RSI-Adaptive, GKYZ-Filtered DEMA [Loxx]RSI-Adaptive, GKYZ-Filtered DEMA is a Garman-Klass-Yang-Zhang Historical Volatility Filtered, RSI-Adaptive Double Exponential Moving Average. This is an experimental indicator. The way this is calculated is by turning RSI into an alpha value that is then injected into a DEMA function to output price. Price is then filtered using GKYZ Historical volatility. This process of creating an alpha out of RSI is only relevant to EMA-based moving averages that use an alpha value for it's calculation.
What is Garman-Klass-Yang-Zhang Historical Volatility?
Yang and Zhang derived an extension to the Garman Klass historical volatility estimator that allows for opening jumps. It assumes Brownian motion with zero drift. This is currently the preferred version of open-high-low-close volatility estimator for zero drift and has an efficiency of 8 times the classic close-to-close estimator. Note that when the drift is nonzero, but instead relative large to the volatility , this estimator will tend to overestimate the volatility . The Garman-Klass-Yang-Zhang Historical Volatility calculation is as follows:
GKYZHV = sqrt((Z/n) * sum((log(open(k)/close( k-1 )))^2 + (0.5*(log(high(k)/low(k)))^2) - (2*log(2) - 1)*(log(close(k)/open(2:end)))^2))
Included
Alerts
Signals
Loxx's Expanded Source Types
Bar coloring
歷史波動率
Z.A.H.It's a scalping script, which can be used using Heikin Ashi candle on 5min time frame (I personally use it for BINANCE:BTCUSDT and BINANCE:ETHUSDT scalping).
We've tried to include SL and target (1.5R and 2R) in this as well, and it works well but sometimes (please note SOMETIMES..SOMETIMES, it can be few..few pips here and there)
Idea is simple, you take the trade based on the signal given by the script and place your SL as per the script and then target 1.5R for 80% of your position and 20% for the remaining 20% of your position.
One extra thing which I've found useful is, you can use Awesome Oscillator (default setting) as well for placing SLs. If you use this method for placing SL, then just place your SL at the last green awesome oscillator candle for shorts and at the last red awesome oscillator candle for longs. From there target 1.5R and 2R.
Overall results are same for either, so you can use it as per your convenience. For any query, you can send me a DM on discord or twitter.
Roger & Satchell Estimator Historical Volatility Bands [Loxx]Roger & Satchell Estimator Historical Volatility Bands are constructed using:
Average as the middle line.
Upper and lower bands using theRoger & Satchell Estimator Historical Volatility Bands for bands calculation.
What is Roger & Satchell Estimator Historical Volatility?
The Rogers–Satchell estimator does not handle opening jumps; therefore, it underestimates the volatility. It accurately explains the volatility portion that can be attributed entirely to a trend in the price evolution. Rogers and Satchell try to embody the frequency of price observations in the model in order to overcome the drawback. They claim that the corrected estimator outperforms the uncorrected one in a study based on simulated data.
RSEHV = sqrt((Z/n) * sum((log(high/close)*log(high/open)) + (log(low/close)*log(low/open))))
The color of the middle line, unlike the bands colors, has 3 colors. When colors of the bands are the same, then the middle line has the same color, otherwise it's white.
Included
Alerts
Signals
Loxx's Expanded Source Types
Bar coloring
Garman-Klass-Yang-Zhang Historical Volatility Bands [Loxx]Garman-Klass-Yang-Zhang Historical Volatility Bands are constructed using:
Average as the middle line.
Upper and lower bands using the Garman-Klass-Yang-Zhang Historical Volatility Bands for bands calculation.
What is Garman-Klass-Yang-Zhang Historical Volatility?
