Levels Of Fear [AstrideUnicorn]"Buy at the level of maximum fear when everyone is selling." - says a well-known among traders wisdom. If an asset's price declines significantly from the most recent highest value or established range, traders start to worry. The higher the drawdown gets, the more fear market participants experience. During a sell-off, a feedback loop arises, in which the escalating fear and price decline strengthen each other.
The Levels Of Fear indicator helps analyze price declines and find the best times to buy an asset after a sell-off. In finance, volatility is a term that describes the degree of variation of an asset price over time. It is usually denoted by the letter σ (sigma) and estimated as the standard deviation of the asset price or price returns. The Levels Of Fear indicator helps measure the current price decline in the standard deviation units. It plots seven levels at distances of 1, 2, 3, 4, 5, 6, and 7 standard deviations (sigmas) below the base price (the recent highest price or upper bound of the established range). In what follows, we will refer to these levels as levels of fear.
HOW TO USE
When the price in its decline reaches a certain level of fear, it means that it has declined from its recent highest value by a corresponding number of standard deviations. The indicator helps traders see the minimum levels to which the price may fall and estimate the potential depth of the current decline based on the cause of the actual market shock. Five-seven sigma declines are relatively rare events and correspond to significant market shocks. In the lack of information, 5-7 sigma levels are good for buying an asset. Because when the price falls that deep, it corresponds to the maximum fear and pessimism in the market when most people are selling. In such situations, contrarian logic becomes the best decision.
SETTINGS
Window: the averaging window or period of the indicator. The algorithm uses this parameter to calculate the base level and standard deviations. Higher values are better for measuring deeper and longer declines.
Levels Stability: the parameter used in the decline detection. The higher the value is, the more stable and long the fear levels are, but at the same time, the lag increases. The lower it is, the faster the indicator responds to the price changes, but the fear levels are recalculated more frequently and are less stable. This parameter is mostly for fine-tuning. It does not change the overall picture much.
Mode: the parameter that defines the style for the labels. In the Cool Guys Mode , the indicator displays the labels as emojis. In the Serious Guys Mode , labels show the distance from the base level measured in standard deviation units or sigmas.
Standard Deviation
rv_iv_vrpThis script provides realized volatility (rv), implied volatility (iv), and volatility risk premium (vrp) information for each of CBOE's volatility indices. The individual outputs are:
- Blue/red line: the realized volatility. This is an annualized, 20-period moving average estimate of realized volatility--in other words, the variability in the instrument's actual returns. The line is blue when realized volatility is below implied volatility, red otherwise.
- Fuchsia line (opaque): the median of realized volatility. The median is based on all data between the "start" and "end" dates.
- Gray line (transparent): the implied volatility (iv). According to CBOE's volatility methodology, this is similar to a weighted average of out-of-the-money ivs for options with approximately 30 calendar days to expiration. Notice that we compare rv20 to iv30 because there are about twenty trading periods in thirty calendar days.
- Fuchsia line (transparent): the median of implied volatility.
- Lightly shaded gray background: the background between "start" and "end" is shaded a very light gray.
- Table: the table shows the current, percentile, and median values for iv, rv, and vrp. Percentile means the value is greater than "N" percent of all values for that measure.
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Volatility risk premium (vrp) is simply the difference between implied and realized volatility. Along with implied and realized volatility, traders interpret this measure in various ways. Some prefer to be buying options when there volatility, implied or realized, reaches absolute levels, or low risk premium, whereas others have the opposite opinion. However, all volatility traders like to look at these measures in relation to their past values, which this script assists with.
By the way, this script is similar to my "vol premia," which provides the vrp data for all of these instruments on one page. However, this script loads faster and lets you see historical data. I recommend viewing the indicator and the corresponding instrument at the same time, to see how volatility reacts to changes in the underlying price.
Creentrend Pressure SignalsThe hull moving average is my favorite moving average, as well as slower (55ma Bollinger Bands dev@(1.618)) is my favorite standard deviation indicator. Lets combine the two to evaluate overbought, oversold, and pressure.
Use for all time frames- I PREFER daily.
Bollinger band MA at 55
Hull ma at 55
The Hull is more reactive and faster than any band on the BB (both at 55)
When HULL closes BELOW LOWER BAND- it will print a buy signal, remember- over sold and overbought in VOLATILITY could be dangerous on low time frames, as swift moves typically have short term reversals but return to the main trend eventually, this is why i reccomend daily candles.
When PRICE closes ABOVE UPPER BOLLINGER BAND- it will print a green ^ signal under candle indicating upward pressure.
When HULL closes ABOVE UPPER BOLLINGER BAND- it will print a red resistance line. Complex tops happen a lot with bitcoin so take a 1-5% stop above it if shorting.
