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Volatility Cone [Loxx]When it comes to forecasting volatility, it seems that the old axiom about weather is applicable: "Everyone talks about it, but no one can do much about it!" Volatility cones are a tool that may be useful in one’s attempt to do something about predicting the future volatility of an asset. A "volatility cone" is a plot of the range of volatilities within a fixed probability band around the true parameter, as a function of sample length. Volatility cone is a visualization tool for the display of historical volatility term structure. It was introduced by Burghardt and Lane in early 1990 and is popular in the option trading community. This is mostly a static indicator due to processor load and is restricted to the daily time frame. Why cones? When we enter the options arena, in an effort to "trade volatility," we want to be able to compare current levels of implied volatility with recent historical volatility in an effort to assess the relative value of the option(s) under consideration Volatility cones can be an effective tool to help us with this assessment. A volatility cone is an analytical application designed to help determine if the current levels of historical or implied volatilities for a given underlying, its options, or any of the new volatility instruments, such as VolContractTM futures, VIX futures, or VXX and VXZ ETNs, are likely to persist in the future. As such, volatility cones are intended to help the user assess the likely volatility that an underlying will go on to display over a certain period. Those who employ volatility cones as a diagnostic tool are relying upon the principle of "reversion to the mean." This means that unusually high levels of volatility are expected to drift or move lower (revert) to their average (mean) levels, while relatively low volatility readings are expected to rise, eventually, to more "normal" values. How to use Suppose you want to analyze an options contract expiring in 3-months and this current option has an current implied volatility 25.5%. Suppose also that realized volatility (y-axis) at the 3-month mark (90 on the x-axis) is 45%, median in 35%, the 25th percentile is 30%, and the low is 25%. Comparing this range to the implied volatility you would maybe conclude that this is a relatively "cheap" option contract. To help you visualize implied volatility on the chart given an expiration date in bars, the indicator includes the ability to enter up to three expirations in bars and each expirations current implied volatility By ascertaining the various historical levels of volatility corresponding to a given time horizon for the options futures under consideration, we’re better prepared to judge the relative "cheapness" or "expensiveness" of the instrument. Volatility options Close-to-Close Close-to-Close volatility is a classic and most commonly used volatility measure, sometimes referred to as historical volatility . Volatility is an indicator of the speed of a stock price change. A stock with high volatility is one where the price changes rapidly and with a bigger amplitude. The more volatile a stock is, the riskier it is. Close-to-close historical volatility calculated using only stock's closing prices. It is the simplest volatility estimator. But in many cases, it is not precise enough. Stock prices could jump considerably during a trading session, and return to the open value at the end. That means that a big amount of price information is not taken into account by close-to-close volatility . Despite its drawbacks, Close-to-Close volatility is still useful in cases where the instrument doesn't have intraday prices. For example, mutual funds calculate their net asset values daily or weekly, and thus their prices are not suitable for more sophisticated volatility estimators. Parkinson Parkinson volatility is a volatility measure that uses the stock’s high and low price of the day. The main difference between regular volatility and Parkinson volatility is that the latter uses high and low prices for a day, rather than only the closing price. That is useful as close to close prices could show little difference while large price movements could have happened during the day. Thus Parkinson's volatility is considered to be more precise and requires less data for calculation than the close-close volatility. One drawback of this estimator is that it doesn't take into account price movements after market close. Hence it systematically undervalues volatility. That drawback is taken into account in the Garman-Klass's volatility estimator. Garman-Klass Garman Klass is a volatility estimator that incorporates open, low, high, and close prices of a security. 