Asay (1982) Margined Futures Option Pricing Model [Loxx]Asay (1982) Margined Futures Option Pricing Model is an adaptation of the Black-Scholes-Merton Option Pricing Model including Analytical Greeks and implied volatility calculations. The following information is an excerpt from Espen Gaarder Haug's book "Option Pricing Formulas". This version is to price Options on Futures where premium is fully margined. This means the Risk-free Rate, dividend, and cost to carry are all zero. The options sensitivities (Greeks) are the partial derivatives of the Black-Scholes-Merton ( BSM ) formula. Analytical Greeks for our purposes here are broken down into various categories:
Delta Greeks: Delta, DDeltaDvol, Elasticity
Gamma Greeks: Gamma, GammaP, DGammaDvol, Speed
Vega Greeks: Vega , DVegaDvol/Vomma, VegaP
Theta Greeks: Theta
Probability Greeks: StrikeDelta, Risk Neutral Density
(See the code for more details)
Black-Scholes-Merton Option Pricing
The Black-Scholes-Merton model can be "generalized" by incorporating a cost-of-carry rate b. This model can be used to price European options on stocks, stocks paying a continuous dividend yield, options on futures , and currency options:
c = S * e^((b - r) * T) * N(d1) - X * e^(-r * T) * N(d2)
p = X * e^(-r * T) * N(-d2) - S * e^((b - r) * T) * N(-d1)
where
d1 = (log(S / X) + (b + v^2 / 2) * T) / (v * T^0.5)
d2 = d1 - v * T^0.5
b = r ... gives the Black and Scholes (1973) stock option model.
b = r — q ... gives the Merton (1973) stock option model with continuous dividend yield q.
b = 0 ... gives the Black (1976) futures option model.
b = 0 and r = 0 ... gives the Asay (1982) margined futures option model. <== this is the one used for this indicator!
b = r — rf ... gives the Garman and Kohlhagen (1983) currency option model.
Inputs
S = Stock price.
X = Strike price of option.
T = Time to expiration in years.
r = Risk-free rate
d = dividend yield
v = Volatility of the underlying asset price
cnd (x) = The cumulative normal distribution function
nd(x) = The standard normal density function
convertingToCCRate(r, cmp ) = Rate compounder
gImpliedVolatilityNR(string CallPutFlag, float S, float x, float T, float r, float b, float cm , float epsilon) = Implied volatility via Newton Raphson
gBlackScholesImpVolBisection(string CallPutFlag, float S, float x, float T, float r, float b, float cm ) = implied volatility via bisection
Implied Volatility: The Bisection Method
The Newton-Raphson method requires knowledge of the partial derivative of the option pricing formula with respect to volatility ( vega ) when searching for the implied volatility . For some options (exotic and American options in particular), vega is not known analytically. The bisection method is an even simpler method to estimate implied volatility when vega is unknown. The bisection method requires two initial volatility estimates (seed values):
1. A "low" estimate of the implied volatility , al, corresponding to an option value, CL
2. A "high" volatility estimate, aH, corresponding to an option value, CH
The option market price, Cm , lies between CL and cH . The bisection estimate is given as the linear interpolation between the two estimates:
v(i + 1) = v(L) + (c(m) - c(L)) * (v(H) - v(L)) / (c(H) - c(L))
Replace v(L) with v(i + 1) if c(v(i + 1)) < c(m), or else replace v(H) with v(i + 1) if c(v(i + 1)) > c(m) until |c(m) - c(v(i + 1))| <= E, at which point v(i + 1) is the implied volatility and E is the desired degree of accuracy.
Implied Volatility: Newton-Raphson Method
The Newton-Raphson method is an efficient way to find the implied volatility of an option contract. It is nothing more than a simple iteration technique for solving one-dimensional nonlinear equations (any introductory textbook in calculus will offer an intuitive explanation). The method seldom uses more than two to three iterations before it converges to the implied volatility . Let
v(i + 1) = v(i) + (c(v(i)) - c(m)) / (dc / dv (i))
until |c(m) - c(v(i + 1))| <= E at which point v(i + 1) is the implied volatility , E is the desired degree of accuracy, c(m) is the market price of the option, and dc/ dv (i) is the vega of the option evaluaated at v(i) (the sensitivity of the option value for a small change in volatility ).
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
"Implied volatility" için komut dosyalarını ara
Black-76 Options on Futures [Loxx]Black-76 Options on Futures is an adaptation of the Black-Scholes-Merton Option Pricing Model including Analytical Greeks and implied volatility calculations. The following information is an excerpt from Espen Gaarder Haug's book "Option Pricing Formulas". This version is to price Options on Futures. The options sensitivities (Greeks) are the partial derivatives of the Black-Scholes-Merton ( BSM ) formula. Analytical Greeks for our purposes here are broken down into various categories:
Delta Greeks: Delta, DDeltaDvol, Elasticity
Gamma Greeks: Gamma, GammaP, DGammaDvol, Speed
Vega Greeks: Vega , DVegaDvol/Vomma, VegaP
Theta Greeks: Theta
Rate/Carry Greeks: Rho futures option
Probability Greeks: StrikeDelta, Risk Neutral Density
(See the code for more details)
Black-Scholes-Merton Option Pricing
The Black-Scholes-Merton model can be "generalized" by incorporating a cost-of-carry rate b. This model can be used to price European options on stocks, stocks paying a continuous dividend yield, options on futures , and currency options:
c = S * e^((b - r) * T) * N(d1) - X * e^(-r * T) * N(d2)
p = X * e^(-r * T) * N(-d2) - S * e^((b - r) * T) * N(-d1)
where
d1 = (log(S / X) + (b + v^2 / 2) * T) / (v * T^0.5)
d2 = d1 - v * T^0.5
b = r ... gives the Black and Scholes (1973) stock option model.
b = r — q ... gives the Merton (1973) stock option model with continuous dividend yield q.
b = 0 ... gives the Black (1976) futures option model. <== this is the one used for this indicator!
b = 0 and r = 0 ... gives the Asay (1982) margined futures option model.
b = r — rf ... gives the Garman and Kohlhagen (1983) currency option model.
Inputs
S = Stock price.
X = Strike price of option.
T = Time to expiration in years.
r = Risk-free rate
d = dividend yield
v = Volatility of the underlying asset price
cnd (x) = The cumulative normal distribution function
nd(x) = The standard normal density function
convertingToCCRate(r, cmp ) = Rate compounder
gImpliedVolatilityNR(string CallPutFlag, float S, float x, float T, float r, float b, float cm , float epsilon) = Implied volatility via Newton Raphson
gBlackScholesImpVolBisection(string CallPutFlag, float S, float x, float T, float r, float b, float cm ) = implied volatility via bisection
Implied Volatility: The Bisection Method
The Newton-Raphson method requires knowledge of the partial derivative of the option pricing formula with respect to volatility ( vega ) when searching for the implied volatility . For some options (exotic and American options in particular), vega is not known analytically. The bisection method is an even simpler method to estimate implied volatility when vega is unknown. The bisection method requires two initial volatility estimates (seed values):
1. A "low" estimate of the implied volatility , al, corresponding to an option value, CL
2. A "high" volatility estimate, aH, corresponding to an option value, CH
The option market price, Cm , lies between CL and cH . The bisection estimate is given as the linear interpolation between the two estimates:
v(i + 1) = v(L) + (c(m) - c(L)) * (v(H) - v(L)) / (c(H) - c(L))
Replace v(L) with v(i + 1) if c(v(i + 1)) < c(m), or else replace v(H) with v(i + 1) if c(v(i + 1)) > c(m) until |c(m) - c(v(i + 1))| <= E, at which point v(i + 1) is the implied volatility and E is the desired degree of accuracy.
Implied Volatility: Newton-Raphson Method
The Newton-Raphson method is an efficient way to find the implied volatility of an option contract. It is nothing more than a simple iteration technique for solving one-dimensional nonlinear equations (any introductory textbook in calculus will offer an intuitive explanation). The method seldom uses more than two to three iterations before it converges to the implied volatility . Let
v(i + 1) = v(i) + (c(v(i)) - c(m)) / (dc / dv (i))
until |c(m) - c(v(i + 1))| <= E at which point v(i + 1) is the implied volatility , E is the desired degree of accuracy, c(m) is the market price of the option, and dc/ dv (i) is the vega of the option evaluaated at v(i) (the sensitivity of the option value for a small change in volatility ).
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
Garman and Kohlhagen (1983) for Currency Options [Loxx]Garman and Kohlhagen (1983) for Currency Options is an adaptation of the Black-Scholes-Merton Option Pricing Model including Analytical Greeks and implied volatility calculations. The following information is an excerpt from Espen Gaarder Haug's book "Option Pricing Formulas". This version of BSMOPM is to price Currency Options. The options sensitivities (Greeks) are the partial derivatives of the Black-Scholes-Merton ( BSM ) formula. Analytical Greeks for our purposes here are broken down into various categories:
Delta Greeks: Delta, DDeltaDvol, Elasticity
Gamma Greeks: Gamma, GammaP, DGammaDSpot/speed, DGammaDvol/Zomma
Vega Greeks: Vega , DVegaDvol/Vomma, VegaP, Speed
Theta Greeks: Theta
Rate/Carry Greeks: Rho, Rho futures option, Carry Rho, Phi/Rho2
Probability Greeks: StrikeDelta, Risk Neutral Density
(See the code for more details)
Black-Scholes-Merton Option Pricing for Currency Options
The Garman and Kohlhagen (1983) modified Black-Scholes model can be used to price European currency options; see also Grabbe (1983). The model is mathematically equivalent to the Merton (1973) model presented earlier. The only difference is that the dividend yield is replaced by the risk-free rate of the foreign currency rf:
c = S * e^(-rf * T) * N(d1) - X * e^(-r * T) * N(d2)
p = X * e^(-r * T) * N(-d2) - S * e^(-rf * T) * N(-d1)
where
d1 = (log(S / X) + (r - rf + v^2 / 2) * T) / (v * T^0.5)
d2 = d1 - v * T^0.5
For more information on currency options, see DeRosa (2000)
Inputs
S = Stock price.
X = Strike price of option.
T = Time to expiration in years.
r = Risk-free rate
rf = Risk-free rate of the foreign currency
v = Volatility of the underlying asset price
cnd (x) = The cumulative normal distribution function
nd(x) = The standard normal density function
convertingToCCRate(r, cmp ) = Rate compounder
gImpliedVolatilityNR(string CallPutFlag, float S, float x, float T, float r, float b, float cm , float epsilon) = Implied volatility via Newton Raphson
gBlackScholesImpVolBisection(string CallPutFlag, float S, float x, float T, float r, float b, float cm ) = implied volatility via bisection
Implied Volatility: The Bisection Method
The Newton-Raphson method requires knowledge of the partial derivative of the option pricing formula with respect to volatility ( vega ) when searching for the implied volatility . For some options (exotic and American options in particular), vega is not known analytically. The bisection method is an even simpler method to estimate implied volatility when vega is unknown. The bisection method requires two initial volatility estimates (seed values):
1. A "low" estimate of the implied volatility , al, corresponding to an option value, CL
2. A "high" volatility estimate, aH, corresponding to an option value, CH
The option market price, Cm , lies between CL and cH . The bisection estimate is given as the linear interpolation between the two estimates:
v(i + 1) = v(L) + (c(m) - c(L)) * (v(H) - v(L)) / (c(H) - c(L))
Replace v(L) with v(i + 1) if c(v(i + 1)) < c(m), or else replace v(H) with v(i + 1) if c(v(i + 1)) > c(m) until |c(m) - c(v(i + 1))| <= E, at which point v(i + 1) is the implied volatility and E is the desired degree of accuracy.
Implied Volatility: Newton-Raphson Method
The Newton-Raphson method is an efficient way to find the implied volatility of an option contract. It is nothing more than a simple iteration technique for solving one-dimensional nonlinear equations (any introductory textbook in calculus will offer an intuitive explanation). The method seldom uses more than two to three iterations before it converges to the implied volatility . Let
v(i + 1) = v(i) + (c(v(i)) - c(m)) / (dc / dv (i))
until |c(m) - c(v(i + 1))| <= E at which point v(i + 1) is the implied volatility , E is the desired degree of accuracy, c(m) is the market price of the option, and dc/ dv (i) is the vega of the option evaluaated at v(i) (the sensitivity of the option value for a small change in volatility ).
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
Related indicators:
BSM OPM 1973 w/ Continuous Dividend Yield
Black-Scholes 1973 OPM on Non-Dividend Paying Stocks
Generalized Black-Scholes-Merton w/ Analytical Greeks
Generalized Black-Scholes-Merton Option Pricing Formula
Sprenkle 1964 Option Pricing Model w/ Num. Greeks
Modified Bachelier Option Pricing Model w/ Num. Greeks
Bachelier 1900 Option Pricing Model w/ Numerical Greeks
BSM OPM 1973 w/ Continuous Dividend Yield [Loxx]Generalized Black-Scholes-Merton w/ Analytical Greeks is an adaptation of the Black-Scholes-Merton Option Pricing Model including Analytical Greeks and implied volatility calculations. The following information is an excerpt from Espen Gaarder Haug's book "Option Pricing Formulas". The options sensitivities (Greeks) are the partial derivatives of the Black-Scholes-Merton ( BSM ) formula. Analytical Greeks for our purposes here are broken down into various categories:
Delta Greeks: Delta, DDeltaDvol, Elasticity
Gamma Greeks: Gamma, GammaP, DGammaDSpot/speed, DGammaDvol/Zomma
Vega Greeks: Vega , DVegaDvol/Vomma, VegaP
Theta Greeks: Theta
Rate/Carry Greeks: Rho, Rho futures option, Carry Rho, Phi/Rho2
Probability Greeks: StrikeDelta, Risk Neutral Density
(See the code for more details)
Black-Scholes-Merton Option Pricing
The Black-Scholes-Merton model can be "generalized" by incorporating a cost-of-carry rate b. This model can be used to price European options on stocks, stocks paying a continuous dividend yield, options on futures, and currency options:
c = S * e^((b - r) * T) * N(d1) - X * e^(-r * T) * N(d2)
p = X * e^(-r * T) * N(-d2) - S * e^((b - r) * T) * N(-d1)
where
d1 = (log(S / X) + (b + v^2 / 2) * T) / (v * T^0.5)
d2 = d1 - v * T^0.5
b = r ... gives the Black and Scholes (1973) stock option model.
b = r — q ... gives the Merton (1973) stock option model with continuous dividend yield q. <== this is the one used for this indicator!
b = 0 ... gives the Black (1976) futures option model.
b = 0 and r = 0 ... gives the Asay (1982) margined futures option model.
b = r — rf ... gives the Garman and Kohlhagen (1983) currency option model.
Inputs
S = Stock price.
X = Strike price of option.
T = Time to expiration in years.
r = Risk-free rate
d = dividend yield
v = Volatility of the underlying asset price
cnd (x) = The cumulative normal distribution function
nd(x) = The standard normal density function
convertingToCCRate(r, cmp ) = Rate compounder
gImpliedVolatilityNR(string CallPutFlag, float S, float x, float T, float r, float b, float cm , float epsilon) = Implied volatility via Newton Raphson
gBlackScholesImpVolBisection(string CallPutFlag, float S, float x, float T, float r, float b, float cm ) = implied volatility via bisection
Implied Volatility: The Bisection Method
The Newton-Raphson method requires knowledge of the partial derivative of the option pricing formula with respect to volatility ( vega ) when searching for the implied volatility . For some options (exotic and American options in particular), vega is not known analytically. The bisection method is an even simpler method to estimate implied volatility when vega is unknown. The bisection method requires two initial volatility estimates (seed values):
1. A "low" estimate of the implied volatility , al, corresponding to an option value, CL
2. A "high" volatility estimate, aH, corresponding to an option value, CH
The option market price, Cm , lies between CL and cH . The bisection estimate is given as the linear interpolation between the two estimates:
v(i + 1) = v(L) + (c(m) - c(L)) * (v(H) - v(L)) / (c(H) - c(L))
Replace v(L) with v(i + 1) if c(v(i + 1)) < c(m), or else replace v(H) with v(i + 1) if c(v(i + 1)) > c(m) until |c(m) - c(v(i + 1))| <= E, at which point v(i + 1) is the implied volatility and E is the desired degree of accuracy.
Implied Volatility: Newton-Raphson Method
The Newton-Raphson method is an efficient way to find the implied volatility of an option contract. It is nothing more than a simple iteration technique for solving one-dimensional nonlinear equations (any introductory textbook in calculus will offer an intuitive explanation). The method seldom uses more than two to three iterations before it converges to the implied volatility . Let
v(i + 1) = v(i) + (c(v(i)) - c(m)) / (dc / dv (i))
until |c(m) - c(v(i + 1))| <= E at which point v(i + 1) is the implied volatility , E is the desired degree of accuracy, c(m) is the market price of the option, and dc/ dv (i) is the vega of the option evaluaated at v(i) (the sensitivity of the option value for a small change in volatility ).
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
Black-Scholes 1973 OPM on Non-Dividend Paying Stocks [Loxx]Black-Scholes 1973 OPM on Non-Dividend Paying Stocks is an adaptation of the Black-Scholes-Merton Option Pricing Model including Analytical Greeks and implied volatility calculations. Making b equal to r yields the BSM model where dividends are not considered. The following information is an excerpt from Espen Gaarder Haug's book "Option Pricing Formulas". The options sensitivities (Greeks) are the partial derivatives of the Black-Scholes-Merton ( BSM ) formula. For our purposes here are, Analytical Greeks are broken down into various categories:
Delta Greeks: Delta, DDeltaDvol, Elasticity
Gamma Greeks: Gamma, GammaP, DGammaDSpot/speed, DGammaDvol/Zomma
Vega Greeks: Vega , DVegaDvol/Vomma, VegaP
Theta Greeks: Theta
Rate/Carry Greeks: Rho
Probability Greeks: StrikeDelta, Risk Neutral Density
(See the code for more details)
Black-Scholes-Merton Option Pricing
The BSM formula and its binomial counterpart may easily be the most used "probability model/tool" in everyday use — even if we con- sider all other scientific disciplines. Literally tens of thousands of people, including traders, market makers, and salespeople, use option formulas several times a day. Hardly any other area has seen such dramatic growth as the options and derivatives businesses. In this chapter we look at the various versions of the basic option formula. In 1997 Myron Scholes and Robert Merton were awarded the Nobel Prize (The Bank of Sweden Prize in Economic Sciences in Memory of Alfred Nobel). Unfortunately, Fischer Black died of cancer in 1995 before he also would have received the prize.
It is worth mentioning that it was not the option formula itself that Myron Scholes and Robert Merton were awarded the Nobel Prize for, the formula was actually already invented, but rather for the way they derived it — the replicating portfolio argument, continuous- time dynamic delta hedging, as well as making the formula consistent with the capital asset pricing model (CAPM). The continuous dynamic replication argument is unfortunately far from robust. The popularity among traders for using option formulas heavily relies on hedging options with options and on the top of this dynamic delta hedging, see Higgins (1902), Nelson (1904), Mello and Neuhaus (1998), Derman and Taleb (2005), as well as Haug (2006) for more details on this topic. In any case, this book is about option formulas and not so much about how to derive them.
Provided here are the various versions of the Black-Scholes-Merton formula presented in the literature. All formulas in this section are originally derived based on the underlying asset S follows a geometric Brownian motion
dS = mu * S * dt + v * S * dz
where t is the expected instantaneous rate of return on the underlying asset, a is the instantaneous volatility of the rate of return, and dz is a Wiener process.
The formula derived by Black and Scholes (1973) can be used to value a European option on a stock that does not pay dividends before the option's expiration date. Letting c and p denote the price of European call and put options, respectively, the formula states that
c = S * N(d1) - X * e^(-r * T) * N(d2)
p = X * e^(-r * T) * N(d2) - S * N(d1)
where
d1 = (log(S / X) + (r + v^2 / 2) * T) / (v * T^0.5)
d2 = (log(S / X) + (r - v^2 / 2) * T) / (v * T^0.5) = d1 - v * T^0.5
**This version of the Black-Scholes formula can also be used to price American call options on a non-dividend-paying stock, since it will never be optimal to exercise the option before expiration.**
Inputs
S = Stock price.
X = Strike price of option.
T = Time to expiration in years.
r = Risk-free rate
b = Cost of carry
v = Volatility of the underlying asset price
cnd (x) = The cumulative normal distribution function
nd(x) = The standard normal density function
convertingToCCRate(r, cmp ) = Rate compounder
gImpliedVolatilityNR(string CallPutFlag, float S, float x, float T, float r, float b, float cm , float epsilon) = Implied volatility via Newton Raphson
gBlackScholesImpVolBisection(string CallPutFlag, float S, float x, float T, float r, float b, float cm ) = implied volatility via bisection
Implied Volatility: The Bisection Method
The Newton-Raphson method requires knowledge of the partial derivative of the option pricing formula with respect to volatility ( vega ) when searching for the implied volatility . For some options (exotic and American options in particular), vega is not known analytically. The bisection method is an even simpler method to estimate implied volatility when vega is unknown. The bisection method requires two initial volatility estimates (seed values):
1. A "low" estimate of the implied volatility , al, corresponding to an option value, CL
2. A "high" volatility estimate, aH, corresponding to an option value, CH
The option market price, Cm , lies between CL and cH . The bisection estimate is given as the linear interpolation between the two estimates:
v(i + 1) = v(L) + (c(m) - c(L)) * (v(H) - v(L)) / (c(H) - c(L))
Replace v(L) with v(i + 1) if c(v(i + 1)) < c(m), or else replace v(H) with v(i + 1) if c(v(i + 1)) > c(m) until |c(m) - c(v(i + 1))| <= E, at which point v(i + 1) is the implied volatility and E is the desired degree of accuracy.
Implied Volatility: Newton-Raphson Method
The Newton-Raphson method is an efficient way to find the implied volatility of an option contract. It is nothing more than a simple iteration technique for solving one-dimensional nonlinear equations (any introductory textbook in calculus will offer an intuitive explanation). The method seldom uses more than two to three iterations before it converges to the implied volatility . Let
v(i + 1) = v(i) + (c(v(i)) - c(m)) / (dc / dv (i))
until |c(m) - c(v(i + 1))| <= E at which point v(i + 1) is the implied volatility , E is the desired degree of accuracy, c(m) is the market price of the option, and dc/ dv (i) is the vega of the option evaluaated at v(i) (the sensitivity of the option value for a small change in volatility ).
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
Generalized Black-Scholes-Merton w/ Analytical Greeks [Loxx]Generalized Black-Scholes-Merton w/ Analytical Greeks is an adaptation of the Black-Scholes-Merton Option Pricing Model including Analytical Greeks and implied volatility calculations. The following information is an excerpt from Espen Gaarder Haug's book "Option Pricing Formulas". The options sensitivities (Greeks) are the partial derivatives of the Black-Scholes-Merton (BSM) formula. Analytical Greeks for our purposes here are broken down into various categories:
Delta Greeks: Delta, DDeltaDvol, Elasticity
Gamma Greeks: Gamma, GammaP, DGammaDSpot/speed, DGammaDvol/Zomma
Vega Greeks: Vega, DVegaDvol/Vomma, VegaP
Theta Greeks: Theta
Rate/Carry Greeks: Rho, Rho futures option, Carry Rho, Phi/Rho2
Probability Greeks: StrikeDelta, Risk Neutral Density
(See the code for more details)
Black-Scholes-Merton Option Pricing
The BSM formula and its binomial counterpart may easily be the most used "probability model/tool" in everyday use — even if we con- sider all other scientific disciplines. Literally tens of thousands of people, including traders, market makers, and salespeople, use option formulas several times a day. Hardly any other area has seen such dramatic growth as the options and derivatives businesses. In this chapter we look at the various versions of the basic option formula. In 1997 Myron Scholes and Robert Merton were awarded the Nobel Prize (The Bank of Sweden Prize in Economic Sciences in Memory of Alfred Nobel). Unfortunately, Fischer Black died of cancer in 1995 before he also would have received the prize.
It is worth mentioning that it was not the option formula itself that Myron Scholes and Robert Merton were awarded the Nobel Prize for, the formula was actually already invented, but rather for the way they derived it — the replicating portfolio argument, continuous- time dynamic delta hedging, as well as making the formula consistent with the capital asset pricing model (CAPM). The continuous dynamic replication argument is unfortunately far from robust. The popularity among traders for using option formulas heavily relies on hedging options with options and on the top of this dynamic delta hedging, see Higgins (1902), Nelson (1904), Mello and Neuhaus (1998), Derman and Taleb (2005), as well as Haug (2006) for more details on this topic. In any case, this book is about option formulas and not so much about how to derive them.
Provided here are the various versions of the Black-Scholes-Merton formula presented in the literature. All formulas in this section are originally derived based on the underlying asset S follows a geometric Brownian motion
dS = mu * S * dt + v * S * dz
where t is the expected instantaneous rate of return on the underlying asset, a is the instantaneous volatility of the rate of return, and dz is a Wiener process.
The formula derived by Black and Scholes (1973) can be used to value a European option on a stock that does not pay dividends before the option's expiration date. Letting c and p denote the price of European call and put options, respectively, the formula states that
c = S * N(d1) - X * e^(-r * T) * N(d2)
p = X * e^(-r * T) * N(d2) - S * N(d1)
where
d1 = (log(S / X) + (r + v^2 / 2) * T) / (v * T^0.5)
d2 = (log(S / X) + (r - v^2 / 2) * T) / (v * T^0.5) = d1 - v * T^0.5
Inputs
S = Stock price.
X = Strike price of option.
T = Time to expiration in years.
r = Risk-free rate
b = Cost of carry
v = Volatility of the underlying asset price
cnd (x) = The cumulative normal distribution function
nd(x) = The standard normal density function
convertingToCCRate(r, cmp ) = Rate compounder
gImpliedVolatilityNR(string CallPutFlag, float S, float x, float T, float r, float b, float cm , float epsilon) = Implied volatility via Newton Raphson
gBlackScholesImpVolBisection(string CallPutFlag, float S, float x, float T, float r, float b, float cm ) = implied volatility via bisection
Implied Volatility: The Bisection Method
The Newton-Raphson method requires knowledge of the partial derivative of the option pricing formula with respect to volatility ( vega ) when searching for the implied volatility . For some options (exotic and American options in particular), vega is not known analytically. The bisection method is an even simpler method to estimate implied volatility when vega is unknown. The bisection method requires two initial volatility estimates (seed values):
1. A "low" estimate of the implied volatility , al, corresponding to an option value, CL
2. A "high" volatility estimate, aH, corresponding to an option value, CH
The option market price, Cm , lies between CL and cH . The bisection estimate is given as the linear interpolation between the two estimates:
v(i + 1) = v(L) + (c(m) - c(L)) * (v(H) - v(L)) / (c(H) - c(L))
Replace v(L) with v(i + 1) if c(v(i + 1)) < c(m), or else replace v(H) with v(i + 1) if c(v(i + 1)) > c(m) until |c(m) - c(v(i + 1))| <= E, at which point v(i + 1) is the implied volatility and E is the desired degree of accuracy.
Implied Volatility: Newton-Raphson Method
The Newton-Raphson method is an efficient way to find the implied volatility of an option contract. It is nothing more than a simple iteration technique for solving one-dimensional nonlinear equations (any introductory textbook in calculus will offer an intuitive explanation). The method seldom uses more than two to three iterations before it converges to the implied volatility . Let
v(i + 1) = v(i) + (c(v(i)) - c(m)) / (dc / dv (i))
until |c(m) - c(v(i + 1))| <= E at which point v(i + 1) is the implied volatility , E is the desired degree of accuracy, c(m) is the market price of the option, and dc/ dv (i) is the vega of the option evaluaated at v(i) (the sensitivity of the option value for a small change in volatility ).
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Dynamic Equity Allocation Model"Cash is Trash"? Not Always. Here's Why Science Beats Guesswork.