Time-of-Day DeviationCreates a 'Time-of-Day' Deviation cone starting from the first bar of the session based upon data from previous days.
NVTNetwork Value to Transactions Ratio (NVT) is defined as the ratio of market capitalization divided by transacted volume.
NVT Ratio can be thought of as an indicator that measures whether the blockchain network is overvalued or not.
If it is upper than red line, it means overvalued.
NVT Golden Cross targets to generate short or long signals by comparing the short-term trend of NVT and the long-term trend of NVT. If the short-term trend is way greater than the long-term trend is, the network can be interpreted as overpriced and will soon revert to mean value, meaning short signal. Similarly, the opposite case may imply a long signal.
Over the red line is short signal and under the green line is long signal.
You can find divergence in this indicator.
There are two sources
cryptocap
glassnode
DMI & ST DEV zone intersection [LM]Hello Traders,
This indicator uses two indicators st dev extremes and DMI extremes and visualize intersection of both indicators extreme zones using crosses. It means where cross is rendered intersection of extremes has occurred.
The standard deviation uses the same calculation as my Standard deviation zones Support & Resistance indicator, DMI indicator measures both the strength and direction of a price movement. I am using both indicators to find the intersection of extreme zones between them.
ST DEV settings:
source
tops setting
bottom setting
DMI settings:
length settings
extreme zone setting
Enjoy,
Lukas
STDev BandsReally simple script for dynamic support and resistance. Takes means over last 1440 bars (1440 minutes in a day) and calculates seven stdevs up and down.
Exponential Regression Channel with novel volatilityThis code is a modified version of the built-in "linear regression" script of Tradingviews which can be plotted correctly on logarithmic charts
The log reg code of Forza was adjusted by altustro to generate an exponential regression (or a correct linear regression on the log scale, this is equivalent).
The standard deviation in the log scale is a better volatility measure which we call novola, and which defines the trend channel displayed in addition to the main indicator.
The exponential regression slope and channel also defines the typical holding time of the stock and the SL/TP boundaries, which are calculated and displayed at the last bar.
The display works both in log and regular scale. But only in the log scale it can be compared to the linear extension, which can also be plotted when activated in the properties.
The underlying exponential fit can not be displayed in regular scale as only lines can be plotted by TV. But with the related script Exponental Regression also the exponential regression can be exactly displayed using a workaround.
SMADIF4 IndicatorIt shows a percentage difference between close and 4-SMA, 20, 50, 100 and 200. As it turns greener, the stock is more expensive, and vice versa, it turns redder when it becomes cheaper relative to the SMA. It will print the green backgraound as long as the bar closes above the 200 SMA and red as long as the bar closes below the 200 SMA. It uses by default 1.3 sigma to discriminate non-representative values and 100 bars in the past.
Bar StatisticsThis script calculates and displays some bar statistics.
For the bar length statistics, it takes every length of upper or lower movements and calculates their average (with SD), median, and max. That way, you can see whether there is a bias in the market or not.
Eg.: If for 10 bars, the market moved 2 up, then 1 down, then 3 up, then 2 down, and 2 up, the average up bars length would be at 2.33, while the average for the down length would be at 1.5, showing that upper movements last longer than down movements.
For the range statistics, it takes the true range of each bar and calculates where the close of the bar is in relation to the true low of it. So if the closing of the bar is at 10.0, the low is at 9.0, and the high is at 10.2, the candle closed in the upper third of the bar. This process is calculated for every bar and for both closing prices and open prices. It is very useful to locate biasses, and they can you a better view of the market, since for most of the time a bar will open on an extreme and close on another extreme.
Eg.: Here on the DJI, we can see that for most of the time, a month opens at the lower third (near the low) and closes at the upper third (near the high). We can also see that it is very difficult for a month to open or close on the middle of the candle, showing how important the first and the last day are for determining the trend of the rest of the month.
Exponential RegressionIn Tradingview it is not possible to actually display arbitrary non-linear functions retrospectively.
Series objects can only depend on the current or past bars
Thus, while regression is possible, display of a non-linear curve into the past is not possible
This script is a workaround to be able to still display an exponential fit of the last n bars.
It is based on a linear regression of the log(close). The parameters of this regression are printed in the label.
To create the correct plot, these parameters have to be written into the properties of the indicator.
The functions displayed follow the expression exp(A)* exp(pot*t+d)
where d =0 for the center line, and d = +-std * upperMult for the upper and lower line respectiveley.
The parameters of the function are:
amplitude in log scale A
exponent of the exponential function pot
standard deviation of the linear regression std
number of bars of the current chart bindex
multiplicator of the std of the upper and lower exponential line upperMult and lowerMult +
This code is a version of the built-in "linear regression" script of Tradingview alztered by Forza so it can be plotted correctly on logarithmic charts
The code of Forza was further adjusted by altustro to be able to plot the full exponential curve also in regular scale
myRangestatCalculates the average daily range as well as the standard deviation of the daily range over a given period.