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. Researchers Rogers and Satchel have proposed a more efficient method for assessing historical volatility that takes into account price trends. See Rogers-Satchell Volatility for more detail. Rogers-Satchell Rogers-Satchell is an estimator for measuring the volatility of securities with an average return not equal to zero. Unlike Parkinson and Garman-Klass estimators, Rogers-Satchell incorporates drift term (mean return not equal to zero). As a result, it provides a better volatility estimation when the underlying is trending. The main disadvantage of this method is that it does not take into account price movements between trading sessions. It means an underestimation of volatility since price jumps periodically occur in the market precisely at the moments between sessions. A more comprehensive estimator that also considers the gaps between sessions was developed based on the Rogers-Satchel formula in the 2000s by Yang-Zhang. See Yang Zhang Volatility for more detail. Yang-Zhang Yang Zhang is a historical volatility estimator that handles both opening jumps and the drift and has a minimum estimation error. We can think of the Yang-Zhang volatility as the combination of the overnight (close-to-open volatility ) and a weighted average of the Rogers-Satchell volatility and the day’s open-to-close volatility . It considered being 14 times more efficient than the close-to-close estimator. Garman-Klass-Yang-Zhang Garman Klass is a volatility estimator that incorporates open, low, high, and close prices of a security. 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. Researchers Rogers and Satchel have proposed a more efficient method for assessing historical volatility that takes into account price trends. See Rogers-Satchell Volatility for more detail. Exponential Weighted Moving Average The Exponentially Weighted Moving Average (EWMA) is a quantitative or statistical measure used to model or describe a time series. The EWMA is widely used in finance, the main applications being technical analysis and volatility modeling. The moving average is designed as such that older observations are given lower weights. The weights fall exponentially as the data point gets older – hence the name exponentially weighted. The only decision a user of the EWMA must make is the parameter lambda. The parameter decides how important the current observation is in the calculation of the EWMA. The higher the value of lambda, the more closely the EWMA tracks the original time series. Standard Deviation of Log Returns This is the simplest calculation of volatility . It's the standard deviation of ln(close/close(1)) Sampling periods used 5, 10, 20, 30, 60, 90, 120, 150, 180, 210, 240, 270, 300, 330, and 360 Historical Volatility plot Purple outer lines: High and low volatility values corresponding to x-axis time Blue inner lines: 25th and 75th percentiles of volatility corresponding to x-axis time Green line: Median volatility values corresponding to x-axis time White dashed line: Realized volatility corresponding to x-axis time Additional things to know Due to UI constraints on TradingView it will be easier to visualize this indicator by double-clicking the bottom pane where it appears and then expanded the y- and x-axis to view the entire chart. You can click on each point on the graph to see what the volatility of that point is. Option expiration dates will show up as large dots on the graph. You can input your own values in the settings.
Pine Script® indicator
by loxx
3
Variety Distribution Probability Cone [Loxx]Variety Distribution Probability Cone forecasts price within a range of confidence using Geometric Brownian Motion (GBM) calculated using selected probability distribution, volatility, and drift. Below is detailed explanation of the inner workings of the indicator and the math involved. While normally this indicator would be used by options traders, this can also be used by regular directional traders who wish to observe a forecast of the confidence interval of possible prices over time. What is a Random Walk A random walk is a path which consists of a set of random steps. The starting point is zero and following movement may be one step to the left or to the right with equal probability. In the random walk process, there is no observable trend or pattern which are followed by the objects that is the movements are completely random. That is why the prices of a stock as it moves up and down can be modeled by random a walk process. Stock Prices and Geometric Brownian Motion Brownian motion, as first conceived by the botanist Robert Brown (1827), is a mathematical model used to describe random movements of small particles in a fluid or gas. These random movements are observed in the stock markets where the prices move up and down, randomly; hence, Brownian motion is considered as a mathematical model for stock prices. P(exp(lnS0 + (mu + 1/2*sigma^2)t - z(0.05)*sigma*t^0.5) <= St <= exp(lnS0 + (mu + 1/2*sigma^2)t + z(0.05)*sigma*t^0.5)) = 0.95 Probability Distributions Typically the normal distribution is used, but for our purposes here we extend this to Student t-distribution, Cauchy, Gaussian KDE, and Laplace Student's t-Distribution The probability density function of the Student’s t distribution is given by g(x) = (L(v+1)/2) / L(v/2) * 1 / L(sqrt(v)) * (1 + x^2/v) ^ (-(v+1)/2) with v degrees of freedom and v >= 0, denoted by X ~ t(v). The mean is 0 and the variance is v/(v-2). It is known that as v tends to infinity, the Student’s t-distribution tends to a standard normal probability density function, which has a variance of one. Blattberg and Gonedes were the first to propose that stock returns could be modeled by this distribution. (Blattberg and Gonedes, 1974) Platen and Sidorowicz later reaffirmed these findings.