Yang and Zhang derived an extension to the Garman Klass historical volatility estimator that allows for opening jumps. It assumes Brownian motion with zero drift. This is currently the preferred version of open-high-low-close volatility estimator for zero drift and has an efficiency of 8 times the classic close-to-close estimator. Note that when the drift is nonzero, but instead relative large to the volatility, this estimator will tend to overestimate the volatility. The Garman-Klass-Yang-Zhang Historical Volatility calculation is as follows:
GKYZHV = sqrt((Z/n) * sum((log(open(k)/close(k-1)))^2 + (0.5*(log(high(k)/low(k)))^2) - (2*log(2) - 1)*(log(close(k)/open(2:end)))^2))
The color of the middle line, unlike the bands colors, has 3 colors. When colors of the bands are the same, then the middle line has the same color, otherwise it's white.
Included
Alerts
Signals
Loxx's Expanded Source Types
Bar coloring
Related Indicators
Garman & Klass Estimator Historical Volatility Bands
Garman & Klass Estimator Historical Volatility Bands [Loxx]Garman & Klass Estimator Historical Volatility Bands are constructed using:
Average as the middle line.
Upper and lower bands using the Garman & Klass Estimator Historical Volatility (instead of "regular" Historical Volatility ) for bands calculation.
What is Garman & Klaus Historical Volatility?
Garman Klass is a volatility estimator that incorporates open, low, high, and close prices of a security. The Garman and Klass estimator for estimating historical volatility assumes Brownian motion with zero drift and no opening jumps (i.e. the opening = close of the previous period). This estimator is 7.4 times more efficient than the close-to-close estimator. Garman-Klass volatility extends Parkinson's volatility by taking into account the opening and closing price. As markets are most active during the opening and closing of a trading session, it makes volatility estimation more accurate. Garman and Klass also assumed that the process of price change is a process of continuous diffusion (geometric Brownian motion). However, this assumption has several drawbacks. The method is not robust for opening jumps in price and trend movements. Despite its drawbacks, the Garman-Klass estimator is still more effective than the basic formula since it takes into account not only the price at the beginning and end of the time interval but also intraday price extremums.
The Garman & Klass Estimator is as follows:
GKE = sqrt((Z/n)* sum((0.5*(log(high./low)).^2) - (2*log(2) - 1).*(log(close./open)).^2))
The color of the middle line, unlike the bands colors, has 3 colors. When colors of the bands are the same, then the middle line has the same color, otherwise it's white.
Included
Alerts
Signals
Loxx's Expanded Source Types
Bar coloring
Related indicators:
Parkinson's Historical Volatility Bands
High/Low Historical Volatility Bands [Loxx]High/Low Historical Volatility Bands are constructed using:
Average as the middle line.
Upper and lower bands using the Historical Volatility high/low (instead of "regular" Historical Volatility) for bands calculation.
What is Historical Volatility?
Historical Volatility (HV) is a statistical measure of the dispersion of returns for a given security or market index over a given period of time. Generally, this measure is calculated by determining the average deviation from the average price of a financial instrument in the given time period. Using standard deviation is the most common, but not the only, way to calculate Historical Volatility .
The higher the Historical Volatility value, the riskier the security. However, that is not necessarily a bad result as risk works both ways - bullish and bearish , i.e: Historical Volatility is not a directional indicator and should not be used as other directional indicators are used. Use to to determine the rising and falling price change volatility .
SH is stock's High price in t day.
SL is stock's Low price in t day.
High/Low Return (xt^HL) is calculated as the natural logarithm of the ratio of a stock's High price to stock's Low price.
Return:
And Parkinson's number: 1 / (4 * math.log(2)) * 252 / n * Σ (n, t =1) {math.log(Ht/Lt)^2}
An important use of the Parkinson's number is the assessment of the distribution prices during the day as well as a better understanding of the market dynamics. Comparing the Parkinson's number and periodically sampled volatility helps traders understand the tendency towards mean reversion in the market as well as the distribution of stop-losses.
The color of the middle line, unlike the bands colors, has 3 colors. When colors of the bands are the same, then the middle line has the same color, otherwise it's white.
Included
Alerts
Signals
Loxx's Expanded Source Types
Bar coloring
Related indicators:
Parkinson's Historical Volatility Bands
Historical Volatility Bands
Parkinson's Historical Volatility Bands [Loxx]Parkinson's Historical Volatility Bands are constructed using:
Average as the middle line.