Adding both values gives you a statistical range (bottom to top or top to bottom) in which price can be expected to move.
[KL] Double Bollinger Bands Strategy (for Crypto/FOREX)This strategy uses a setup consisting of two Bollinger Bands based on the 20 period 20-SMA +/-
(a) upper/lower bands of two standard deviations apart, and
(b) upper/lower bands of one standard deviation apart.
We consider price at +/- one standard deviation apart from 20-SMA as the "Neutral Zone".
If price closes above Neutral Zone after a period of consolidation, then it's an opportunity for entry. Strategy will long, anticipating for breakout.
The illustration below shows price closing above the Neutral Zone after a period of consolidation.
a.c-dn.net
Position is exited when prices closes at Neutral Zone (being lower than prior bars)
Ultimate Moving Average Bands [CC+RedK]The Ultimate Moving Average Bands were created by me and @RedKTrader and this converts our Ultimate Moving Average into volatility bands that use the same adaptive logic to create the bands. I have enabled everything to be fully adjustable so please let me know if you find a more useful setting than what I have here by default. I'm sure everyone is familiar with volatility bands but generally speaking if a price goes above the volatility bands then this is either a sign of an extremely strong uptrend or a potential reversal point and vice versa. I have included strong buy and sell signals in addition to normal ones so darker colors are strong signals and lighter colors are normal ones. Buy when the lines turn green and sell when they turn red.
Let me know if there are any other scripts you would like to see me publish!
Zigzag CloudThis is Bollinger Band built on top of Zigzags instead of regular price + something more.
Indicator presents 7 lines and cloud around it. This can be used to visualize how low or high price is with respect to its past movement.
Middle line is moving average of last N zigzag pivots
Lines adjacent to moving average are also moving averages. But, they are made of only pivot highs and pivot lows. Means, line above moving average is pivot high moving average and line below moving average is pivot low moving average.
Lines after pivot high/low moving averages are upper and lower bolllinger bands based on Moving Average Line with 2 standard deviation difference.
Outermost lines are bollinger band top of Moving average pivot high and bollinger band bottom of moving average pivot low.
pricing_tableThis script helps you evaluate the fair value of an option. It poses the question "if I bought or sold an option under these circumstances in the past, would it have expired in the money, or worthless? What would be its expected value, at expiration, if I opened a position at N standard deviations, given the volatility forecast, with M days to expiration at the close of every previous trading day?"
The default (and only) "hv" volatility forecast is based on the assumption that today's volatility will hold for the next M days.
To use this script, only one step is mandatory. You must first select days to expiration. The script will not do anything until this value is changed from the default (-1). These should be CALENDAR days. The script will convert to these to business days for forecasting and valuation, as trading in most contracts occurs over ~250 business days per year.
Adjust any other variables as desired:
model: the volatility forecasting model
window: the number of periods for a lagged model (e.g. hv)
filter: a filter to remove forecasts from the sample
filter type: "none" (do not use the filter), "less than" (keep forecasts when filter < volatility), "greater than" (keep forecasts when filter > volatility)
filter value: a whole number percentage. see example below
discount rate: to discount the expected value to present value
precision: number of decimals in output
trim outliers: omit upper N % of (generally itm) contracts
The theoretical values are based on history. For example, suppose days to expiration is 30. On every bar, the 30 days ago N deviation forecast value is compared to the present price. If the price is above the forecast value, the contract has expired in the money; otherwise, it has expired worthless. The theoretical value is the average of every such sample. The itm probabilities are calculated the same way.
The default (and only) volatility model is a 20 period EWMA derived historical (realized) volatility. Feel free to extend the script by adding your own.
The filter parameters can be used to remove some forecasts from the sample.
Example A:
filter:
filter type: none
filter value:
Default: the filter is not used; all forecasts are included in the the sample.
Example B:
filter: model
filter type: less than
filter value: 50
If the model is "hv", this will remove all forecasts when the historical volatility is greater than fifty.
Example C:
filter: rank
filter type: greater than
filter value: 75
If the model volatility is in the top 25% of the previous year's range, the forecast will be included in the sample apart from "model" there are some common volatility indexes to choose from, such as Nasdaq (VXN), crude oil (OVX), emerging markets (VXFXI), S&P; (VIX) etc.
Refer to the middle-right table to see the current forecast value, its rank among the last 252 days, and the number of business days until
expiration.
NOTE: This script is meant for the daily chart only.
STDev % by Alejandro PThis is a simple indicator that expands the usability of Standard deviation into a universally usable indicator.
