dual goal of reducing lag and reducing the lag of high-frequency components relative to the lag of lower-frequency components.
A gaussian filter with...
One Pole: f = alpha*g + (1-alpha)f
Two Poles: f = alpha*2g + 2(1-alpha)f - (1-alpha)2f
Three Poles: f = alpha*3g + 3(1-alpha)f - 3(1-alpha)2f + (1-alpha)3f
Four Poles: f = alpha*4g + 4(1-alpha)f - 6(1-alpha)2f + 4(1-alpha)3f - (1-alpha)4f
and so on...
For an equivalent number of poles the lag of a Gaussian is about half the lag of a Butterworth filters: Lag = N*P / pi^2, where,
N is the number of poles, and
P is the critical period
Special initialization of filter stages ensures proper working in scans with as few bars as possible.
From Ehlers Book: "The first objective of using smoothers is to eliminate or reduce the undesired high-frequency components in the eprice data. Therefore these smoothers are called low-pass filters, and they all work by some form of averaging. Butterworth low-pass filters can do this job, but nothing comes for free. A higher degree of filtering is necessarily accompanied by a larger amount of lag. We have come to see that is a fact of life."
References John F. Ehlers: "Rocket Science For Traders, Digital Signal Processing Applications", Chapter 15: "Infinite Impulse Response Filters"
Included
Loxx's Expanded Source Types
Signals
Alerts
Bar coloring
Related indicators
STD-Filtered, Gaussian Moving Average (GMA)
STD-Filtered, Gaussian-Kernel-Weighted Moving Average
One-Sided Gaussian Filter w/ Channels
Fisher Transform w/ Dynamic Zones
R-sqrd Adapt. Fisher Transform w/ D. Zones & Divs .
STD-Filtered, Gaussian Moving Average (GMA) [Loxx]STD-Filtered, Gaussian Moving Average (GMA) is a 1-4 pole Ehlers Gaussian Filter with standard deviation filtering. This indicator should perform similar to Ehlers Fisher Transform.
The purpose of the standard deviation filter is to filter out noise by and by default it will filter 1 standard deviation. Adjust this number and the filter selections (price, both, GMA, none) to reduce the signal noise.
What is Ehlers Gaussian filter?
This filter can be used for smoothing. It rejects high frequencies (fast movements) better than an EMA and has lower lag. published by John F. Ehlers in "Rocket Science For Traders". First implemented in Wealth-Lab by Dr René Koch.
A Gaussian filter is one whose transfer response is described by the familiar Gaussian bell-shaped curve. In the case of low-pass filters, only the upper half of the curve describes the filter. The use of gaussian filters is a move toward achieving the dual goal of reducing lag and reducing the lag of high-frequency components relative to the lag of lower-frequency components.
A gaussian filter with...
one pole is equivalent to an EMA filter.
two poles is equivalent to EMA(EMA())
three poles is equivalent to EMA(EMA(EMA()))
and so on...
For an equivalent number of poles the lag of a Gaussian is about half the lag of a Butterworth filters: Lag = N * P / (2 * ¶2), where,
N is the number of poles, and
P is the critical period
Special initialization of filter stages ensures proper working in scans with as few bars as possible.
From Ehlers Book: "The first objective of using smoothers is to eliminate or reduce the undesired high-frequency components in the eprice data. Therefore these smoothers are called low-pass filters, and they all work by some form of averaging. Butterworth low-pass filtters can do this job, but nothing comes for free. A higher degree of filtering is necessarily accompanied by a larger amount of lag. We have come to see that is a fact of life."
References John F. Ehlers: "Rocket Science For Traders, Digital Signal Processing Applications", Chapter 15: "Infinite Impulse Response Filters"
Included
Loxx's Expanded Source Types
Signals
Alerts
Bar coloring
Related indicators
STD-Filtered, Gaussian-Kernel-Weighted Moving Average
One-Sided Gaussian Filter w/ Channels
Fisher Transform w/ Dynamic Zones
R-sqrd Adapt. Fisher Transform w/ D. Zones & Divs.
