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Volatility

[ChasinAlts] The Great Reset Hello fellow tradeurs, "The Great Reset" just tracks the % change of a coin. For whichever reset hour is chosen, once the reset time is reached the % changes of all the coins reset to 0. This is great to find which coins have been moving the most and to be able to see how all of them are moving compared to the rest. Once the reset interval is up and the % change resets to 0, you can see the "*" at the end of the plots and if you hover over it the coin's name is shown in a tooltip. Lastly, if a threshold of 5 is selected and alerts are also used then it will alert you at that % change level as well as threshold*2 and threshold*3 so you can be notified if a coin is going on a tear and pumping through those % change levels (the threshold, threshold*2, and threshold*3 levels are also printed as Hlines on the chart) There is also the Printed Bar Filter to only show the coins that have been moving the most according to the values set in the filter (if you choose to use/select to use the filter). This is the same filter on many of my other scripts so as not to clutter up the chart with coins that have not been moving much. Hope it comes of some use to anyone. Peace and love people...peace and love. -ChasinAlts
Pine Script™ indicator
by A_Traders_Edge
24
HMA w/ SSE-Dynamic EWMA Volatility Bands [Loxx]This indicator is for educational purposes to lay the groundwork for future closed/open source indicators. Some of thee future indicators will employ parameter estimation methods described below, others will require complex solvers such as the Nelder-Mead algorithm on log likelihood estimations to derive optimal parameter values for omega, gamma, alpha, and beta for GARCH(1,1) MLE and other volatility metrics. For our purposes here, we estimate the rolling lambda (λ) value used to calculate EWMA by minimizing of the sum of the squared errors minus the long-run variance--a rolling window of the one year mean of squared log-returns. In practice, practitioners will use a λ equal to a standardized value put out by institutions such as JP Morgan. Even simpler than this, others use a ratio of (per - 1) / (per + 1) to derive λ where per is the lookback period for EWMA. Due to computation limits in Pine, we'll likely not see a true GARCH(1,1) MLE on Pine for quite some time, but future closed source indicators will contain some very interesting industry hacks to get close by employing modifications to EWMA. Enjoy! Exponentially weighted volatility and its relationship to GARCH(1,1) Exponentially weighted volatility--also called exponentially weighted moving average volatility (EWMA)--puts more weight on more recent observations. EWMA is calculated as follows: σ*2 = λσ(n - 1)^2 + (1 − λ)u(n - 1)^2 The estimate, σn, of the volatility for day n (made at the end of day n − 1) is calculated from σn −1 (the estimate that was made at the end of day n − 2 of the volatility for day n − 1) and u^n−1 (the most recent daily percentage change). The EWMA approach has the attractive feature that the data storage requirements are modest. At any given time, we need to remember only the current estimate of the variance rate and the most recent observation on the value of the market variable. When we get a new observation on the value of the market variable, we calculate a new daily percentage change to update our estimate of the variance rate. The old estimate of the variance rate and the old value of the market variable can then be discarded. The EWMA approach is designed to track changes in the volatility. Suppose there is a big move in the market variable on day n − 1 so that u2n−1 is large. This causes our estimate of the current volatility to move upward. The value of λ governs how responsive the estimate of the daily volatility is to the most recent daily percentage change. A low value of λ leads to a great deal of weight being given to the u(n−1)^2 when σn is calculated. In this case, the estimates produced for the volatility on successive days are themselves highly volatile. A high value of λ (i.e., a value close to 1.0) produces estimates of the daily volatility that respond relatively slowly to new information provided by the daily percentage change. The RiskMetrics database, which was originally created by JPMorgan and made publicly available in 1994, used the EWMA model with λ = 0.94 for updating daily volatility estimates. The company found that, across a range of different market variables, this value of λ gives forecasts of the variance rate that come closest to the realized variance rate. In 2006, RiskMetrics switched to using a long memory model. This is a model where the weights assigned to the u(n -i)^2 as i increases decline less fast than in EWMA. GARCH(1,1) Model The EWMA model is a particular case of GARCH(1,1) where γ = 0, α = 1 − λ, and β = λ. The “(1,1)” in GARCH(1,1) indicates that σ^2 is based on the most recent observation of u^2 and the most recent estimate of the variance rate. The more general GARCH(p, q) model calculates σ^2 from the most recent p observations on u2 and the most recent q estimates of the variance rate.7 GARCH(1,1) is by far the most popular of the GARCH models. Setting ω = γVL, the GARCH(1,1) model can also be written: σ(n)^2 = ω + αu(n-1)^2 + βσ(n-1)^2 What this indicator does Calculate log returns log(close/close(1)) Calculates Lambda (λ) dynamically by minimizing the sum of squared errors. I've restricted this to the daily timeframe so as to not bloat the code with additional logic required to derive an annualized EWMA historical volatility metric. After the Lambda is derived, EWMA is calculated one last time and the result is the daily volatility This daily volatility is multiplied by the source and the multiplier +/- the HMA to create the volatility bands Finally, daily volatility is multiplied by the square-root of days per year to derive annualized volatility. Years are trading days for the asset, for most everything but crypto, its 252, for crypto is 365.