(Platen and Rendek, 2007) Finally, Cassidy, Hamp, and Ouyed used these findings to derive the Gosset formula, which is the Student t version of the Black-Scholes model.(Cassidy et al., 2010) They found that v = 2.65 provides the best fit when looking at the past 100 years of returns. They realized that as markets become more turbulent, the degrees of freedom should be adjusted to a smaller value.(Cassidy et al., 2010) Cauchy Distribution The probability density function of the Cauchy distribution is given by f(x) = 1 / (theta*pi*(1 + ((x-n)/v))) where n is the location parameter and theta is the scale parameter, for -infinity < x < infinity and is denoted by X ~ CAU(L,v). This model is similar to the normal distribution in that it is symmetric about zero, but the tails are fatter. This would mean that the probability of an extreme event occurring lies far out in the distributions tail. Using a crude example, if the normal distribution gave a probability of an extreme event occurring of 0.05% and the “best case” scenario of this event occurring 300 years, then using the Cauchy distribution one would find that the probability of occurring would be around 5% and now the “best case” scenario might have been reduced to only 63 years. Thus giving extreme events more of a likelihood of occurring. The mean, variance, and higher order moments are not defined (they are infinite); this implies that n and theta cannot be related to a mean and standard deviation. The Cauchy distribution is related to the Student’s t distribution T ~ CAU(1,0) when v = 1. In 1963, Benoit Mandelbrot was the first to suggest that stock returns follow a stable distribution, in particular, the Cauchy distribution.(Mandelbrot, 1963) His work was validated by Eugene Fama in 1965.(Fama, 1965) Recent research by Nassim Taleb came to the same conclusion as Mandelbrot, saying that stock returns follow a Cauchy distribution, as reported in his New York Times best-seller book “The Black Swan”.(Taleb, 2010) Laplace Distribution In probability theory and statistics, the Laplace distribution is a continuous probability distribution named after Pierre-Simon Laplace. It is also sometimes called the double exponential distribution, because it can be thought of as two exponential distributions (with an additional location parameter) spliced together along the abscissa, although the term is also sometimes used to refer to the Gumbel distribution. The difference between two independent identically distributed exponential random variables is governed by a Laplace distribution, as is a Brownian motion evaluated at an exponentially distributed random time. Increments of Laplace motion or a variance gamma process evaluated over the time scale also have a Laplace distribution. The probability density function of the Cauchy distribution is given by f(x) = 1/2b * exp(-|x-µ|/b) Here, µ is a location parameter and b > 0, which is sometimes referred to as the "diversity", is a scale parameter. If µ = 0 and b=1, the positive half-line is exactly an exponential distribution scaled by 1/2. The probability density function of the Laplace distribution is also reminiscent of the normal distribution; however, whereas the normal distribution is expressed in terms of the squared difference from the mean µ, the Laplace density is expressed in terms of the absolute difference from the mean. Consequently, the Laplace distribution has fatter tails than the normal distribution. Gaussian Kernel Density Estimation In statistics, kernel density estimation (KDE) is the application of kernel smoothing for probability density estimation, i.e., a non-parametric method to estimate the probability density function of a random variable based on kernels as weights. KDE is a fundamental data smoothing problem where inferences about the population are made, based on a finite data sample. In some fields such as signal processing and econometrics it is also termed the Parzen–Rosenblatt window method, after Emanuel Parzen and Murray Rosenblatt, who are usually credited with independently creating it in its current form. One of the famous applications of kernel density estimation is in estimating the class-conditional marginal densities of data when using a naive Bayes classifier, which can improve its prediction accuracy. Let (x1, x2, ..., xn) be independent and identically distributed samples drawn from some univariate distribution with an unknown density f at any given point x. We are interested in estimating the shape of this function f. Its kernel density estimator is: f(x) = 1/nh * sum(k(x-xi)/h, n) where K is the kernel—a non-negative function—and h > 0 is a smoothing parameter called the bandwidth. A kernel with subscript h is called the scaled kernel and defined as Kh(x) = 1/h K(x/h). Intuitively one wants to choose h as small as the data will allow; however, there is always a trade-off between the bias of the estimator and its variance. The probability density function of Gaussian Kernel Density Estimation