Volatility of Volatility MA - LayeringProvides a volatility of volatility moving average to show trends in Vol of Vol. Meant to be used with Volume MA, and Volatility MA, layered on top of eachother.
Wyszukaj w skryptach "Volatility"
Volatility IndicatorThe Volatility Index measures the market volatility by plotting a smoothed average of the True Range.
Based on HPotter's idea (),
it returns an average of the TrueRange over a specific number of bars.
Here the result is passed through the Fisher's transform and normalized to 0/1-range.
This indicator may be used to identify stretches in the price movements, suitable for entry.
Volatility Across CoinsCompare the recent volatility of 8 cryptocurrencies, based on percentage change per candle.
Useful for volatility strategies to find the highest volatility coins over recent periods or to get an at-a-glance view of volatility correlations.
Options to change the resolution and find average % change per candle over user defined length.
Key:
BTC = Yellow/Gold
ETH = Purple
LTC = Gray
NEO = Green
IOTA = Light Blue
XMR = Orange
BCH = Red
Dash = Blue
Volatility SystemDespite its crude name, the volatility system strategy, described by Richard Bookstaber in 1984, follows the simple premise that once there is a big volatile movement, the market tends to follow it. Thus, it uses the ATR to measure the volatility, and issues orders when the current change of the closing price exceeds the threshold, calculated by the ATR times a configurable constant.
It yields good results for some very specific charts, as you can see. However, I doubt it would work in the current market conditions, since it has no stop loss and no take profit , and the current noise levels obliterate this strategy, especially in small time frames. Maybe their integration to the strategy would yield better results, so feel free to add your own modifications.
Volatility MeasureThis indicator is super simple, it gives you the average amount of volatility (IN PERCENT) in any given asset over any given timeframe over the last 100 periods. Adjustable. This is useful for gauging volatility, risk, reward, opportunity set, and more. It can help you set stop losses, tell how much risk you are actually taking based on historical measures, and how much of what you do is based on skill or luck. Enjoy!
Pholesolus
Simplest volatility bandsVolatility bands based on average candle percentage spread. Tested on BTCUSD charts only.
Based on the 68-95-99.7 rule, it seems that the spread, for daily and 4-H candles, follows a normal distribution: that means, around 85% of candles have a %-spread within sma(low/high, some_len) and sma(high/low, some_len) , and around 95% of candles within the pow2 of that range.
If you take the mean between the boundaries of the first %-spreads band, and calculate the 1.5 standard deviation of past some_len candles (I'm speaking from memory, it has been a while since I did them), the 1.5 standard deviation bands match similarly the %-spread bands, and around 85% of the candles are within these %-spread bands.
If you then take the pow2 of the bands, it will be similar to the 2 * std of the original bands, with around 95% of data within the pow2 bands.
You can take ema or other similar means with similar results, and the same for different lengths, but it seems that sma with a len of 14 is the more stable ones for both daily and 4-H, and taken other average calculations doesn't cause too many differences respect to the sma. I haven't tested too much for lower or higher timeframes.
With those %-spread bands, I multiple and divide those spreads to the open value of a new candle to get the two bands.
So, in short, you know that 85% of candles are within the closer bands, and around 95% of candles, around the bigger one. Once a new candle is born, the bands won't move (the bands are calculated from the previous candle, so the current candle's price movement doesn't move the band).
Going out the bands implies a sudden increase in volality, which usually causes rejection. They happen mostly at breakouts and ends of heavy trends. If a candle closes above the bigger band, you have probably got a breakout (a rejection rarely happens if the candle have already closed), although a breakout can happen without closing above the bands if volatility was already high.
If a trend is already stablished and is healthy, you won't probably see candles going out the bands, not even with a wick. When the trend is parabolic, and goes above the candle, the trend has probably ended, although the trend can be exhausted without going out the bands as well.