Pine Script™ indicator
by loxx
4
Normalized VolatilityOVERVIEW The Normalized Volatility indicator is a technical indicator that gauges the amount of volatility currently present in the market, relative to the average volatility in the market. The purpose of this indicator is to filter out with-trend signals during ranging/non-trending/consolidating conditions. CONCEPTS This indicator assists traders in capitalizing on the assumption that trends are more likely to start during periods of high volatility compared to periods of low volatility. This is because high volatility indicates that there are bigger players currently in the market, which is necessary to begin a sustained trending move. So, to determine whether the current volatility is "high", it is compared to an average volatility for however number of candles back the user specifies. If the current volatility is greater than the average volatility, it is reasonable to assume we are in a high-volatility period. Thus, this is the ideal time to enter a trending trade due to the assumption that trends are more likely to start during these high-volatility periods. HOW DO I READ THIS INDICATOR When the column's color is red, don't take any trend trades since the current volatility is less than the average volatility experienced in the market. When the column's color is green, take all valid with-trend trades since the current volatility is greater than the average volatility experienced in the market.
Pine Script™ indicator
by bjr117
Normalized VolumeOVERVIEW The Normalized Volume indicator is a technical indicator that gauges the amount of volume currently present in the market, relative to the average volume in the market. The purpose of this indicator is to filter out with-trend signals during ranging/non-trending/consolidating conditions. CONCEPTS This indicator assists traders in capitalizing on the assumption that trends are more likely to start during periods of high volume compared to periods of low volume. This is because high volume indicates that there are bigger players currently in the market, which is necessary to begin a sustained trending move. So, to determine whether the current volume is "high", it is compared to an average volume for however number of candles back the user specifies. If the current volume is greater than the average volume, it is reasonable to assume we are in a high-volume period. Thus, this is the ideal time to enter a trending trade due to the assumption that trends are more likely to start during these high-volume periods. More information on this indicator can be found on NNFX's video on it in his Indicator Profile series and on Stonehill Forex's blog post on it . HOW DO I READ THIS INDICATOR When the column's color is red, don't take any trend trades since the current volume is less than the average volume experienced in the market. When the column's color is green, take all valid with-trend trades since the current volume is greater than the average volume experienced in the market.
Pine Script™ indicator
by bjr117
5
Unified Composite Index [UCI] [KuraiBlu] [LazyBear]The purpose of this indicator is to combine the four basic types of indicators (Trend, Volatility, Momentum and Volume) to create a singular, composite index in order to provide a more holistic means of observing potential changes within the market, known as the Unified Composite Index . The indicators used in this index are as follows: Trend - Trend Composite Index Volatility - Bollinger Bands %b Momentum - Relative Strength Index Volume - Money Flow Index The average price source can’t be altered as I’ve made it an average between ((open + close) / 2) and ((high + low) / 2). The best way to use this is by observing several of the indicators at once in conjunction with the average, rather than simply using the average produced to determine the right moment to enter, or exit a trade by itself. I've found when one indicator goes way out of bounds relative to the other three (and subsequently, the average array), then it presents a good buying, or selling opportunity. Some adjustments were made to several of the indicators in order to standardize them on a scale of 1-100 so that they could better accommodate the average array that was finally produced. Thanks to LazyBear for letting me strip down the WaveTrend Oscillator.