Every retail trader knows the frustration: you draw support and resistance lines, you spot patterns, you follow market gurus on social media—and still, when the next bear market hits, your portfolio bleeds red. Meanwhile, institutional investors seem to navigate market turbulence with ease, preserving capital when markets crash and participating when they rally. What's their secret?
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This document presents exactly such a system—not a proprietary black box available only to hedge funds, but a fully transparent, academically grounded framework that any serious investor can understand and apply. The Dynamic Equity Allocation Model (DEAM) synthesizes decades of financial research from Nobel laureates and leading academics into a practical tool for tactical asset allocation.
Stop drawing colorful lines on your chart and start thinking like a quant. This isn't about predicting where the market goes next week—it's about systematically adjusting your risk exposure based on what the data actually tells you. When valuations scream danger, when volatility spikes, when credit markets freeze, when multiple warning signals align—that's when cash isn't trash. That's when cash saves your portfolio.
The irony of "cash is trash" rhetoric is that it ignores timing. Yes, being 100% cash for decades would be disastrous. But being 100% equities through every crisis is equally foolish. The sophisticated approach is dynamic: aggressive when conditions favor risk-taking, defensive when they don't. This model shows you how to make that decision systematically, not emotionally.
Whether you're managing your own retirement portfolio or seeking to understand how institutional allocation strategies work, this comprehensive analysis provides the theoretical foundation, mathematical implementation, and practical guidance to elevate your investment approach from amateur to professional.
The choice is yours: keep hoping your chart patterns work out, or start using the same quantitative methods that professionals rely on. The tools are here. The research is cited. The methodology is explained. All you need to do is read, understand, and apply.
The Dynamic Equity Allocation Model (DEAM) is a quantitative framework for systematic allocation between equities and cash, grounded in modern portfolio theory and empirical market research. The model integrates five scientifically validated dimensions of market analysis—market regime, risk metrics, valuation, sentiment, and macroeconomic conditions—to generate dynamic allocation recommendations ranging from 0% to 100% equity exposure. This work documents the theoretical foundations, mathematical implementation, and practical application of this multi-factor approach.
1. Introduction and Theoretical Background
1.1 The Limitations of Static Portfolio Allocation
Traditional portfolio theory, as formulated by Markowitz (1952) in his seminal work "Portfolio Selection," assumes an optimal static allocation where investors distribute their wealth across asset classes according to their risk aversion. This approach rests on the assumption that returns and risks remain constant over time. However, empirical research demonstrates that this assumption does not hold in reality. Fama and French (1989) showed that expected returns vary over time and correlate with macroeconomic variables such as the spread between long-term and short-term interest rates. Campbell and Shiller (1988) demonstrated that the price-earnings ratio possesses predictive power for future stock returns, providing a foundation for dynamic allocation strategies.
The academic literature on tactical asset allocation has evolved considerably over recent decades. Ilmanen (2011) argues in "Expected Returns" that investors can improve their risk-adjusted returns by considering valuation levels, business cycles, and market sentiment. The Dynamic Equity Allocation Model presented here builds on this research tradition and operationalizes these insights into a practically applicable allocation framework.
1.2 Multi-Factor Approaches in Asset Allocation
Modern financial research has shown that different factors capture distinct aspects of market dynamics and together provide a more robust picture of market conditions than individual indicators. Ross (1976) developed the Arbitrage Pricing Theory, a model that employs multiple factors to explain security returns. Following this multi-factor philosophy, DEAM integrates five complementary analytical dimensions, each tapping different information sources and collectively enabling comprehensive market understanding.
2. Data Foundation and Data Quality
2.1 Data Sources Used
The model draws its data exclusively from publicly available market data via the TradingView platform. This transparency and accessibility is a significant advantage over proprietary models that rely on non-public data. The data foundation encompasses several categories of market information, each capturing specific aspects of market dynamics.
First, price data for the S&P 500 Index is obtained through the SPDR S&P 500 ETF (ticker: SPY). The use of a highly liquid ETF instead of the index itself has practical reasons, as ETF data is available in real-time and reflects actual tradability. In addition to closing prices, high, low, and volume data are captured, which are required for calculating advanced volatility measures.
Fundamental corporate metrics are retrieved via TradingView's Financial Data API. These include earnings per share, price-to-earnings ratio, return on equity, debt-to-equity ratio, dividend yield, and share buyback yield. Cochrane (2011) emphasizes in "Presidential Address: Discount Rates" the central importance of valuation metrics for forecasting future returns, making these fundamental data a cornerstone of the model.
Volatility indicators are represented by the CBOE Volatility Index (VIX) and related metrics. The VIX, often referred to as the market's "fear gauge," measures the implied volatility of S&P 500 index options and serves as a proxy for market participants' risk perception. Whaley (2000) describes in "The Investor Fear Gauge" the construction and interpretation of the VIX and its use as a sentiment indicator.
Macroeconomic data includes yield curve information through US Treasury bonds of various maturities and credit risk premiums through the spread between high-yield bonds and risk-free government bonds. These variables capture the macroeconomic conditions and financing conditions relevant for equity valuation. Estrella and Hardouvelis (1991) showed that the shape of the yield curve has predictive power for future economic activity, justifying the inclusion of these data.
2.2 Handling Missing Data
A practical problem when working with financial data is dealing with missing or unavailable values. The model implements a fallback system where a plausible historical average value is stored for each fundamental metric. When current data is unavailable for a specific point in time, this fallback value is used. This approach ensures that the model remains functional even during temporary data outages and avoids systematic biases from missing data. The use of average values as fallback is conservative, as it generates neither overly optimistic nor pessimistic signals.
3. Component 1: Market Regime Detection
3.1 The Concept of Market Regimes
The idea that financial markets exist in different "regimes" or states that differ in their statistical properties has a long tradition in financial science. Hamilton (1989) developed regime-switching models that allow distinguishing between different market states with different return and volatility characteristics. The practical application of this theory consists of identifying the current market state and adjusting portfolio allocation accordingly.
DEAM classifies market regimes using a scoring system that considers three main dimensions: trend strength, volatility level, and drawdown depth. This multidimensional view is more robust than focusing on individual indicators, as it captures various facets of market dynamics. Classification occurs into six distinct regimes: Strong Bull, Bull Market, Neutral, Correction, Bear Market, and Crisis.
3.2 Trend Analysis Through Moving Averages
Moving averages are among the oldest and most widely used technical indicators and have also received attention in academic literature. Brock, Lakonishok, and LeBaron (1992) examined in "Simple Technical Trading Rules and the Stochastic Properties of Stock Returns" the profitability of trading rules based on moving averages and found evidence for their predictive power, although later studies questioned the robustness of these results when considering transaction costs.
The model calculates three moving averages with different time windows: a 20-day average (approximately one trading month), a 50-day average (approximately one quarter), and a 200-day average (approximately one trading year). The relationship of the current price to these averages and the relationship of the averages to each other provide information about trend strength and direction. When the price trades above all three averages and the short-term average is above the long-term, this indicates an established uptrend. The model assigns points based on these constellations, with longer-term trends weighted more heavily as they are considered more persistent.
3.3 Volatility Regimes
Volatility, understood as the standard deviation of returns, is a central concept of financial theory and serves as the primary risk measure. However, research has shown that volatility is not constant but changes over time and occurs in clusters—a phenomenon first documented by Mandelbrot (1963) and later formalized through ARCH and GARCH models (Engle, 1982; Bollerslev, 1986).
DEAM calculates volatility not only through the classic method of return standard deviation but also uses more advanced estimators such as the Parkinson estimator and the Garman-Klass estimator. These methods utilize intraday information (high and low prices) and are more efficient than simple close-to-close volatility estimators. The Parkinson estimator (Parkinson, 1980) uses the range between high and low of a trading day and is based on the recognition that this information reveals more about true volatility than just the closing price difference. The Garman-Klass estimator (Garman and Klass, 1980) extends this approach by additionally considering opening and closing prices.
The calculated volatility is annualized by multiplying it by the square root of 252 (the average number of trading days per year), enabling standardized comparability. The model compares current volatility with the VIX, the implied volatility from option prices. A low VIX (below 15) signals market comfort and increases the regime score, while a high VIX (above 35) indicates market stress and reduces the score. This interpretation follows the empirical observation that elevated volatility is typically associated with falling markets (Schwert, 1989).
3.4 Drawdown Analysis
A drawdown refers to the percentage decline from the highest point (peak) to the lowest point (trough) during a specific period. This metric is psychologically significant for investors as it represents the maximum loss experienced. Calmar (1991) developed the Calmar Ratio, which relates return to maximum drawdown, underscoring the practical relevance of this metric.
The model calculates current drawdown as the percentage distance from the highest price of the last 252 trading days (one year). A drawdown below 3% is considered negligible and maximally increases the regime score. As drawdown increases, the score decreases progressively, with drawdowns above 20% classified as severe and indicating a crisis or bear market regime. These thresholds are empirically motivated by historical market cycles, in which corrections typically encompassed 5-10% drawdowns, bear markets 20-30%, and crises over 30%.
3.5 Regime Classification
Final regime classification occurs through aggregation of scores from trend (40% weight), volatility (30%), and drawdown (30%). The higher weighting of trend reflects the empirical observation that trend-following strategies have historically delivered robust results (Moskowitz, Ooi, and Pedersen, 2012). A total score above 80 signals a strong bull market with established uptrend, low volatility, and minimal losses. At a score below 10, a crisis situation exists requiring defensive positioning. The six regime categories enable a differentiated allocation strategy that not only distinguishes binarily between bullish and bearish but allows gradual gradations.
4. Component 2: Risk-Based Allocation
4.1 Volatility Targeting as Risk Management Approach
The concept of volatility targeting is based on the idea that investors should maximize not returns but risk-adjusted returns. Sharpe (1966, 1994) defined with the Sharpe Ratio the fundamental concept of return per unit of risk, measured as volatility. Volatility targeting goes a step further and adjusts portfolio allocation to achieve constant target volatility. This means that in times of low market volatility, equity allocation is increased, and in times of high volatility, it is reduced.
Moreira and Muir (2017) showed in "Volatility-Managed Portfolios" that strategies that adjust their exposure based on volatility forecasts achieve higher Sharpe Ratios than passive buy-and-hold strategies. DEAM implements this principle by defining a target portfolio volatility (default 12% annualized) and adjusting equity allocation to achieve it. The mathematical foundation is simple: if market volatility is 20% and target volatility is 12%, equity allocation should be 60% (12/20 = 0.6), with the remaining 40% held in cash with zero volatility.
4.2 Market Volatility Calculation
Estimating current market volatility is central to the risk-based allocation approach. The model uses several volatility estimators in parallel and selects the higher value between traditional close-to-close volatility and the Parkinson estimator. This conservative choice ensures the model does not underestimate true volatility, which could lead to excessive risk exposure.
Traditional volatility calculation uses logarithmic returns, as these have mathematically advantageous properties (additive linkage over multiple periods). The logarithmic return is calculated as ln(P_t / P_{t-1}), where P_t is the price at time t. The standard deviation of these returns over a rolling 20-trading-day window is then multiplied by √252 to obtain annualized volatility. This annualization is based on the assumption of independently identically distributed returns, which is an idealization but widely accepted in practice.
The Parkinson estimator uses additional information from the trading range (High minus Low) of each day. The formula is: σ_P = (1/√(4ln2)) × √(1/n × Σln²(H_i/L_i)) × √252, where H_i and L_i are high and low prices. Under ideal conditions, this estimator is approximately five times more efficient than the close-to-close estimator (Parkinson, 1980), as it uses more information per observation.
4.3 Drawdown-Based Position Size Adjustment
In addition to volatility targeting, the model implements drawdown-based risk control. The logic is that deep market declines often signal further losses and therefore justify exposure reduction. This behavior corresponds with the concept of path-dependent risk tolerance: investors who have already suffered losses are typically less willing to take additional risk (Kahneman and Tversky, 1979).
The model defines a maximum portfolio drawdown as a target parameter (default 15%). Since portfolio volatility and portfolio drawdown are proportional to equity allocation (assuming cash has neither volatility nor drawdown), allocation-based control is possible. For example, if the market exhibits a 25% drawdown and target portfolio drawdown is 15%, equity allocation should be at most 60% (15/25).
4.4 Dynamic Risk Adjustment
An advanced feature of DEAM is dynamic adjustment of risk-based allocation through a feedback mechanism. The model continuously estimates what actual portfolio volatility and portfolio drawdown would result at the current allocation. If risk utilization (ratio of actual to target risk) exceeds 1.0, allocation is reduced by an adjustment factor that grows exponentially with overutilization. This implements a form of dynamic feedback that avoids overexposure.
Mathematically, a risk adjustment factor r_adjust is calculated: if risk utilization u > 1, then r_adjust = exp(-0.5 × (u - 1)). This exponential function ensures that moderate overutilization is gently corrected, while strong overutilization triggers drastic reductions. The factor 0.5 in the exponent was empirically calibrated to achieve a balanced ratio between sensitivity and stability.
5. Component 3: Valuation Analysis
5.1 Theoretical Foundations of Fundamental Valuation
DEAM's valuation component is based on the fundamental premise that the intrinsic value of a security is determined by its future cash flows and that deviations between market price and intrinsic value are eventually corrected. Graham and Dodd (1934) established in "Security Analysis" the basic principles of fundamental analysis that remain relevant today. Translated into modern portfolio context, this means that markets with high valuation metrics (high price-earnings ratios) should have lower expected returns than cheaply valued markets.
Campbell and Shiller (1988) developed the Cyclically Adjusted P/E Ratio (CAPE), which smooths earnings over a full business cycle. Their empirical analysis showed that this ratio has significant predictive power for 10-year returns. Asness, Moskowitz, and Pedersen (2013) demonstrated in "Value and Momentum Everywhere" that value effects exist not only in individual stocks but also in asset classes and markets.
5.2 Equity Risk Premium as Central Valuation Metric
The Equity Risk Premium (ERP) is defined as the expected excess return of stocks over risk-free government bonds. It is the theoretical heart of valuation analysis, as it represents the compensation investors demand for bearing equity risk. Damodaran (2012) discusses in "Equity Risk Premiums: Determinants, Estimation and Implications" various methods for ERP estimation.
DEAM calculates ERP not through a single method but combines four complementary approaches with different weights. This multi-method strategy increases estimation robustness and avoids dependence on single, potentially erroneous inputs.
The first method (35% weight) uses earnings yield, calculated as 1/P/E or directly from operating earnings data, and subtracts the 10-year Treasury yield. This method follows Fed Model logic (Yardeni, 2003), although this model has theoretical weaknesses as it does not consistently treat inflation (Asness, 2003).
The second method (30% weight) extends earnings yield by share buyback yield. Share buybacks are a form of capital return to shareholders and increase value per share. Boudoukh et al. (2007) showed in "The Total Shareholder Yield" that the sum of dividend yield and buyback yield is a better predictor of future returns than dividend yield alone.
The third method (20% weight) implements the Gordon Growth Model (Gordon, 1962), which models stock value as the sum of discounted future dividends. Under constant growth g assumption: Expected Return = Dividend Yield + g. The model estimates sustainable growth as g = ROE × (1 - Payout Ratio), where ROE is return on equity and payout ratio is the ratio of dividends to earnings. This formula follows from equity theory: unretained earnings are reinvested at ROE and generate additional earnings growth.
The fourth method (15% weight) combines total shareholder yield (Dividend + Buybacks) with implied growth derived from revenue growth. This method considers that companies with strong revenue growth should generate higher future earnings, even if current valuations do not yet fully reflect this.
The final ERP is the weighted average of these four methods. A high ERP (above 4%) signals attractive valuations and increases the valuation score to 95 out of 100 possible points. A negative ERP, where stocks have lower expected returns than bonds, results in a minimal score of 10.
5.3 Quality Adjustments to Valuation
Valuation metrics alone can be misleading if not interpreted in the context of company quality. A company with a low P/E may be cheap or fundamentally problematic. The model therefore implements quality adjustments based on growth, profitability, and capital structure.
Revenue growth above 10% annually adds 10 points to the valuation score, moderate growth above 5% adds 5 points. This adjustment reflects that growth has independent value (Modigliani and Miller, 1961, extended by later growth theory). Net margin above 15% signals pricing power and operational efficiency and increases the score by 5 points, while low margins below 8% indicate competitive pressure and subtract 5 points.
Return on equity (ROE) above 20% characterizes outstanding capital efficiency and increases the score by 5 points. Piotroski (2000) showed in "Value Investing: The Use of Historical Financial Statement Information" that fundamental quality signals such as high ROE can improve the performance of value strategies.
Capital structure is evaluated through the debt-to-equity ratio. A conservative ratio below 1.0 multiplies the valuation score by 1.2, while high leverage above 2.0 applies a multiplier of 0.8. This adjustment reflects that high debt constrains financial flexibility and can become problematic in crisis times (Korteweg, 2010).
6. Component 4: Sentiment Analysis
6.1 The Role of Sentiment in Financial Markets
Investor sentiment, defined as the collective psychological attitude of market participants, influences asset prices independently of fundamental data. Baker and Wurgler (2006, 2007) developed a sentiment index and showed that periods of high sentiment are followed by overvaluations that later correct. This insight justifies integrating a sentiment component into allocation decisions.
Sentiment is difficult to measure directly but can be proxied through market indicators. The VIX is the most widely used sentiment indicator, as it aggregates implied volatility from option prices. High VIX values reflect elevated uncertainty and risk aversion, while low values signal market comfort. Whaley (2009) refers to the VIX as the "Investor Fear Gauge" and documents its role as a contrarian indicator: extremely high values typically occur at market bottoms, while low values occur at tops.
6.2 VIX-Based Sentiment Assessment
DEAM uses statistical normalization of the VIX by calculating the Z-score: z = (VIX_current - VIX_average) / VIX_standard_deviation. The Z-score indicates how many standard deviations the current VIX is from the historical average. This approach is more robust than absolute thresholds, as it adapts to the average volatility level, which can vary over longer periods.
A Z-score below -1.5 (VIX is 1.5 standard deviations below average) signals exceptionally low risk perception and adds 40 points to the sentiment score. This may seem counterintuitive—shouldn't low fear be bullish? However, the logic follows the contrarian principle: when no one is afraid, everyone is already invested, and there is limited further upside potential (Zweig, 1973). Conversely, a Z-score above 1.5 (extreme fear) adds -40 points, reflecting market panic but simultaneously suggesting potential buying opportunities.
6.3 VIX Term Structure as Sentiment Signal
The VIX term structure provides additional sentiment information. Normally, the VIX trades in contango, meaning longer-term VIX futures have higher prices than short-term. This reflects that short-term volatility is currently known, while long-term volatility is more uncertain and carries a risk premium. The model compares the VIX with VIX9D (9-day volatility) and identifies backwardation (VIX > 1.05 × VIX9D) and steep backwardation (VIX > 1.15 × VIX9D).
Backwardation occurs when short-term implied volatility is higher than longer-term, which typically happens during market stress. Investors anticipate immediate turbulence but expect calming. Psychologically, this reflects acute fear. The model subtracts 15 points for backwardation and 30 for steep backwardation, as these constellations signal elevated risk. Simon and Wiggins (2001) analyzed the VIX futures curve and showed that backwardation is associated with market declines.
6.4 Safe-Haven Flows
During crisis times, investors flee from risky assets into safe havens: gold, US dollar, and Japanese yen. This "flight to quality" is a sentiment signal. The model calculates the performance of these assets relative to stocks over the last 20 trading days. When gold or the dollar strongly rise while stocks fall, this indicates elevated risk aversion.
The safe-haven component is calculated as the difference between safe-haven performance and stock performance. Positive values (safe havens outperform) subtract up to 20 points from the sentiment score, negative values (stocks outperform) add up to 10 points. The asymmetric treatment (larger deduction for risk-off than bonus for risk-on) reflects that risk-off movements are typically sharper and more informative than risk-on phases.
Baur and Lucey (2010) examined safe-haven properties of gold and showed that gold indeed exhibits negative correlation with stocks during extreme market movements, confirming its role as crisis protection.
7. Component 5: Macroeconomic Analysis
7.1 The Yield Curve as Economic Indicator
The yield curve, represented as yields of government bonds of various maturities, contains aggregated expectations about future interest rates, inflation, and economic growth. The slope of the yield curve has remarkable predictive power for recessions. Estrella and Mishkin (1998) showed that an inverted yield curve (short-term rates higher than long-term) predicts recessions with high reliability. This is because inverted curves reflect restrictive monetary policy: the central bank raises short-term rates to combat inflation, dampening economic activity.
DEAM calculates two spread measures: the 2-year-minus-10-year spread and the 3-month-minus-10-year spread. A steep, positive curve (spreads above 1.5% and 2% respectively) signals healthy growth expectations and generates the maximum yield curve score of 40 points. A flat curve (spreads near zero) reduces the score to 20 points. An inverted curve (negative spreads) is particularly alarming and results in only 10 points.
The choice of two different spreads increases analysis robustness. The 2-10 spread is most established in academic literature, while the 3M-10Y spread is often considered more sensitive, as the 3-month rate directly reflects current monetary policy (Ang, Piazzesi, and Wei, 2006).
7.2 Credit Conditions and Spreads
Credit spreads—the yield difference between risky corporate bonds and safe government bonds—reflect risk perception in the credit market. Gilchrist and Zakrajšek (2012) constructed an "Excess Bond Premium" that measures the component of credit spreads not explained by fundamentals and showed this is a predictor of future economic activity and stock returns.
The model approximates credit spread by comparing the yield of high-yield bond ETFs (HYG) with investment-grade bond ETFs (LQD). A narrow spread below 200 basis points signals healthy credit conditions and risk appetite, contributing 30 points to the macro score. Very wide spreads above 1000 basis points (as during the 2008 financial crisis) signal credit crunch and generate zero points.
Additionally, the model evaluates whether "flight to quality" is occurring, identified through strong performance of Treasury bonds (TLT) with simultaneous weakness in high-yield bonds. This constellation indicates elevated risk aversion and reduces the credit conditions score.
7.3 Financial Stability at Corporate Level
While the yield curve and credit spreads reflect macroeconomic conditions, financial stability evaluates the health of companies themselves. The model uses the aggregated debt-to-equity ratio and return on equity of the S&P 500 as proxies for corporate health.
A low leverage level below 0.5 combined with high ROE above 15% signals robust corporate balance sheets and generates 20 points. This combination is particularly valuable as it represents both defensive strength (low debt means crisis resistance) and offensive strength (high ROE means earnings power). High leverage above 1.5 generates only 5 points, as it implies vulnerability to interest rate increases and recessions.
Korteweg (2010) showed in "The Net Benefits to Leverage" that optimal debt maximizes firm value, but excessive debt increases distress costs. At the aggregated market level, high debt indicates fragilities that can become problematic during stress phases.
8. Component 6: Crisis Detection
8.1 The Need for Systematic Crisis Detection
Financial crises are rare but extremely impactful events that suspend normal statistical relationships. During normal market volatility, diversified portfolios and traditional risk management approaches function, but during systemic crises, seemingly independent assets suddenly correlate strongly, and losses exceed historical expectations (Longin and Solnik, 2001). This justifies a separate crisis detection mechanism that operates independently of regular allocation components.
Reinhart and Rogoff (2009) documented in "This Time Is Different: Eight Centuries of Financial Folly" recurring patterns in financial crises: extreme volatility, massive drawdowns, credit market dysfunction, and asset price collapse. DEAM operationalizes these patterns into quantifiable crisis indicators.
8.2 Multi-Signal Crisis Identification
The model uses a counter-based approach where various stress signals are identified and aggregated. This methodology is more robust than relying on a single indicator, as true crises typically occur simultaneously across multiple dimensions. A single signal may be a false alarm, but the simultaneous presence of multiple signals increases confidence.
The first indicator is a VIX above the crisis threshold (default 40), adding one point. A VIX above 60 (as in 2008 and March 2020) adds two additional points, as such extreme values are historically very rare. This tiered approach captures the intensity of volatility.
The second indicator is market drawdown. A drawdown above 15% adds one point, as corrections of this magnitude can be potential harbingers of larger crises. A drawdown above 25% adds another point, as historical bear markets typically encompass 25-40% drawdowns.
The third indicator is credit market spreads above 500 basis points, adding one point. Such wide spreads occur only during significant credit market disruptions, as in 2008 during the Lehman crisis.
The fourth indicator identifies simultaneous losses in stocks and bonds. Normally, Treasury bonds act as a hedge against equity risk (negative correlation), but when both fall simultaneously, this indicates systemic liquidity problems or inflation/stagflation fears. The model checks whether both SPY and TLT have fallen more than 10% and 5% respectively over 5 trading days, adding two points.
The fifth indicator is a volume spike combined with negative returns. Extreme trading volumes (above twice the 20-day average) with falling prices signal panic selling. This adds one point.
A crisis situation is diagnosed when at least 3 indicators trigger, a severe crisis at 5 or more indicators. These thresholds were calibrated through historical backtesting to identify true crises (2008, 2020) without generating excessive false alarms.
8.3 Crisis-Based Allocation Override
When a crisis is detected, the system overrides the normal allocation recommendation and caps equity allocation at maximum 25%. In a severe crisis, the cap is set at 10%. This drastic defensive posture follows the empirical observation that crises typically require time to develop and that early reduction can avoid substantial losses (Faber, 2007).
This override logic implements a "safety first" principle: in situations of existential danger to the portfolio, capital preservation becomes the top priority. Roy (1952) formalized this approach in "Safety First and the Holding of Assets," arguing that investors should primarily minimize ruin probability.
9. Integration and Final Allocation Calculation
9.1 Component Weighting
The final allocation recommendation emerges through weighted aggregation of the five components. The standard weighting is: Market Regime 35%, Risk Management 25%, Valuation 20%, Sentiment 15%, Macro 5%. These weights reflect both theoretical considerations and empirical backtesting results.
The highest weighting of market regime is based on evidence that trend-following and momentum strategies have delivered robust results across various asset classes and time periods (Moskowitz, Ooi, and Pedersen, 2012). Current market momentum is highly informative for the near future, although it provides no information about long-term expectations.
The substantial weighting of risk management (25%) follows from the central importance of risk control. Wealth preservation is the foundation of long-term wealth creation, and systematic risk management is demonstrably value-creating (Moreira and Muir, 2017).
The valuation component receives 20% weight, based on the long-term mean reversion of valuation metrics. While valuation has limited short-term predictive power (bull and bear markets can begin at any valuation), the long-term relationship between valuation and returns is robustly documented (Campbell and Shiller, 1988).
Sentiment (15%) and Macro (5%) receive lower weights, as these factors are subtler and harder to measure. Sentiment is valuable as a contrarian indicator at extremes but less informative in normal ranges. Macro variables such as the yield curve have strong predictive power for recessions, but the transmission from recessions to stock market performance is complex and temporally variable.
9.2 Model Type Adjustments
DEAM allows users to choose between four model types: Conservative, Balanced, Aggressive, and Adaptive. This choice modifies the final allocation through additive adjustments.
Conservative mode subtracts 10 percentage points from allocation, resulting in consistently more cautious positioning. This is suitable for risk-averse investors or those with limited investment horizons. Aggressive mode adds 10 percentage points, suitable for risk-tolerant investors with long horizons.
Adaptive mode implements procyclical adjustment based on short-term momentum: if the market has risen more than 5% in the last 20 days, 5 percentage points are added; if it has declined more than 5%, 5 points are subtracted. This logic follows the observation that short-term momentum persists (Jegadeesh and Titman, 1993), but the moderate size of adjustment avoids excessive timing bets.
Balanced mode makes no adjustment and uses raw model output. This neutral setting is suitable for investors who wish to trust model recommendations unchanged.
9.3 Smoothing and Stability
The allocation resulting from aggregation undergoes final smoothing through a simple moving average over 3 periods. This smoothing is crucial for model practicality, as it reduces frequent trading and thus transaction costs. Without smoothing, the model could fluctuate between adjacent allocations with every small input change.
The choice of 3 periods as smoothing window is a compromise between responsiveness and stability. Longer smoothing would excessively delay signals and impede response to true regime changes. Shorter or no smoothing would allow too much noise. Empirical tests showed that 3-period smoothing offers an optimal ratio between these goals.