Heavy but not yet exhausted trends (specially recently started heavy downtrends), usually reach the bottom of the bigger bands during 4 o 5 contiguous candles (check visually looking at bitcoin history though, I'm speaking from memory).
So, the possibilities are multiple and you cannot use the bands to form a strategy, as usual. It can be comfortable enough psycologically for going to sleep, by moving your stop-loss to a point out of the bands in the opposite direction of your trade, and adjusting your position size accordingly; or just to check momentum looking at how close are the candle limits to the bands.
But, as usual, you are responsible of what you do with your money :)
Volatility Backtest The Volatility function measures the market volatility by plotting a
smoothed average of the True Range. It returns an average of the TrueRange
over a specific number of bars, giving higher weight to the TrueRange of
the most recent bar.
You can change long to short in the Input Settings
WARNING:
- For purpose educate only
- This script to change bars colors.
Volatility Strategy The Volatility function measures the market volatility by plotting a
smoothed average of the True Range. It returns an average of the TrueRange
over a specific number of bars, giving higher weight to the TrueRange of
the most recent bar.
WARNING:
- This script to change bars colors.
Volatility Shield ProConcept: Volatility Shield Pro is a multi-dimensional execution engine designed to filter high-probability entries by triangulating Trend, Institutional Volume, and Statistical Exhaustion.
Why this is original: Unlike standard indicators that look at price in a vacuum, this uses a Volume-Weighted ATR (VWATR) to distinguish between retail noise and institutional "Strikes." It integrates an ADR (Average Daily Range) Fuel gauge to prevent entries into exhausted moves, solving the common problem of buying the "top" of a trend.
Components & Logic:
Institutional Strike Engine: Uses VWATR normalized against a 50-period SMA to find momentum backed by volume.
ADR Fuel Gauge: Calculated by comparing current price travel to the 10-day ADR. A "State" of EXHAUSTED is triggered at 120% to warn of mean reversion.
HTF Anchor: A built-in Higher Time Frame EMA filter (default 4H) to ensure local trades align with the macro tide.
Live EDGE Tracker: A real-time backtesting module that calculates the win rate of the "Strike" signals on the current chart history using a 1.5:1 Reward-to-Risk ratio.
This combined tool addresses the three main reasons most trading systems fail by integrating higher-timeframe bias, daily range exhaustion, and volume confirmation into one framework:
Fighting the Tide (HTF Ribbon): Keeps traders aligned with the dominant higher-timeframe trend to avoid counter-trend entries.
Running Out of Gas (ADR Fuel): Measures a symbol’s average daily range to prevent chasing moves that have already reached their statistical limit.
Ghost Volume (RVOL/VWATR): Filters out low-quality, retail-driven activity by requiring institutional-level volume spikes before taking trades.
In essence, it combines trend alignment, range exhaustion detection, and real-volume filtering to eliminate the most common account-killing mistakes.
The "Triple-Threat" Trade Setup
This is the highest-probability setup the tool can produce. When these three things align, the "Edge" is at its peak:
The Anchor: HTF Ribbon is Bright Green.
The Local: Atlas Trend Bias is BULLISH and State is STRIKE.
The Value: ADR Fuel is Low (40-60%), meaning the stock has massive room to move before hitting daily resistance.
Volatility Calculator for Daily Top and Bottom RangeWith the usage of ATR, applied on the close of the daily candle, I am calculated the volatility channels for the TOP and BOTTOM
Based on this logic, we can estimate, with a huge confidence factor, where the prices are going to be compressed for the trading day.