Upper and lower bands using the Parkinson's historical volatility (instead of "regular" Historical Volatility) for bands calculation.
What is Parkinson's Historical Volatility?
The Parkinson's number, or High Low Range Volatility developed by the physicist, Michael Parkinson in 1980, aims to estimate the Volatility of returns for a random walk using the High and Low in any particular period. IVolatility.com calculates daily Parkinson values. Prices are observed on a fixed time interval: n = 10, 20, 30, 60, 90, 120, 150, 180 days.
SH is stock's High price in t day.
SL is stock's Low price in t day.
High/Low Return (xt^HL) is calculated as the natural logarithm of the ratio of a stock's High price to stock's Low price.
Return:
And Parkinson's number: 1 / (4 * math.log(2)) * 252 / n * Σ (n, t =1) {math.log(Ht/Lt)^2}
An important use of the Parkinson's number is the assessment of the distribution prices during the day as well as a better understanding of the market dynamics. Comparing the Parkinson's number and periodically sampled volatility helps traders understand the tendency towards mean reversion in the market as well as the distribution of stop-losses.
The color of the middle line, unlike the bands colors, has 3 colors. When colors of the bands are the same, then the middle line has the same color, otherwise it's white.
Included
Alerts
Signals
Loxx's Expanded Source Types
Bar coloring
Historical Volatility Bands [Loxx]Historical Volatility Bands are constructed using:
Average as the middle line.
Upper and lower bands using the Historical Volatility for bands calculation.
What is Historical Volatility?
Historical Volatility (HV) is a statistical measure of the dispersion of returns for a given security or market index over a given period of time. Generally, this measure is calculated by determining the average deviation from the average price of a financial instrument in the given time period. Using standard deviation is the most common, but not the only, way to calculate Historical Volatility.
The higher the Historical Volatility value, the riskier the security. However, that is not necessarily a bad result as risk works both ways - bullish and bearish, i.e: Historical Volatility is not a directional indicator and should not be used as other directional indicators are used. Use to to determine the rising and falling price change volatility.
The color of the middle line, unlike the bands colors, has 3 colors. When colors of the bands are the same, then the middle line has the same color, otherwise it's white.
Included
Alerts
Signals
Loxx's Expanded Source Types
Bar coloring
Full Volatility Statistics and Forecast
This is a tool designed to translate the data from the expected volatility of different assets, such as for example VIX, which measures the volatility of SP500 index.
Once get the data from the volatility asset we want to measure(for this test I have used VIX), we are going to translate it the required timeframe expected move by dividing the initial value into :
252 = if we want to use the daily timeframe, since there are ~252 aproximative daily trading days
52 = if we want to use the weekly timeframe, since there 52 trading weeks in a year
12 = if we want to use the monthly timeframe, since there are 12 months in a year
For this example I have used 252 with the daily timeframe.
In this scenario, we can see that we had 5711 total cnadles which we analysed, and in this case, we had 942 crosses, where the daily movement ended up either above or below the channel made from the opening daily candle value + expected movement from the volatility, giving as a total of 16.5% of occurances that volatility was higher than expected, and in 83.5% of the times, we can see that the price stayed within our channel.
At the same time, we can see that we had 6 max losses in a row ( OUT) AND 95 max wins in a row (IN), and at the same time in those moments when the volatility crosses happen we had a 0.51% avg movements when the top crossed happened, and 0.67% avg movements when the bot happened.
Lastly on the second part of the panel, we had E which means the expected movement of today, for example it has 61.056$ , so lets say price opened on 4083, our top is 4083 + 61 and our bot is 4083 - 61 ( giving us the daily channel). At continuation we can see that overall the avg bull candle os 0.714% and avg bear candle was 0.805% .
I hope this tool will help you with your future analysis and trades !
If you have any questions please let me know !
vol_coneDraws a volatility cone on the chart, using the contract's realized volatility (rv). The inputs are:
- window: the number of past periods to use for computing the realized volatility. VIX uses 30 calendar days, which is 21 trading days, so 21 is the default.