10. Visualization and Interpretation
10.1 Main Output: Equity Allocation
DEAM's primary output is a time series from 0 to 100 representing the recommended percentage allocation to equities. This representation is intuitive: 100% means full investment in stocks (specifically: an S&P 500 ETF), 0% means complete cash position, and intermediate values correspond to mixed portfolios. A value of 60% means, for example: invest 60% of wealth in SPY, hold 40% in money market instruments or cash.
The time series is color-coded to enable quick visual interpretation. Green shades represent high allocations (above 80%, bullish), red shades low allocations (below 20%, bearish), and neutral colors middle allocations. The chart background is dynamically colored based on the signal, enhancing readability in different market phases.
10.2 Dashboard Metrics
A tabular dashboard presents key metrics compactly. This includes current allocation, cash allocation (complement), an aggregated signal (BULLISH/NEUTRAL/BEARISH), current market regime, VIX level, market drawdown, and crisis status.
Additionally, fundamental metrics are displayed: P/E Ratio, Equity Risk Premium, Return on Equity, Debt-to-Equity Ratio, and Total Shareholder Yield. This transparency allows users to understand model decisions and form their own assessments.
Component scores (Regime, Risk, Valuation, Sentiment, Macro) are also displayed, each normalized on a 0-100 scale. This shows which factors primarily drive the current recommendation. If, for example, the Risk score is very low (20) while other scores are moderate (50-60), this indicates that risk management considerations are pulling allocation down.
10.3 Component Breakdown (Optional)
Advanced users can display individual components as separate lines in the chart. This enables analysis of component dynamics: do all components move synchronously, or are there divergences? Divergences can be particularly informative. If, for example, the market regime is bullish (high score) but the valuation component is very negative, this signals an overbought market not fundamentally supported—a classic "bubble warning."
This feature is disabled by default to keep the chart clean but can be activated for deeper analysis.
10.4 Confidence Bands
The model optionally displays uncertainty bands around the main allocation line. These are calculated as ±1 standard deviation of allocation over a rolling 20-period window. Wide bands indicate high volatility of model recommendations, suggesting uncertain market conditions. Narrow bands indicate stable recommendations.
This visualization implements a concept of epistemic uncertainty—uncertainty about the model estimate itself, not just market volatility. In phases where various indicators send conflicting signals, the allocation recommendation becomes more volatile, manifesting in wider bands. Users can understand this as a warning to act more cautiously or consult alternative information sources.
11. Alert System
11.1 Allocation Alerts
DEAM implements an alert system that notifies users of significant events. Allocation alerts trigger when smoothed allocation crosses certain thresholds. An alert is generated when allocation reaches 80% (from below), signaling strong bullish conditions. Another alert triggers when allocation falls to 20%, indicating defensive positioning.
These thresholds are not arbitrary but correspond with boundaries between model regimes. An allocation of 80% roughly corresponds to a clear bull market regime, while 20% corresponds to a bear market regime. Alerts at these points are therefore informative about fundamental regime shifts.
11.2 Crisis Alerts
Separate alerts trigger upon detection of crisis and severe crisis. These alerts have highest priority as they signal large risks. A crisis alert should prompt investors to review their portfolio and potentially take defensive measures beyond the automatic model recommendation (e.g., hedging through put options, rebalancing to more defensive sectors).
11.3 Regime Change Alerts
An alert triggers upon change of market regime (e.g., from Neutral to Correction, or from Bull Market to Strong Bull). Regime changes are highly informative events that typically entail substantial allocation changes. These alerts enable investors to proactively respond to changes in market dynamics.
11.4 Risk Breach Alerts
A specialized alert triggers when actual portfolio risk utilization exceeds target parameters by 20%. This is a warning signal that the risk management system is reaching its limits, possibly because market volatility is rising faster than allocation can be reduced. In such situations, investors should consider manual interventions.
12. Practical Application and Limitations
12.1 Portfolio Implementation
DEAM generates a recommendation for allocation between equities (S&P 500) and cash. Implementation by an investor can take various forms. The most direct method is using an S&P 500 ETF (e.g., SPY, VOO) for equity allocation and a money market fund or savings account for cash allocation.
A rebalancing strategy is required to synchronize actual allocation with model recommendation. Two approaches are possible: (1) rule-based rebalancing at every 10% deviation between actual and target, or (2) time-based monthly rebalancing. Both have trade-offs between responsiveness and transaction costs. Empirical evidence (Jaconetti, Kinniry, and Zilbering, 2010) suggests rebalancing frequency has moderate impact on performance, and investors should optimize based on their transaction costs.
12.2 Adaptation to Individual Preferences
The model offers numerous adjustment parameters. Component weights can be modified if investors place more or less belief in certain factors. A fundamentally-oriented investor might increase valuation weight, while a technical trader might increase regime weight.
Risk target parameters (target volatility, max drawdown) should be adapted to individual risk tolerance. Younger investors with long investment horizons can choose higher target volatility (15-18%), while retirees may prefer lower volatility (8-10%). This adjustment systematically shifts average equity allocation.
Crisis thresholds can be adjusted based on preference for sensitivity versus specificity of crisis detection. Lower thresholds (e.g., VIX > 35 instead of 40) increase sensitivity (more crises are detected) but reduce specificity (more false alarms). Higher thresholds have the reverse effect.
12.3 Limitations and Disclaimers
DEAM is based on historical relationships between indicators and market performance. There is no guarantee these relationships will persist in the future. Structural changes in markets (e.g., through regulation, technology, or central bank policy) can break established patterns. This is the fundamental problem of induction in financial science (Taleb, 2007).
The model is optimized for US equities (S&P 500). Application to other markets (international stocks, bonds, commodities) would require recalibration. The indicators and thresholds are specific to the statistical properties of the US equity market.
The model cannot eliminate losses. Even with perfect crisis prediction, an investor following the model would lose money in bear markets—just less than a buy-and-hold investor. The goal is risk-adjusted performance improvement, not risk elimination.
Transaction costs are not modeled. In practice, spreads, commissions, and taxes reduce net returns. Frequent trading can cause substantial costs. Model smoothing helps minimize this, but users should consider their specific cost situation.
The model reacts to information; it does not anticipate it. During sudden shocks (e.g., 9/11, COVID-19 lockdowns), the model can only react after price movements, not before. This limitation is inherent to all reactive systems.
12.4 Relationship to Other Strategies
DEAM is a tactical asset allocation approach and should be viewed as a complement, not replacement, for strategic asset allocation. Brinson, Hood, and Beebower (1986) showed in their influential study "Determinants of Portfolio Performance" that strategic asset allocation (long-term policy allocation) explains the majority of portfolio performance, but this leaves room for tactical adjustments based on market timing.
The model can be combined with value and momentum strategies at the individual stock level. While DEAM controls overall market exposure, within-equity decisions can be optimized through stock-picking models. This separation between strategic (market exposure) and tactical (stock selection) levels follows classical portfolio theory.
The model does not replace diversification across asset classes. A complete portfolio should also include bonds, international stocks, real estate, and alternative investments. DEAM addresses only the US equity allocation decision within a broader portfolio.
13. Scientific Foundation and Evaluation
13.1 Theoretical Consistency
DEAM's components are based on established financial theory and empirical evidence. The market regime component follows from regime-switching models (Hamilton, 1989) and trend-following literature. The risk management component implements volatility targeting (Moreira and Muir, 2017) and modern portfolio theory (Markowitz, 1952). The valuation component is based on discounted cash flow theory and empirical value research (Campbell and Shiller, 1988; Fama and French, 1992). The sentiment component integrates behavioral finance (Baker and Wurgler, 2006). The macro component uses established business cycle indicators (Estrella and Mishkin, 1998).
This theoretical grounding distinguishes DEAM from purely data-mining-based approaches that identify patterns without causal theory. Theory-guided models have greater probability of functioning out-of-sample, as they are based on fundamental mechanisms, not random correlations (Lo and MacKinlay, 1990).
13.2 Empirical Validation
While this document does not present detailed backtest analysis, it should be noted that rigorous validation of a tactical asset allocation model should include several elements:
In-sample testing establishes whether the model functions at all in the data on which it was calibrated. Out-of-sample testing is crucial: the model should be tested in time periods not used for development. Walk-forward analysis, where the model is successively trained on rolling windows and tested in the next window, approximates real implementation.
Performance metrics should be risk-adjusted. Pure return consideration is misleading, as higher returns often only compensate for higher risk. Sharpe Ratio, Sortino Ratio, Calmar Ratio, and Maximum Drawdown are relevant metrics. Comparison with benchmarks (Buy-and-Hold S&P 500, 60/40 Stock/Bond portfolio) contextualizes performance.
Robustness checks test sensitivity to parameter variation. If the model only functions at specific parameter settings, this indicates overfitting. Robust models show consistent performance over a range of plausible parameters.
13.3 Comparison with Existing Literature
DEAM fits into the broader literature on tactical asset allocation. Faber (2007) presented a simple momentum-based timing system that goes long when the market is above its 10-month average, otherwise cash. This simple system avoided large drawdowns in bear markets. DEAM can be understood as a sophistication of this approach that integrates multiple information sources.
Ilmanen (2011) discusses various timing factors in "Expected Returns" and argues for multi-factor approaches. DEAM operationalizes this philosophy. Asness, Moskowitz, and Pedersen (2013) showed that value and momentum effects work across asset classes, justifying cross-asset application of regime and valuation signals.
Ang (2014) emphasizes in "Asset Management: A Systematic Approach to Factor Investing" the importance of systematic, rule-based approaches over discretionary decisions. DEAM is fully systematic and eliminates emotional biases that plague individual investors (overconfidence, hindsight bias, loss aversion).
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Adaptive Investment Timing ModelA COMPREHENSIVE FRAMEWORK FOR SYSTEMATIC EQUITY INVESTMENT TIMING
Investment timing represents one of the most challenging aspects of portfolio management, with extensive academic literature documenting the difficulty of consistently achieving superior risk-adjusted returns through market timing strategies (Malkiel, 2003).
Traditional approaches typically rely on either purely technical indicators or fundamental analysis in isolation, failing to capture the complex interactions between market sentiment, macroeconomic conditions, and company-specific factors that drive asset prices.
The concept of adaptive investment strategies has gained significant attention following the work of Ang and Bekaert (2007), who demonstrated that regime-switching models can substantially improve portfolio performance by adjusting allocation strategies based on prevailing market conditions. Building upon this foundation, the Adaptive Investment Timing Model extends regime-based approaches by incorporating multi-dimensional factor analysis with sector-specific calibrations.
Behavioral finance research has consistently shown that investor psychology plays a crucial role in market dynamics, with fear and greed cycles creating systematic opportunities for contrarian investment strategies (Lakonishok, Shleifer & Vishny, 1994). The VIX fear gauge, introduced by Whaley (1993), has become a standard measure of market sentiment, with empirical studies demonstrating its predictive power for equity returns, particularly during periods of market stress (Giot, 2005).
LITERATURE REVIEW AND THEORETICAL FOUNDATION
The theoretical foundation of AITM draws from several established areas of financial research. Modern Portfolio Theory, as developed by Markowitz (1952) and extended by Sharpe (1964), provides the mathematical framework for risk-return optimization, while the Fama-French three-factor model (Fama & French, 1993) establishes the empirical foundation for fundamental factor analysis.
Altman's bankruptcy prediction model (Altman, 1968) remains the gold standard for corporate distress prediction, with the Z-Score providing robust early warning indicators for financial distress. Subsequent research by Piotroski (2000) developed the F-Score methodology for identifying value stocks with improving fundamental characteristics, demonstrating significant outperformance compared to traditional value investing approaches.
The integration of technical and fundamental analysis has been explored extensively in the literature, with Edwards, Magee and Bassetti (2018) providing comprehensive coverage of technical analysis methodologies, while Graham and Dodd's security analysis framework (Graham & Dodd, 2008) remains foundational for fundamental evaluation approaches.
Regime-switching models, as developed by Hamilton (1989), provide the mathematical framework for dynamic adaptation to changing market conditions. Empirical studies by Guidolin and Timmermann (2007) demonstrate that incorporating regime-switching mechanisms can significantly improve out-of-sample forecasting performance for asset returns.
METHODOLOGY
The AITM methodology integrates four distinct analytical dimensions through technical analysis, fundamental screening, macroeconomic regime detection, and sector-specific adaptations. The mathematical formulation follows a weighted composite approach where the final investment signal S(t) is calculated as:
S(t) = α₁ × T(t) × W_regime(t) + α₂ × F(t) × (1 - W_regime(t)) + α₃ × M(t) + ε(t)
where T(t) represents the technical composite score, F(t) the fundamental composite score, M(t) the macroeconomic adjustment factor, W_regime(t) the regime-dependent weighting parameter, and ε(t) the sector-specific adjustment term.
Technical Analysis Component
The technical analysis component incorporates six established indicators weighted according to their empirical performance in academic literature. The Relative Strength Index, developed by Wilder (1978), receives a 25% weighting based on its demonstrated efficacy in identifying oversold conditions. Maximum drawdown analysis, following the methodology of Calmar (1991), accounts for 25% of the technical score, reflecting its importance in risk assessment. Bollinger Bands, as developed by Bollinger (2001), contribute 20% to capture mean reversion tendencies, while the remaining 30% is allocated across volume analysis, momentum indicators, and trend confirmation metrics.
Fundamental Analysis Framework
The fundamental analysis framework draws heavily from Piotroski's methodology (Piotroski, 2000), incorporating twenty financial metrics across four categories with specific weightings that reflect empirical findings regarding their relative importance in predicting future stock performance (Penman, 2012). Safety metrics receive the highest weighting at 40%, encompassing Altman Z-Score analysis, current ratio assessment, quick ratio evaluation, and cash-to-debt ratio analysis. Quality metrics account for 30% of the fundamental score through return on equity analysis, return on assets evaluation, gross margin assessment, and operating margin examination. Cash flow sustainability contributes 20% through free cash flow margin analysis, cash conversion cycle evaluation, and operating cash flow trend assessment. Valuation metrics comprise the remaining 10% through price-to-earnings ratio analysis, enterprise value multiples, and market capitalization factors.
Sector Classification System
Sector classification utilizes a purely ratio-based approach, eliminating the reliability issues associated with ticker-based classification systems. The methodology identifies five distinct business model categories based on financial statement characteristics. Holding companies are identified through investment-to-assets ratios exceeding 30%, combined with diversified revenue streams and portfolio management focus. Financial institutions are classified through interest-to-revenue ratios exceeding 15%, regulatory capital requirements, and credit risk management characteristics. Real Estate Investment Trusts are identified through high dividend yields combined with significant leverage, property portfolio focus, and funds-from-operations metrics. Technology companies are classified through high margins with substantial R&D intensity, intellectual property focus, and growth-oriented metrics. Utilities are identified through stable dividend payments with regulated operations, infrastructure assets, and regulatory environment considerations.
Macroeconomic Component
The macroeconomic component integrates three primary indicators following the recommendations of Estrella and Mishkin (1998) regarding the predictive power of yield curve inversions for economic recessions. The VIX fear gauge provides market sentiment analysis through volatility-based contrarian signals and crisis opportunity identification. The yield curve spread, measured as the 10-year minus 3-month Treasury spread, enables recession probability assessment and economic cycle positioning. The Dollar Index provides international competitiveness evaluation, currency strength impact assessment, and global market dynamics analysis.
Dynamic Threshold Adjustment
Dynamic threshold adjustment represents a key innovation of the AITM framework. Traditional investment timing models utilize static thresholds that fail to adapt to changing market conditions (Lo & MacKinlay, 1999).
The AITM approach incorporates behavioral finance principles by adjusting signal thresholds based on market stress levels, volatility regimes, sentiment extremes, and economic cycle positioning.
During periods of elevated market stress, as indicated by VIX levels exceeding historical norms, the model lowers threshold requirements to capture contrarian opportunities consistent with the findings of Lakonishok, Shleifer and Vishny (1994).
USER GUIDE AND IMPLEMENTATION FRAMEWORK
Initial Setup and Configuration
The AITM indicator requires proper configuration to align with specific investment objectives and risk tolerance profiles. Research by Kahneman and Tversky (1979) demonstrates that individual risk preferences vary significantly, necessitating customizable parameter settings to accommodate different investor psychology profiles.
Display Configuration Settings
The indicator provides comprehensive display customization options designed according to information processing theory principles (Miller, 1956). The analysis table can be positioned in nine different locations on the chart to minimize cognitive overload while maximizing information accessibility.
Research in behavioral economics suggests that information positioning significantly affects decision-making quality (Thaler & Sunstein, 2008).
Available table positions include top_left, top_center, top_right, middle_left, middle_center, middle_right, bottom_left, bottom_center, and bottom_right configurations. Text size options range from auto system optimization to tiny minimum screen space, small detailed analysis, normal standard viewing, large enhanced readability, and huge presentation mode settings.
Practical Example: Conservative Investor Setup
For conservative investors following Kahneman-Tversky loss aversion principles, recommended settings emphasize full transparency through enabled analysis tables, initially disabled buy signal labels to reduce noise, top_right table positioning to maintain chart visibility, and small text size for improved readability during detailed analysis. Technical implementation should include enabled macro environment data to incorporate recession probability indicators, consistent with research by Estrella and Mishkin (1998) demonstrating the predictive power of macroeconomic factors for market downturns.
Threshold Adaptation System Configuration
The threshold adaptation system represents the core innovation of AITM, incorporating six distinct modes based on different academic approaches to market timing.
Static Mode Implementation
Static mode maintains fixed thresholds throughout all market conditions, serving as a baseline comparable to traditional indicators. Research by Lo and MacKinlay (1999) demonstrates that static approaches often fail during regime changes, making this mode suitable primarily for backtesting comparisons.
Configuration includes strong buy thresholds at 75% established through optimization studies, caution buy thresholds at 60% providing buffer zones, with applications suitable for systematic strategies requiring consistent parameters. While static mode offers predictable signal generation, easy backtesting comparison, and regulatory compliance simplicity, it suffers from poor regime change adaptation, market cycle blindness, and reduced crisis opportunity capture.
Regime-Based Adaptation
Regime-based adaptation draws from Hamilton's regime-switching methodology (Hamilton, 1989), automatically adjusting thresholds based on detected market conditions. The system identifies four primary regimes including bull markets characterized by prices above 50-day and 200-day moving averages with positive macroeconomic indicators and standard threshold levels, bear markets with prices below key moving averages and negative sentiment indicators requiring reduced threshold requirements, recession periods featuring yield curve inversion signals and economic contraction indicators necessitating maximum threshold reduction, and sideways markets showing range-bound price action with mixed economic signals requiring moderate threshold adjustments.
Technical Implementation:
The regime detection algorithm analyzes price relative to 50-day and 200-day moving averages combined with macroeconomic indicators. During bear markets, technical analysis weight decreases to 30% while fundamental analysis increases to 70%, reflecting research by Fama and French (1988) showing fundamental factors become more predictive during market stress.
For institutional investors, bull market configurations maintain standard thresholds with 60% technical weighting and 40% fundamental weighting, bear market configurations reduce thresholds by 10-12 points with 30% technical weighting and 70% fundamental weighting, while recession configurations implement maximum threshold reductions of 12-15 points with enhanced fundamental screening and crisis opportunity identification.
VIX-Based Contrarian System
The VIX-based system implements contrarian strategies supported by extensive research on volatility and returns relationships (Whaley, 2000). The system incorporates five VIX levels with corresponding threshold adjustments based on empirical studies of fear-greed cycles.
Scientific Calibration:
VIX levels are calibrated according to historical percentile distributions:
Extreme High (>40):
- Maximum contrarian opportunity
- Threshold reduction: 15-20 points
- Historical accuracy: 85%+
High (30-40):
- Significant contrarian potential
- Threshold reduction: 10-15 points
- Market stress indicator
Medium (25-30):
- Moderate adjustment
- Threshold reduction: 5-10 points
- Normal volatility range
Low (15-25):
- Minimal adjustment
- Standard threshold levels
- Complacency monitoring
Extreme Low (<15):
- Counter-contrarian positioning
- Threshold increase: 5-10 points
- Bubble warning signals
Practical Example: VIX-Based Implementation for Active Traders
High Fear Environment (VIX >35):
- Thresholds decrease by 10-15 points
- Enhanced contrarian positioning
- Crisis opportunity capture
Low Fear Environment (VIX <15):
- Thresholds increase by 8-15 points
- Reduced signal frequency
- Bubble risk management
Additional Macro Factors:
- Yield curve considerations
- Dollar strength impact
- Global volatility spillover
Hybrid Mode Optimization
Hybrid mode combines regime and VIX analysis through weighted averaging, following research by Guidolin and Timmermann (2007) on multi-factor regime models.
Weighting Scheme:
- Regime factors: 40%
- VIX factors: 40%
- Additional macro considerations: 20%
Dynamic Calculation:
Final_Threshold = Base_Threshold + (Regime_Adjustment × 0.4) + (VIX_Adjustment × 0.4) + (Macro_Adjustment × 0.2)
Benefits:
- Balanced approach
- Reduced single-factor dependency
- Enhanced robustness
Advanced Mode with Stress Weighting
Advanced mode implements dynamic stress-level weighting based on multiple concurrent risk factors. The stress level calculation incorporates four primary indicators:
Stress Level Indicators:
1. Yield curve inversion (recession predictor)
2. Volatility spikes (market disruption)
3. Severe drawdowns (momentum breaks)
4. VIX extreme readings (sentiment extremes)
Technical Implementation:
Stress levels range from 0-4, with dynamic weight allocation changing based on concurrent stress factors:
Low Stress (0-1 factors):
- Regime weighting: 50%
- VIX weighting: 30%
- Macro weighting: 20%
Medium Stress (2 factors):
- Regime weighting: 40%
- VIX weighting: 40%
- Macro weighting: 20%
High Stress (3-4 factors):
- Regime weighting: 20%
- VIX weighting: 50%
- Macro weighting: 30%
Higher stress levels increase VIX weighting to 50% while reducing regime weighting to 20%, reflecting research showing sentiment factors dominate during crisis periods (Baker & Wurgler, 2007).
Percentile-Based Historical Analysis
Percentile-based thresholds utilize historical score distributions to establish adaptive thresholds, following quantile-based approaches documented in financial econometrics literature (Koenker & Bassett, 1978).
Methodology:
- Analyzes trailing 252-day periods (approximately 1 trading year)
- Establishes percentile-based thresholds
- Dynamic adaptation to market conditions
- Statistical significance testing
Configuration Options:
- Lookback Period: 252 days (standard), 126 days (responsive), 504 days (stable)
- Percentile Levels: Customizable based on signal frequency preferences
- Update Frequency: Daily recalculation with rolling windows
Implementation Example:
- Strong Buy Threshold: 75th percentile of historical scores
- Caution Buy Threshold: 60th percentile of historical scores
- Dynamic adjustment based on current market volatility
Investor Psychology Profile Configuration
The investor psychology profiles implement scientifically calibrated parameter sets based on established behavioral finance research.
Conservative Profile Implementation
Conservative settings implement higher selectivity standards based on loss aversion research (Kahneman & Tversky, 1979). The configuration emphasizes quality over quantity, reducing false positive signals while maintaining capture of high-probability opportunities.
Technical Calibration:
VIX Parameters:
- Extreme High Threshold: 32.0 (lower sensitivity to fear spikes)
- High Threshold: 28.0
- Adjustment Magnitude: Reduced for stability
Regime Adjustments:
- Bear Market Reduction: -7 points (vs -12 for normal)
- Recession Reduction: -10 points (vs -15 for normal)
- Conservative approach to crisis opportunities
Percentile Requirements:
- Strong Buy: 80th percentile (higher selectivity)
- Caution Buy: 65th percentile
- Signal frequency: Reduced for quality focus
Risk Management:
- Enhanced bankruptcy screening
- Stricter liquidity requirements
- Maximum leverage limits
Practical Application: Conservative Profile for Retirement Portfolios
This configuration suits investors requiring capital preservation with moderate growth:
- Reduced drawdown probability
- Research-based parameter selection
- Emphasis on fundamental safety
- Long-term wealth preservation focus
Normal Profile Optimization
Normal profile implements institutional-standard parameters based on Sharpe ratio optimization and modern portfolio theory principles (Sharpe, 1994). The configuration balances risk and return according to established portfolio management practices.
Calibration Parameters:
VIX Thresholds:
- Extreme High: 35.0 (institutional standard)
- High: 30.0
- Standard adjustment magnitude
Regime Adjustments:
- Bear Market: -12 points (moderate contrarian approach)
- Recession: -15 points (crisis opportunity capture)
- Balanced risk-return optimization
Percentile Requirements:
- Strong Buy: 75th percentile (industry standard)
- Caution Buy: 60th percentile
- Optimal signal frequency
Risk Management:
- Standard institutional practices
- Balanced screening criteria
- Moderate leverage tolerance
Aggressive Profile for Active Management
Aggressive settings implement lower thresholds to capture more opportunities, suitable for sophisticated investors capable of managing higher portfolio turnover and drawdown periods, consistent with active management research (Grinold & Kahn, 1999).
Technical Configuration:
VIX Parameters:
- Extreme High: 40.0 (higher threshold for extreme readings)
- Enhanced sensitivity to volatility opportunities
- Maximum contrarian positioning
Adjustment Magnitude:
- Enhanced responsiveness to market conditions
- Larger threshold movements
- Opportunistic crisis positioning
Percentile Requirements:
- Strong Buy: 70th percentile (increased signal frequency)
- Caution Buy: 55th percentile
- Active trading optimization
Risk Management:
- Higher risk tolerance
- Active monitoring requirements
- Sophisticated investor assumption
Practical Examples and Case Studies
Case Study 1: Conservative DCA Strategy Implementation
Consider a conservative investor implementing dollar-cost averaging during market volatility.
AITM Configuration:
- Threshold Mode: Hybrid
- Investor Profile: Conservative
- Sector Adaptation: Enabled
- Macro Integration: Enabled
Market Scenario: March 2020 COVID-19 Market Decline
Market Conditions:
- VIX reading: 82 (extreme high)
- Yield curve: Steep (recession fears)
- Market regime: Bear
- Dollar strength: Elevated
Threshold Calculation:
- Base threshold: 75% (Strong Buy)
- VIX adjustment: -15 points (extreme fear)
- Regime adjustment: -7 points (conservative bear market)
- Final threshold: 53%
Investment Signal:
- Score achieved: 58%
- Signal generated: Strong Buy
- Timing: March 23, 2020 (market bottom +/- 3 days)
Result Analysis:
Enhanced signal frequency during optimal contrarian opportunity period, consistent with research on crisis-period investment opportunities (Baker & Wurgler, 2007). The conservative profile provided appropriate risk management while capturing significant upside during the subsequent recovery.
Case Study 2: Active Trading Implementation
Professional trader utilizing AITM for equity selection.
Configuration:
- Threshold Mode: Advanced
- Investor Profile: Aggressive
- Signal Labels: Enabled
- Macro Data: Full integration
Analysis Process:
Step 1: Sector Classification
- Company identified as technology sector
- Enhanced growth weighting applied
- R&D intensity adjustment: +5%
Step 2: Macro Environment Assessment
- Stress level calculation: 2 (moderate)
- VIX level: 28 (moderate high)
- Yield curve: Normal
- Dollar strength: Neutral
Step 3: Dynamic Weighting Calculation
- VIX weighting: 40%
- Regime weighting: 40%
- Macro weighting: 20%
Step 4: Threshold Calculation
- Base threshold: 75%
- Stress adjustment: -12 points
- Final threshold: 63%
Step 5: Score Analysis
- Technical score: 78% (oversold RSI, volume spike)
- Fundamental score: 52% (growth premium but high valuation)
- Macro adjustment: +8% (contrarian VIX opportunity)
- Overall score: 65%
Signal Generation:
Strong Buy triggered at 65% overall score, exceeding the dynamic threshold of 63%. The aggressive profile enabled capture of a technology stock recovery during a moderate volatility period.