Having said that, lets take a look at the data gathered among the most important financial markets:
SPX
TOP CROSSES : 2116
BOT CROSSES : 1954
Total Daily Candles : 18908
Occurance ratio = 0.215
NDX
TOP CROSSES : 1212
BOT CROSSES : 1183
Total Daily Candles : 9386
Occurance ratio = 0.255
DIA
TOP CROSSES : 759
BOT CROSSES : 769
Total Daily Candles : 6109
Occurance ratio = 0.25
DXY
TOP CROSSES : 1597
BOT CROSSES : 1598
Total Daily Candles : 13156
Occurance ratio = 0.243
DAX
TOP CROSSES : 1878
BOT CROSSES : 1848
Total Daily Candles : 13155
Occurance ratio = 0.283
BTC USD
TOP CROSSES : 416
BOT CROSSES : 417
Total Daily Candles : 4290
Occurance ratio = 0.194
ETH USD
TOP CROSSES : 247
BOT CROSSES : 268
Total Daily Candles : 2452
Occurance ratio = 0.21
EUR USD
TOP CROSSES : 820
BOT CROSSES : 805
Total Daily Candles : 7489
Occurance ratio = 0.217
GOLD
TOP CROSSES : 1722
BOT CROSSES : 1569
Total Daily Candles : 13747
Occurance ratio = 0.239
USOIL
TOP CROSSES : 1077
BOT CROSSES : 1089
Total Daily Candles : 10231
Occurance ratio = 0.212
US 10Y
TOP CROSSES : 1302
BOT CROSSES : 1365
Total Daily Candles : 9075
Occurance ratio = 0.294
Based on this, we can assume with a very high confidence ( 70-80%) that the market is going to stay, within the range created from the BOT and TOP ATR points.
Volatility Adapted Relative StrengthVARS uses a stock's ALPHA in comparison to the SPX to determine whether there is RS on an volatility adjusted basis.
Volatility Breakout StrategyThis is a strategy used by Larry R. Williams called Volatility Breakout.
By identifying a strong uptrend that exceeds 'a certain level' on a daily basis as a breakout signal, enter long position, take advantage of long at the the next day's open.
'a certain level (Entry Price)' is calculated by { close + 'k' * high -low }, and applied logarithmic calculation.
Stop loss level is calculated by half of the previous day's Low and Entry Price.
Strategy exit always at UTC+0.
And expressed day session with the background color.
Thanks and hope this helps you.
(kor)
이건 Larry R. Williams가 사용하는 변동성 돌파 전략입니다.
특정 수준을 넘는 강한 상승세를 돌파 신호로 파악하여 매수하고, 장시간이 종료될때에 빠르게 수익을 실현하는 전략입니다.
"특정 레벨(진입가)"은 close + 'k' * high -low 로 계산되며 로그 계산을 적용했습니다.
손절 레벨은 전일 저가 및 진입 가격의 절반으로 계산됩니다.
전략은 항상 UTC+0에서 종료합니다. ( 한국 기준 오전 9시 )
그리고 배경색으로 일일세션을 표현했습니다.
트뷰에 오픈소스의 래리윌리엄스 변동성전략이 없는것 같아 간단하게나마 만들어 보았습니다. 도움이 되었으면 좋겟워요..
Volatility Quality Histogram (NicoadW)This indicator is based on the Volatility Quality Index ( VQI ) by Thomas Stridsman.
It shows the slope of the VQI in form of a histogram.
The VQI is calculated in the following Steps:
1. Applying a WeightedMovingAverage with the onto the low, open, high, close and prior close
2. Calculating the true range and the range from the results of step 1 and setting them into relation
3. Calculating a weighted lumpsum of the results from step 2 (This is the value of the widely known VQI )
4. The change from the current value of step 3 compared to the last value is the VQI-Slope
5. The VQI-Slope is filtered by only recogning changes greater as the User-Input
6. If the Slope is rising, its considered a long trend and if its falling its considered a short trend
User Inputs
Valuation Period: Length of the WMAs
Filter in Pips: minimum change of the VQI-Slope to result in a Trend-Change
Visuals
Inrease the size of the Signal: Highligts the Trend-Changes
Color Chart Bars: Colors the bars of the main chart depending on the trend given by the VQI
Volatility OscillatorThis tool displays relative volatility and directional trend. Excellent way to pickup diversions and reversals. Length can be lowered to 11 or 13 in settings to show price range.