- stdevs: the number of standard deviations that the cone will cover.
- periods to project: the length of the volatility cone.
- periods per year: the number of periods in a year. for a daily chart, this is 252. for a thirty minute chart on a contract that trades 23 hours a day, this is 23 * 2 * 252 = 11592. for an accurate cone, this input must be set correctly, according to the chart's time frame.
- history: show the lagged projections. in other words, if the cone is set to project 21 periods in the future, the lines drawn show the top and bottom edges of the cone from 23 periods ago.
- rate: the current interest or discount rate. this is used to compute the forward price of the underlying contract. using an accurate forward price allows you to compare the realized volatility projection to the implied volatility projections derived from options prices.
Example settings for a 30 minute chart of a contract that trades 23 hours per day, with 1 standard deviation, a 21 day rv calculation, and half a day projected:
- stdevs: 1
- periods to project: 23
- window: 23 * 2 * 21 = 966
- periods per year: 23 * 2 * 252 = 11592
Additionally, a table is drawn in the upper right hand corner, with several values:
- rv: the contract's current realized volatility.
- rnk: the rv's percentile rank, compared to the rv values on past bars.
- acc: the proportion of times price settled inside, versus outside, the volatility cone, "periods to project" into the future. this should be around 65-70% for most contracts when the cone is set to 1 standard deviation.
- up: the upper bound of the cone for the projection period.
- dn: the lower bound of the cone for the projection period.
Limitations:
- pinescript only seems to be able to draw a limited distance into the future. If you choose too many "periods to project", the cone will start drawing vertically at some limit.
- the cone is not totally smooth owing to the facts a) it is comprised of a limited number of lines and b) each bar does not represent the same amount of time in pinescript, as some cross weekends, session gaps, etc.
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)
Average Daily Pip Ranges by monthShows historical average daily pip ranges for specific months for FOREX pairs
useful for guaging typical seasonal volatility; or rough expected daily pip ranges for different months
works on both DXY and foreign currencies
option to plot 10yrs worth of data; with 10yr average of the average daily range for specific months
cast back to any previous 10yrs of your choosing
@twingall
TDV IndicatorThis indicator measures the volatility of an asset based on the price distance from the baseline used in my TDR indicator. IT calculates the average distance the price will move from the baseline and shows a visual representation of that data. If the scatterplot is white, the price is within the average, if it turns yellow, the price is above the average. This can be used to determine if the price action is over extended.
Jurik Composite Fractal Behavior (CFB) on EMA [Loxx]Jurik Composite Fractal Behavior (CFB) on EMA is an exponential moving average with adaptive price trend duration inputs. This purpose of this indicator is to introduce the formulas for the calculation Composite Fractal Behavior. As you can see from the chart above, price reacts wildly to shifts in volatility--smoothing out substantially while riding a volatility wave and cutting sharp corners when volatility drops. Notice the chop zone on BTC around August 2021, this was a time of extremely low relative volatility.
This indicator uses three previous indicators from my public scripts. These are:
JCFBaux Volatility
Jurik Filter
Jurik Volty
The CFB is also related to the following indicator
Jurik Velocity ("smoother moment")
Now let's dive in...
What is Composite Fractal Behavior (CFB)?
All around you mechanisms adjust themselves to their environment. From simple thermostats that react to air temperature to computer chips in modern cars that respond to changes in engine temperature, r.p.m.'s, torque, and throttle position. It was only a matter of time before fast desktop computers applied the mathematics of self-adjustment to systems that trade the financial markets.
Unlike basic systems with fixed formulas, an adaptive system adjusts its own equations. For example, start with a basic channel breakout system that uses the highest closing price of the last N bars as a threshold for detecting breakouts on the up side. An adaptive and improved version of this system would adjust N according to market conditions, such as momentum, price volatility or acceleration.
Since many systems are based directly or indirectly on cycles, another useful measure of market condition is the periodic length of a price chart's dominant cycle, (DC), that cycle with the greatest influence on price action.