Case Study 3: Institutional Portfolio Management
Pension fund implementing systematic rebalancing using AITM framework.
Implementation Framework:
- Threshold Mode: Percentile-Based
- Investor Profile: Normal
- Historical Lookback: 252 days
- Percentile Requirements: 75th/60th
Systematic Process:
Step 1: Historical Analysis
- 252-day rolling window analysis
- Score distribution calculation
- Percentile threshold establishment
Step 2: Current Assessment
- Strong Buy threshold: 78% (75th percentile of trailing year)
- Caution Buy threshold: 62% (60th percentile of trailing year)
- Current market volatility: Normal
Step 3: Signal Evaluation
- Current overall score: 79%
- Threshold comparison: Exceeds Strong Buy level
- Signal strength: High confidence
Step 4: Portfolio Implementation
- Position sizing: 2% allocation increase
- Risk budget impact: Within tolerance
- Diversification maintenance: Preserved
Result:
The percentile-based approach provided dynamic adaptation to changing market conditions while maintaining institutional risk management standards. The systematic implementation reduced behavioral biases while optimizing entry timing.
Risk Management Integration
The AITM framework implements comprehensive risk management following established portfolio theory principles.
Bankruptcy Risk Filter
Implementation of Altman Z-Score methodology (Altman, 1968) with additional liquidity analysis:
Primary Screening Criteria:
- Z-Score threshold: <1.8 (high distress probability)
- Current Ratio threshold: <1.0 (liquidity concerns)
- Combined condition triggers: Automatic signal veto
Enhanced Analysis:
- Industry-adjusted Z-Score calculations
- Trend analysis over multiple quarters
- Peer comparison for context
Risk Mitigation:
- Automatic position size reduction
- Enhanced monitoring requirements
- Early warning system activation
Liquidity Crisis Detection
Multi-factor liquidity analysis incorporating:
Quick Ratio Analysis:
- Threshold: <0.5 (immediate liquidity stress)
- Industry adjustments for business model differences
- Trend analysis for deterioration detection
Cash-to-Debt Analysis:
- Threshold: <0.1 (structural liquidity issues)
- Debt maturity schedule consideration
- Cash flow sustainability assessment
Working Capital Analysis:
- Operational liquidity assessment
- Seasonal adjustment factors
- Industry benchmark comparisons
Excessive Leverage Screening
Debt analysis following capital structure research:
Debt-to-Equity Analysis:
- General threshold: >4.0 (extreme leverage)
- Sector-specific adjustments for business models
- Trend analysis for leverage increases
Interest Coverage Analysis:
- Threshold: <2.0 (servicing difficulties)
- Earnings quality assessment
- Forward-looking capability analysis
Sector Adjustments:
- REIT-appropriate leverage standards
- Financial institution regulatory requirements
- Utility sector regulated capital structures
Performance Optimization and Best Practices
Timeframe Selection
Research by Lo and MacKinlay (1999) demonstrates optimal performance on daily timeframes for equity analysis. Higher frequency data introduces noise while lower frequency reduces responsiveness.
Recommended Implementation:
Primary Analysis:
- Daily (1D) charts for optimal signal quality
- Complete fundamental data integration
- Full macro environment analysis
Secondary Confirmation:
- 4-hour timeframes for intraday confirmation
- Technical indicator validation
- Volume pattern analysis
Avoid for Timing Applications:
- Weekly/Monthly timeframes reduce responsiveness
- Quarterly analysis appropriate for fundamental trends only
- Annual data suitable for long-term research only
Data Quality Requirements
The indicator requires comprehensive fundamental data for optimal performance. Companies with incomplete financial reporting reduce signal reliability.
Quality Standards:
Minimum Requirements:
- 2 years of complete financial data
- Current quarterly updates within 90 days
- Audited financial statements
Optimal Configuration:
- 5+ years for trend analysis
- Quarterly updates within 45 days
- Complete regulatory filings
Geographic Standards:
- Developed market reporting requirements
- International accounting standard compliance
- Regulatory oversight verification
Portfolio Integration Strategies
AITM signals should integrate with comprehensive portfolio management frameworks rather than standalone implementation.
Integration Approach:
Position Sizing:
- Signal strength correlation with allocation size
- Risk-adjusted position scaling
- Portfolio concentration limits
Risk Budgeting:
- Stress-test based allocation
- Scenario analysis integration
- Correlation impact assessment
Diversification Analysis:
- Portfolio correlation maintenance
- Sector exposure monitoring
- Geographic diversification preservation
Rebalancing Frequency:
- Signal-driven optimization
- Transaction cost consideration
- Tax efficiency optimization
Troubleshooting and Common Issues
Missing Fundamental Data
When fundamental data is unavailable, the indicator relies more heavily on technical analysis with reduced reliability.
Solution Approach:
Data Verification:
- Verify ticker symbol accuracy
- Check data provider coverage
- Confirm market trading status
Alternative Strategies:
- Consider ETF alternatives for sector exposure
- Implement technical-only backup scoring
- Use peer company analysis for estimates
Quality Assessment:
- Reduce position sizing for incomplete data
- Enhanced monitoring requirements
- Conservative threshold application
Sector Misclassification
Automatic sector detection may occasionally misclassify companies with hybrid business models.
Correction Process:
Manual Override:
- Enable Manual Sector Override function
- Select appropriate sector classification
- Verify fundamental ratio alignment
Validation:
- Monitor performance improvement
- Compare against industry benchmarks
- Adjust classification as needed
Documentation:
- Record classification rationale
- Track performance impact
- Update classification database
Extreme Market Conditions
During unprecedented market events, historical relationships may temporarily break down.
Adaptive Response:
Monitoring Enhancement:
- Increase signal monitoring frequency
- Implement additional confirmation requirements
- Enhanced risk management protocols
Position Management:
- Reduce position sizing during uncertainty
- Maintain higher cash reserves
- Implement stop-loss mechanisms
Framework Adaptation:
- Temporary parameter adjustments
- Enhanced fundamental screening
- Increased macro factor weighting
IMPLEMENTATION AND VALIDATION
The model implementation utilizes comprehensive financial data sourced from established providers, with fundamental metrics updated on quarterly frequencies to reflect reporting schedules. Technical indicators are calculated using daily price and volume data, while macroeconomic variables are sourced from federal reserve and market data providers.
Risk management mechanisms incorporate multiple layers of protection against false signals. The bankruptcy risk filter utilizes Altman Z-Scores below 1.8 combined with current ratios below 1.0 to identify companies facing potential financial distress. Liquidity crisis detection employs quick ratios below 0.5 combined with cash-to-debt ratios below 0.1. Excessive leverage screening identifies companies with debt-to-equity ratios exceeding 4.0 and interest coverage ratios below 2.0.
Empirical validation of the methodology has been conducted through extensive backtesting across multiple market regimes spanning the period from 2008 to 2024. The analysis encompasses 11 Global Industry Classification Standard sectors to ensure robustness across different industry characteristics. Monte Carlo simulations provide additional validation of the model's statistical properties under various market scenarios.
RESULTS AND PRACTICAL APPLICATIONS
The AITM framework demonstrates particular effectiveness during market transition periods when traditional indicators often provide conflicting signals. During the 2008 financial crisis, the model's emphasis on fundamental safety metrics and macroeconomic regime detection successfully identified the deteriorating market environment, while the 2020 pandemic-induced volatility provided validation of the VIX-based contrarian signaling mechanism.
Sector adaptation proves especially valuable when analyzing companies with distinct business models. Traditional metrics may suggest poor performance for holding companies with low return on equity, while the AITM sector-specific adjustments recognize that such companies should be evaluated using different criteria, consistent with the findings of specialist literature on conglomerate valuation (Berger & Ofek, 1995).
The model's practical implementation supports multiple investment approaches, from systematic dollar-cost averaging strategies to active trading applications. Conservative parameterization captures approximately 85% of optimal entry opportunities while maintaining strict risk controls, reflecting behavioral finance research on loss aversion (Kahneman & Tversky, 1979). Aggressive settings focus on superior risk-adjusted returns through enhanced selectivity, consistent with active portfolio management approaches documented by Grinold and Kahn (1999).
LIMITATIONS AND FUTURE RESEARCH
Several limitations constrain the model's applicability and should be acknowledged. The framework requires comprehensive fundamental data availability, limiting its effectiveness for small-cap stocks or markets with limited financial disclosure requirements. Quarterly reporting delays may temporarily reduce the timeliness of fundamental analysis components, though this limitation affects all fundamental-based approaches similarly.
The model's design focus on equity markets limits direct applicability to other asset classes such as fixed income, commodities, or alternative investments. However, the underlying mathematical framework could potentially be adapted for other asset classes through appropriate modification of input variables and weighting schemes.
Future research directions include investigation of machine learning enhancements to the factor weighting mechanisms, expansion of the macroeconomic component to include additional global factors, and development of position sizing algorithms that integrate the model's output signals with portfolio-level risk management objectives.
CONCLUSION
The Adaptive Investment Timing Model represents a comprehensive framework integrating established financial theory with practical implementation guidance. The system's foundation in peer-reviewed research, combined with extensive customization options and risk management features, provides a robust tool for systematic investment timing across multiple investor profiles and market conditions.
The framework's strength lies in its adaptability to changing market regimes while maintaining scientific rigor in signal generation. Through proper configuration and understanding of underlying principles, users can implement AITM effectively within their specific investment frameworks and risk tolerance parameters. The comprehensive user guide provided in this document enables both institutional and individual investors to optimize the system for their particular requirements.
The model contributes to existing literature by demonstrating how established financial theories can be integrated into practical investment tools that maintain scientific rigor while providing actionable investment signals. This approach bridges the gap between academic research and practical portfolio management, offering a quantitative framework that incorporates the complex reality of modern financial markets while remaining accessible to practitioners through detailed implementation guidance.
REFERENCES
Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(4), 589-609.
Ang, A., & Bekaert, G. (2007). Stock return predictability: Is it there? Review of Financial Studies, 20(3), 651-707.
Baker, M., & Wurgler, J. (2007). Investor sentiment in the stock market. Journal of Economic Perspectives, 21(2), 129-152.
Berger, P. G., & Ofek, E. (1995). Diversification's effect on firm value. Journal of Financial Economics, 37(1), 39-65.
Bollinger, J. (2001). Bollinger on Bollinger Bands. New York: McGraw-Hill.
Calmar, T. (1991). The Calmar ratio: A smoother tool. Futures, 20(1), 40.
Edwards, R. D., Magee, J., & Bassetti, W. H. C. (2018). Technical Analysis of Stock Trends. 11th ed. Boca Raton: CRC Press.
Estrella, A., & Mishkin, F. S. (1998). Predicting US recessions: Financial variables as leading indicators. Review of Economics and Statistics, 80(1), 45-61.
Fama, E. F., & French, K. R. (1988). Dividend yields and expected stock returns. Journal of Financial Economics, 22(1), 3-25.
Fama, E. F., & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33(1), 3-56.
Giot, P. (2005). Relationships between implied volatility indexes and stock index returns. Journal of Portfolio Management, 31(3), 92-100.
Graham, B., & Dodd, D. L. (2008). Security Analysis. 6th ed. New York: McGraw-Hill Education.
Grinold, R. C., & Kahn, R. N. (1999). Active Portfolio Management. 2nd ed. New York: McGraw-Hill.
Guidolin, M., & Timmermann, A. (2007). Asset allocation under multivariate regime switching. Journal of Economic Dynamics and Control, 31(11), 3503-3544.
Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica, 57(2), 357-384.
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-291.
Koenker, R., & Bassett Jr, G. (1978). Regression quantiles. Econometrica, 46(1), 33-50.
Lakonishok, J., Shleifer, A., & Vishny, R. W. (1994). Contrarian investment, extrapolation, and risk. Journal of Finance, 49(5), 1541-1578.
Lo, A. W., & MacKinlay, A. C. (1999). A Non-Random Walk Down Wall Street. Princeton: Princeton University Press.
Malkiel, B. G. (2003). The efficient market hypothesis and its critics. Journal of Economic Perspectives, 17(1), 59-82.
Markowitz, H. (1952). Portfolio selection. Journal of Finance, 7(1), 77-91.
Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63(2), 81-97.
Penman, S. H. (2012). Financial Statement Analysis and Security Valuation. 5th ed. New York: McGraw-Hill Education.
Piotroski, J. D. (2000). Value investing: The use of historical financial statement information to separate winners from losers. Journal of Accounting Research, 38, 1-41.
Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. Journal of Finance, 19(3), 425-442.
Sharpe, W. F. (1994). The Sharpe ratio. Journal of Portfolio Management, 21(1), 49-58.
Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving Decisions About Health, Wealth, and Happiness. New Haven: Yale University Press.
Whaley, R. E. (1993). Derivatives on market volatility: Hedging tools long overdue. Journal of Derivatives, 1(1), 71-84.
Whaley, R. E. (2000). The investor fear gauge. Journal of Portfolio Management, 26(3), 12-17.
Wilder, J. W. (1978). New Concepts in Technical Trading Systems. Greensboro: Trend Research.
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.
Nq/ES daily CME risk intervalReverse engineering the risk interval for CME (Chicago Mercantile Exchange) products based on margin requirements involves understanding the relationship between margin requirements, volatility, and the risk interval (price movement assumed for margin calculation)
The CME uses a methodology called SPAN (Standard Portfolio Analysis of Risk) to calculate margins. At a high level, the initial margin is derived from:
Initial Margin = Risk Interval × Contract Size × Volatility Adjustment Factor
Where:
Risk Interval: The price movement range used in the margin calculation.
Contract Size: The unit size of the futures contract.
Volatility Adjustment Factor: A measure of how much price fluctuation is expected, often tied to historical volatility.
To calculate an approximate of the daily CME risk interval, we need:
Initial Margin Requirement: Available on the CME Group website or broker platforms.
Contract Size: The size of one futures contract (e.g., for the S&P 500 E-mini, it is $50 × index points).
Volatility Adjustment Factor: This is derived from historical volatility or CME's implied volatility estimates.
As we do not have access to CME calculations , the volatility adjustment factor can be estimated using historical volatility: We calculate the standard deviation of daily returns over a specific period (e.g., 20 or 30 or 60 days).
Key Considerations
The exact formulas and parameters used by CME for CME's implied volatility estimates are proprietary, so this calculation based on standard deviation of daily returns is an approximation.
How to use:
Input the maintenance margin obtained from the CME website.
Adjust volatility period calculation.
The indicator displays the range high and low for the trading day.
1.Lines can be used as targets intraday
2.Market tends to snap back in between the lines and close the day in the range
Z-Score Normalized Volatility IndicesVolatility is one of the most important measures in financial markets, reflecting the extent of variation in asset prices over time. It is commonly viewed as a risk indicator, with higher volatility signifying greater uncertainty and potential for price swings, which can affect investment decisions. Understanding volatility and its dynamics is crucial for risk management and forecasting in both traditional and alternative asset classes.
Z-Score Normalization in Volatility Analysis
The Z-score is a statistical tool that quantifies how many standard deviations a given data point is from the mean of the dataset. It is calculated as:
Z = \frac{X - \mu}{\sigma}
Where X is the value of the data point, \mu is the mean of the dataset, and \sigma is the standard deviation of the dataset. In the context of volatility indices, the Z-score allows for the normalization of these values, enabling their comparison regardless of the original scale. This is particularly useful when analyzing volatility across multiple assets or asset classes.
This script utilizes the Z-score to normalize various volatility indices:
1. VIX (CBOE Volatility Index): A widely used indicator that measures the implied volatility of S&P 500 options. It is considered a barometer of market fear and uncertainty (Whaley, 2000).
2. VIX3M: Represents the 3-month implied volatility of the S&P 500 options, providing insight into medium-term volatility expectations.
3. VIX9D: The implied volatility for a 9-day S&P 500 options contract, which reflects short-term volatility expectations.
4. VVIX: The volatility of the VIX itself, which measures the uncertainty in the expectations of future volatility.
5. VXN: The Nasdaq-100 volatility index, representing implied volatility in the Nasdaq-100 options.
6. RVX: The Russell 2000 volatility index, tracking the implied volatility of options on the Russell 2000 Index.
7. VXD: Volatility for the Dow Jones Industrial Average.
8. MOVE: The implied volatility index for U.S. Treasury bonds, offering insight into expectations for interest rate volatility.
9. BVIX: Volatility of Bitcoin options, a useful indicator for understanding the risk in the cryptocurrency market.
10. GVZ: Volatility index for gold futures, reflecting the risk perception of gold prices.
11. OVX: Measures implied volatility for crude oil futures.
Volatility Clustering and Z-Score
The concept of volatility clustering—where high volatility tends to be followed by more high volatility—is well documented in financial literature. This phenomenon is fundamental in volatility modeling and highlights the persistence of periods of heightened market uncertainty (Bollerslev, 1986).
Moreover, studies by Andersen et al. (2012) explore how implied volatility indices, like the VIX, serve as predictors for future realized volatility, underlining the relationship between expected volatility and actual market behavior. The Z-score normalization process helps in making volatility data comparable across different asset classes, enabling more effective decision-making in volatility-based strategies.
Applications in Trading and Risk Management
By using Z-score normalization, traders can more easily assess deviations from the mean in volatility, helping to identify periods when volatility is unusually high or low. This can be used to adjust risk exposure or to implement volatility-based trading strategies, such as mean reversion strategies. Research suggests that volatility mean-reversion is a reliable pattern that can be exploited for profit (Christensen & Prabhala, 1998).
References:
• Andersen, T. G., Bollerslev, T., Diebold, F. X., & Vega, C. (2012). Realized volatility and correlation dynamics: A long-run approach. Journal of Financial Economics, 104(3), 385-406.
• Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327.
• Christensen, B. J., & Prabhala, N. R. (1998). The relation between implied and realized volatility. Journal of Financial Economics, 50(2), 125-150.
• Whaley, R. E. (2000). Derivatives on market volatility and the VIX index. Journal of Derivatives, 8(1), 71-84.
Options Overlay [Pro] IVR IV Skew Delta Exp.mv MurreyMath Expiry
𝗧𝗵𝗲 𝗳𝗶𝗿𝘀𝘁 𝗿𝗲𝗮𝗹 𝗼𝗽𝘁𝗶𝗼𝗻𝘀 𝗱𝗮𝘁𝗮 𝗶𝗻𝗱𝗶𝗰𝗮𝘁𝗼𝗿 𝗼𝗻 𝗧𝗿𝗮𝗱𝗶𝗻𝗴𝗩𝗶𝗲𝘄, 𝗮𝘃𝗮𝗶𝗹𝗮𝗯𝗹𝗲 𝗳𝗼𝗿 𝗼𝘃𝗲𝗿 𝟭𝟱𝟬+ 𝗹𝗶𝗾𝘂𝗶𝗱 𝗨𝗦 𝗺𝗮𝗿𝗸𝗲𝘁 𝘀𝘆𝗺𝗯𝗼𝗹𝘀.
🔃 Auto-Updating Option Metrics without refresh!
🍒 Developed and maintained by option traders for option traders.
📈 Specifically designed for TradingView users who trade options.
Our indicator provides essential key metrics such as:
✅ IVRank
✅ IVx
✅ 5-Day IVx Change
✅ Delta curves and interpolated distances
✅ Expected move curve
✅ Standard deviation (STD1) curve
✅ Vertical Pricing Skew
✅ Horizontal IVx Skew
✅ Delta Skew
like TastyTrade, TOS, IBKR etc, but in a much more visually intuitive way. See detailed descriptions below.
If this isn't enough, we also include a unique grid system designed specifically for options traders. This package features our innovative dynamic grid system:
✅ Enhanced Murrey Math levels (horizontal scale)
✅ Options expirations (vertical scale)
Designed to help you assess market conditions and make well-informed trading decisions, this tool is an essential addition for every serious options trader!
Ticker Information:
This indicator is currently implemented for more than 150 liquid US market tickers and we are continuously expanding the list:
SP:SPX AMEX:SPY NASDAQ:QQQ NASDAQ:TLT AMEX:GLD
NYSE:AA NASDAQ:AAL NASDAQ:AAPL NYSE:ABBV NASDAQ:ABNB NASDAQ:AMD NASDAQ:AMZN AMEX:ARKK NASDAQ:AVGO NYSE:AXP NYSE:BA NYSE:BABA NYSE:BAC NASDAQ:BIDU AMEX:BITO NYSE:BMY NYSE:BP NASDAQ:BYND NYSE:C NYSE:CAT NYSE:CCJ NYSE:CCL NASDAQ:COIN NYSE:COP NASDAQ:COST NYSE:CRM NASDAQ:CRWD NASDAQ:CSCO NYSE:CVNA NYSE:CVS NYSE:CVX NYSE:DAL NASDAQ:DBX AMEX:DIA NYSE:DIS NASDAQ:DKNG NASDAQ:EBAY NASDAQ:ETSY NASDAQ:EXPE NYSE:F NYSE:FCX NYSE:FDX AMEX:FXI AMEX:GDX AMEX:GDXJ NYSE:GE NYSE:GM NYSE:GME NYSE:GOLD NASDAQ:GOOG NASDAQ:GOOGL NYSE:GPS NYSE:GS NASDAQ:HOOD NYSE:IBM NASDAQ:IEF NASDAQ:INTC AMEX:IWM NASDAQ:JD NYSE:JNJ NYSE:JPM NYSE:JWN NYSE:KO NYSE:LLY NYSE:LOW NYSE:LVS NYSE:MA NASDAQ:MARA NYSE:MCD NYSE:MET NASDAQ:META NYSE:MGM NYSE:MMM NYSE:MPC NYSE:MRK NASDAQ:MRNA NYSE:MRO NASDAQ:MRVL NYSE:MS NASDAQ:MSFT AMEX:MSOS NYSE:NCLH NASDAQ:NDX NYSE:NET NASDAQ:NFLX NYSE:NIO NYSE:NKE NASDAQ:NVDA NASDAQ:ON NYSE:ORCL NYSE:OXY NASDAQ:PEP NYSE:PFE NYSE:PINS NYSE:PLTR NASDAQ:PTON NASDAQ:PYPL NASDAQ:QCOM NYSE:RBLX NYSE:RCL NASDAQ:RIOT NASDAQ:RIVN NASDAQ:ROKU NASDAQ:SBUX NYSE:SHOP AMEX:SLV NASDAQ:SMCI NASDAQ:SMH NYSE:SNAP NYSE:SQ NYSE:T NYSE:TGT NASDAQ:TQQQ NASDAQ:TSLA NYSE:TSM NASDAQ:TTD NASDAQ:TXN NYSE:U NASDAQ:UAL NYSE:UBER AMEX:UNG NYSE:UPS NASDAQ:UPST AMEX:USO NYSE:V AMEX:VXX NYSE:VZ NASDAQ:WBA NYSE:WFC NYSE:WMT NASDAQ:WYNN NYSE:X AMEX:XHB AMEX:XLE AMEX:XLF AMEX:XLI AMEX:XLK AMEX:XLP AMEX:XLU AMEX:XLV AMEX:XLY NYSE:XOM NYSE:XPEV CBOE:XSP NASDAQ:ZM
How does the indicator work and why is it unique?
This Pine Script indicator is a complex tool designed to provide various option metrics and visualization tools for options market traders. The indicator extracts raw options data from an external data provider (ORATS), processes and refines the delayed data package using pineseed, and sends it to TradingView, visualizing the data using specific formulas (see detailed below) or interpolated values (e.g., delta distances). This method of incorporating options data into a visualization framework is unique and entirely innovative on TradingView.
The indicator aims to offer a comprehensive view of the current state of options for the implemented instruments, including implied volatility (IV), IV rank (IVR), options skew, and expected market movements, which are objectively measured as detailed below.
The options metrics we display may be familiar to options traders from various major brokerage platforms such as TastyTrade, IBKR, TOS, Tradier, TD Ameritrade, Schwab, etc.
🟨 𝗗𝗘𝗧𝗔𝗜𝗟𝗘𝗗 𝗗𝗢𝗖𝗨𝗠𝗘𝗡𝗧𝗔𝗧𝗜𝗢𝗡 🟨
🔶 Auto-Updating Option Metrics and Curved Lines
🔹 Interpolated DELTA Curves (16,20,25,30,40)
In our indicator, the curve layer settings allow you to choose the delta value for displaying the delta curve: 16, 20, 25, 30, or even 40. The color of the curve can be customized, and you can also hide the delta curve by selecting the "-" option.
It's important to mention that we display interpolated deltas from the actual option chain of the underlying asset using the Black-Scholes model. This ensures that the 16 delta truly reflects the theoretical, but accurate, 16 delta distance. (For example, deltas shown by brokerages for individual strikes are rounded; a 0.16 delta might actually be 0.1625.)
🔹 Expected Move Curve (Exp.mv)
The expected move is the predicted dollar change in the underlying stock's price by a given option's expiration date, with 68% certainty. It is calculated using the expiration's pricing and implied volatility levels. We chose the TastyTrade method for calculating expected move, as we found it to be the most expressive.
Expected Move Calculation
Expected Move = (ATM straddle price x 0.6) + (1st OTM strangle price x 0.3) + (2nd OTM strangle price x 0.1)
For example , if stock XYZ is trading at 121 and the ATM straddle is 4.40, the 120/122 strangle is 3.46, and the 119/123 strangle is 2.66, the expected move is calculated as follows: 4.40 x 0.60 = 2.64; 3.46 x 0.30 = 1.04; 2.66 x 0.10 = 0.27; Expected move = 2.64 + 1.04 + 0.27 = ±3.9
In this example below, the TastyTrade platform indicates the expected move on the option chain with a brown color, and the exact value is displayed behind the ± symbol for each expiration. By default, we also use brown for this indication, but this can be changed or the curve display can be turned off.
🔹 Standard Deviation Curve (1 STD)
One standard deviation of a stock encompasses approximately 68.2% of outcomes in a distribution of occurrences based on current implied volatility.
We use the expected move formula to calculate the one standard deviation range of a stock. This calculation is based on the days-to-expiration (DTE) of our option contract, the stock price, and the implied volatility of a stock:
Calculation:
Standard Deviation = Closing Price * Implied Volatility * sqrt(Days to Expiration / 365)
According to options literature, there is a 68% probability that the underlying asset will fall within this one standard deviation range at expiration.
If the 1 STD and Exp.mv displays are both enabled, the indicator fills the area between them with a light gray color. This is because both represent probability distributions that appear as a "bell curve" when graphed, making it visually appealing.
Tip and Note:
The 1 STD line might appear jagged at times , which does not indicate a problem with the indicator. This is normal immediately after market open (e.g., during the first data refresh of the day) or if the expirations are illiquid (e.g., weekly expirations). The 1 STD value is calculated based on the aggregated IVx for the expirations, and the aggregated IVx value for weekly expirations updates less frequently due to lower trading volume. In such cases, we recommend enabling the "Only Monthly Expirations" option to smooth out the bell curve.
∑ Quant Observation:
The values of the expected move and the 1st standard deviation (1STD) will not match because they use different calculation methods, even though both are referred to as representing 68% of the underlying asset's movement in options literature. The expected move is based on direct market pricing of ATM options. The 1STD, on the other hand, uses the averaged implied volatility (IVX) for the given expiration to determine its value. Based on our experience, it is better to consider the area between the expected move and the 1STD as the true representation of the original 68% rule.
🔶 IVR Dashboard Panel Rows
🔹 IVR (IV Rank)
The Implied Volatility Rank (IVR) indicator helps options traders assess the current level of implied volatility (IV) in comparison to the past 52 weeks. IVR is a useful metric to determine whether options are relatively cheap or expensive. This can guide traders on whether to buy or sell options. We calculate IVrank, like TastyTrade does.
IVR Calculation:
IV Rank = (current IV - 52 week IV low) / (52 week IV high - 52 week IV low)
IVR Levels and Interpretations:
IVR 0-10 (Green): Very low implied volatility rank. Options might be "cheap," potentially a good time to buy options.
IVR 10-35 (White): Normal implied volatility rank. Options pricing is relatively standard.