Can be used to identify patterns such as parallel channels and likely direction of price action as pictured below.
Volatility Support & Resistance [LM]Hello guys,
I would like to introduce you volatility support and resistance horizontals. It draws line horizontal on high and low vhighly volatile candles. You can select lines on this timeframe and/or another one. It's done in the same spirit like my other indicators for horizontals
The colors are dynamic depending whether price is below or above the line:
for the current timeframe green is used for support and blue for resistance
for the other timeframe by default 4h orange is used for support and violet for resistance
There are various sections in setting:
general settings - here you can select ATR length, multiplier and show labels
show and hide section of the current timeframe
other timeframe
show and hide other timeframe horizontals
Any suggestions are welcome
Volatility Adjusted Bands - JD This indicator gives a likely trading range based on the volatility of the past x amount of bars, measured against a certain moving average.
The indicator can be used as an alternative for BBands.
It gives approx. the same "trend-side" lines (upper line in uptrend, lower line in down trend) as the Bollinger Bands, but the opposite line follows closer on price,
Instead of "flying out" to the other direction like in BBands.
As a comparison, the BBands for the same length (50 period) are added on the chart.
JD.
#NotTradingAdvice #DYOR
I build these indicators for myself and provide them open source, to use for free to use and improve upon,
as I believe the best way to learn is toghether.
Volatility Direction DetectorThis script helps trader identify volatility direction in order to classify each market move. It also provides good entry points.
Volatility RatioVolatility Ratio indicator script. This indicator was originally developed by Jack Schwager.
Volatility Finite Volume Elements Backtest This version has an important enhancement to the previous one that`s
especially useful with intraday minute charts.
Due to the volatility had not been taken into account to avoid the extra
complication in the formula, the previous formula has some drawbacks:
The main drawback is that the constant cutoff coefficient will overestimate
price changes in minute charts and underestimate corresponding changes in
weekly or monthly charts.
And now the indicator uses adaptive cutoff coefficient which will adjust to
all time frames automatically.
You can change long to short in the Input Settings
Please, use it only for learning or paper trading. Do not for real trading.
Volatility Finite Volume Elements Strategy This version has an important enhancement to the previous one that`s
especially useful with intraday minute charts.
Due to the volatility had not been taken into account to avoid the extra
complication in the formula, the previous formula has some drawbacks:
The main drawback is that the constant cutoff coefficient will overestimate
price changes in minute charts and underestimate corresponding changes in
weekly or monthly charts.
And now the indicator uses adaptive cutoff coefficient which will adjust to
all time frames automatically.
WARNING:
This script to change bars colors.
VolatilityIndicatorsLibrary "VolatilityIndicators"
This is a library of Volatility Indicators .
It aims to facilitate the grouping of this category of indicators, and also offer the customized supply of
the parameters and sources, not being restricted to just the closing price.
@Thanks and credits:
1. Dynamic Zones: Leo Zamansky, Ph.D., and David Stendahl
2. Deviation: Karl Pearson (code by TradingView)
3. Variance: Ronald Fisher (code by TradingView)
4. Z-score: Veronique Valcu (code by HPotter)
5. Standard deviation: Ronald Fisher (code by TradingView)
6. ATR (Average True Range): J. Welles Wilder (code by TradingView)
7. ATRP (Average True Range Percent): millerrh
8. Historical Volatility: HPotter
9. Min-Max Scale Normalization: gorx1
10. Mean Normalization: gorx1
11. Standardization: gorx1
12. Scaling to unit length: gorx1
13. LS Volatility Index: Alexandre Wolwacz (Stormer), Fabrício Lorenz, Fábio Figueiredo (Vlad) (code by me)
14. Bollinger Bands: John Bollinger (code by TradingView)
15. Bollinger Bands %: John Bollinger (code by TradingView)
16. Bollinger Bands Width: John Bollinger (code by TradingView)
dev(source, length, anotherSource)
Deviation. Measure the difference between a source in relation to another source
Parameters:
source (float)
length (simple int) : (int) Sequential period to calculate the deviation
anotherSource (float) : (float) Source to compare
Returns: (float) Bollinger Bands Width
variance(src, mean, length, biased, degreesOfFreedom)
Variance. A statistical measurement of the spread between numbers in a data set. More specifically,
variance measures how far each number in the set is from the mean (average), and thus from every other number in the set.