The utility of this new DC measure was noted by author Murray Ruggiero in the January '96 issue of Futures Magazine. In it. Mr. Ruggiero used it to adaptive adjust the value of N in a channel breakout system. He then simulated trading 15 years of D-Mark futures in order to compare its performance to a similar system that had a fixed optimal value of N. The adaptive version produced 20% more profit!
This DC index utilized the popular MESA algorithm (a formulation by John Ehlers adapted from Burg's maximum entropy algorithm, MEM). Unfortunately, the DC approach is problematic when the market has no real dominant cycle momentum, because the mathematics will produce a value whether or not one actually exists! Therefore, we developed a proprietary indicator that does not presuppose the presence of market cycles. It's called CFB (Composite Fractal Behavior) and it works well whether or not the market is cyclic.
CFB examines price action for a particular fractal pattern, categorizes them by size, and then outputs a composite fractal size index. This index is smooth, timely and accurate
Essentially, CFB reveals the length of the market's trending action time frame. Long trending activity produces a large CFB index and short choppy action produces a small index value. Investors have found many applications for CFB which involve scaling other existing technical indicators adaptively, on a bar-to-bar basis.
What is Jurik Volty used in the Juirk Filter?
One of the lesser known qualities of Juirk smoothing is that the Jurik smoothing process is adaptive. "Jurik Volty" (a sort of market volatility ) is what makes Jurik smoothing adaptive. The Jurik Volty calculation can be used as both a standalone indicator and to smooth other indicators that you wish to make adaptive.
What is the Jurik Moving Average?
Have you noticed how moving averages add some lag (delay) to your signals? ... especially when price gaps up or down in a big move, and you are waiting for your moving average to catch up? Wait no more! JMA eliminates this problem forever and gives you the best of both worlds: low lag and smooth lines.
Ideally, you would like a filtered signal to be both smooth and lag-free. Lag causes delays in your trades, and increasing lag in your indicators typically result in lower profits. In other words, late comers get what's left on the table after the feast has already begun.
Modifications and improvements
1. Jurik's original calculation for CFB only allowed for depth lengths of 24, 48, 96, and 192. For theoretical purposes, this indicator allows for up to 20 different depth inputs to sample volatility. These depth lengths are
2, 3, 4, 6, 8, 12, 16, 24, 32, 48, 64, 96, 128, 192, 256, 384, 512, 768, 1024, 1536
Including these additional length inputs is arguable useless, but they are are included for completeness of the algorithm.
2. The result of the CFB calculation is forced to be an integer greater than or equal to 1.
3. The result of the CFB calculation is double filtered using an advanced, (and adaptive itself) filtering algorithm called the Jurik Filter. This filter and accompanying internal algorithm are discussed above.
Sentient levelThe indicator presented here is made based on the study published on NSE:INDIAVIX . Basically it shows 2 sigma (by default) trading ranges of the next day (by default) of indices e.g. NSE:NIFTY & NSE:BANKNIFTY . Everyday three new lines get plotted automatically on the chart of the instrument (preferably NSE:NIFTY & NSE:BANKNIFTY ) you want to trade. Generally it's expected that the index to be traded within the ranges however in case of major gap-up or gap-down if the index opens above the higher range or below the lower range then it's assumed that the day to remain very volatile. This three lines can be considered as important support/resistance . Default parameters are set in consideration of day trading however user can modify them manually as per their trading style.
If you like my work you can donate through Tradingview coin. Thanks
Historical Volatility RatioHistorical volatility is an indicator of the extent to which a price may diverge from its average in a given period. Hence, increased price fluctuation results in a higher historical volatility value. It is important to keep in mind that the historical volatility figure does not indicate the price direction but rather how unstable a price is.
Volatility is generally a measure of the riskiness of an investment. Increased volatility serves as an indication of increased uncertainty and risk. The opposite is also true; decreased volatility serves as an indication for lowered uncertainty and risk. As commonly expected in financial instrument trading, HV can be used along with other trading patterns, trends, and other indicators to identify instruments that they consider to be risky or highly volatile.