IVR 35-50 (Orange): Almost high implied volatility rank.
IVR 50-75 (Red): Definitely high implied volatility rank. Options might be "expensive," potentially a good time to sell options for higher premiums.
IVR above 75 (Highlighted Red): Ultra high implied volatility rank. Indicates very high levels, suggesting a favorable time for selling options.
The panel refreshes automatically if the symbol is implemented. You can hide the panel or change the position and size.
🔹IVx (Implied Volatility Index)
The Implied Volatility Index (IVx) displayed in the option chain is calculated similarly to the VIX. The Cboe uses standard and weekly SPX options to measure the expected volatility of the S&P 500. A similar method is utilized to calculate IVx for each option expiration cycle.
For our purposes on the IVR Panel, we aggregate the IVx values specifically for the 35-70 day monthly expiration cycle . This aggregated value is then presented in the screener and info panel, providing a clear and concise measure of implied volatility over this period.
IVx Color coding:
IVx above 30 is displayed in orange.
IVx above 60 is displayed in red
IVx on curve:
The IVx values for each expiration can be viewed by hovering the mouse over the colored tooltip labels above the Curve.
IVx avg on IVR panel :
If the option is checked in the IVR panel settings, the IVR panel will display the average IVx values up to the optimal expiration.
Important Note:
The IVx value alone does not provide sufficient context. There are stocks that inherently exhibit high IVx values. Therefore, it is crucial to consider IVx in conjunction with the Implied Volatility Rank (IVR), which measures the IVx relative to its own historical values. This combined view helps in accurately assessing the significance of the IVx in relation to the specific stock's typical volatility behavior.
This indicator offers traders a comprehensive view of implied volatility, assisting them in making informed decisions by highlighting both the absolute and relative volatility measures.
🔹IVx 5 days change %
We are displaying the five-day change of the IV Index (IVx value). The IV Index 5-Day Change column provides quick insight into recent expansions or decreases in implied volatility over the last five trading days.
Traders who expect the value of options to decrease might view a decrease in IVX as a positive signal. Strategies such as Strangle and Ratio Spread can benefit from this decrease.
On the other hand, traders anticipating further increases in IVX will focus on the rising IVX values. Strategies like Calendar Spread or Diagonal Spread can take advantage of increasing implied volatility.
This indicator helps traders quickly assess changes in implied volatility, enabling them to make informed decisions based on their trading strategies and market expectations.
🔹 Vertical Pricing Skew
At TanukiTrade, Vertical Pricing Skew refers to the difference in pricing between put and call options with the same expiration date at the same distance (at expected move). We analyze this skew to understand market sentiment. This is the same formula used by TastyTrade for calculations.
We calculate the interpolated strike price based on the expected move , taking into account the neighboring option prices and their distances. This allows us to accurately determine whether the CALL or PUT options are more expensive.
PUT Skew (red): Put options are more expensive than call options, indicating the market expects a downward move (▽). If put options are more expensive by more than 20% at the same expected move distance, we color it lighter red.
CALL Skew (green): Call options are more expensive than put options, indicating the market expects an upward move (△). If call options are priced more than 30% higher at the examined expiration, we color it lighter green.
Vertical Skew on Curve:
The degree of vertical pricing skew for each expiration can be viewed by hovering over the points above the curve. Hover with mouse for more information.
Vertical Skew on IVR panel:
We focus on options with 35-70 days to expiration (DTE) for optimal analysis in case of vertical skew. Hover with mouse for more information.
This approach helps us gauge market expectations accurately, providing insights into potential price movements. Remember, we always evaluate the skew at the expected move using linear interpolation to determine the theoretical pricing of options.
🔹 Delta Skew 🌪️ (Twist)
We have a new metric that examines which monthly expiration indicates a "Delta Skew Twist" where the 16 delta deviates from the monthly STD. This is important because, under normal circumstances, the 16 delta is positioned between the expected move and the standard deviation (STD1) line (see Exp.mv & 1STD exact definitions above). However, if the interpolated 16 delta line exceeds the STD1 line either upwards or downwards, it represents a special case of vertical skew on the option chain.
Normal case : exp.move < delta16 < std1
Delta Skew Twist: exp.move < std1 < delta16
We indicate this with direction-specific colors (red/green) on the delta line. We also color the section of the delta curve affected by the delta skew in this case, even if you choose to display a lower delta, such as 30, instead of 16.
If "Colored Labels with Tooltips" is enabled, we also display a 🌪️ symbol in the tooltip for the expirations affected by Delta Skew.
If you have enabled the display of 'Vertical Pricing Skew' on the IVR Panel, a 🌪️ symbol will also appear next to the value of the vertical skew, and the tooltip will indicate from which expiration Delta Skew is observed.
🔹 Horizontal IVx Skew
In options pricing, it is typically expected that the implied volatility (IVx) increases for options with later expiration dates. This means that options further out in time are generally more expensive. At TanukiTrade, we refer to the phenomenon where this expectation is reversed—when the IVx decreases between two consecutive expirations—as Horizontal Skew or IVx Skew.
Horizontal IVx Skew occurs when: Front Expiry IVx < Back Expiry IVx
This scenario can create opportunities for traders who prefer diagonal or calendar strategies . Based on our experience, we categorize Horizontal Skew into two types:
Weekly Horizontal Skew:
When IVx skew is observed between two consecutive non-monthly expirations, the displayed value is the rounded-up percentage difference. On hover, the approximate location of this skew is also displayed. The precise location can be seen on this indicator.
Monthly Horizontal Skew:
When IVx skew is observed between two consecutive monthly expirations , the displayed value is the rounded-up percentage difference. On hover, the approximate location of this skew is also displayed. The precise location can be seen on our Overlay indicator.
The Monthly Vertical IVx skew is consistently more liquid than the weekly vertical IVx skew. Weekly Horizontal IVx Skew may not carry relevant information for symbols not included in the 'Weeklies & Volume Masters' preset in our Options Screener indicator.
If the options chain follows the normal IVx pattern, no skew value is displayed.
Color codes or tooltip labels above curve:
Gray - No horizontal skew;
Purple - Weekly horizontal skew;
BigBlue - Monthly horizontal skew
The display of monthly and weekly IVx skew can be toggled on or off on the IVR panel. However, if you want to disable the colored tooltips above the curve, this can only be done using the "Colored labels with tooltips" switch.
We indicate this range with colorful information bubbles above the upper STD line.
🔶 The Option Trader’s GRID System: Adaptive MurreyMath + Expiry Lines
At TanukiTrade, we utilize Enhanced MurreyMath and Expiry lines to create a dynamic grid system, unlike the basic built-in vertical grids in TradingView, which provide no insight into specific price levels or option expirations.
These grids are beneficial because they provide a structured layout, making important price levels visible on the chart. The grid automatically resizes as the underlying asset's volatility changes, helping traders identify expected movements for various option expirations.
The Option Trader’s GRID System part of this indicator can be used without limitations for all instruments . There are no type or other restrictions, and it automatically scales to fit every asset. Even if we haven't implemented the option metrics for a particular underlying asset, the GRID system will still function!
🔹 SETUP OF YOUR OPTIONS GRID SYSTEM
You can setup your new grid system in 3 easy steps!
STEP1: Hide default horizontal grid lines in TradingView
Right-click on an empty area of your chart, then select “Settings.” In the Chart settings -> Canvas -> Grid lines section, disable the display of horizontal lines to avoid distraction.
SETUP STEP2: Scaling fix
Right-click on the price scale on the right side, then select "Scale price chart only" to prevent the chart from scaling to the new horizontal lines!
STEP3: Enable Tanuki Options Grid
As a final step, make sure that both the vertical (MurreyMath) and horizontal (Expiry) lines are enabled in the Grid section of our indicator.
You are done, enjoy the new grid system!
🔹 HORIZONTAL: Enhanced MurreyMath Lines
Murrey Math lines are based on the principles observed by William Gann, renowned for his market symmetry forecasts. Gann's techniques, such as Gann Angles, have been adapted by Murrey to make them more accessible to ordinary investors. According to Murrey, markets often correct at specific price levels, and breakouts or returns to these levels can signal good entry points for trades.
At TanukiTrade, we enhance these price levels based on our experience , ensuring a clear display. We acknowledge that while MurreyMath lines aren't infallible predictions, they are useful for identifying likely price movements over a given period (e.g., one month) if the market trend aligns.
Our opinion: MurreyMath lines are not crystal balls (like no other tool). They should be used to identify that if we are trading in the right direction, the price is likely to reach the next unit step within a unit time (e.g. monthly expiration).
One unit step is the distance between Murrey Math lines, such as between the 0/8 and 1/8 lines. This interval helps identify different quadrants and is crucial for recognizing support and resistance levels.
Some option traders use Murrey Math lines to gauge the movement speed of an instrument over a unit time. A quadrant encompasses 4 unit steps.
Key levels, according to TanukiTrade, include:
Of course, the lines can be toggled on or off, and their default color can also be changed.
🔹 VERTICAL: Expiry Lines
The indicator can display monthly and weekly expirations as dashed lines, with customizable colors. Weekly expirations will always appear in a lighter shade compared to monthly expirations.
Monthly Expiry Lines:
You can turn off the lines indicating monthly expirations, or set the direction (past/future/both) and the number of lines to be drawn.
Weekly Expiry Lines:
You can display weekly expirations pointing to the future. You can also turn them off or specify how many weeks ahead the lines should be drawn.
Of course, the lines can be toggled on or off, and their default color can also be changed.
TIP: Hide default vertical grid lines in TradingView
Right-click on an empty area of your chart, then select “Settings.” In the Chart settings -> Canvas -> Grid lines section, disable the display of vertical lines to avoid distraction. Same, like steps above at MurreyMath lines.
🔶 ADDITIONAL IMPORTANT COMMENTS
- U.S. market only:
Since we only deal with liquid option chains: this option indicator only works for the USA options market and do not include future contracts; we have implemented each selected symbol individually.
- Why is there a slight difference between the displayed data and my live brokerage data? There are two reasons for this, and one is beyond our control.
- Brokerage Calculation Differences:
Every brokerage has slight differences in how they calculate metrics like IV and IVx. If you open three windows for TOS, TastyTrade, and IBKR side by side, you will notice that the values are minimally different. We had to choose a standard, so we use the formulas and mathematical models described by TastyTrade when analyzing the options chain and drawing conclusions.
- Option-data update frequency:
According to TradingView's regulations and guidelines, we can update external data a maximum of 5 times per day. We strive to use these updates in the most optimal way:
(1st update) 15 minutes after U.S. market open
(2nd, 3rd, 4th updates) 1.5–3 hours during U.S. market open hours
(5th update) 10 minutes before market close.
You don’t need to refresh your window, our last refreshed data-pack is always automatically applied to your indicator , and you can see the time elapsed since the last update at the bottom of your indicator.
- Skewed Curves:
The delta, expected move, and standard deviation curves also appear relevantly on a daily or intraday timeframe. Data loss is experienced above a daily timeframe: this is a TradingView limitation.
- Weekly illiquid expiries:
Especially for instruments where weekly options are illiquid: the weekly expiration STD1 data is not relevant. In these cases, we recommend checking in the "Display only Monthly labels" checkbox to avoid displaying not relevant weekly options expirations.
-Timeframe Issues:
Our option indicator visualizes relevant data on a daily resolution. If you see strange or incorrect data (e.g., when the options data was last updated), always switch to a daily (1D) timeframe. If you still see strange data, please contact us.
Disclaimer:
Our option indicator uses approximately 15min-3 hour delayed option market snapshot data to calculate the main option metrics. Exact realtime option contract prices are never displayed; only derived metrics and interpolated delta are shown to ensure accurate and consistent visualization. Due to the above, this indicator can only be used for decision support; exclusive decisions cannot be made based on this indicator . We reserve the right to make errors.This indicator is designed for options traders who understand what they are doing. It assumes that they are familiar with options and can make well-informed, independent decisions. We work with public data and are not a data provider; therefore, we do not bear any financial or other liability.
Options Screener [Pro] - IVRank, IVx, Deltas, Exp.move, Skew
𝗢𝗽𝘁𝗶𝗼𝗻 𝘀𝗰𝗿𝗲𝗲𝗻𝗲𝗿 𝗼𝗻 𝗧𝗿𝗮𝗱𝗶𝗻𝗴𝗩𝗶𝗲𝘄 𝘄𝗶𝘁𝗵 𝗿𝗲𝗮𝗹 𝗱𝗮𝘁𝗮, 𝗮𝘃𝗮𝗶𝗹𝗮𝗯𝗹𝗲 𝗳𝗼𝗿 𝗼𝘃𝗲𝗿 𝟭𝟱𝟬+ 𝗹𝗶𝗾𝘂𝗶𝗱 𝗨𝗦 𝗺𝗮𝗿𝗸𝗲𝘁 𝘀𝘆𝗺𝗯𝗼𝗹𝘀!
𝗢𝘂𝗿 𝘀𝗰𝗿𝗲𝗲𝗻𝗲𝗿 𝗽𝗿𝗼𝘃𝗶𝗱𝗲𝘀 𝗲𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗸𝗲𝘆 𝗺𝗲𝘁𝗿𝗶𝗰𝘀 𝘀𝘂𝗰𝗵 𝗮𝘀:
✅ IVRank
✅ IVx
✅ 5-Day IVx Change
✅ Vertical Pricing Skew
✅ Horizontal IVx Skew
✅ Delta Skew
like TastyTrade, TOS, IBKR etc.
Designed to help you assess option market conditions and make well-informed trading decisions, this tool is an essential addition for every serious options trader!
Ticker Information:
This screener is currently implemented for more than 150 liquid US market tickers and we are continuously expanding the list:
SP:SPX AMEX:SPY NASDAQ:QQQ NASDAQ:TLT AMEX:GLD
NYSE:AA NASDAQ:AAL NASDAQ:AAPL NYSE:ABBV NASDAQ:ABNB NASDAQ:AMD NASDAQ:AMZN AMEX:ARKK NASDAQ:AVGO NYSE:AXP NYSE:BA NYSE:BABA NYSE:BAC NASDAQ:BIDU AMEX:BITO NYSE:BMY NYSE:BP NASDAQ:BYND NYSE:C NYSE:CAT NYSE:CCJ NYSE:CCL NASDAQ:COIN NYSE:COP NASDAQ:COST NYSE:CRM NASDAQ:CRWD NASDAQ:CSCO NYSE:CVNA NYSE:CVS NYSE:CVX NYSE:DAL NASDAQ:DBX AMEX:DIA NYSE:DIS NASDAQ:DKNG NASDAQ:EBAY NASDAQ:ETSY NASDAQ:EXPE NYSE:F NYSE:FCX NYSE:FDX AMEX:FXI AMEX:GDX AMEX:GDXJ NYSE:GE NYSE:GM NYSE:GME NYSE:GOLD NASDAQ:GOOG NASDAQ:GOOGL NYSE:GPS NYSE:GS NASDAQ:HOOD NYSE:IBM NASDAQ:IEF NASDAQ:INTC AMEX:IWM NASDAQ:JD NYSE:JNJ NYSE:JPM NYSE:JWN NYSE:KO NYSE:LLY NYSE:LOW NYSE:LVS NYSE:MA NASDAQ:MARA NYSE:MCD NYSE:MET NASDAQ:META NYSE:MGM NYSE:MMM NYSE:MPC NYSE:MRK NASDAQ:MRNA NYSE:MRO NASDAQ:MRVL NYSE:MS NASDAQ:MSFT AMEX:MSOS NYSE:NCLH NASDAQ:NDX NYSE:NET NASDAQ:NFLX NYSE:NIO NYSE:NKE NASDAQ:NVDA NASDAQ:ON NYSE:ORCL NYSE:OXY NASDAQ:PEP NYSE:PFE NYSE:PINS NYSE:PLTR NASDAQ:PTON NASDAQ:PYPL NASDAQ:QCOM NYSE:RBLX NYSE:RCL NASDAQ:RIOT NASDAQ:RIVN NASDAQ:ROKU NASDAQ:SBUX NYSE:SHOP AMEX:SLV NASDAQ:SMCI NASDAQ:SMH NYSE:SNAP NYSE:SQ NYSE:T NYSE:TGT NASDAQ:TQQQ NASDAQ:TSLA NYSE:TSM NASDAQ:TTD NASDAQ:TXN NYSE:U NASDAQ:UAL NYSE:UBER AMEX:UNG NYSE:UPS NASDAQ:UPST AMEX:USO NYSE:V AMEX:VXX NYSE:VZ NASDAQ:WBA NYSE:WFC NYSE:WMT NASDAQ:WYNN NYSE:X AMEX:XHB AMEX:XLE AMEX:XLF AMEX:XLI AMEX:XLK AMEX:XLP AMEX:XLU AMEX:XLV AMEX:XLY NYSE:XOM NYSE:XPEV CBOE:XSP NASDAQ:ZM
How does the screener work and why is it unique?
This Pine Script screener is an expert tool created to provide various option metrics and visualization tools for options market traders. The screener extracts raw options data from an external data provider (ORATS), processes, and refines the delayed data package using pineseed, and sends it to TradingView. The data is calculated using specific formulas or interpolated values, such as delta distances. This method of integrating options data into a screener framework is unique and innovative on TradingView.
The screener aims to offer a comprehensive view of the current state of options for the implemented instruments, including implied volatility index (IVx), IV rank (IVR), options skew, and expected market movements, which are objectively measured as detailed below.
The options metrics displayed may be familiar to options traders from various major brokerage platforms such as TastyTrade, IBKR, TOS, Tradier, TD Ameritrade, Schwab, etc.
🟨 𝗗𝗘𝗧𝗔𝗜𝗟𝗘𝗗 𝗗𝗢𝗖𝗨𝗠𝗘𝗡𝗧𝗔𝗧𝗜𝗢𝗡 🟨
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🔶 Auto-Updating Option Metrics
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🔹 IVR (IV Rank)
The Implied Volatility Rank (IVR) indicator helps options traders assess the current level of implied volatility (IV) in comparison to the past 52 weeks. IVR is a useful metric to determine whether options are relatively cheap or expensive. This can guide traders on whether to buy or sell options. We calculate IVrank, like TastyTrade does.
IVR Calculation: IV Rank = (current IV - 52 week IV low) / (52 week IV high - 52 week IV low)
IVR Levels and Interpretations:
IVR 0-10 (Green): Very low implied volatility rank. Options might be "cheap," potentially a good time to buy options.
IVR 10-35 (White): Normal implied volatility rank. Options pricing is relatively standard.
IVR 35-50 (Orange): Almost high implied volatility rank.
IVR 50-75 (Red): Definitely high implied volatility rank. Options might be "expensive," potentially a good time to sell options for higher premiums.
IVR above 75 (Highlighted Red): Ultra high implied volatility rank. Indicates very high levels, suggesting a favorable time for selling options.
Extra: If the IVx value is also greater than 30, the background will be dark highlighted, because a high IVR alone doesn’t mean much without high IVx.
🔹IVx (Implied Volatility Index)
The Implied Volatility Index (IVx) displayed in the option chain is calculated similarly to the VIX. The Cboe employs standard and weekly SPX options to measure the expected volatility of the S&P 500. A similar method is utilized to calculate IVx for each option expiration cycle.
For our purposes, we aggregate the IVx values specifically for the 35-70 day monthly expiration cycle . This aggregated value is then presented in the screener and info panel, providing a clear and concise measure of implied volatility over this period.
We will display a warning if the option chain is heavily skewed and valid, symmetric 16 delta options are not found at optimal monthly expirations.
IVx Color coding:
IVx above 30 is displayed in orange.
IVx above 60 is displayed in red
Important Note: The IVx value alone does not provide sufficient context. There are stocks that inherently exhibit high IVx values. Therefore, it is crucial to consider IVx in conjunction with the Implied Volatility Rank (IVR), which measures the IVx relative to its own historical values. This combined view helps in accurately assessing the significance of the IVx in relation to the specific stock's typical volatility behavior.
This indicator offers traders a comprehensive view of implied volatility, assisting them in making informed decisions by highlighting both the absolute and relative volatility measures.
🔹IVx 5 days change %
We are displaying the five-day change of the IV Index (IVx value). The IV Index 5-Day Change column provides quick insight into recent expansions or decreases in implied volatility over the last five trading days.
Traders who expect the value of options to decrease might view a decrease in IVX as a positive signal. Strategies such as Strangle and Ratio Spread can benefit from this decrease.
On the other hand, traders anticipating further increases in IVX will focus on the rising IVX values. Strategies like Calendar Spread or Diagonal Spread can take advantage of increasing implied volatility.
This indicator helps traders quickly assess changes in implied volatility, enabling them to make informed decisions based on their trading strategies and market expectations.
🔹 Vertical Pricing Skew
At TanukiTrade, Vertical Pricing Skew refers to the difference in pricing between put and call options with the same expiration date at the same distance (at expected move). We analyze this skew to understand market sentiment. This is the same formula used by TastyTrade for calculations.
PUT Skew (red): Put options are more expensive than call options, indicating the market expects a downward move (▽). If put options are more expensive by more than 20% at the same expected move distance, we color it lighter red.
CALL Skew (green): Call options are more expensive than put options, indicating the market expects an upward move (△). If call options are priced more than 30% higher at the examined expiration, we color it lighter green.
We focus on options with 35-70 days to expiration (DTE) for optimal analysis. We always evaluate the skew at the expected move using linear interpolation to determine the theoretical pricing of options. If the pricing have more than C50%/P35% we are highlighting the cell.
This approach helps us gauge market expectations accurately, providing insights into potential price movements.
🔹 Horizontal IVx Skew
In options pricing, it is typically expected that the implied volatility (IVx) increases for options with later expiration dates. This means that options further out in time are generally more expensive. At TanukiTrade, we refer to the phenomenon where this expectation is reversed—when the IVx decreases between two consecutive expirations—as Horizontal Skew or IVx Skew.
Horizontal IVx Skew occurs when: Front Month IVx < Back Month IVx
This scenario can create opportunities for traders who prefer diagonal or calendar strategies. Based on our experience, we categorize Horizontal Skew into two types:
Weekly Horizontal Skew: When IVx skew is observed between two consecutive non-monthly expirations , the displayed value is the rounded-up percentage difference. On hover, the approximate location of this skew is also displayed. The precise location can be seen on the Overlay indicator.
Monthly Horizontal Skew: When IVx skew is observed between two consecutive monthly expirations , the displayed value is the rounded-up percentage difference. On hover, the approximate location of this skew is also displayed. The precise location can be seen on the Overlay indicator.
The Monthly Vertical IVx skew is consistently stronger (more liquid) on average symbols than the weekly vertical IVx skew. Weekly Horizontal IVx Skew may not carry relevant information for symbols not included in the 'Weeklies & Volume Masters' preset.
If the options chain follows the normal IVx pattern, no skew value is displayed.
Additionally , if the Implied Volatility Rank (IVR) is low (indicated by green), the Horizontal Skew background turns black, because this environment is good for Calendar+Diagonal.
Additionally , if the % of the skew is greater than 10, the Horizontal Skew font color turns lighter.
🔹 Delta Skew 🌪️ (Twist)
We have a metric that examines which monthly expiration indicates a "Delta Skew Twist" where the 16 delta deviates from the monthly STD. This is important because, under normal circumstances, the 16 delta is positioned between the expected move and the standard deviation (STD1) line. However, if the interpolated 16 delta line exceeds the STD1 line either upwards or downwards, it represents a special case of vertical skew.
Normal case : exp.move < delta16 < std1
Delta Skew Twist: exp.move < std1 < delta16
If the Days to Expiration of the twist is less than 75, we use a lighter color.
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🔶 HOW WE CALCULATE
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🔹 Expected Move
The expected move is the predicted dollar change in the underlying stock's price by a given option's expiration date, with 68% certainty. It is calculated using the expiration's pricing and implied volatility levels.
Expected Move Calculation
Expected Move = (ATM straddle price x 0.6) + (1st OTM strangle price x 0.3) + (2nd OTM strangle price x 0.1)
For example , if stock XYZ is trading at 121 and the ATM straddle is 4.40, the 120/122 strangle is 3.46, and the 119/123 strangle is 2.66, the expected move is calculated as follows: 4.40 x 0.60 = 2.64; 3.46 x 0.30 = 1.04; 2.66 x 0.10 = 0.27; Expected move = 2.64 + 1.04 + 0.27 = ±3.9
🔹 Standard deviation
One standard deviation of a stock encompasses approximately 68.2% of outcomes in a distribution of occurrences based on current implied volatility.
We use the expected move formula to calculate the one standard deviation range of a stock. This calculation is based on the days-to-expiration (DTE) of our option contract, the stock price, and the implied volatility of a stock:
Calculation:
Standard Deviation = Closing Price * Implied Volatility * sqrt(Days to Expiration / 365)
According to options literature, there is a 68% probability that the underlying asset will fall within this one standard deviation range at expiration.
∑ Quant Observation: The values of the expected move and the 1st standard deviation (1STD) will not match because they use different calculation methods, even though both are referred to as representing 68% of the underlying asset's movement in options literature. The expected move is based on direct market pricing of ATM options. The 1STD, on the other hand, uses the averaged implied volatility (IVX) for the given expiration to determine its value. Based on our experience, it is better to consider the area between the expected move and the 1STD as the true representation of the original 68% rule.
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🔶 USAGE
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🔹 Create a new empty layout for the screener!
You can access this from the dropdown menu in the upper right corner. In the popup window, name it as you like, for example, "Option Screener."
🔹 Hide the candlestick chart
Make the chart invisible using the "Hide" option from the three-dot dropdown menu located in the upper left corner.
🔹 Other Unwanted Elements
If other unnecessary elements are distracting you (e.g., economic data, volume, default grid), you can easily remove them from the layout. Right-click on the empty chart area. Here, click on the gear (Settings) icon and remove everything from the "Events" tab, as well as from the "Trading" tab. Under the "Canvas" tab, it is recommended to set the "Grid lines" setting to "None."
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
🔶 Screener Settings
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Naturally, the font size and position can be easily adjusted.
Additionally, there are two basic usage modes: manual input or using the preset list.
🔹If you selected “Manual Below” in the preset dropdown, the tickers you chose from the dropdown (up to a maximum of 40) will be displayed. The panel name will be the one you specified.
🔹If you selected a pre-assembled list , the manually entered list will be ignored, and the preset list will be displayed. (In the future, we will expand the preset list based on your feedback!).
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
🔶 Best Practices for TanukiTrade Option Screener:
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
🔹 Every Preset on a New Layout:
If you following the steps above, you easy can setup this screener in one window with one split layout:
🔹 Split Layout:
- Left Side: The underlying asset with our Options IV Overlay (IVR, Deltas, Expected Move, STD1, Skew visualized) along with the Enhanced Murrey Math Indicator and Option Expiry.
- Right Side: Searching for opportunities using our Options Screener.
Opportunities Search
🔹 Everything in One Layout + One Window:
This is the all-in-one view:
- The underlying asset with our Options IV Overlay (IVR, Deltas, Expected Move, STD1, Skew visualized)
- Enhanced Murrey Math Indicator and Option Expiry
- Options Screener on the left
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
🔶 ADDITIONAL IMPORTANT COMMENTS
- U.S. market only:
Since we only deal with liquid option chains: this option indicator only works for the USA options market and do not include future contracts; we have implemented each selected symbol individually.
- Why is there a slight difference between the displayed data and my live brokerage data? There are two reasons for this, and one is beyond our control.
- Brokerage Calculation Differences:
Every brokerage has slight differences in how they calculate metrics like IV and IVx. If you open three windows for TOS, TastyTrade, and IBKR side by side, you will notice that the values are minimally different. We had to choose a standard, so we use the formulas and mathematical models described by TastyTrade when analyzing the options chain and drawing conclusions.
- Option-data update frequency:
According to TradingView's regulations and guidelines, we can update external data a maximum of 5 times per day. We strive to use these updates in the most optimal way:
(1st update) 15 minutes after U.S. market open
(2nd, 3rd, 4th updates) 1.5–3 hours during U.S. market open hours
(5th update) 10 minutes before market close.