Variance is often depicted by this symbol: σ2. It is used by both analysts and traders to determine volatility and market security.
Parameters:
src (float) : (float) Source to calculate variance
mean (float) : (float) Mean (Moving average)
length (simple int) : (int) The sequential period to calcule the variance (number of values in data set)
biased (simple bool) : (bool) Defines the type of standard deviation. If true, uses biased sample variance (n),
degreesOfFreedom (simple int) : (int) Degrees of freedom. The number of values in the final calculation of a statistic that are free to vary.
Default value is n-1, where n here is length. Only applies when biased parameter is defined as true.
Returns: (float) Standard deviation
stDev(src, length, mean, biased, degreesOfFreedom)
Measure the Standard deviation from a source in relation to it's moving average.
In this implementation, you pass the average as a parameter, allowing a more personalized calculation.
Parameters:
src (float) : (float) Source to calculate standard deviation
length (simple int) : (int) The sequential period to calcule the standard deviation
mean (float) : (float) Moving average.
biased (simple bool) : (bool) Defines the type of standard deviation. If true, uses biased sample variance (n),
else uses unbiased sample variance (n-1 or another value, as long as it is in the range between 1 and n-1), where n=length.
degreesOfFreedom (simple int) : (int) Degrees of freedom. The number of values in the final calculation of a statistic that are free to vary.
Default value is n-1, where n here is length.
Returns: (float) Standard deviation
zscore(src, mean, length, biased, degreesOfFreedom)
Z-Score. A z-score is a statistical measurement that indicates how many standard deviations a data point is from
the mean of a data set. It is also known as a standard score. The formula for calculating a z-score is (x - μ) / σ,
where x is the individual data point, μ is the mean of the data set, and σ is the standard deviation of the data set.
Z-scores are useful in identifying outliers or extreme values in a data set. A positive z-score indicates that the
data point is above the mean, while a negative z-score indicates that the data point is below the mean. A z-score of
0 indicates that the data point is equal to the mean.
Z-scores are often used in hypothesis testing and determining confidence intervals. They can also be used to compare
data sets with different units or scales, as the z-score standardizes the data. Overall, z-scores provide a way to
measure the relative position of a data point in a data
Parameters:
src (float) : (float) Source to calculate z-score
mean (float) : (float) Moving average.
length (simple int) : (int) The sequential period to calcule the standard deviation
biased (simple bool) : (bool) Defines the type of standard deviation. If true, uses biased sample variance (n),
else uses unbiased sample variance (n-1 or another value, as long as it is in the range between 1 and n-1), where n=length.
degreesOfFreedom (simple int) : (int) Degrees of freedom. The number of values in the final calculation of a statistic that are free to vary.
Default value is n-1, where n here is length.
Returns: (float) Z-score
atr(source, length)
ATR: Average True Range. Customized version with source parameter.
Parameters:
source (float) : (float) Source
length (simple int) : (int) Length (number of bars back)
Returns: (float) ATR
atrp(length, sourceP)
ATRP (Average True Range Percent)
Parameters:
length (simple int) : (int) Length (number of bars back) for ATR
sourceP (float) : (float) Source for calculating percentage relativity
Returns: (float) ATRP
atrp(source, length, sourceP)
ATRP (Average True Range Percent). Customized version with source parameter.