is given by f(x) = 1 / (v * 2*pi)^0.5 * exp(-(x - m)^2 / (2 * v)) where v is the bandwidth component h squared KDE Bandwidth Estimation Bandwidth selection strongly influences the estimate obtained from the KDE (much more so than the actual shape of the kernel). Bandwidth selection can be done by a "rule of thumb", by cross-validation, by "plug-in methods" or by other means. The default is Scott's Rule. Scott's Rule n ^ (-1/(d+4)) with n the number of data points and d the number of dimensions. In the case of unequally weighted points, this becomes neff^(-1/(d+4)) with neff the effective number of datapoints. Silverman's Rule (n * (d + 2) / 4)^(-1 / (d + 4)) or in the case of unequally weighted points: (neff * (d + 2) / 4)^(-1 / (d + 4)) With a set of weighted samples, the effective number of datapoints neff is defined by: neff = sum(weights)^2 / sum(weights^2) Manual input You can provide your own bandwidth input. This is useful for those who wish to run external to TradingView Grid Search Machine Learning algorithms to solve for the bandwidth per ticker. Inverse CDF of KDE Calculation 1. Create an array of random normalized numbers, using an inverse CDF of a normal distribution of mean of zero and standard deviation one 2. Create a line space range of values -3 to 3 3. Create a Gaussian Kernel Density Estimate CDF by iterating over the line space array created in step 2. For each line space item, find the mean difference between the line space and the random variable divided by the bandwidth. 4. Derive test statistics from the resulting KDE inverse CDF, we use cubic spline interpolation to solve for line space value for a given alpha computed using the user selected probability percent value in the settings. Volatility Close-to-Close Close-to-Close volatility is a classic and most commonly used volatility measure, sometimes referred to as historical volatility. Volatility is an indicator of the speed of a stock price change. A stock with high volatility is one where the price changes rapidly and with a bigger amplitude. The more volatile a stock is, the riskier it is. Close-to-close historical volatility calculated using only stock's closing prices. It is the simplest volatility estimator. But in many cases, it is not precise enough. Stock prices could jump considerably during a trading session, and return to the open value at the end. That means that a big amount of price information is not taken into account by close-to-close volatility. Despite its drawbacks, Close-to-Close volatility is still useful in cases where the instrument doesn't have intraday prices. For example, mutual funds calculate their net asset values daily or weekly, and thus their prices are not suitable for more sophisticated volatility estimators. Parkinson Parkinson volatility is a volatility measure that uses the stock’s high and low price of the day. The main difference between regular volatility and Parkinson volatility is that the latter uses high and low prices for a day, rather than only the closing price. That is useful as close to close prices could show little difference while large price movements could have happened during the day. Thus Parkinson's volatility is considered to be more precise and requires less data for calculation than the close-close volatility. One drawback of this estimator is that it doesn't take into account price movements after market close. Hence it systematically undervalues volatility. That drawback is taken into account in the Garman-Klass's volatility estimator. Garman-Klass Garman Klass is a volatility estimator that incorporates open, low, high, and close prices of a security. 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. Researchers Rogers and Satchel have proposed a more efficient method for assessing historical volatility that takes into account price trends. See Rogers-Satchell Volatility for more detail. Rogers-Satchell Rogers-Satchell is an estimator for measuring the volatility of securities with an average return not equal to zero. Unlike Parkinson and Garman-Klass estimators, Rogers-Satchell incorporates drift term (mean return not equal to zero). As a result, it provides a better volatility estimation when the underlying is trending. The main disadvantage of this method is that it does not take into account price movements between trading sessions. It means an underestimation of volatility since price jumps periodically occur in the market precisely at the moments between sessions. A more comprehensive estimator that also considers the gaps between sessions was developed based on the Rogers-Satchel formula in the 2000s by Yang-Zhang. See Yang Zhang Volatility for more detail. Yang-Zhang Yang Zhang is a historical volatility estimator that handles both opening jumps and the drift and has a minimum estimation error. We can think of the Yang-Zhang volatility as the combination of the overnight (close-to-open volatility) and a weighted average of the Rogers-Satchell volatility and the day’s open-to-close volatility. It considered being 14 times more efficient than the close-to-close estimator. Garman-Klass-Yang-Zhang Garman Klass is a volatility estimator that incorporates open, low, high, and close prices of a security. 