You don’t need to refresh your window, our last refreshed data-pack is always automatically applied to your indicator , and you can see the time elapsed since the last update at the bottom of your indicator.
- Weekly illiquid expiries:
The Weekly Horizontal IVx Skew may not carry relevant information for instruments not included in the 'Weeklies & Volume Masters' preset package.
-Timeframe Issues:
Our option indicator visualizes relevant data on a daily resolution. If you see strange or incorrect data (e.g., when the options data was last updated), always switch to a daily (1D) timeframe. If you still see strange data, please contact us.
Disclaimer:
Our option indicator uses approximately 15min-3 hour delayed option market snapshot data to calculate the main option metrics. Exact realtime option contract prices are never displayed; only derived metrics and interpolated delta are shown to ensure accurate and consistent visualization. Due to the above, this indicator can only be used for decision support; exclusive decisions cannot be made based on this indicator . We reserve the right to make errors.This indicator is designed for options traders who understand what they are doing. It assumes that they are familiar with options and can make well-informed, independent decisions. We work with public data and are not a data provider; therefore, we do not bear any financial or other liability.
Options SCREENER [Lite] - IVRank, IVx, Deltas, Exp.move, Skew
𝗢𝗽𝘁𝗶𝗼𝗻 𝘀𝗰𝗿𝗲𝗲𝗻𝗲𝗿 𝗼𝗻 𝗧𝗿𝗮𝗱𝗶𝗻𝗴𝗩𝗶𝗲𝘄 𝘄𝗶𝘁𝗵 𝗿𝗲𝗮𝗹 𝗱𝗮𝘁𝗮, 𝗼𝗻𝗹𝘆 𝗳𝗼𝗿 𝟱 𝗹𝗶𝗾𝘂𝗶𝗱 𝗨𝗦 𝗺𝗮𝗿𝗸𝗲𝘁 𝘀𝘆𝗺𝗯𝗼𝗹𝘀
𝗢𝘂𝗿 𝘀𝗰𝗿𝗲𝗲𝗻𝗲𝗿 𝗽𝗿𝗼𝘃𝗶𝗱𝗲𝘀 𝗲𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗸𝗲𝘆 𝗺𝗲𝘁𝗿𝗶𝗰𝘀 𝘀𝘂𝗰𝗵 𝗮𝘀:
✅ IVRank
✅ IVx
✅ 5-Day IVx Change
✅ Vertical Pricing Skew
✅ Horizontal IVx Skew
✅ Delta Skew
like TastyTrade, TOS, IBKR etc.
Designed to help you assess option market conditions and make well-informed trading decisions, this tool is an essential addition for every serious options trader!
Ticker Information:
This screener is currently implemented only for 5 liquid US market tickers:
NASDAQ:AAPL NASDAQ:AMZN AMEX:DIA NYSE:ORCL and NASDAQ:TSLA
How does the screener work and why is it unique?
This Pine Script screener is an expert tool created to provide various option metrics and visualization tools for options market traders. The screener extracts raw options data from an external data provider (ORATS), processes, and refines the delayed data package using pineseed, and sends it to TradingView. The data is calculated using specific formulas or interpolated values, such as delta distances. This method of integrating options data into a screener framework is unique and innovative on TradingView.
The screener aims to offer a comprehensive view of the current state of options for the implemented instruments, including implied volatility index (IVx), IV rank (IVR), options skew, and expected market movements, which are objectively measured as detailed below.
The options metrics displayed may be familiar to options traders from various major brokerage platforms such as TastyTrade, IBKR, TOS, Tradier, TD Ameritrade, Schwab, etc.
🟨 𝗗𝗘𝗧𝗔𝗜𝗟𝗘𝗗 𝗗𝗢𝗖𝗨𝗠𝗘𝗡𝗧𝗔𝗧𝗜𝗢𝗡 🟨
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🔶 Auto-Updating Option Metrics
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🔹 IVR (IV Rank)
The Implied Volatility Rank (IVR) indicator helps options traders assess the current level of implied volatility (IV) in comparison to the past 52 weeks. IVR is a useful metric to determine whether options are relatively cheap or expensive. This can guide traders on whether to buy or sell options. We calculate IVrank, like TastyTrade does.
IVR Calculation: IV Rank = (current IV - 52 week IV low) / (52 week IV high - 52 week IV low)
IVR Levels and Interpretations:
IVR 0-10 (Green): Very low implied volatility rank. Options might be "cheap," potentially a good time to buy options.
IVR 10-35 (White): Normal implied volatility rank. Options pricing is relatively standard.
IVR 35-50 (Orange): Almost high implied volatility rank.
IVR 50-75 (Red): Definitely high implied volatility rank. Options might be "expensive," potentially a good time to sell options for higher premiums.
IVR above 75 (Highlighted Red): Ultra high implied volatility rank. Indicates very high levels, suggesting a favorable time for selling options.
Extra: If the IVx value is also greater than 30, the background will be dark highlighted, because a high IVR alone doesn’t mean much without high IVx.
🔹IVx (Implied Volatility Index)
The Implied Volatility Index (IVx) displayed in the option chain is calculated similarly to the VIX. The Cboe employs standard and weekly SPX options to measure the expected volatility of the S&P 500. A similar method is utilized to calculate IVx for each option expiration cycle.
For our purposes, we aggregate the IVx values specifically for the 35-70 day monthly expiration cycle . This aggregated value is then presented in the screener and info panel, providing a clear and concise measure of implied volatility over this period.
We will display a warning if the option chain is heavily skewed and valid, symmetric 16 delta options are not found at optimal monthly expirations.
IVx Color coding:
IVx above 30 is displayed in orange.
IVx above 60 is displayed in red
Important Note: The IVx value alone does not provide sufficient context. There are stocks that inherently exhibit high IVx values. Therefore, it is crucial to consider IVx in conjunction with the Implied Volatility Rank (IVR), which measures the IVx relative to its own historical values. This combined view helps in accurately assessing the significance of the IVx in relation to the specific stock's typical volatility behavior.
This indicator offers traders a comprehensive view of implied volatility, assisting them in making informed decisions by highlighting both the absolute and relative volatility measures.
🔹IVx 5 days change %
We are displaying the five-day change of the IV Index (IVx value). The IV Index 5-Day Change column provides quick insight into recent expansions or decreases in implied volatility over the last five trading days.
Traders who expect the value of options to decrease might view a decrease in IVX as a positive signal. Strategies such as Strangle and Ratio Spread can benefit from this decrease.
On the other hand, traders anticipating further increases in IVX will focus on the rising IVX values. Strategies like Calendar Spread or Diagonal Spread can take advantage of increasing implied volatility.
This indicator helps traders quickly assess changes in implied volatility, enabling them to make informed decisions based on their trading strategies and market expectations.
🔹 Vertical Pricing Skew
At TanukiTrade, Vertical Pricing Skew refers to the difference in pricing between put and call options with the same expiration date at the same distance (at expected move). We analyze this skew to understand market sentiment. This is the same formula used by TastyTrade for calculations.
PUT Skew (red): Put options are more expensive than call options, indicating the market expects a downward move (▽). If put options are more expensive by more than 20% at the same expected move distance, we color it lighter red.
CALL Skew (green): Call options are more expensive than put options, indicating the market expects an upward move (△). If call options are priced more than 30% higher at the examined expiration, we color it lighter green.
We focus on options with 35-70 days to expiration (DTE) for optimal analysis. We always evaluate the skew at the expected move using linear interpolation to determine the theoretical pricing of options. If the pricing have more than C50%/P35% we are highlighting the cell.
This approach helps us gauge market expectations accurately, providing insights into potential price movements.
🔹 Horizontal IVx Skew
In options pricing, it is typically expected that the implied volatility (IVx) increases for options with later expiration dates. This means that options further out in time are generally more expensive. At TanukiTrade, we refer to the phenomenon where this expectation is reversed—when the IVx decreases between two consecutive expirations—as Horizontal Skew or IVx Skew.
Horizontal IVx Skew occurs when: Front Month IVx < Back Month IVx
This scenario can create opportunities for traders who prefer diagonal or calendar strategies. Based on our experience, we categorize Horizontal Skew into two types:
Weekly Horizontal Skew: When IVx skew is observed between two consecutive non-monthly expirations , the displayed value is the rounded-up percentage difference. On hover, the approximate location of this skew is also displayed. The precise location can be seen on the Overlay indicator.
Monthly Horizontal Skew: When IVx skew is observed between two consecutive monthly expirations , the displayed value is the rounded-up percentage difference. On hover, the approximate location of this skew is also displayed. The precise location can be seen on the Overlay indicator.
The Monthly Vertical IVx skew is consistently stronger (more liquid) on average symbols than the weekly vertical IVx skew. Weekly Horizontal IVx Skew may not carry relevant information for symbols not included in the 'Weeklies & Volume Masters' preset.
If the options chain follows the normal IVx pattern, no skew value is displayed.
Additionally , if the Implied Volatility Rank (IVR) is low (indicated by green), the Horizontal Skew background turns black, because this environment is good for Calendar+Diagonal.
Additionally , if the % of the skew is greater than 10, the Horizontal Skew font color turns lighter.
🔹 Delta Skew 🌪️ (Twist)
We have a metric that examines which monthly expiration indicates a "Delta Skew Twist" where the 16 delta deviates from the monthly STD. This is important because, under normal circumstances, the 16 delta is positioned between the expected move and the standard deviation (STD1) line. However, if the interpolated 16 delta line exceeds the STD1 line either upwards or downwards, it represents a special case of vertical skew.
Normal case : exp.move < delta16 < std1
Delta Skew Twist: exp.move < std1 < delta16
If the Days to Expiration of the twist is less than 75, we use a lighter color.
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🔶 HOW WE CALCULATE
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
🔹 Expected Move
The expected move is the predicted dollar change in the underlying stock's price by a given option's expiration date, with 68% certainty. It is calculated using the expiration's pricing and implied volatility levels.
Expected Move Calculation
Expected Move = (ATM straddle price x 0.6) + (1st OTM strangle price x 0.3) + (2nd OTM strangle price x 0.1)
For example , if stock XYZ is trading at 121 and the ATM straddle is 4.40, the 120/122 strangle is 3.46, and the 119/123 strangle is 2.66, the expected move is calculated as follows: 4.40 x 0.60 = 2.64; 3.46 x 0.30 = 1.04; 2.66 x 0.10 = 0.27; Expected move = 2.64 + 1.04 + 0.27 = ±3.9
🔹 Standard deviation
One standard deviation of a stock encompasses approximately 68.2% of outcomes in a distribution of occurrences based on current implied volatility.
We use the expected move formula to calculate the one standard deviation range of a stock. This calculation is based on the days-to-expiration (DTE) of our option contract, the stock price, and the implied volatility of a stock:
Calculation:
Standard Deviation = Closing Price * Implied Volatility * sqrt(Days to Expiration / 365)
According to options literature, there is a 68% probability that the underlying asset will fall within this one standard deviation range at expiration.
∑ Quant Observation: The values of the expected move and the 1st standard deviation (1STD) will not match because they use different calculation methods, even though both are referred to as representing 68% of the underlying asset's movement in options literature. The expected move is based on direct market pricing of ATM options. The 1STD, on the other hand, uses the averaged implied volatility (IVX) for the given expiration to determine its value. Based on our experience, it is better to consider the area between the expected move and the 1STD as the true representation of the original 68% rule.
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🔶 USAGE
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
🔹 Create a new empty layout for the screener!
You can access this from the dropdown menu in the upper right corner. In the popup window, name it as you like, for example, "Option Screener."
🔹 Hide the candlestick chart
Make the chart invisible using the "Hide" option from the three-dot dropdown menu located in the upper left corner.
🔹 Other Unwanted Elements
If other unnecessary elements are distracting you (e.g., economic data, volume, default grid), you can easily remove them from the layout. Right-click on the empty chart area. Here, click on the gear (Settings) icon and remove everything from the "Events" tab, as well as from the "Trading" tab. Under the "Canvas" tab, it is recommended to set the "Grid lines" setting to "None."
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
🔶 Screener Settings
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Naturally, the font size and position can be easily adjusted.
Additionally, there are two basic usage modes: manual input or using the preset list.
🔹If you selected “Manual Below” in the preset dropdown, the tickers you chose from the dropdown (up to a maximum of 40) will be displayed. The panel name will be the one you specified.
🔹If you selected a pre-assembled list , the manually entered list will be ignored, and the preset list will be displayed. (In the future, we will expand the preset list based on your feedback!).
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
🔶 Best Practices for TanukiTrade Option Screener:
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
🔹 Every Preset on a New Layout:
If you following the steps above, you easy can setup this screener in one window with one split layout:
🔹 Split Layout:
- Left Side: The underlying asset with our Options IV Overlay (IVR, Deltas, Expected Move, STD1, Skew visualized) along with the Enhanced Murrey Math Indicator and Option Expiry.
- Right Side: Searching for opportunities using our Options Screener.
Opportunities Search
🔹 Everything in One Layout + One Window:
This is the all-in-one view:
- The underlying asset with our Options IV Overlay (IVR, Deltas, Expected Move, STD1, Skew visualized)
- Enhanced Murrey Math Indicator and Option Expiry
- Options Screener on the left
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
🔶 ADDITIONAL IMPORTANT COMMENTS
- U.S. market only:
Since we only deal with liquid option chains: this option indicator only works for the USA options market and do not include future contracts; we have implemented each selected symbol individually.
- Why is there a slight difference between the displayed data and my live brokerage data? There are two reasons for this, and one is beyond our control.
- Brokerage Calculation Differences:
Every brokerage has slight differences in how they calculate metrics like IV and IVx. If you open three windows for TOS, TastyTrade, and IBKR side by side, you will notice that the values are minimally different. We had to choose a standard, so we use the formulas and mathematical models described by TastyTrade when analyzing the options chain and drawing conclusions.
- Option-data update frequency:
According to TradingView's regulations and guidelines, we can update external data a maximum of 5 times per day. We strive to use these updates in the most optimal way:
(1st update) 15 minutes after U.S. market open
(2nd, 3rd, 4th updates) 1.5–3 hours during U.S. market open hours
(5th update) 10 minutes before market close.
You don’t need to refresh your window, our last refreshed data-pack is always automatically applied to your indicator , and you can see the time elapsed since the last update at the bottom of your indicator.
- Weekly illiquid expiries:
The Weekly Horizontal IVx Skew may not carry relevant information for instruments not included in the 'Weeklies & Volume Masters' preset package.
-Timeframe Issues:
Our option indicator visualizes relevant data on a daily resolution. If you see strange or incorrect data (e.g., when the options data was last updated), always switch to a daily (1D) timeframe. If you still see strange data, please contact us.
Disclaimer:
Our option indicator uses approximately 15min-3 hour delayed option market snapshot data to calculate the main option metrics. Exact realtime option contract prices are never displayed; only derived metrics and interpolated delta are shown to ensure accurate and consistent visualization. Due to the above, this indicator can only be used for decision support; exclusive decisions cannot be made based on this indicator . We reserve the right to make errors.This indicator is designed for options traders who understand what they are doing. It assumes that they are familiar with options and can make well-informed, independent decisions. We work with public data and are not a data provider; therefore, we do not bear any financial or other liability.
Options Overlay [Lite] IVR IV Skew Delta Expmv MurreyMath Expiry𝗡𝗼𝗻-𝗼𝗳𝗳𝗶𝗰𝗶𝗮𝗹 𝗧𝗢𝗦 𝗮𝗻𝗱 𝗧𝗮𝘀𝘁𝘆𝗧𝗿𝗮𝗱𝗲 𝗹𝗶𝗸𝗲 𝗜𝗩𝗥 𝗢𝗽𝘁𝗶𝗼𝗻𝘀 𝘃𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝘁𝗼𝗼𝗹 𝘄𝗶𝘁𝗵 𝗱𝗲𝗹𝗮𝘆𝗲𝗱 𝗼𝗽𝘁𝗶𝗼𝗻 𝗰𝗵𝗮𝗶𝗻 𝗱𝗮𝘁𝗮
Are you an options trader who uses TradingView for technical analysis for the US market?
➡️ Do you want to see the IV Rank of an instrument on TradingView?
➡️ Can’t you check the key options metrics while charting?
➡️ Have you never visualized the options chain before?
➡️ Would you like to see how the IVx has changed for a specific ticker?
If you answered "yes" to any of these questions, then we have the solution for you!
🔃 Auto-Updating Option Metrics without refresh!
🍒 Developed and maintained by option traders for option traders.
📈 Specifically designed for TradingView users who trade options.
Our indicator provides essential key metrics such as:
✅ IVRank
✅ IVx
✅ 5-Day IVx Change
✅ Delta curves and interpolated distances
✅ Expected move curve
✅ Standard deviation (STD1) curve
✅ Vertical Pricing Skew
✅ Horizontal IVx Skew
✅ Delta Skew
like TastyTrade, TOS, IBKR etc, but in a much more visually intuitive way. See detailed descriptions below.
If this isn't enough, we also include a unique grid system designed specifically for options traders. This package features our innovative dynamic grid system:
✅ Enhanced Murrey Math levels (horizontal scale)
✅ Options expirations (vertical scale)
Designed to help you assess market conditions and make well-informed trading decisions, this tool is an essential addition for every serious options trader!
Ticker Information:
This indicator is currently implemented for 5 liquid tickers: NASDAQ:AAPL NASDAQ:AMZN AMEX:DIA NYSE:ORCL and NASDAQ:TSLA
How does the indicator work and why is it unique?
This Pine Script indicator is a complex tool designed to provide various option metrics and visualization tools for options market traders. The indicator extracts raw options data from an external data provider (ORATS), processes and refines the delayed data package using pineseed, and sends it to TradingView, visualizing the data using specific formulas (see detailed below) or interpolated values (e.g., delta distances). This method of incorporating options data into a visualization framework is unique and entirely innovative on TradingView.
The indicator aims to offer a comprehensive view of the current state of options for the implemented instruments, including implied volatility (IV), IV rank (IVR), options skew, and expected market movements, which are objectively measured as detailed below.
The options metrics we display may be familiar to options traders from various major brokerage platforms such as TastyTrade, IBKR, TOS, Tradier, TD Ameritrade, Schwab, etc.
Key Features:
IV Rank (IVR) : The implied volatility rank compares the current IV to the lowest and highest values over the past 52 weeks. The IVR indicator helps determine whether options are relatively cheap or expensive.
IV Average (IVx) : The implied volatility displayed in the options chain, calculated similarly to the VIX. IVx values are aggregated within the 35-70 day expiration cycle.
IV Change (5 days) : The change in implied volatility over the past five trading days. This indicator provides a quick insight into the recent changes in IV.
Expected Move (Exp. Move) : The expected movement for the options expiration cycle, calculated using the price of the ATM (at-the-money) straddle, the first OTM (out-of-the-money) strangle, and the second OTM strangle.
Options Skew : The price difference between put and call options with the same expiration date. Vertical and horizontal skew indicators help understand market sentiment and potential price movements.
Visualization Tools:
Informational IVR Panel : A tabular display mode that presents the selected indicators on the chart. The panel’s placement, size, and content are customizable, including color and tooltip settings.
1 STD, Delta, and Expected Move : Visualization of fundamental classic options metrics corresponding to expirations with bell curves.
Colored Label Tooltips : Detailed tooltips above the bell curves showing options metrics for each expiration.
Adaptive Murrey Math Lines : A horizontal line system based on the principles of Murrey Math Lines, helping identify important price levels and market structures.
Expiration Lines : Displays both monthly and weekly options expirations. The indicator supports various color and style settings, as well as the regulation of the number of expirations displayed.
🟨 𝗗𝗘𝗧𝗔𝗜𝗟𝗘𝗗 𝗗𝗢𝗖𝗨𝗠𝗘𝗡𝗧𝗔𝗧𝗜𝗢𝗡 🟨
🔶 Auto-Updating Option Metrics and Curved Lines
🔹 Interpolated DELTA Curves (16,20,25,30,40)
In our indicator, the curve layer settings allow you to choose the delta value for displaying the delta curve: 16, 20, 25, 30, or even 40. The color of the curve can be customized, and you can also hide the delta curve by selecting the "-" option.
It's important to mention that we display interpolated deltas from the actual option chain of the underlying asset using the Black-Scholes model. This ensures that the 16 delta truly reflects the theoretical, but accurate, 16 delta distance. (For example, deltas shown by brokerages for individual strikes are rounded; a 0.16 delta might actually be 0.1625.)
🔹 Expected Move Curve (Exp.mv)
The expected move is the predicted dollar change in the underlying stock's price by a given option's expiration date, with 68% certainty. It is calculated using the expiration's pricing and implied volatility levels. We chose the TastyTrade method for calculating expected move, as we found it to be the most expressive.
Expected Move Calculation
Expected Move = (ATM straddle price x 0.6) + (1st OTM strangle price x 0.3) + (2nd OTM strangle price x 0.1)
For example , if stock XYZ is trading at 121 and the ATM straddle is 4.40, the 120/122 strangle is 3.46, and the 119/123 strangle is 2.66, the expected move is calculated as follows: 4.40 x 0.60 = 2.64; 3.46 x 0.30 = 1.04; 2.66 x 0.10 = 0.27; Expected move = 2.64 + 1.04 + 0.27 = ±3.9
In this example below, the TastyTrade platform indicates the expected move on the option chain with a brown color, and the exact value is displayed behind the ± symbol for each expiration. By default, we also use brown for this indication, but this can be changed or the curve display can be turned off.
🔹 Standard Deviation Curve (1 STD)
One standard deviation of a stock encompasses approximately 68.2% of outcomes in a distribution of occurrences based on current implied volatility.
We use the expected move formula to calculate the one standard deviation range of a stock. This calculation is based on the days-to-expiration (DTE) of our option contract, the stock price, and the implied volatility of a stock:
Calculation:
Standard Deviation = Closing Price * Implied Volatility * sqrt(Days to Expiration / 365)
According to options literature, there is a 68% probability that the underlying asset will fall within this one standard deviation range at expiration.
If the 1 STD and Exp.mv displays are both enabled, the indicator fills the area between them with a light gray color. This is because both represent probability distributions that appear as a "bell curve" when graphed, making it visually appealing.
Tip and Note:
The 1 STD line might appear jagged at times , which does not indicate a problem with the indicator. This is normal immediately after market open (e.g., during the first data refresh of the day) or if the expirations are illiquid (e.g., weekly expirations). The 1 STD value is calculated based on the aggregated IVx for the expirations, and the aggregated IVx value for weekly expirations updates less frequently due to lower trading volume. In such cases, we recommend enabling the "Only Monthly Expirations" option to smooth out the bell curve.
∑ Quant Observation:
The values of the expected move and the 1st standard deviation (1STD) will not match because they use different calculation methods, even though both are referred to as representing 68% of the underlying asset's movement in options literature. The expected move is based on direct market pricing of ATM options. The 1STD, on the other hand, uses the averaged implied volatility (IVX) for the given expiration to determine its value. Based on our experience, it is better to consider the area between the expected move and the 1STD as the true representation of the original 68% rule.
🔶 IVR Dashboard Panel Rows
🔹 IVR (IV Rank)
The Implied Volatility Rank (IVR) indicator helps options traders assess the current level of implied volatility (IV) in comparison to the past 52 weeks. IVR is a useful metric to determine whether options are relatively cheap or expensive. This can guide traders on whether to buy or sell options. We calculate IVrank, like TastyTrade does.
IVR Calculation:
IV Rank = (current IV - 52 week IV low) / (52 week IV high - 52 week IV low)
IVR Levels and Interpretations:
IVR 0-10 (Green): Very low implied volatility rank. Options might be "cheap," potentially a good time to buy options.
IVR 10-35 (White): Normal implied volatility rank. Options pricing is relatively standard.
IVR 35-50 (Orange): Almost high implied volatility rank.
IVR 50-75 (Red): Definitely high implied volatility rank. Options might be "expensive," potentially a good time to sell options for higher premiums.
IVR above 75 (Highlighted Red): Ultra high implied volatility rank. Indicates very high levels, suggesting a favorable time for selling options.
The panel refreshes automatically if the symbol is implemented. You can hide the panel or change the position and size.
🔹IVx (Implied Volatility Index)
The Implied Volatility Index (IVx) displayed in the option chain is calculated similarly to the VIX. The Cboe uses standard and weekly SPX options to measure the expected volatility of the S&P 500. A similar method is utilized to calculate IVx for each option expiration cycle.
For our purposes on the IVR Panel, we aggregate the IVx values specifically for the 35-70 day monthly expiration cycle . This aggregated value is then presented in the screener and info panel, providing a clear and concise measure of implied volatility over this period.
IVx Color coding:
IVx above 30 is displayed in orange.
IVx above 60 is displayed in red
IVx on curve:
The IVx values for each expiration can be viewed by hovering the mouse over the colored tooltip labels above the Curve.
IVx avg on IVR panel :
If the option is checked in the IVR panel settings, the IVR panel will display the average IVx values up to the optimal expiration.
Important Note:
The IVx value alone does not provide sufficient context. There are stocks that inherently exhibit high IVx values. Therefore, it is crucial to consider IVx in conjunction with the Implied Volatility Rank (IVR), which measures the IVx relative to its own historical values. This combined view helps in accurately assessing the significance of the IVx in relation to the specific stock's typical volatility behavior.
This indicator offers traders a comprehensive view of implied volatility, assisting them in making informed decisions by highlighting both the absolute and relative volatility measures.
🔹IVx 5 days change %
We are displaying the five-day change of the IV Index (IVx value). The IV Index 5-Day Change column provides quick insight into recent expansions or decreases in implied volatility over the last five trading days.
Traders who expect the value of options to decrease might view a decrease in IVX as a positive signal. Strategies such as Strangle and Ratio Spread can benefit from this decrease.
On the other hand, traders anticipating further increases in IVX will focus on the rising IVX values. Strategies like Calendar Spread or Diagonal Spread can take advantage of increasing implied volatility.
This indicator helps traders quickly assess changes in implied volatility, enabling them to make informed decisions based on their trading strategies and market expectations.
🔹 Vertical Pricing Skew
At TanukiTrade, Vertical Pricing Skew refers to the difference in pricing between put and call options with the same expiration date at the same distance (at expected move). We analyze this skew to understand market sentiment. This is the same formula used by TastyTrade for calculations.
We calculate the interpolated strike price based on the expected move , taking into account the neighboring option prices and their distances. This allows us to accurately determine whether the CALL or PUT options are more expensive.
PUT Skew (red): Put options are more expensive than call options, indicating the market expects a downward move (▽). If put options are more expensive by more than 20% at the same expected move distance, we color it lighter red.
CALL Skew (green): Call options are more expensive than put options, indicating the market expects an upward move (△). If call options are priced more than 30% higher at the examined expiration, we color it lighter green.
Vertical Skew on Curve:
The degree of vertical pricing skew for each expiration can be viewed by hovering over the points above the curve. Hover with mouse for more information.
Vertical Skew on IVR panel:
We focus on options with 35-70 days to expiration (DTE) for optimal analysis in case of vertical skew. Hover with mouse for more information.
This approach helps us gauge market expectations accurately, providing insights into potential price movements. Remember, we always evaluate the skew at the expected move using linear interpolation to determine the theoretical pricing of options.
🔹 Delta Skew 🌪️ (Twist)
We have a new metric that examines which monthly expiration indicates a "Delta Skew Twist" where the 16 delta deviates from the monthly STD. This is important because, under normal circumstances, the 16 delta is positioned between the expected move and the standard deviation (STD1) line (see Exp.mv & 1STD exact definitions above). However, if the interpolated 16 delta line exceeds the STD1 line either upwards or downwards, it represents a special case of vertical skew on the option chain.
Normal case : exp.move < delta16 < std1
Delta Skew Twist: exp.move < std1 < delta16
We indicate this with direction-specific colors (red/green) on the delta line. We also color the section of the delta curve affected by the delta skew in this case, even if you choose to display a lower delta, such as 30, instead of 16.