Pine Script™ indicator
by kuraiblu
2
Reset Strike Options-Type 2 (Gray Whaley) [Loxx]For a reset option type 2, the strike is reset in a similar way as a reset option 1. That is, the strike is reset to the asset price at a predetermined future time, if the asset price is below (above) the initial strike price for a call (put). The payoff for such a reset call is max(S - X, 0), and max(X - S, 0) for a put, where X is equal to the original strike X if not reset, and equal to the reset strike if reset. Gray and Whaley (1999) have derived a closed-form solution for the price of European reset strike options. The price of the call option is then given by (via "The Complete Guide to Option Pricing Formulas") c = Se^(b-r)T2 * M(a1, y1; p) - Xe^(-rT2) * M(a2, y2; p) - Se^(b-r)T1 * N(-a1) * N(z2) * e^-r(T2-T1) + Se^(b-r)T2 * N(-a1) * N(z1) p = Se^(b-r)T1 * N(a1) * N(-z2) * e^-r(T2-T1) + Se^(b-r)T2 * N(a1) * N(-z1) + Xe^(-rT2) * M(-a2, -y2; p) - Se^(b-r)T2 * M(-a1, -y1; p) where b is the cost-of-carry of the underlying asset, a is the volatility of the relative price changes in the asset, and r is the risk-free interest rate. K is the strike price of the option, T1 the time to reset (in years), and T2 is its time to expiration. N(x) and M(a,b; p) are, respectively, the univariate and bivariate cumulative normal distribution functions. Further a1 = (log(S/X) + (b+v^2/2)T1) / v*T1^0.5 ... a2 = a1 - v*T1^0.5 z1 = ((b+v^2/2)(T2-T1)) / v*(T2-T1)^0.5 ... z2 = z1 - v*(T2-T1)^0.5 y1 = (log(S/X) + (b+v^2/2)T1) / v*T1^0.5 ... y2 = a1 - v*T1^0.5 and p = (T1/T2)^0.5. For reset options with multiple reset rights, see Dai, Kwok, and Wu (2003) and Liao and Wang (2003). Inputs Asset price ( S ) Strike price ( K ) Reset time ( T1 ) Time to maturity ( T2 ) Risk-free rate ( r ) Cost of carry ( b ) Volatility ( s ) Numerical Greeks or Greeks by Finite Difference Analytical Greeks are the standard approach to estimating Delta, Gamma etc... That is what we typically use when we can derive from closed form solutions. Normally, these are well-defined and available in text books. Previously, we relied on closed form solutions for the call or put formulae differentiated with respect to the Black Scholes parameters. When Greeks formulae are difficult to develop or tease out, we can alternatively employ numerical Greeks - sometimes referred to finite difference approximations. A key advantage of numerical Greeks relates to their estimation independent of deriving mathematical Greeks. This could be important when we examine American options where there may not technically exist an exact closed form solution that is straightforward to work with. (via VinegarHill FinanceLabs) Numerical Greeks Outputs Delta D Elasticity L Gamma G DGammaDvol GammaP G Vega DvegaDvol VegaP Theta Q (1 day) Rho r Rho futures option r Phi/Rho2 Carry DDeltaDvol Speed Strike Delta Strike gamma Things to know Only works on the daily timeframe and for the current source price. You can adjust the text size to fit the screen
Pine Script™ indicator
by loxx
Writer Extendible Option [Loxx]These options can be exercised at their initial maturity date /I but are extended to T2 if the option is out-of-the-money at ti. The payoff from a writer-extendible call option at time T1 (T1 < T2) is (via "The Complete Guide to Option Pricing Formulas") c(S, X1, X2, t1, T2) = (S - X1) if S>= X1 else cBSM(S, X2, T2-T1) and for a writer-extendible put is c(S, X1, X2, T1, T2) = (X1 - S) if S< X1 else pBSM(S, X2, T2-T1) Writer-Extendible Call c = cBSM(S, X1, T1) + Se^(b-r)T2 * M(Z1, -Z2; -p) - X2e^-rT2 * M(Z1 - vT^0.5, -Z2 + vT^0.5; -p) Writer-Extendible Put p = cBSM(S, X1, T1) + X2e^-rT2 * M(-Z1 + vT^0.5, Z2 - vT^0.5; -p) - Se^(b-r)T2 * M(-Z1, Z2; -p) b=r options on non-dividend paying stock b=r-q options on stock or index paying a dividend yield of q b=0 options on futures b=r-rf currency options (where rf is the rate in the second currency) Inputs Asset price ( S ) Initial strike price ( X1 ) Extended strike price ( X2 ) Initial time to maturity ( t1 ) Extended time to maturity ( T2 ) Risk-free rate ( r ) Cost of carry ( b ) Volatility ( s ) Numerical Greeks or Greeks by Finite Difference Analytical Greeks are the standard approach to estimating Delta, Gamma etc... That is what we typically use when we can derive from closed form solutions. Normally, these are well-defined and available in text books. Previously, we relied on closed form solutions for the call or put formulae differentiated with respect to the Black Scholes parameters. When Greeks formulae are difficult to develop or tease out, we can alternatively employ numerical Greeks - sometimes referred to finite difference approximations. A key advantage of numerical Greeks relates to their estimation independent of deriving mathematical Greeks. This could be important when we examine American options where there may not technically exist an exact closed form solution that is straightforward to work with. (via VinegarHill FinanceLabs) Numerical Greeks Output Delta Elasticity Gamma DGammaDvol GammaP Vega DvegaDvol VegaP Theta (1 day) Rho Rho futures option Phi/Rho2 Carry DDeltaDvol Speed Things to know Only works on the daily timeframe and for the current source price. You can adjust the text size to fit the screen
Pine Script™ indicator
by loxx
Reset Strike Options-Type 1 [Loxx]In a reset call (put) option, the strike is reset to the asset price at a predetermined future time, if the asset price is below (above) the initial strike price. This makes the strike path-dependent. The payoff for a call at maturity is equal to max((S-X)/X, 0) where is equal to the original strike X if not reset, and equal to the reset strike if reset. Similarly, for a put, the payoff is max((X-S)/X, 0) Gray and Whaley (1997) x have derived a closed-form solution for such an option. For a call, we have c = e^(b-r)(T2-T1) * N(-a2) * N(z1) * e^(-rt1) - e^(-rT2) * N(-a2)*N(z2) - e^(-rT2) * M(a2, y2; p) + (S/X) * e^(b-r)T2 * M(a1, y1; p) and for a put, p = e^(-rT2) * N(a2) * N(-z2) - e^(b-r)(T2-T1) * N(a2) * N(-z1) * e^(-rT1) + e^(-rT2) * M(-a2, -y2; p) - (S/X) * e^(b-r)T2 * M(-a1, -y1; p) where b is the cost-of-carry of the underlying asset, a is the volatil- ity of the relative price changes in the asset, and r is the risk-free interest rate. X is the strike price of the option, r the time to reset (in years), and T is its time to expiration. N(x) and M(a, b; p) are, respec- tively, the univariate and bivariate cumulative normal distribution functions. The remaining parameters are p = (T1/T2)^0.5 and a1 = (log(S/X) + (b+v^2/2)T1) / vT1^0.5 ... a2 = a1 - vT1^0.5 z1 = (b+v^2/2)(T2-T1)/v(T2-T1)^0.5 ... z2 = z1 - v(T2-T1)^0.5 y1 = log(S/X) + (b+v^2)T2 / vT2^0.5 ... y2 = y1 - vT2^0.5 b=r options on non-dividend paying stock b=r-q options on stock or index paying a dividend yield of q b=0 options on futures b=r-rf currency options (where rf is the rate in the second currency) Inputs Asset price ( S ) Initial strike price ( X1 ) Extended strike price ( X2 ) Initial time to maturity ( t1 ) Extended time to maturity ( T2 ) Risk-free rate ( r ) Cost of carry ( b ) Volatility ( s ) Numerical Greeks or Greeks by Finite Difference Analytical Greeks are the standard approach to estimating Delta, Gamma etc... That is what we typically use when we can derive from closed form solutions. Normally, these are well-defined and available in text books. Previously, we relied on closed form solutions for the call or put formulae differentiated with respect to the Black Scholes parameters. When Greeks formulae are difficult to develop or tease out, we can alternatively employ numerical Greeks - sometimes referred to finite difference approximations. A key advantage of numerical Greeks relates to their estimation independent of deriving mathematical Greeks. This could be important when we examine American options where there may not technically exist an exact closed form solution that is straightforward to work with. (via VinegarHill FinanceLabs) Numerical Greeks Ouput Delta Elasticity Gamma DGammaDvol GammaP Vega DvegaDvol VegaP Theta (1 day) Rho Rho futures option Phi/Rho2 Carry DDeltaDvol Speed Things to know Only works on the daily timeframe and for the current source price. You can adjust the text size to fit the screen