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. Researchers Rogers and Satchel have proposed a more efficient method for assessing historical volatility that takes into account price trends. See Rogers-Satchell Volatility for more detail. Exponential Weighted Moving Average The Exponentially Weighted Moving Average (EWMA) is a quantitative or statistical measure used to model or describe a time series. The EWMA is widely used in finance, the main applications being technical analysis and volatility modeling. The moving average is designed as such that older observations are given lower weights. The weights fall exponentially as the data point gets older – hence the name exponentially weighted. The only decision a user of the EWMA must make is the parameter lambda. The parameter decides how important the current observation is in the calculation of the EWMA. The higher the value of lambda, the more closely the EWMA tracks the original time series. Standard Deviation of Log Returns This is the simplest calculation of volatility. It's the standard deviation of ln(close/close(1)) Pseudo GARCH(2,2) This is calculated using a short- and long-run mean of variance multiplied by θ. θavg(var ;M) + (1 − θ)avg(var ;N) = 2θvar/(M+1-(M-1)L) + 2(1-θ)var/(M+1-(M-1)L) Solving for θ can be done by minimizing the mean squared error of estimation; that is, regressing L^-1var - avg(var; N) against avg(var; M) - avg(var; N) and using the resulting beta estimate as θ. Manual User input % value Drift Cost of Equity / Required Rate of Return (CAPM) Standard Capital Asset Pricing Model used to solve for Cost of Equity of Required Rate of Return. Due to the processor overhead required to compute CAPM, the user must plug in values for beta, alpha, and expected market return using Loxx's CAPM indicator series. Used for stocks. Mean of Log Returns Average of the log returns for the underlying ticker over the user selected period of evaluation. General purpose use. Risk-free Rate (r) 10, 20, or 30 year bond yields for the user selected currency. Under equilibrium the drift of the empirical GBM must be the risk-free rate. If the price process is a GBM under the empirical measure, then a consequence of viability is that it is also a GBM under an equivalent (risk-neutral) measure. Risk-free Rate adjusted for Dividends (r-q) This is the Risk-free Rate minus the Dividend Yield. Forex (r-rf) This is derived from the Garman and Kohlhagen (1983) modified Black-Scholes model can be used to price European currency options. This is simply the diffeence between Risk-free Rate of the Forex currency in question. This is used for Forex pricing. Martingale (0) When the drift parameter is 0, geometric Brownian motion is a martingale. In probability theory, a martingale is a sequence of random variables (i.e., a stochastic process) for which, at a particular time, the conditional expectation of the next value in the sequence is equal to the present value, regardless of all prior values. Typically used for futures or margined futures. Manual User input % value Additional notes Indicator can be used on any timeframe. The T (time) variable used to annualize volatility and inside the GBM formula is automatically calculated based on the timeframe of the chart. Confidence interval of volatility is calculated using an inverse CDF of a Chi-Squared Distribution. You change the volatility input used to create the probability cones from from realized volatility to upper or lower confidence levels of volatility to better visualize extremes of range. Generally, you'd stick with realized volatility. Days per year should be 252 for everything but Cryptocurrency. These are days trader per year. Maximum future forecast bars is 365. Forecast bars are limited to the maximum of selected days per year. Includes the ability to overlay option expiration dates by bars to see the range of prices for that date at that bar You can select confidence % you wish for both the cone in general and the volatility. There are three levels for the cones, this will show on the three different levels up and down on the chart. The table on the right displays important calculated values so you don't have to remember what they are or what settings you selected All values are annualized no matter the timeframe. Additional distributions and measures of volatility and drift will be added in future releases.
Pine Script® indicator
by loxx
13
5MSM MAHESH 15It´s just the histogram of the MACD . (Actually it´s not a histogram, I like the Area visualisation more. But you can switch.) 5min stock market property When I´m using the MACD , I´m just searching for a divergence between Price and the MACD-histogram. I´m not interested in the MACD-signalline or the MACD-line in any way. As you can see, The omission of them leads to better visualisation. It´s much easier to spot a divergence. On the one hand because that way the histogram scales bigger, on the other hand becauce the lines can´t overdraw the histogram. Rules bullish Divergence: Price makes a lower low, oscillator makes higher low. Rules bearish Divergence: Price makes a higher high, oscillator makes lower high.