Parameters:
source (float) : (float) Source for ATR
length (simple int) : (int) Length (number of bars back) for ATR
sourceP (float) : (float) Source for calculating percentage relativity
Returns: (float) ATRP
historicalVolatility(lengthATR, lengthHist)
Historical Volatility
Parameters:
lengthATR (simple int) : (int) Length (number of bars back) for ATR
lengthHist (simple int) : (int) Length (number of bars back) for Historical Volatility
Returns: (float) Historical Volatility
historicalVolatility(source, lengthATR, lengthHist)
Historical Volatility
Parameters:
source (float) : (float) Source for ATR
lengthATR (simple int) : (int) Length (number of bars back) for ATR
lengthHist (simple int) : (int) Length (number of bars back) for Historical Volatility
Returns: (float) Historical Volatility
minMaxNormalization(src, numbars)
Min-Max Scale Normalization. Maximum and minimum values are taken from the sequential range of
numbars bars back, where numbars is a number defined by the user.
Parameters:
src (float) : (float) Source to normalize
numbars (simple int) : (int) Numbers of sequential bars back to seek for lowest and hightest values.
Returns: (float) Normalized value
minMaxNormalization(src, numbars, minimumLimit, maximumLimit)
Min-Max Scale Normalization. Maximum and minimum values are taken from the sequential range of
numbars bars back, where numbars is a number defined by the user.
In this implementation, the user explicitly provides the desired minimum (min) and maximum (max) values for the scale,
rather than using the minimum and maximum values from the data.
Parameters:
src (float) : (float) Source to normalize
numbars (simple int) : (int) Numbers of sequential bars back to seek for lowest and hightest values.
minimumLimit (simple float) : (float) Minimum value to scale
maximumLimit (simple float) : (float) Maximum value to scale
Returns: (float) Normalized value
meanNormalization(src, numbars, mean)
Mean Normalization
Parameters:
src (float) : (float) Source to normalize
numbars (simple int) : (int) Numbers of sequential bars back to seek for lowest and hightest values.
mean (float) : (float) Mean of source
Returns: (float) Normalized value
standardization(src, mean, stDev)
Standardization (Z-score Normalization). How "outside the mean" values relate to the standard deviation (ratio between first and second)
Parameters:
src (float) : (float) Source to normalize
mean (float) : (float) Mean of source
stDev (float) : (float) Standard Deviation
Returns: (float) Normalized value
scalingToUnitLength(src, numbars)
Scaling to unit length
Parameters:
src (float) : (float) Source to normalize
numbars (simple int) : (int) Numbers of sequential bars back to seek for lowest and hightest values.
Returns: (float) Normalized value
lsVolatilityIndex(movingAverage, sourceHvol, lengthATR, lengthHist, lenNormal, lowerLimit, upperLimit)
LS Volatility Index. Measures the volatility of price in relation to an average.
Parameters:
movingAverage (float) : (float) A moving average
sourceHvol (float) : (float) Source for calculating the historical volatility
lengthATR (simple int) : (float) Length for calculating the ATR (Average True Range)
lengthHist (simple int) : (float) Length for calculating the historical volatility
lenNormal (simple int) : (float) Length for normalization
lowerLimit (simple int)
upperLimit (simple int)
Returns: (float) LS Volatility Index
lsVolatilityIndex(sourcePrice, movingAverage, sourceHvol, lengthATR, lengthHist, lenNormal, lowerLimit, upperLimit)
LS Volatility Index. Measures the volatility of price in relation to an average.
Parameters:
sourcePrice (float) : (float) Source for measure the distance
movingAverage (float) : (float) A moving average
sourceHvol (float) : (float) Source for calculating the historical volatility
lengthATR (simple int) : (float) Length for calculating the ATR (Average True Range)
lengthHist (simple int) : (float) Length for calculating the historical volatility
lenNormal (simple int)
lowerLimit (simple int)
upperLimit (simple int)
Returns: (float) LS Volatility Index
bollingerBands(src, length, mult, basis)
Bollinger Bands. A Bollinger Band is a technical analysis tool defined by a set of lines plotted
two standard deviations (positively and negatively) away from a simple moving average (SMA) of the security's price,
but can be adjusted to user preferences. In this version you can pass a customized basis (moving average), not only SMA.