Historical volatility can be utilized as an instrument by traders who only trade underlying financial instruments. Measuring the instability of a market can impact the expectation of an investor on how much or to what extent the market may change and offers some guidance in making price forecasts and executing a trade.
A high volatility can imply a possible change of trend when aggressive buying/selling enters the market because the large transaction volumes will trigger notable price reversals.
Furthermore, historical volatility does not assess the probability of loss primarily, even though it can be used to provide an indication thereof.
HV can be used to assess by how much the price of a security shifts from its average value. In markets where a predominant trend exists, historical volatility provides an overview of the extent to which traded prices may have deviated from a central or moving average price. In smooth markets with a strong predominant trend, low volatility levels can be expected even though prices may fluctuate drastically as time passes.
This version is just a smoother version of standard HV. This is achieved by dividing HV of 2 different periods.
trailing_drawdown
Description:
Drawdown was a tool to measure historical risk, derived from measuring current wealth from its previous peak, casually from portfolio construction (weights allocation), will consider to having a minimum drawdown. In this indicator, the drawdown for individual assets is utilized to measure its value or percentage from its trailing peak (default to 1-yr period).
Drawdown:
drawdown = (price/peaks)-1
Feature:
Static: display drawdown as percentage
Dynamic: display drawdown as value
Index Reversal Range with Volatility Index or VIXWhat is the Indicator?
• The indicator is a visualization of maximum price in which the respective index can go up to in comparison with it's Volatility Index or VIX.
Who to use?
• Intraday
• Swing
• Position
• Long term Investors
• Futures
• Options
• Portfolio Managers
• Mutual Fund Managers
• Index Traders
• Volatility based Traders
• Long term Investors and Options Traders gets the maximum benefit
What timeframe to use?
• 1 Year: Position & Investors
• 6 Months: Position & Investors
• 3 Months: Swing & Position
• 1 Month: Swing & Position
• 1 Week: Swing
• 1 Day: Swing
• 1 Hour: Intraday & Swing
What are Upper and Lower lines?
• Upper Line: If the index price reach closer to the Upper line there is a high chance of reversal to Bearish trend.
• Lower Line: If the index price reach closer to the Lower line there is a high chance of reversal to Bullish trend.
• This need to be confirmed with multiple levels like Daily, Weekly, Monthly etc.
How to use?
• If the price reach closer to that level there is a high chance of reversal from the current trend.
• To identify the reversal zone of the index.
• To identify the trend.
• Option Traders can Sell a Call or Put Option from that level.
• Long term Investors, Position or Swing traders can plan for a Long entry.
• Intraday traders can use lower timeframes to do the same.
Indicator Menu
• Input VIX: Identify the VIX Symbol of your Index and type it in the box.
• For example for NIFTY Index chart type INDIAVIX in the box.
• Choose multiple timeframes according to your convenience.
How to turn on indicator Name and Value labels?
• Right side of the screen >
• Right click on the Price scale >
• Labels > Indicators and financial name labels, Indicators and financial value labels
Further Reading:
• Various videos and reading materials are available about this method.
Donchian with QQW MOD AND EMA strategythe 1st indicator is E M A , and the 2nd indicator is donchian trend , and the final one is Q Q E MODe , and we have to change some settings , change this E M A length from 9 to 200 ,
and change some settings on donchian indicator , so lets change Donchian channel period from 20 to 30 , and Q Q E MOD on default sittings
for a long signal to be valid , the price must be above 200 E M A ,with NEW blue histogram appeared on our q q e mode , if , donchian trend is red
for a short signal to be valid , the price must be below 200 E M A ,with NEW red histogram appeared on our q q e mode ,if ,donchian trend is green
ATR with MAOVERVIEW
The Average True Range Moving Average (ATRMA) is a technical indicator that gauges the amount of volatility currently present in the market, relative to the historical average volatility that was present before. It adds a moving average to the Average True Range (ATR) indicator.
This indicator is extremely similar to the VOXI indicator, but instead of measuring volume, it measures volatility. Volume measures the amount of shares/lots/units/contracts exchanged per unit of time. Volatility, on the other hand, measures the range of price movement per unit of time.