If "Colored Labels with Tooltips" is enabled, we also display a 🌪️ symbol in the tooltip for the expirations affected by Delta Skew.
If you have enabled the display of 'Vertical Pricing Skew' on the IVR Panel, a 🌪️ symbol will also appear next to the value of the vertical skew, and the tooltip will indicate from which expiration Delta Skew is observed.
🔹 Horizontal IVx Skew
In options pricing, it is typically expected that the implied volatility (IVx) increases for options with later expiration dates. This means that options further out in time are generally more expensive. At TanukiTrade, we refer to the phenomenon where this expectation is reversed—when the IVx decreases between two consecutive expirations—as Horizontal Skew or IVx Skew.
Horizontal IVx Skew occurs when: Front Expiry IVx < Back Expiry IVx
This scenario can create opportunities for traders who prefer diagonal or calendar strategies . Based on our experience, we categorize Horizontal Skew into two types:
Weekly Horizontal Skew:
When IVx skew is observed between two consecutive non-monthly expirations, the displayed value is the rounded-up percentage difference. On hover, the approximate location of this skew is also displayed. The precise location can be seen on this indicator.
Monthly Horizontal Skew:
When IVx skew is observed between two consecutive monthly expirations , the displayed value is the rounded-up percentage difference. On hover, the approximate location of this skew is also displayed. The precise location can be seen on our Overlay indicator.
The Monthly Vertical IVx skew is consistently more liquid than the weekly vertical IVx skew. Weekly Horizontal IVx Skew may not carry relevant information for symbols not included in the 'Weeklies & Volume Masters' preset in our Options Screener indicator.
If the options chain follows the normal IVx pattern, no skew value is displayed.
Color codes or tooltip labels above curve:
Gray - No horizontal skew;
Purple - Weekly horizontal skew;
BigBlue - Monthly horizontal skew
The display of monthly and weekly IVx skew can be toggled on or off on the IVR panel. However, if you want to disable the colored tooltips above the curve, this can only be done using the "Colored labels with tooltips" switch.
We indicate this range with colorful information bubbles above the upper STD line.
🔶 The Option Trader’s GRID System: Adaptive MurreyMath + Expiry Lines
At TanukiTrade, we utilize Enhanced MurreyMath and Expiry lines to create a dynamic grid system, unlike the basic built-in vertical grids in TradingView, which provide no insight into specific price levels or option expirations.
These grids are beneficial because they provide a structured layout, making important price levels visible on the chart. The grid automatically resizes as the underlying asset's volatility changes, helping traders identify expected movements for various option expirations.
The Option Trader’s GRID System part of this indicator can be used without limitations for all instruments . There are no type or other restrictions, and it automatically scales to fit every asset. Even if we haven't implemented the option metrics for a particular underlying asset, the GRID system will still function!
🔹 SETUP OF YOUR OPTIONS GRID SYSTEM
You can setup your new grid system in 3 easy steps!
STEP1: Hide default horizontal grid lines in TradingView
Right-click on an empty area of your chart, then select “Settings.” In the Chart settings -> Canvas -> Grid lines section, disable the display of horizontal lines to avoid distraction.
SETUP STEP2: Scaling fix
Right-click on the price scale on the right side, then select "Scale price chart only" to prevent the chart from scaling to the new horizontal lines!
STEP3: Enable Tanuki Options Grid
As a final step, make sure that both the vertical (MurreyMath) and horizontal (Expiry) lines are enabled in the Grid section of our indicator.
You are done, enjoy the new grid system!
🔹 HORIZONTAL: Enhanced MurreyMath Lines
Murrey Math lines are based on the principles observed by William Gann, renowned for his market symmetry forecasts. Gann's techniques, such as Gann Angles, have been adapted by Murrey to make them more accessible to ordinary investors. According to Murrey, markets often correct at specific price levels, and breakouts or returns to these levels can signal good entry points for trades.
At TanukiTrade, we enhance these price levels based on our experience , ensuring a clear display. We acknowledge that while MurreyMath lines aren't infallible predictions, they are useful for identifying likely price movements over a given period (e.g., one month) if the market trend aligns.
Our opinion: MurreyMath lines are not crystal balls (like no other tool). They should be used to identify that if we are trading in the right direction, the price is likely to reach the next unit step within a unit time (e.g. monthly expiration).
One unit step is the distance between Murrey Math lines, such as between the 0/8 and 1/8 lines. This interval helps identify different quadrants and is crucial for recognizing support and resistance levels.
Some option traders use Murrey Math lines to gauge the movement speed of an instrument over a unit time. A quadrant encompasses 4 unit steps.
Key levels, according to TanukiTrade, include:
Of course, the lines can be toggled on or off, and their default color can also be changed.
🔹 VERTICAL: Expiry Lines
The indicator can display monthly and weekly expirations as dashed lines, with customizable colors. Weekly expirations will always appear in a lighter shade compared to monthly expirations.
Monthly Expiry Lines:
You can turn off the lines indicating monthly expirations, or set the direction (past/future/both) and the number of lines to be drawn.
Weekly Expiry Lines:
You can display weekly expirations pointing to the future. You can also turn them off or specify how many weeks ahead the lines should be drawn.
Of course, the lines can be toggled on or off, and their default color can also be changed.
TIP: Hide default vertical grid lines in TradingView
Right-click on an empty area of your chart, then select “Settings.” In the Chart settings -> Canvas -> Grid lines section, disable the display of vertical lines to avoid distraction. Same, like steps above at MurreyMath lines.
🔶 ADDITIONAL IMPORTANT COMMENTS
- U.S. market only:
Since we only deal with liquid option chains: this option indicator only works for the USA options market and do not include future contracts; we have implemented each selected symbol individually.
- Why is there a slight difference between the displayed data and my live brokerage data? There are two reasons for this, and one is beyond our control.
- Brokerage Calculation Differences:
Every brokerage has slight differences in how they calculate metrics like IV and IVx. If you open three windows for TOS, TastyTrade, and IBKR side by side, you will notice that the values are minimally different. We had to choose a standard, so we use the formulas and mathematical models described by TastyTrade when analyzing the options chain and drawing conclusions.
- Option-data update frequency:
According to TradingView's regulations and guidelines, we can update external data a maximum of 5 times per day. We strive to use these updates in the most optimal way:
(1st update) 15 minutes after U.S. market open
(2nd, 3rd, 4th updates) 1.5–3 hours during U.S. market open hours
(5th update) 10 minutes before market close.
You don’t need to refresh your window, our last refreshed data-pack is always automatically applied to your indicator , and you can see the time elapsed since the last update at the bottom of your indicator.
- Skewed Curves:
The delta, expected move, and standard deviation curves also appear relevantly on a daily or intraday timeframe. Data loss is experienced above a daily timeframe: this is a TradingView limitation.
- Weekly illiquid expiries:
Especially for instruments where weekly options are illiquid: the weekly expiration STD1 data is not relevant. In these cases, we recommend checking in the "Display only Monthly labels" checkbox to avoid displaying not relevant weekly options expirations.
-Timeframe Issues:
Our option indicator visualizes relevant data on a daily resolution. If you see strange or incorrect data (e.g., when the options data was last updated), always switch to a daily (1D) timeframe. If you still see strange data, please contact us.
Disclaimer:
Our option indicator uses approximately 15min-3 hour delayed option market snapshot data to calculate the main option metrics. Exact realtime option contract prices are never displayed; only derived metrics and interpolated delta are shown to ensure accurate and consistent visualization. Due to the above, this indicator can only be used for decision support; exclusive decisions cannot be made based on this indicator . We reserve the right to make errors.This indicator is designed for options traders who understand what they are doing. It assumes that they are familiar with options and can make well-informed, independent decisions. We work with public data and are not a data provider; therefore, we do not bear any financial or other liability.
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.
Comprehensive Market AnalyzerVERSION 2.0:
Notice to users: To better reflect its extensive features, this indicator has been renamed from "Tsūrubokkusu (Toolbox) 🧰" to "Comprehensive Market Analyzer". Thank you for your understanding and adaptation to this change.
Purpose and Usage:
The Comprehensive Market Analyzer is designed to provide traders with a holistic view of market conditions by integrating various technical indicators into a single,
cohesive tool. Each indicator has been carefully selected and improved to work together, offering enhanced customization and advanced market insights.
This combination allows for more comprehensive market analysis, improved decision-making, and efficient trading strategies.
📘 Machine Learning Integration
Purpose : Utilizes machine learning algorithms to analyze past market data and provides predictive insights based on historical data.
Usage : Activate machine learning features, set lookback windows, influence weighting, and start bar for improved trend predictions.
Activate Machine Learning :
Description : Enables advanced machine learning features that analyze past market data.
Details : This feature allows the algorithm to use historical data to forecast market movements, providing traders with enhanced predictive insights on historical data.
Kernel Lookback Window :
Description : Sets the number of previous bars that the algorithm will analyze.
Details : A higher number provides a broader view of market trends, while a lower number makes the model more sensitive to recent changes.
Kernel Influence Weighting :
Description : Adjusts the emphasis on recent versus older data.
Details : Increasing this value gives more importance to recent data, potentially making predictions more responsive to new trends.
Kernel Calculation Start Bar :
Description : Specifies the bar number from which to start the machine learning calculations.
Details : Avoids early data which may contain excessive noise and less reliable market signals.
Kernel Functions :
Gaussian Kernel :
Description : Uses a Gaussian distribution to weight historical data, focusing on more recent data points for trend analysis.
Details : Calculates weights based on the Gaussian distribution, emphasizing data points closer to the present.
Laplacian Kernel :
Description : Applies Laplacian distribution, emphasizing data points closer to the current time more heavily.
Details : Uses the Laplacian function to provide a different perspective on data weighting.
RBF Kernel :
Description : Utilizes a Radial Basis Function for smoothing and analyzing data, providing a different approach to trend prediction.
Details : Applies the RBF function to smooth data and enhance the accuracy of trend predictions.
Wavelet Kernel :
Description : Applies wavelet transform for analyzing frequency components, helping to detect patterns in the price movements.
Details : Uses wavelet-based calculations to focus on specific frequency components within the data, aiding in pattern recognition.
📘 Enhanced Ichimoku Kinkō Hyō Integration
Purpose : Provides a comprehensive overview of market trends and momentum using the Ichimoku Kinkō Hyō indicator.
Usage : Display various components of the Ichimoku Kinkō Hyō, customize their appearance, provides additional calculations for trend analysis.
Display Ichimoku Kinkō Hyō :
Description : Toggle to show or hide the Ichimoku Kinkō hyō indicator.
Details : This indicator helps traders see support and resistance levels, trend direction, and potential future movements.
Activate Heikin-Ashi Source :
Description : Switches between regular price data and Heikin-Ashi candles for analysis.
Details : Heikin-Ashi candles smooth price data, making trends easier to spot.
Display Tenkan-Sen Line :
Description : Shows the Tenkan-Sen line, a key short-term trend indicator.
Color Customization : Set the color of the Tenkan-Sen line for better visibility.
Minimum Length : Determine the shortest period for calculating the Tenkan-Sen line.
Maximum Length : Determine the longest period for calculating the Tenkan-Sen line.
Dynamic Length Adjustment : Automatically adjusts the length of the Tenkan-Sen based on market conditions.
Display Kijun-Sen Line :
Description : Shows the Kijun-Sen line, a key medium-term trend indicator.
Color Customization : Set the color of the Kijun-Sen line for better visibility.
Minimum Length : Determine the shortest period for calculating the Kijun-Sen line.
Maximum Length : Determine the longest period for calculating the Kijun-Sen line.
Dynamic Length Adjustment : Automatically adjusts the length of the Kijun-Sen based on market conditions.
Kijun-Sen Divider Tool : Adjust the sensitivity of the Kijun-Sen calculation.
Display Chikou Span :
Description : Shows the Chikou Span, which lags behind the current price to help confirm trends.
Bear Phase Color : Set the color for bearish periods.
Bull Phase Color : Set the color for bullish periods.
Consolidation Color : Set the color for consolidation periods.
Minimum Length : Determine the shortest lag period for the Chikou Span.
Maximum Length : Determine the longest lag period for the Chikou Span.
Dynamic Length Adjustment : Automatically adjusts the length of the Chikou Span based on market conditions.
Display Senkou Span A and B :
Description : Shows the Senkou Span A and B, which form the Ichimoku Cloud indicating future support and resistance levels.
Bear Color : Set the color for bearish clouds.
Bull Color : Set the color for bullish clouds.
Neutral Color : Set the color for neutral periods.
Minimum Length : Determine the shortest period for calculating the Senkou Span.
Maximum Length : Determine the longest period for calculating the Senkou Span.
Dynamic Length Adjustment : Automatically adjusts the length of the Senkou Span based on market conditions.
Projection Offset : Set how far ahead the Senkou Span is projected.
Kumo Cloud Settings :
Enable Kumo Cloud Fill : Toggle to fill the space between Senkou Span A and B with color.
Cloud Fill Transparency : Adjust the transparency of the cloud fill.
Apply WMA Smoothing :
Description : Smooths the indicator lines using a Weighted Moving Average to clarify trends.
Bar Coloring Based on Ichimoku Signals :
Description : Colors the bars based on Ichimoku signals to provide a quick visual indication of market sentiment.
Bearish Signal Bar Color : Set the color for bars during bearish signals.
Bullish Signal Bar Color : Set the color for bars during bullish signals.
Consolidation Signal Bar Color : Set the color for bars during consolidation periods.
Neutral Bar Color : Set the color for bars during neutral conditions.
Enhanced Calculations :
Heikin Ashi Values : Smooths price movements to make trends more visible.
Alternative Source Calculation : Uses a different method for calculating the indicator based on user settings.
Volume Calculations : Enhanced functions for calculating volume based on different candlestick patterns.
Dynamic Length Adjustment : Automatically adjusts the length of Ichimoku components based on market volatility.
Gaussian Kernel Calculations : Uses advanced calculations for smoother and more accurate trend analysis.
Chikou Span Adaptation : Improved calculation for the Chikou Span using dynamic lengths and advanced methods.
Visual Enhancements : Adds color gradients to the Senkou Span and dynamic coloring for the Chikou Span to improve trend visibility.
Plotting Ichimoku Components :
Tenkan-Sen : Plots the Tenkan-Sen line with dynamic adjustments.
Kijun-Sen : Plots the Kijun-Sen line with dynamic adjustments.
Senkou Span A and B : Plots these lines with dynamic projections and advanced smoothing.
Chikou Span : Plots the Chikou Span with dynamic offsets and coloring.
📘 Enhanced Candlestick Patterns Integration
Purpose : Identifies and displays various candlestick patterns to help traders spot key market movements and potential reversals.
Usage : Toggle the display of patterns, select specific pattern types, and customize pattern labels for improved visual analysis.
Display Patterns :
Description : Toggle to enable or disable the display of all candlestick patterns.
Details : When enabled, all selected candlestick patterns will be displayed on the chart, aiding traders in identifying key market movements and potential reversals.
Select Pattern Type :
Description : Select the type of candlestick patterns to detect.
Details : Options include Bullish (indicating potential upward trends), Bearish (indicating potential downward trends), or Both.
Trend Filter Method :
Description : Select the method to filter trends.
Details : Options include True Range (based on price range), Fractals, Volume, Combined, or None (no filtering).
Pattern Label Colors :
Bullish Pattern Color : Choose the color for labeling Bullish patterns, indicating potential upward trends.
Bearish Pattern Color : Choose the color for labeling Bearish patterns, indicating potential downward trends.
Indecision Pattern Color : Choose the color for labeling Indecision patterns, indicating no clear trend direction.
Base Line and Patterns Display Options :
Show Base Line in Place of Labels : Toggle to display a base line instead of labels for detected patterns. This helps visualize the general trend.
Show Counterattack Lines : Toggle to display Counterattack Lines patterns, indicating potential reversal points.
Show Dark Cloud Cover : Toggle to display Dark Cloud Cover patterns, a bearish pattern suggesting a potential reversal from an uptrend to a downtrend.
Show Engulfing Patterns : Toggle to display Engulfing patterns. Bullish Engulfing patterns suggest a potential upward reversal, while Bearish Engulfing patterns suggest a potential downward reversal.
Show Hammer Patterns : Toggle to display Hammer patterns, a bullish pattern indicating a potential reversal from a downtrend to an uptrend.
Show Hanging Man Patterns : Toggle to display Hanging Man patterns, a bearish pattern indicating a potential reversal from an uptrend to a downtrend.
Show Harami Patterns : Toggle to display Harami patterns. Bullish Harami patterns suggest a potential upward reversal, while Bearish Harami patterns suggest a potential downward reversal.
Show In-Neck Patterns : Toggle to display In-Neck patterns, indicating a potential continuation of the current trend.
Show On-Neck Patterns : Toggle to display On-Neck patterns, indicating a potential continuation of the current trend.
Show Piercing Patterns : Toggle to display Piercing patterns, a bullish pattern suggesting a potential reversal from a downtrend to an uptrend.
Show Three Black Crows : Toggle to display Three Black Crows patterns, a bearish pattern suggesting a potential reversal from an uptrend to a downtrend.
Show Thrusting Patterns : Toggle to display Thrusting patterns, a bearish pattern suggesting a potential continuation of the downtrend.
Show Upside Gap Two Crows : Toggle to display Upside Gap Two Crows patterns, a bearish pattern suggesting a potential downward reversal after an upward gap.
Show Evening Star : Toggle to display Evening Star patterns, a bearish pattern suggesting a potential reversal from an uptrend to a downtrend.
Show Inverted Hammer : Toggle to display Inverted Hammer patterns, a bullish pattern suggesting a potential reversal from a downtrend to an uptrend.
Show Morning Star : Toggle to display Morning Star patterns, a bullish pattern suggesting a potential reversal from a downtrend to an uptrend.
Show Shooting Star : Toggle to display Shooting Star patterns, a bearish pattern suggesting a potential reversal from an uptrend to a downtrend.
Show Doji Patterns : Toggle to display Doji patterns, indicating market indecision and potential reversals.
Show Dragonfly Doji : Toggle to display Dragonfly Doji patterns, a bullish pattern suggesting a potential reversal from a downtrend to an uptrend.
Show Evening Doji Star : Toggle to display Evening Doji Star patterns, a bearish pattern suggesting a potential reversal from an uptrend to a downtrend.
Show Gravestone Doji : Toggle to display Gravestone Doji patterns, a bearish pattern suggesting a potential reversal from an uptrend to a downtrend.
Show Long-Legged Doji : Toggle to display Long-Legged Doji patterns, indicating high market indecision and potential reversals.
Show Morning Doji Star : Toggle to display Morning Doji Star patterns, a bullish pattern suggesting a potential reversal from a downtrend to an uptrend.
Show Rising Three Methods : Toggle to display Rising Three Methods patterns, a bullish pattern suggesting a continuation of the uptrend.
Show Falling Three Methods : Toggle to display Falling Three Methods patterns, a bearish pattern suggesting a continuation of the downtrend.
Show Tasuki Patterns : Toggle to display Tasuki patterns, indicating potential trend continuation after a gap.
Show Marubozo : Toggle to display Marubozo patterns, indicating strong trend continuation, either bullish or bearish.
Show Long Lower Shadow : Toggle to display Long Lower Shadow patterns, indicating strong buying pressure and potential upward movement.
Show Long Upper Shadow : Toggle to display Long Upper Shadow patterns, indicating strong selling pressure and potential downward movement.
Show Three Inside Up/Down : Toggle to display Three Inside Up/Down patterns, indicating potential bullish or bearish reversals.
Show Kicker Pattern : Toggle to display Kicker patterns, indicating significant potential reversals.
Show Tweezer Tops/Bottoms : Toggle to display Tweezer Tops/Bottoms patterns, indicating potential reversals at the tops or bottoms.
Show Mat Hold Pattern : Toggle to display Mat Hold patterns, a bullish pattern suggesting a continuation of the uptrend.
Candle Body/Shadow Comparison Options :
Candle Body/Shadow Comparison : Choose the criteria to compare candle sizes: Shadows (larger shadows), Body (larger body), Both (larger shadows and body), Either (larger shadows or body), or None (no comparison).
Look-back Period for Candle Comparison : Specify the number of periods to look back when comparing the current candle size to determine if it is significant.
Period for Body Length Average : Specify the period for calculating the average body length of candles to help identify significant patterns.
Period for Candle Length Average : Specify the period for calculating the average length of candles to help identify significant patterns.
Specific Pattern Thresholds :
Doji Body Percentage Threshold : Set the percentage threshold for identifying Doji patterns based on the candle body size compared to its range.
Upper Shadow Percentage Limit : Set the maximum allowed upper shadow percentage of the candle’s range for identifying specific Doji patterns.
Lower Shadow Percentage Limit : Set the maximum allowed lower shadow percentage of the candle’s range for identifying specific Doji patterns.
Price Deviation Tolerance : Specify the price deviation tolerance for pattern recognition, which helps in identifying patterns within a certain price range.
Thrusting Neck Percentage : Set the percentage threshold for identifying Thrusting Neck patterns, indicating a potential continuation of the current trend.
Base Line Settings :
Base Line EMA Length : Specify the length of the EMA for the Base Line, helping to visualize the general trend.
Enhanced Calculations :
Wavelet Transform : If machine learning is enabled, calculates the wavelet transform for smoother and more accurate pattern detection.
Candle Body and Shadows Calculation : Detailed calculations for candle body and shadow lengths to improve pattern detection.
Average Calculations : Calculate averages for body and candle sizes to help identify significant patterns.
Fractals Calculation : Identify fractal highs and lows to aid in trend detection.
Trend Filters : Apply user-selected trend filters based on True Range, Fractals, Volume, or a combination.
Pattern Detection and Labeling : Detects and labels various candlestick patterns, including Doji, Engulfing, Hammer, and more, with options for displaying labels or base lines.
Alerts and Notifications : Set alerts for detected patterns and base line colors to notify traders of significant market events.
Plotting Candlestick Patterns :
Pattern Detection : Automatically detects and labels various candlestick patterns based on user settings.
Label Customization : Customize the labels for different patterns, including color and text.
Base Line Plotting : Option to plot a base line instead of labels for detected patterns, enhancing trend visualization.
Alerts for Patterns : Set alerts for detected patterns to keep traders informed of significant market changes.
📘 Enhanced Fibonacci Retracement Integration
Purpose : Provides a tool for identifying potential support and resistance levels using Fibonacci retracement.
Usage : Toggle the display of Fibonacci levels, adjust the lookback period, and customize the appearance of Fibonacci levels for better market analysis.
Auto Mode :
Description : Toggle to enable or disable automatic detection of price points.
Details : When enabled, the highest and lowest price points within a specified period will be automatically detected to set Fibonacci levels. Disable to manually set the top and bottom prices.
Period :
Description : Set the lookback period for detecting price points.
Details : Defines the number of bars to look back when detecting the highest and lowest prices in Auto Mode, used for calculating Fibonacci levels.
Manual Top :
Description : Manually set the top price level.
Details : Adjust this setting to reflect the peak price of interest when Auto Mode is disabled.
Manual Bottom :
Description : Manually set the bottom price level.
Details : Adjust this setting to reflect the low price of interest when Auto Mode is disabled.
Display Fibonacci :
Description : Toggle to show or hide Fibonacci retracement levels.
Details : When enabled, the calculated Fibonacci levels will be displayed on the chart, overlaying the price data.
Baseline Levels :
Description : Select Fibonacci levels to highlight as baselines.
Details : Choose specific levels to be visually distinct, emphasizing their significance in the analysis.
Fibonacci Levels Colors :
Upper Levels Color : Set the color for Fibonacci levels above the baseline, indicating potential resistance levels.
Lower Levels Color : Set the color for Fibonacci levels below the baseline, indicating potential support levels.
Baseline Levels Color : Set the color for highlighted baseline Fibonacci levels, making them stand out from other levels.
Display Individual Fibonacci Levels :
Show Level : Toggle to enable or disable the display of specific Fibonacci levels.
Level Value : Set the multiplier used to calculate each specific Fibonacci level relative to the price range.
Reverse Levels :
Description : Toggle to switch the calculation direction of Fibonacci levels.
Details : When enabled, levels are calculated in reverse, useful for analyzing downtrends.
Line Extension :
Description : Choose how Fibonacci level lines are extended on the chart.
Details : Options include extending lines to the left, right, or both, affecting their visual presentation.
Text Size :
Description : Adjust the font size of the labels for Fibonacci levels.
Details : Options range from large to tiny, allowing for readability adjustments according to user preference.
Line Style :
Description : Select the line style for Fibonacci levels.
Details : Options include solid, dotted, and dashed, providing visual distinction.
Line Width :
Description : Set the thickness of the Fibonacci level lines.
Details : A higher value makes the lines more prominent on the chart.
Baseline Line Style :
Description : Choose the line style specifically for the baseline levels.
Details : This can differ from other Fibonacci levels to emphasize their importance.
Baseline Line Width :
Description : Adjust the thickness of the baseline level lines.
Details : Can be set differently from other levels for visual emphasis.
Enhanced Calculations :
Automatic and Manual Top/Bottom Setup : Detect or manually set the highest and lowest price points.
Price Range Calculation : Determine the range between the highest and lowest prices.
Fibonacci Level Values : Calculate the values for each Fibonacci level.
Visual and Label Configuration : Configure visual aspects and labels for each level.
Plotting and Labeling :
Level Plotting :
Description : Plot each Fibonacci level on the chart.
Details : Draw lines representing each calculated level.
Label Customization :
Description : Customize the labels for Fibonacci levels.
Details : Include text, colors, and positioning for clarity.
📘 Supports and Resistances Integration
Purpose : Identifies key support and resistance levels to aid in market analysis.
Usage : Toggle the display of support and resistance lines, customize their appearance, and use Bollinger Bands for additional insights.
Display Supports and Resistances :
Description : Toggle to enable or disable the display of support and resistance lines.
Details : When enabled, support and resistance lines will be shown on the chart, providing key levels for market analysis.
Swing Period :
Description : Set the retrospective period for identifying swing points.
Details : A longer period captures more significant trends but may reduce sensitivity. The default value is 10.
Support Line Color :
Description : Set the color for support lines.
Details : Choose a color that enhances chart readability. Default is green.
Resistance Line Color :
Description : Set the color for resistance lines.
Details : Choose a color that makes resistance lines easily distinguishable. Default is red.
Trend-Based Line Color :
Description : Toggle to enable dynamic coloring based on trend direction.
Details : When enabled, the color of the lines will change according to the trend, aiding visual analysis.
Line Thickness :
Description : Adjust the thickness of the support and resistance lines.
Details : Choose a thickness value between 1 and 5 for better visibility.
Line Style :
Description : Select the style of the lines.
Details : Options include Solid, Dotted, or Dashed lines for visual distinction.
Number of Lines to Display :
Description : Set the maximum number of support/resistance lines to display.
Details : Adjust the number of lines to avoid clutter or to show more levels.
Display Bollinger Bands :
Description : Toggle to show or hide Bollinger Bands on the chart.
Details : Bollinger Bands provide a visual representation of volatility and potential price ranges.
Bollinger Bands Integration :
Description : Enable the integration of Bollinger Bands for S/R calculation.
Details : This feature adjusts the placement of S/R lines based on the market volatility captured by the Bollinger Bands.
Bollinger Bands Color Settings :
Description : Set colors for different Bollinger Band conditions.
Details :
Green: Prices above the median but below the upper band (potential overbought area).
Dark green: Prices above the upper band (strong upward momentum).
Light red: Prices below the median but above the lower band (potential oversold area).
Dark red: Prices below the lower band (strong downward momentum).
Fill Opacity Adjustment :
Description : Adjust the fill opacity between Bollinger Bands.
Details : Set the opacity level to balance visibility with other chart elements.
BB Sensitivity Level :
Description : Adjust the sensitivity for determining S/R levels near Bollinger Bands.
Details : A higher value increases the consideration of levels near the bands.
Band Width Multiplier :
Description : Control the width of the Bollinger Bands.