Pine Script™ indicator
by loxx
SUPER MACDthis indicator serves to differentiate the classic source of MACD and add the: DYNAMIC MACD and DYNAMIC BAND with these inputs you can modify the inputs of the different Bar's, you can choose between: Candles = classic Candles Heikin Hashi Kagi Line break Pointfigure Renko To use the Dynamic Macd and Band just check the box: Use Dynamic Rsi??? = this input will change the Rsi in the Dynamic Rsi Use Dynamic Band??? = this input will change the Bands to the Dynamic Band Selecting the input: "Use Different Source ???" you can use a source with multiple elements of your choice: 2 = (Source 1 + Source 2) / 2 3 = (Source 1 + Source 2 + Source 3) / 3 4 = (Source 1 + Source 2 + Source 3 + Source 4) / 4 5 = (Source 1 + Source 2 + Source 3 + Source 4 + Source 5) / 5
Pine Script™ indicator
by Cometreon
Fade-in Options [Loxx]A fade-in call has the same payoff as a standard call except the size of the payoff is weighted by how many fixings the asset price were inside a predefined range (L, U). If the asset price is inside the range for every fixing, the payoff will be identical to a plain vanilla option. More precisely, for a call option, the payoff will be max(S(T) - X, 0) X 1/n Sum(n(i)), where n is the total number of fixings and n(i) = 1 if at fixing i the asset price is inside the range, and n(i) = 0 otherwise. Similarly, for a put, the payoff is max(X - S(T), 0) X 1/n Sum(n(i)). Brockhaus, Ferraris, Gallus, Long, Martin, and Overhaus (1999) describe a closed-form formula for fade-in options. For a call the value is given by max(X - S(T), 0) X 1/n Sum(n(i)) describe a closed-form formula for fade-in options. For a call the value is given by c = 1/n * Sum(S^((b-r)*T) * (M(-d5, d1; -p) - M(-d3, d1; -p)) - Xe^(-rT) * (M(-d6, d2; -p) - M(-d4, d2; -p)) where n is the number of fixings, p = (t1^0.5/T^0.5), t1 = iT/n d1 = (log(S/X) + (b + v^2/2)*T) / (v * T^0.5) ... d2 = d1 - v*T^0.5 d3 = (log(S/L) + (b + v^2/2)*t1) / (v * t1^0.5) ... d4 = d3 - v*t1^0.5 d5 = (log(S/U) + (b + v^2/2)*t1) / (v * t1^0.5) ... d6 = d5 - v*t1^0.5 The value of a put is similarly p = 1/n * Sum(Xe^(-rT) * (M(-d6, -d2; -p) - M(-d4, -d2; -p))) - S^((b-r)*T) * (M(-d5, -d1; -p) - M(-d3, -d1; -p) b=r options on non-dividend paying stock b=r-q options on stock or index paying a dividend yield of q b=0 options on futures b=r-rf currency options (where rf is the rate in the second currency) Inputs Asset price ( S ) Strike price ( K ) Lower barrier ( L ) Upper barrier ( U ) Time to maturity ( T ) Risk-free rate ( r ) Cost of carry ( b ) Volatility ( s ) Fixings ( n ) cnd1(x) = Cumulative Normal Distribution nd(x) = Standard Normal Density Function cbnd3() = Cumulative Bivariate Distribution convertingToCCRate(r, cmp ) = Rate compounder Numerical Greeks or Greeks by Finite Difference Analytical Greeks are the standard approach to estimating Delta, Gamma etc... That is what we typically use when we can derive from closed form solutions. Normally, these are well-defined and available in text books. Previously, we relied on closed form solutions for the call or put formulae differentiated with respect to the Black Scholes parameters. When Greeks formulae are difficult to develop or tease out, we can alternatively employ numerical Greeks - sometimes referred to finite difference approximations. A key advantage of numerical Greeks relates to their estimation independent of deriving mathematical Greeks. This could be important when we examine American options where there may not technically exist an exact closed form solution that is straightforward to work with. (via VinegarHill FinanceLabs) Things to know Only works on the daily timeframe and for the current source price. You can adjust the text size to fit the screen
Pine Script™ indicator
by loxx