Pine Script® indicator
by patrick199415
Price Pivots for NASDQ 100 StocksPrice Pivots for NASDQ 100 Stocks What is this Indicator? • This indicator calculates the price range a Stock can move in a Day. Advantages of this Indicator • This is a Leading indicator, not Dynamic or Repaint. • Helps to identify the tight range of price movement. • Can easily identify the Options strike price. • Develops a discipline in placing Targets. Disadvantages of this Indicator • The indicator is specifically made for NASDQ 100 stocks. The levels won't work for other stocks. • The indicator shows nothing for other indexes and stocks other than above mentioned. • The data need to be entered manually. Who to use? Highly beneficial for Day Traders, it can be used for Swing and Positions as well. What timeframe to use? • Any timeframe. • The highlighted levels in Red and Green will not show correct levels in 1 minute timeframe. • 5min is recommended for Day Traders. When to use? • Wait for proper swing to form. • Recommended to avoid 1st 1 hour or market open, that is 9.15am to 10.15 or 10.30am. • Within this time a proper swing will be formed. What are the Lines? • The concept is the price will move from one pivot to another. • Entry and Exit can be these levels as Reversal or Retracement. Gray Lines: • Every lines with price labels are the Strike Prices in the Option Chain. • Price moves from 1 Strike Price level to another. • The dashed lines are average levels of 2 Strike Prices. Red & Green Lines: • The Red and Green Lines will appear only after the first 1 hour. • The levels are calculated based on the 1st 1 hour. • Red Lines are important Resistance levels, these are strong Bearish reversal points. It is also a breakout level, this need to be figured out from the past levels, trend, percentage change and consolidation. • Green Lines are important Support levels, these are strong Bullish reversal points. It is also a breakdown level, this need to be figured out from the past levels, trend, percentage change and consolidation. What are the Labels? • First Number: Price of that level. • Numbers in (): Percentage change and Change of price from LTP (Last Traded Price) to that Level. How to use? Entry: • Enter when price is closer to the Red or Green lines. • Enter after considering previous Swing and Trend. • Note the 50% of previous Swing. • Enter Short when price reverse from each level. • If 50% of swing and the pivot level is closer it can be a good entry. Exit: • Use the logic of Entry, each level can be a target. • Exit when price is closer to the Red or Green lines. Indicator Menu Source • Custom: Enter the price manually after choosing the Source as Custom to show the Pivots at that price. • LTP: Pivot is calculated based on Last Traded Price. • Day Open: Pivot is calculated based on current day opening price. • PD Close: Pivot is calculated based on previous day closing price. • PD HL2: Pivot is calculated based on previous day average of High and Low. • PD HLC3: Pivot is calculated based on previous day average of High, Low and Close. "Time (Vertical Lines)" • This is a marker of every 1 hour. • Usually major price movement happen between previous day last 1 hour to today first 1 hour. • Two swings can happen between first 2 hour of current day. • At the end of the day last 1 hour another important movement will happen. • Usually rest of the time won't show any interesting movement. To the Users • Certain symbols may show the levels as a single line. For such symbols choose a different Source or Timeframe from the indicator menu. • Please inform if any of the Symbol's price levels don't react to the pivots , include the Symbol a well. • Also inform if you notice any wrong values, errors or abnormal behavior in the indicator. • Feel free to suggest or adding new features and options. General Tips • It is good if Stock trend is same as that of Index trend. • Lots of indicators creates lots of confusion. • Keep the chart simple and clean. • Buy Low and Sell High. • Master averages or 50%. • Previous Swing High and Swing Low are crucial. Important Note • Currently the levels are in testing stage. • Eventually the levels of certain symbols will be corrected after each update and test.
Pine Script® indicator
by Arun_K_Bhaskar
4
Straddle MoverStraddle Mover is an indicator especially made for option writer / seller who wants to do straddle and adjust the position based on the market trend / movement. It can be use for iron fly strategy too. Settings: User must know the settings of the indicator before using. First one is Option Strike Difference , user need to enter the correct option strike difference of the particular instrument / stock / indices, one can get it from option chain. For example, Nifty having 50 points differences in each option strike and bank nifty having 100 points. So, Nifty user must enter 50 and Bank nifty user must enter 100 in this setting. Second is Straddle Type based on , user can choose the type of the straddle mover. There are two options to choose, 1) High/Low is based on current day high/low average near strike to make straddle and 2) Trend is based on recent N number of candle(s) average near strike to make straddle. Third is Trend Length , default value is 20 which generally used in vwma , donchian channel and other trend finding indicators. User can change if need. If user select high/low based type then length is not important. Note : Option Strike Difference and Straddle type based on is very important setting to use this indicator. User must do adjustments based on their own risk and strategy. This indicator is only for education purpose.