This indicator displays the volatility as standard deviation as a % of asset value, this allows using more standardized and comparable values across multiple instruments and asset classes.
Standard Deviation PercentageThis indicator plots Standard Deviation in Percentage. Standard deviation depicts how far is price from its mean.
By default it shows Standard Deviation Percentage for 10 periods.
While price will be moving away from mean, it will be printed in green, while price will retrace towards mean, it will be printed in red.
Currently, we have indicators available to print Standard Deviation but value of standard deviation depends upon value of underlying. This indicator will show deviation from mean in terms of percentage.
Probability Distribution HistogramProbability Distribution Histogram
During data exploration it is often useful to plot the distribution of the data one is exploring. This indicator plots the distribution of data between different bins.
Essentially, what we do is we look at the min and max of the entire data set to determine its range. When we have the range of the data, we decide how many bins we want to divide this range into, so that the more bins we get, the smaller the range (a.k.a. width) for each bin becomes. We then place each data point in its corresponding bin, to see how many of the data points end up in each bin. For instance, if we have a data set where the smallest number is 5 and the biggest number is 105, we get a range of 100. If we then decide on 20 bins, each bin will have a width of 5. So the left-most bin would therefore correspond to values between 5 and 10, and the bin to the right would correspond to values between 10 and 15, and so on.
Once we have distributed all the data points into their corresponding bins, we compare the count in each bin to the total number of data points, to get a percentage of the total for each bin. So if we have 100 data points, and the left-most bin has 2 data points in it, that would equal 2%. This is also known as probability mass (or well, an approximation of it at least, since we're dealing with a bin, and not an exact number).
Usage
This is not an indicator that will give you any trading signals. This indicator is made to help you examine data. It can take any input you give it and plot how that data is distributed.
The indicator can transform the data in a few ways to help you get the most out of your data exploration. For instance, it is usually more accurate to use logarithmic data than raw data, so there is an option to transform the data using the natural logarithmic function. There is also an option to transform the data into %-Change form or by using data differencing.
Another option that the indicator has is the ability to trim data from the data set before plotting the distribution. This can help if you know there are outliers that are made up of corrupted data or data that is not relevant to your research.
I also included the option to plot the normal distribution as well, for comparison. This can be useful when the data is made up of residuals from a prediction model, to see if the residuals seem to be normally distributed or not.
Sigma Spikes [CC]Sigma Spikes were created by Adam Grimes and this is one of the best volatility indicators out there. This indicator not only gives you positive or negative volatility but with my version I can identify any sudden changes from the underlying trend. Buy when the line turns green and sell when it turns red.
Let me know if there were any other indicators you wanted to see me publish!
Linear Regression + Moving Average1. Linear Regression including 2 x Standard Deviation + High / Low. Middle line colour depends on colour change of Symmetrically Weighted Moving Average . Green zones indicate good long positions. Red zones indicate good short positions. (Custom)
2. Symmetrically Weighted Moving Average. Colour change depending on cross of offset -1. (Fixed)
3. Exponentially Weighted Moving Average. Colour change depending on cross with Symmetrically Weighted Moving Average. (Custom)
Intrangle - Straddle / StrangleIntrangle is an indicator to assist Nifty / Bank Nifty Option Writers / Sellers to identify the PE / CE legs to Sell for Straddle and Strangle positions for Intraday.
Basic Idea : (My Conclusion for making this Indicator)
1) Last 10 Years data says Nifty / Bank Nifty More than 66% of times Index are sideways or rangebound (within 1% day) .
2) Mostly, First one hour high and low working as good support and resistance.
Once First one hour complete, this indicator will show Strangle High (CE), Strangle Low (PE) and Straddle (CE/PE).
Straddle:
If you want to do straddle strategy, sell at the money strike (CE/PE) when price comes near to the straddle line (black line),
Strangle:
If you want to do Strangle strategy, sell Strangle High (CE) and Strangle Low (PE) when price comes near to the straddle line (black line). Both Strangle High and Low will be out of the money when price near to the straddle line (black line).
Adjustment: option adjustment to be done based on the price movement. Adjustment purely up to the user / trader.
Note1: If price not comes to near straddle line after first hour, better to stay light…
Note2: If first hour not giving wide High / Low, don’t use strangle strike based on this indicator. Straddle can be done any day with require adjustment / hedge. This Indicator is purely for education purpose, user / trader has to be back-tested before their start using it.
This indicator will work in Nifty / Bank Nifty only. Best Time frames are 3/5/15 Mins. This is purely made for Intraday
Happy Trading 😊
Coefficient of variation (standard deviation over mean)Shows the coefficient of variation defined as standard deviation over mean (for the specified window).