Parameters:
src (float) : (float) Source to calculate standard deviation used in Bollinger Bands
length (simple int) : (int) The time period to be used in calculating the standard deviation
mult (simple float) : (float) Multiplier used in standard deviation. Basically, the upper/lower bands are standard deviation multiplied by this.
basis (float) : (float) Basis of Bollinger Bands (a moving average)
Returns: (float) A tuple of Bollinger Bands, where index 1=basis; 2=basis+dev; 3=basis-dev; and dev=multiplier*stdev
bollingerBands(src, length, aMult, basis)
Bollinger Bands. A Bollinger Band is a technical analysis tool defined by a set of lines plotted
two standard deviations (positively and negatively) away from a simple moving average (SMA) of the security's price,
but can be adjusted to user preferences. In this version you can pass a customized basis (moving average), not only SMA.
Also, various multipliers can be passed, thus getting more bands (instead of just 2).
Parameters:
src (float) : (float) Source to calculate standard deviation used in Bollinger Bands
length (simple int) : (int) The time period to be used in calculating the standard deviation
aMult (float ) : (float ) An array of multiplies used in standard deviation. Basically, the upper/lower bands are standard deviation multiplied by this.
This array of multipliers permit the use of various bands, not only 2.
basis (float) : (float) Basis of Bollinger Bands (a moving average)
Returns: (float ) An array of Bollinger Bands, where:
index 1=basis; 2=basis+dev1; 3=basis-dev1; 4=basis+dev2, 5=basis-dev2, 6=basis+dev2, 7=basis-dev2, Nup=basis+devN, Nlow=basis-devN
and dev1, dev2, devN are ```multiplier N * stdev```
bollingerBandsB(src, length, mult, basis)
Bollinger Bands %B - or Percent Bandwidth (%B).
Quantify or display where price (or another source) is in relation to the bands.
%B can be useful in identifying trends and trading signals.
Calculation:
%B = (Current Price - Lower Band) / (Upper Band - Lower Band)
Parameters:
src (float) : (float) Source to calculate standard deviation used in Bollinger Bands
length (simple int) : (int) The time period to be used in calculating the standard deviation
mult (simple float) : (float) Multiplier used in standard deviation
basis (float) : (float) Basis of Bollinger Bands (a moving average)
Returns: (float) Bollinger Bands %B
bollingerBandsB(src, length, aMult, basis)
Bollinger Bands %B - or Percent Bandwidth (%B).
Quantify or display where price (or another source) is in relation to the bands.
%B can be useful in identifying trends and trading signals.
Calculation
%B = (Current Price - Lower Band) / (Upper Band - Lower Band)
Parameters:
src (float) : (float) Source to calculate standard deviation used in Bollinger Bands
length (simple int) : (int) The time period to be used in calculating the standard deviation
aMult (float ) : (float ) Array of multiplier used in standard deviation. Basically, the upper/lower bands are standard deviation multiplied by this.
This array of multipliers permit the use of various bands, not only 2.
basis (float) : (float) Basis of Bollinger Bands (a moving average)
Returns: (float ) An array of Bollinger Bands %B. The number of results in this array is equal the numbers of multipliers passed via parameter.
bollingerBandsW(src, length, mult, basis)
Bollinger Bands Width. Serve as a way to quantitatively measure the width between the Upper and Lower Bands
Calculation:
Bollinger Bands Width = (Upper Band - Lower Band) / Middle Band
Parameters:
src (float) : (float) Source to calculate standard deviation used in Bollinger Bands
length (simple int) : (int) Sequential period to calculate the standard deviation
mult (simple float) : (float) Multiplier used in standard deviation
basis (float) : (float) Basis of Bollinger Bands (a moving average)
Returns: (float) Bollinger Bands Width
bollingerBandsW(src, length, aMult, basis)
Bollinger Bands Width. Serve as a way to quantitatively measure the width between the Upper and Lower Bands
Calculation
Bollinger Bands Width = (Upper Band - Lower Band) / Middle Band
Parameters:
src (float) : (float) Source to calculate standard deviation used in Bollinger Bands
length (simple int) : (int) Sequential period to calculate the standard deviation
aMult (float ) : (float ) Array of multiplier used in standard deviation. Basically, the upper/lower bands are standard deviation multiplied by this.