Pine Script® indicator
by Saravanan_Ragavan
3
Weekly Put SaleWeekly Put Sale This study is a tool I use for selling weekly puts at the suggested strike prices. 1. The suggested strike prices are based on the weekly high minus an ATR multiple which can be adjusted in the settings 2. You can also adjust the settings to Monthly strike prices if you prefer selling options further out 3. I suggest looking for Put sale premium that is between 0.25% to 0.75% of the strike price for weekly Puts and 1% to 3% of the strike price for monthly Puts Disclaimers: Selling Puts is an advanced strategy that is risky if you are not prepared to acquire the stock at the strike price you sell at on the expiration date. You must make your own decisions as you will bear the risks associated with any trades you place. To sum it up, trading is risky, and do so at your own risk.
Pine Script® indicator
by Dustin_D_RLT
4
Options Scalping V2This Indicator is Owned by Team Option Scalping. It has 4 Plots and 2 Tables. This indicator to be used only in BankNifty Futures VWAP ( Volume weighted average price ) • User can input the source and enable/disable the VWAP from input section. • When price is more than the VWAP its Bullish Trend and vice versa. VWMA ( Volume weighted moving average ) • Default value of 20 is used in VWMA . User can enable/disable it from input section. • When price is more than the VWMA its Bullish Trend and vice versa. Parabolic SAR • User can input “start”, “increment” and “maximum” values from input section and can enable/disable SAR also. • When price is more than the Parabolic SAR its Bullish Trend and vice versa. SuperTrend • User can input ATR Period and ATR Multiplier values from input section. By defaults it’s 10 and 2. • User have option of enable/disable “Change ATR calculation Method”, if enabled then ATR is calculated differently for SuperTrend. • Enable/disable “BUY/SELL signals” on SuperTrend. • When price is more than the SuperTrend its Bullish Trend and vice versa. Top Right Corner TABLE ( 6 , 10 ) When you are trading in Banknifty futures , we have to check major Banks which is contributing to Banknifty move. So we have given that in this tab. This table consist data of 9 following stocks: • BankNifty • Nifty • Dow • INDIA • VIX • HDFC • ICICI • KOTAK • AXIS • SBI And following data of each stock has been provided: • LTP • Daily Change • Daily Percentage Change • 15-minute Change Percentage • 1-Hour Change Percentage Bottom Right Corner TABLE (3, 6 ) This table consist of 4 indicators values and Up/Down indicator: • VWMA (When price is more than the VWMA its Bullish and vice versa) • SuperTrend (10.2, When price is more than the SuperTrend its Bullish and vice versa.) • RSI (14) • VWAP (When price is more than the VWAP its Bullish and vice versa.)
Pine Script® indicator
by OptionsScalper123
74
Apex Transformation Band EliteApex Transformation Band Elite Version Gauge the mean range of price on an annual/yearly basis of the market. Determine if price is in an uptrend (above the zone), neutral (inside the zone) or downtrend (below the zone). Works on 'all' time frames. Works for 'all' asset classes. Customize settings for better interpretation of trend Buy Signals (green cross) Sell Signals (red cross) Alert Conditions for Buy/Sell Signals Alert Conditions for Trend change: Uptrend/Neutral/Downtrend
Pine Script® indicator
by imzeeshan
1
(JS) S&P 500 Volatility Oscillator For OptionsThe idea for this started here: www.tradingview.com with the user @dime This should only be used on SPX or SPY (though you could use it on other things for correlation I suppose) given that the instrument used to create this calculation is derived from the S&P 500 (thank you VIX). There's a lot of moving parts here though, so allow me to explain... First: The main signal is when Implied Volatility (from VIX) drops beneath Historical Volatility - which is what you want to see so you aren't purchasing a ton of premium on long options. Green and above 0 means that IV% has dropped lower than Historical Volatility. (this signal, for example, would suggest using a Long Call or Put depending on your sentiment) Second: The green line running underneath zero is the bottom portion of the "Average True Range" derived from the values used to create the oscillator. the closer the bottom histogram is to the green line, the more "normal" IV% is. Obviously, if this gets far away from the line then it could be setting up nicely to short options and sell the IV premium to someone else. (this signal, for example, would suggest using something like a Bull Put Spread) Third: The red background along with the white line that drops down below zero signals when (and how far) the IV% from 3 months out (from VIX3M) is less than the current IV%. This would signal the current environment has IV way too high, a signal to short options once again (and don't take any long option positions!). Tried to make this simple, yet effective. If you trade options on SPX, SPY, even ES1! futures - this is a tool tailored specifically for you! As I said before, if you want you can use it for correlation on other securities. Any other ideas or suggestions surrounding this, please let me know! Enjoy!
Pine Script® indicator
by J-Streak
3