The purpose of this indicator is to help traders filter between non-volatile periods in the market from volatile periods in the market without introducing subjectivity. It can also help long-term investors to determine market regime using volatility without introducing subjectivity.
CONCEPTS
This indicator assumes that trends are more likely to start during periods of high volatility, and consolidation is more likely to persist during periods of low volatility. The indicator also assumes that the average true range (ATR) of the last 14 candles is reflective of the current volatility in the market. ATR is the average height of all the candles, where height = |high - low|.
Suppose the ATR of the last 14 candles is greater than a moving average of the ATR(14) of the last 20 candles (this occurs whenever the indicator's filled region is colored BLUE). In that case, we can assume that the current volatility in the market is high.
Suppose the ATR of the last 14 candles is less than the moving average of the ATR(14) of the last 20 candles (this occurs whenever the indicator's filled region is colored RED). In that case, we can assume that the current volatility in the market is low.
HOW DO I READ THIS INDICATOR?
If the ATR line is above the ATR MA line (indicated by the blue color), the current volatility is greater than the historical average volatility.
If the ATR line is above the ATR MA line (indicated by the red color), the current volatility is less than the historical average volatility.
EWMA Implied Volatility based on Historical VolatilityVolatility is the most common measure of risk.
Volatility in this sense can either be historical volatility (one observed from past data), or it could implied volatility (observed from market prices of financial instruments.)
The main objective of EWMA is to estimate the next-day (or period) volatility of a time series and closely track the volatility as it changes.
The EWMA model allows one to calculate a value for a given time on the basis of the previous day's value.
The EWMA model has an advantage in comparison with SMA, because the EWMA has a memory.
The EWMA remembers a fraction of its past by a factor A, that makes the EWMA a good indicator of the history of the price movement if a wise choice of the term is made.
Full details regarding the formula :
www.investopedia.com
In this scenario, we are looking at the historical volatility using the anual length of 252 trading days and a monthly length of 21.
Once we apply all of that we are going to get the yearly volatility.
After that we just have to divide that by the square root of number of days in a year, or weeks in a year or months in a year in order to get the daily/weekly/monthly expected volatility.
Once we have the expected volatility, we can estimate with a high chance where the market top and bottom is going to be and continue our analysis on that premise.
If you have any questions, please let me know !
LS Volatility Index█ OVERVIEW
This indicator serves to measure the volatility of the price in relation to the average.
It serves four purposes:
1. Identify abnormal prices, extremely stretched in relation to an average;
2. Identify acceptable prices in the context of the main trend;
3. Identify market crashes;
4. Identify divergences.
█ CONCEPTS
The LS Volatility Index was originally described by Brazilian traders Alexandre Wolwacz (Stormer) , Fabrício Lorenz , and Fábio Figueiredo (Vlad)
Basically, this indicator can be used in two ways:
1. In a mean reversion strategy , when there is an unusual distance from it;
2. In a trend following strategy , when the price is in an acceptable region.
Perhaps the version presented here may have some slight differences, but the core is the same.
The original indicator is presented with a 21-period moving average, but here this value is customizable.
I made some fine tuning available, namely:
1. The possibility of smoothing the indicator;
2. Choose the type of moving average;
3. Customizable period;
4. Possibility to show a moving average of the indicator;
5. Color customization.
█ CALCULATION
First, the distance of the price from a given average in percentage terms is measured.
Then, the historical average volatility is obtained.
Finally the indicator is calculated through the ratio between the distance and the historical volatility.
To facilitate visualization, the result is normalized in a range from 0 to 100.
When it reaches 0, it means the price is on average.
When it hits 100, it means the price is way off average (stretched).
█ HOW TO USE IT
Here are some examples:
1. In a return-to-average strategy
2. In a trend following strategy
3. Identification of crashes and divergences
█ THANKS AND CREDITS
- Alexandre Wolwacz (Stormer), Fabrício Lorenz, Fábio Figueiredo (Vlad)
- Feature scaler (for normalization)
- HPotter (for calc of Historical Volatility)