This array of multipliers permit the use of various bands, not only 2.
basis (float) : (float) Basis of Bollinger Bands (a moving average)
Returns: (float ) An array of Bollinger Bands Width. The number of results in this array is equal the numbers of multipliers passed via parameter.
dinamicZone(source, sampleLength, pcntAbove, pcntBelow)
Get Dynamic Zones
Parameters:
source (float) : (float) Source
sampleLength (simple int) : (int) Sample Length
pcntAbove (simple float) : (float) Calculates the top of the dynamic zone, considering that the maximum values are above x% of the sample
pcntBelow (simple float) : (float) Calculates the bottom of the dynamic zone, considering that the minimum values are below x% of the sample
Returns: A tuple with 3 series of values: (1) Upper Line of Dynamic Zone;
(2) Lower Line of Dynamic Zone; (3) Center of Dynamic Zone (x = 50%)
Examples:
Shannon Entropy Volatility AnalyzerThis algorithm aims to measure market uncertainty or volatility using a Shannon entropy-based approach. 🔄📊
Entropy is a measure of disorder or unpredictability, and here we use it to evaluate the structure of price returns within a defined range of periods (window length). 🧩⏳ Thus, the goal is to detect changes to identify conditions of high or low volatility. 🔍⚡
What we seek with Shannon's formula in this algorithm is to measure market uncertainty or volatility through dynamic entropy. This measure helps us understand how unpredictable price behavior is over a given period, which is key to making informed decisions. 📈🧠
Through this formula, we calculate the level of disorder or dispersion in price returns based on their probability of occurrence, enabling us to identify moments of high or low volatility. 💡💥
Shannon Entropy Calculation 📏
• Uses probabilities to measure uncertainty in returns. 🎲
• Entropy is normalized on a scale of 0 to 100, where:
o High Entropy: Unpredictable movements (high uncertainty). ⚠️💥
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o Low Entropy: Structured movements (low uncertainty). 📉🔒
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• With probabilities, we measure the level of dispersion or unpredictability of returns using Shannon's entropy formula. 📊🔍
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Indicator Usefulness 🛠️
• Identify High Volatility: When the market is unpredictable, the indicator signals "High Uncertainty." ⚡🔮
• Detect Market Stability: When the market is more predictable and structured, the indicator highlights "Low Uncertainty." 🔒🧘♂️
• Neutral Zones: Helps monitor markets without extreme conditions, enabling safer entry or exit opportunities. ⚖️🚶♂️
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Uncertainty Zones 🌀
1. High Uncertainty: When entropy exceeds the upper threshold. 🚨🔺
2. Low Uncertainty: When entropy is below the lower threshold. 🔻💡
3. Neutral: When entropy lies between both thresholds. ⚖️🔄
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What We Aim to Achieve with the Formula in Practice 🎯
1. Detection of Volatile Moments: Shannon’s formula helps us identify when the market is unpredictable. This is a good moment to take additional precautions, such as reducing position size or avoiding trading during high volatility phases. ⚠️📉
2. Trading Opportunities in Stable Markets: With low entropy, we can identify when the market is more predictable, favoring trend or momentum strategies with a higher chance of success. 🚀📈
3. Optimization of Risk Management: By measuring market volatility in real-time, we can adjust entry and exit strategies, tailoring risk based on the level of uncertainty detected. 🔄⚖️
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We hope this makes it easy to interpret and use. If you have any questions or comments, please feel free to reach out to us! 📬😊