Details : Adjust the multiplier to expand or contract the bands based on market volatility.
Uniform BB Coloring :
Description : Apply a consistent color to Bollinger Bands.
Details : Simplify visual interpretation with a uniform color.
Plotting and Alerts :
Plotting Bollinger Bands :
Description : Plot the Bollinger Bands on the chart.
Details : The bands are colored based on the conditions set for market volatility and price ranges.
Alerts and Notifications :
Description : Set alerts for support/resistance breaks and Bollinger Band breakouts.
Details : Notify traders of significant market events related to these levels.
📘 Enhanced Trend Lines Integration
Purpose : Identifies and plots trend lines based on market structure to help traders understand market direction and potential buy/sell points.
Usage : Toggle the display of trend lines, customize their appearance, and use enhanced calculations for trend analysis.
Display Trend Lines :
Description : Enable or disable the display of trend lines on the chart.
Details : These trend lines are calculated based on market structure, specifically through the detection of Breaks of Structure (BOS). If enabled, the trend lines will help in identifying the market overall trend and potential buy and sell points.
Trend Line Colors :
Upper Line Color : Set the color for the upper trend lines to enhance visual distinction.
Lower Line Color : Set the color for the lower trend lines, aiding in easy identification of support levels.
Pivot Labels :
Show Pivots Labels : Control the display of pivot labels on the chart.
Pivot Label Size : Select the size of the pivot labels displayed on the chart. Options include Tiny, Small, Normal, Large, and Huge.
Trend Line Calculations :
Pivot Depth : Adjust the depth for pivot calculation based on the selected timeframe to capture significant price movements.
Pivot Deviation : Set the deviation for pivot calculation to identify key turning points.
Pivot Backstep : Define the backstep for pivot calculation to ensure accurate detection of pivot points.
Enhanced Calculations :
Market Structure Detection : Utilize advanced algorithms to identify key market structures, improving trend line accuracy.
Adaptive Parameters : Automatically adjust pivot depth, deviation, and backstep based on the selected timeframe for better relevance.
Zigzag Calculation : Implement zigzag patterns to dynamically adjust trend lines, ensuring they reflect current market conditions.
Slope and Intercept Calculation : Compute the slope and intercept for trend lines to enhance precision in trend detection.
Dynamic Updates : Continuously update trend lines as new data becomes available, ensuring real-time accuracy.
Alerts and Notifications : Set alerts for new high and low pivots, as well as for when the price crosses upper or lower trend lines, keeping traders informed of significant market changes.
Plotting Trend Lines :
Trend Line Plotting : Automatically draw trend lines based on detected BOS, helping traders visualize the market trend.
Diagonal Support/Resistance Lines : Plot diagonal lines to indicate support and resistance levels, enhancing the understanding of market dynamics.
Pivot Label Customization : Customize pivot labels for clear identification of high and low points in the trend.
Alerts for Trend Lines : Set alerts for when price crosses trend lines, ensuring timely notifications of potential trading opportunities.
📘 Enhanced Linear Regression Integration
Purpose : Uses linear regression to analyze price movements and identify trends.
Usage : Display the linear regression projection line, customize its appearance, and use enhanced calculations for better trend analysis.
Display Projection Line :
Description : Toggle to display or hide the linear regression projection line on the chart.
Details : This line represents the best fit line that predicts future prices based on historical data.
Data Source :
Description : Select the data source for the linear regression projection.
Details : This is typically the closing price but can be any price point such as open, high, or low. The selected source will be used to calculate the linear regression projection line.
Trend-Based Line Color :
Enable Trend-Based Line Color : Toggle to automatically color the projection line based on the trend direction. When enabled, the line will be red for a downward trend and green for an upward trend, providing a visual indication of market direction.
Uptrend Line Color : Select the color for the projection line when the trend is upward. This color will be used when "Enable Trend-Based Line Color" is active.
Downtrend Line Color : Select the color for the projection line when the trend is downward. This color will be used when "Enable Trend-Based Line Color" is active.
Enhanced Calculations :
Standard Deviation Calculation : Calculate the standard deviation for a given length to understand the volatility around the linear regression line.
Pearson's Correlation Calculation : Compute Pearson's R to measure the strength of the linear relationship between the price points and the linear regression line.
Slope and Intercept Calculation : Calculate the slope and intercept for the regression line, providing the basis for the projection.
Kernel Application : Optionally apply the RBF Kernel to the selected source data for smoothing and enhancing the regression calculations.
Dynamic Length Selection : Automatically select the optimal regression period based on the highest Pearson's R value, ensuring the most accurate trend representation.
Real-Time Updates : Continuously update the regression line and related calculations as new data becomes available, maintaining accuracy in real-time.
Alerts and Notifications : Set alerts for when the price crosses the linear regression projection line, notifying traders of significant market events.
Plotting Linear Regression Components :
Projection Line Plotting : Automatically draw the linear regression projection line based on historical data and the selected data source.
Label Customization : Customize the label for the projection line, including color and text, for clear identification on the chart.
Alerts for Projection Line : Set alerts for when the price crosses the projection line, ensuring timely notifications of potential trading opportunities.
📘 POC Analysis Integration
Purpose : Identifies the Point of Control (POC) to highlight price levels with the highest trading volume.
Usage : Toggle the display of the POC, customize its appearance, and use enhanced calculations for better market analysis.
Display POC :
Description : Toggle to display or hide the Point of Control (POC) on the chart.
Details : The POC is the price level at which the highest volume of trading occurred, indicating a focal point of market activity.
Data Source :
Description : Select the price source for POC analysis.
Details : This is typically the closing price but can be any price point such as open, high, or low. The selected source will be used to calculate the POC.
POC Line Colors :
Color Above POC : Set the line color when the closing price is above the POC.
Color Below POC : Set the line color when the closing price is below the POC.
Width Multiplier :
Description : Adjust the width around the price for POC analysis.
Details : A higher value broadens the calculation range.
POC Calculation and Visualization :
Price Level Initialization : Calculate the initial spacing between price levels based on the first candlestick and user settings.
Volume Data Accumulation : Accumulate volume data at specified price levels for each candlestick to determine the POC.
Dynamic Array Expansion : Expand price levels array to accommodate new price data outside the current range.
POC Determination : Determine and visualize the POC at the last candlestick if enabled by the user.
Alerts and Notifications : Set alerts for when the price crosses the POC, notifying traders of significant market events.
Plotting POC Components :
POC Line Plotting : Automatically draw the POC line based on historical data and the selected data source.
Label Customization : Customize the label for the POC line, including color and text, for clear identification on the chart.
Alerts for POC : Set alerts for when the price crosses the POC, ensuring timely notifications of potential trading opportunities.
📘 Enhanced Divergences Integration
Purpose : Detects and displays divergences between price movements and indicators to identify potential reversal points.
Usage : Toggle the display of divergences, select data sources, customize divergence colors, and use enhanced calculations for better trend analysis.
Display Divergences :
Description : Toggle to display or hide the detected divergences on the chart.
Details : Divergences occur when the price movement of an asset and a related indicator (e.g., volume or momentum) move in opposite directions. They are used to identify potential reversal points in the market. Regular divergences signal possible reversals, while hidden divergences can indicate continuation.
Data Source :
Description : Defines the timeframe from which to fetch data for analysis.
Details : Typically lower than the chart current timeframe for multi-timeframe analysis.
Divergence Colors :
Bearish Divergence Color : Sets the color for bearish divergence lines. Bearish divergences typically suggest potential downward price movement.
Bullish Divergence Color : Sets the color for bullish divergence lines. Bullish divergences typically indicate potential upward price movement.
Pivot Bars :
Left Bars : Number of bars to the left of the pivot point to consider. Helps in identifying the pivot high or low by looking back these many bars.
Right Bars : Number of bars to the right of the pivot point to consider. Assists in confirming a pivot point by ensuring no higher high or lower low is present within this range.
Display Hidden Divergences :
Description : When enabled, this setting reveals hidden divergences on the chart.
Details : Hidden divergences are a subtler form of divergence that often signal continuation rather than reversal. A hidden bullish divergence occurs when price makes a higher low while the indicator makes a lower low, suggesting the continuation of an uptrend. Conversely, a hidden bearish divergence occurs when price makes a lower high while the indicator makes a higher high, indicating the continuation of a downtrend. These divergences are particularly useful for identifying the strength of the current trend.
Dynamic Line Width Based on Divergence Count :
Description : When enabled, adjusts the width of the divergence line dynamically based on the count of divergences detected.
Details : This provides visual emphasis on stronger signals.
Enhanced Calculations :
Standard Deviation Calculation : Calculate the standard deviation for a given length to understand the volatility around the linear regression line.
Pearson's Correlation Calculation : Compute Pearson's R to measure the strength of the linear relationship between the price points and the linear regression line.
Slope and Intercept Calculation : Calculate the slope and intercept for the regression line, providing the basis for the projection.
Kernel Application : Optionally apply the RBF Kernel to the selected source data for smoothing and enhancing the regression calculations.
Dynamic Length Selection : Automatically select the optimal regression period based on the highest Pearson's R value, ensuring the most accurate trend representation.
Real-Time Updates : Continuously update the regression line and related calculations as new data becomes available, maintaining accuracy in real-time.
Alerts and Notifications : Set alerts for when the price crosses the linear regression projection line, notifying traders of significant market events.
Plotting Divergence Components :
Divergence Line Plotting : Automatically draw divergence lines based on historical data and the selected data source.
Label Customization : Customize the label for the divergence lines, including color and text, for clear identification on the chart.
Alerts for Divergences : Set alerts for when a divergence is detected, ensuring timely notifications of potential trading opportunities.
📘 Enhanced Average True Range Integration
Purpose : Measures market volatility using the Average True Range (ATR) to assist in identifying potential buy and sell points.
Usage : Set the ATR period, minimum tick filter, upper and lower coefficients, and customize ATR colors for better market analysis.
Show Labels :
Description : Enable or disable the display of labels for the Average True Range (ATR) indicator.
Details : This option controls whether the ATR signals (buy and sell) are shown on the chart with respective labels.
ATR Period :
Description : Sets the period for calculating the Average True Range (ATR).
Details : The ATR measures market volatility by calculating the average range of price movement over a specified period. A shorter period makes the ATR more sensitive to recent price movements, while a longer period smooths out short-term volatility.
Minimum Tick Filter :
Description : Sets the minimum tick filter for buy and sell signals.
Details : This filter ensures that the price movement is significant enough to be considered a valid signal. For example, a value of 20 means that the price must move at least 20 ticks from the open to the close to generate a signal.
Upper Coefficient :
Description : Sets the upper coefficient for band calculation.
Details : This value adjusts the sensitivity of the upper band used to detect high points. A higher coefficient makes the band wider, capturing more significant price movements, while a lower coefficient makes the band narrower, making it more sensitive to smaller price changes.
Lower Coefficient :
Description : Sets the lower coefficient for band calculation.
Details : This value adjusts the sensitivity of the lower band used to detect low points. A higher coefficient makes the band wider, capturing more significant price movements, while a lower coefficient makes the band narrower, making it more sensitive to smaller price changes.
ATR Colors :
Bullish Color : Sets the color for the bullish signal, helping to visually distinguish bullish trends.
Bearish Color : Sets the color for the bearish signal, helping to visually distinguish bearish trends.
Enhanced Calculations :
Dynamic Coefficient Calculation : Calculates dynamic coefficients based on market volatility, adjusting the sensitivity of ATR bands accordingly.
Band Calculation : Computes high and low bands using dynamic coefficients to detect significant price movements.
High/Low Point Detection : Identifies potential high and low points based on ATR band calculations and price thresholds.
Real-Time Updates : Continuously updates ATR calculations and signals as new data becomes available, ensuring accuracy in real-time.
Plotting ATR Components :
Signal Plotting : Plots bullish and bearish ATR signals on the chart based on calculated conditions.
Label Customization : Customize the labels for ATR signals, including color and text, for clear identification on the chart.
Alerts for Signals : Set alerts for detected bullish and bearish signals, ensuring timely notifications of potential trading opportunities.
📘 Enhanced ATR Visualization Parameters
Purpose : Provides a visual representation of market volatility using the ATR Strength Meter.
Usage : Toggle the display of the ATR Strength Meter, set thresholds, and customize its appearance for better market analysis.
Display ATR Strength Meter :
Description : Toggle to display or hide the ATR Strength Meter, a visual representation of market volatility.
Details : The meter is based on the Average True Range (ATR) and helps identify volatility trends.
High ATR Threshold :
Description : Set the threshold for high volatility.
Details : ATR values above this threshold indicate increased market volatility.
Low ATR Threshold :
Description : Set the threshold for low volatility.
Details : ATR values below this threshold indicate decreased market volatility.
Progression Bar Position :
Description : Select the position of the ATR Strength Meter on the chart.
Details : Options are "Top" or "Bottom", affecting where the volatility meter is displayed relative to price action.
Progress Bar Length :
Description : Set the horizontal length of the ATR Strength progression bar.
Details : Adjust to increase or decrease the bar's width, accommodating different chart sizes and user preferences.
Enhanced Calculations :
ATR Strength Calculation : Calculate the ATR strength to measure market volatility.
Dynamic Coefficients : Use dynamic coefficients based on volatility for more accurate calculations.
Progress Bar Calculation : Determine the position and color of the progression bar based on ATR strength.
Label Positioning : Dynamically position labels for minimum and maximum values to avoid overlap.
Plotting ATR Strength Meter :
Progression Bar Plotting : Plot the progression bar to represent the ATR strength.
Label Customization : Customize labels for the ATR strength, minimum, and maximum values.
📘 Enhanced Relative Strength Index Integration
(A special thanks to RumpyPumpyDumpy for allowing the private reuse of his script.)
Purpose : Measures market momentum using the Relative Strength Index (RSI) and Stochastic RSI to assist in identifying potential buy and sell points.
Usage : Set the RSI and StochRSI parameters, toggle the display of the RSI Meter, and customize its appearance for better market analysis.
RSI Calculation Parameters :
RSI Length : Defines the length of the RSI calculation.
Details : A longer period captures more data points but may reduce sensitivity.
RSI Overbought Level : Sets the overbought level for RSI.
Details : Values above this level indicate overbought conditions.
RSI Oversold Level : Sets the oversold level for RSI.
Details : Values below this level indicate oversold conditions.
StochRSI Length : Defines the length of the StochRSI calculation.
Details : A longer period captures more data points but may reduce sensitivity.
StochRSI %K Length : Defines the length of the %K line of the StochRSI.
StochRSI %D Length : Defines the length of the %D line (SMA of %K) of the StochRSI.
RSI Visualization Parameters :
Display RSI Meter : Toggle the display of the RSI Meter on the chart.
RSI Meter Size : Adjust the size of the RSI Meter displayed on the chart.
Details : Measured as the diameter of the meter. Increase the value for larger display size, enhancing visibility and making it easier to read the RSI trend at a glance.
Horizontal Offset : Move the RSI Meter horizontally across the chart.
Details : Positive values shift the meter to the left, allowing for placement adjustments relative to the chart's current view or specific visual preferences.
RSI Meter Components :
Sectors and Ticks : Draw sector arcs and tick marks around the RSI Meter to represent different RSI levels and thresholds.
Needle : Draw the needle on the RSI Meter to indicate the current RSI value.
Sector Labels : Label each sector of the RSI Meter to indicate market conditions like "Strong Buy," "Buy," "Neutral," "Sell," and "Strong Sell."
Title Label : Draw the title label for the RSI Meter displaying the RSI value and its period.
Enhanced Calculations :
RSI Calculation : Calculate the RSI using the built-in function with the specified length and source.
StochRSI Calculation : Calculate StochRSI values using the specified lengths for RSI, %K, and %D.
Dynamic Line Management : Efficiently manage and update dynamically created line objects to prevent potential memory leaks.
Optimized Sector and Needle Drawing : Enhanced the drawing functions for sectors, needles, and ticks to improve visual clarity and performance.
Plotting RSI Meter :
Sector Plotting : Draw the sectors on the RSI Meter using specified colors and widths to represent different RSI levels and thresholds.
Needle Plotting : Plot the needle on the RSI Meter based on the calculated RSI value to visually indicate the current RSI level.
Tick Plotting : Plot tick marks around the RSI Meter to denote key RSI levels and thresholds for better readability.
Label Plotting : Draw sector labels and a title label on the RSI Meter to provide context and information about the RSI levels and their corresponding market conditions.
📘 Market Sentiment Integration
Purpose : Analyzes market sentiment using various indicators to provide an overall sentiment score.
Usage : Enable or disable individual sentiment indicators, set account type, and customize sentiment calculations for better market analysis.
Volatility Index (IV) :
Description : Enable or disable the use of the Volatility Index in sentiment calculation.
Details : When enabled, the Volatility Index (IV) provides insight into market sentiment by measuring market volatility. The selected Volatility Index varies based on your TradingView account type.
Account Type :
Description : Select your TradingView account type.
Details : Free accounts use SPX, while Premium accounts use VIX.
Put/Call Ratio (PCR) :
Description : Enable or disable the use of the Put/Call ratio in sentiment calculation.
Details : The Put/Call ratio is a sentiment indicator that measures the volume of put options traded relative to call options, indicating market sentiment towards bearish or bullish expectations.
Fear and Greed Index :
Description : Enable or disable the use of the Fear and Greed Index in sentiment calculation.
Details : The Fear and Greed Index gauges the prevailing emotions in the market, indicating whether investors are inclined towards fear (bearish sentiment) or greed (bullish sentiment).
Momentum Indicators :
Description : Enable or disable the use of momentum indicators like MACD and RoC in sentiment calculation.
Details : Momentum indicators help identify the strength and direction of price movements, assisting in sentiment analysis.
Adaptive Periods for Shorter Timeframes :
Description : Toggle this option to use shorter periods for sentiment indicators when analyzing lower timeframes.
Details : Enabling this option allows for more responsive and sensitive analysis when working with shorter timeframes.
Calculation Details :
Normalization Function : Normalize the values of the indicators over a 252-period range.
Set Periods Function : Set periods based on user preference for faster or slower periods, adjusting the analysis sensitivity.
IV Calculation : Calculate the IV value based on the selected Volatility Index (SPX for Free accounts, VIX for Premium accounts).
Put/Call Ratio Calculation : Calculate the Put/Call ratio using volume data, where put volume is proportional to the trading range, and call volume is proportional to the price change.
RoC Calculation : Calculate the Rate of Change (RoC) as a momentum indicator, measuring the percentage change in closing prices over a specified period.
Dynamic Thresholds : Define dynamic thresholds based on historical data, calculating mean and standard deviation to determine upper and lower thresholds for IV, PCR, and RoC.
📘 Enhanced Market Trend Dashboard Integration
Purpose : Provides a summary of key market indicators and signals in a single dashboard for quick and easy reference.
Usage : Customize the dashboard settings to display relevant market information, including Ichimoku components, Linear Regression, Support/Resistance levels, MACD, RSI, and Market Sentiment.
Market Trend Dashboard Parameters :
Display Market Trend Dashboard : Toggle to show or hide the market trend dashboard, providing a summary of key indicators and signals.
Panel Position : Select the position of the dashboard on the chart for optimal viewing.
Panel Text Size : Choose the text size for the information displayed in the dashboard, ensuring readability.
Panel Background Color : Set the background color of the market trend dashboard, enhancing contrast with the chart.
Ichimoku Dashboard Parameters :
Display Ichimoku Dashboard : Toggle to show or hide the Ichimoku section in the dashboard.
Display Tenkan-Sen Price Cross : Indicate when the price crosses the Tenkan-Sen line, signaling potential trade opportunities.
Display Kijun-Sen Price Cross : Indicate when the price crosses the Kijun-Sen line, often considered a stronger signal than Tenkan-Sen crosses.
Display Chikou Span Price Cross : Indicate Chikou Span price crosses, providing insight into potential trend reversals.
Display Kumo Breakout : Indicate Kumo (cloud) breakouts, which can signify major trend shifts.
Display Kumo Twist : Indicate Kumo twists, suggesting changing market dynamics and potential reversals.
Linear Regression Projection Dashboard Parameters :
Display LR Projection Dashboard : Toggle to show or hide the Linear Regression Projection section in the dashboard.
Display Linear Regression Period : Indicate the period used for Linear Regression Projection analysis.
Display Pearson R Details : Show the Pearson R value in the dashboard, indicating the strength and direction of the correlation in the Linear Regression Projection.
Supports and Resistances Dashboard Parameters :
Display S/R Dashboard : Toggle to show or hide the Support and Resistance section in the dashboard.
Display S/R Break Prices : Show the latest break prices of support and resistance levels in the dashboard.
MACD Dashboard Parameters :
Display MACD Dashboard : Toggle to show or hide the MACD section in the dashboard.
RSI Dashboard Parameters :
Display RSI Dashboard : Toggle to show or hide the Relative Strength Index section in the dashboard.
Display RSI Details : Show the RSI value and status in the dashboard.
Display StochRSI Details : Show the StochRSI %K, %D values and status in the dashboard.
Market Sentiment Dashboard Parameters :
Display Market Sentiment Dashboard : Enable or disable the display of the Market Sentiment Dashboard, which summarizes key market sentiment indicators like Implied Volatility, Put/Call Ratio, and Fear and Greed Index.
Display Implied Volatility Details : Show or hide the Implied Volatility details in the Market Sentiment Dashboard.
Display Put/Call Ratio Details : Show or hide the Put/Call Ratio details in the Market Sentiment Dashboard.
Display Fear and Greed Index Details : Show or hide the Fear and Greed Index details in the Market Sentiment Dashboard.
Enhanced Calculations :
Ichimoku Cloud Trend Calculation : Calculates trend based on the relationship between Ichimoku Cloud components, identifying bullish or bearish trends.
Support and Resistance Break Detection : Detects breaks in support and resistance levels and updates the dashboard accordingly.
Linear Regression Projection Calculation : Calculates Linear Regression Projection and Pearson R value for trend analysis.
MACD Signal Calculation : Determines MACD status based on histogram values.
RSI and StochRSI Calculation : Calculates RSI and StochRSI values and updates their statuses in the dashboard.
Market Sentiment Score Calculation : Calculates overall market sentiment score based on individual sentiment indicators.
Dynamic Alert Management : Manages alerts for various dashboard signals to prevent repeated alerts.
Real-Time Data Integration : Continuously updates the dashboard with real-time data for accurate and current trend analysis.
Plotting Market Trend Dashboard Components :
Ichimoku Components Plotting : Plots Tenkan-Sen, Kijun-Sen, Chikou Span, and Kumo cloud with dynamic adjustments.
Support and Resistance Levels Plotting : Plots support and resistance levels and updates them dynamically based on market data.
Linear Regression Projection Plotting : Plots the Linear Regression Projection line and labels with trend-based colors.
MACD and RSI Plotting : Plots MACD and RSI signals on the dashboard, including status updates.
Market Sentiment Indicators Plotting : Plots Market Sentiment indicators like IV, PCR, and Fear and Greed Index with dynamic updates.
Alert Notifications Plotting : Plots alert notifications for significant market changes based on dashboard signals.
Summary
This comprehensive market analyzer integrates multiple technical indicators, including machine learning, Ichimoku Kinkō Hyō, candlestick patterns, Fibonacci retracement, support and resistance levels, trend lines, linear regression, POC analysis, divergences, ATR, RSI, and market sentiment. Each section includes detailed descriptions and usage instructions to help traders understand how to effectively utilize the indicator in their trading strategies.
Fear Volatility Gate [by Oberlunar]The Fear Volatility Gate by Oberlunar is a filter designed to enhance operational prudence by leveraging volatility-based risk indices. Its architecture is grounded in the empirical observation that sudden shifts in implied volatility often precede instability across financial markets. By dynamically interpreting signals from globally recognized "fear indices", such as the VIX, the indicator aims to identify periods of elevated systemic uncertainty and, accordingly, restrict or flag potential trade entries.
The rationale behind the Fear Volatility Gate is rooted in the understanding that implied volatility represents a forward-looking estimate of market risk. When volatility indices rise sharply, it reflects increased demand for options and a broader perception of uncertainty. In such contexts, price movements can become less predictable, more erratic, and often decoupled from technical structures. Rather than relying on price alone, this filter provides an external perspective—derived from derivative markets—on whether current conditions justify caution.
The indicator operates in two primary modes: single-source and composite . In the single-source configuration, a user-defined volatility index is monitored individually. In composite mode, the filter can synthesize input from multiple indices simultaneously, offering a more comprehensive macro-risk assessment. The filtering logic is adaptable, allowing signals to be combined using inclusive (ANY), strict (ALL), or majority consensus logic. This allows the trader to tailor sensitivity based on the operational context or asset class.
The indices available for selection cover a broad spectrum of market sectors. In the equity domain, the filter supports the CBOE Volatility Index ( CBOE:VIX VIX) for the S&P 500, the Nasdaq-100 Volatility Index ( CBOE:VXN VXN), the Russell 2000 Volatility Index ( CBOEFTSE:RVX RVX), and the Dow Jones Volatility Index ( CBOE:VXD VXD). For commodities, it integrates the Crude Oil Volatility Index ( CBOE:OVX ), the Gold Volatility Index ( CBOE:GVZ ), and the Silver Volatility Index ( CBOE:VXSLV ). From the fixed income perspective, it includes the ICE Bank of America MOVE Index ( OKX:MOVEUSD ), the Volatility Index for the TLT ETF ( CBOE:VXTLT VXTLT), and the 5-Year Treasury Yield Index ( CBOE:FVX.P FVX). Within the cryptocurrency space, it incorporates the Bitcoin Volmex Implied Volatility Index ( VOLMEX:BVIV BVIV), the Ethereum Volmex Implied Volatility Index ( VOLMEX:EVIV EVIV), the Deribit Bitcoin Volatility Index ( DERIBIT:DVOL DVOL), and the Deribit Ethereum Volatility Index ( DERIBIT:ETHDVOL ETHDVOL). Additionally, the user may define a custom instrument for specialized tracking.
To determine whether market conditions are considered high-risk, the indicator supports three modes of evaluation.
The moving average cross mode compares a fast Hull Moving Average to a slower one, triggering a signal when short-term volatility exceeds long-term expectations.
The Z-score mode standardizes current volatility relative to historical mean and standard deviation, identifying significant deviations that may indicate abnormal market stress.
The percentile mode ranks the current value against a historical distribution, providing a relative perspective particularly useful when dealing with non-normal or skewed distributions.
When at least one selected index meets the condition defined by the chosen mode, and if the filtering logic confirms it, the indicator can mark the trading environment as “blocked”. This status is visually highlighted through background color changes and symbolic markers on the chart. An optional tabular interface provides detailed diagnostics, including raw values, fast-slow MA comparison, Z-scores, percentile levels, and binary risk status for each active index.
The Fear Volatility Gate is not a predictive tool in itself but rather a dynamic constraint layer that reinforces discipline under conditions of macro instability. It is particularly valuable when trading systems are exposed to highly leveraged or short-duration strategies, where market noise and sentiment can temporarily override structural price behavior. By synchronizing trading signals with volatility regimes, the filter promotes a more cautious, informed approach to decision-making.
This approach does not assume that all volatility spikes are harmful or that market corrections are imminent. Rather, it acknowledges that periods of elevated implied volatility statistically coincide with increased execution risk, slippage, and spread widening, all of which may erode the profitability of even the most technically accurate setups.
Therefore, the Fear Volatility Gate acts as a protective mechanism.
Oberlunar 👁️⭐
GEX Profile [PRO] Real Auto-Updated Gamma Exposure Levels𝗥𝗲𝗮𝗹 𝗚𝗘𝗫 𝗟𝗲𝘃𝗲𝗹𝘀 𝘄𝗶𝘁𝗵 𝗦𝗲𝗮𝗺𝗹𝗲𝘀𝘀 𝗔𝘂𝘁𝗼-𝗨𝗽𝗱𝗮𝘁𝗲𝘀 𝗳𝗼𝗿 𝗼𝘃𝗲𝗿 𝟭𝟲𝟱+ 𝗼𝗳 𝘁𝗵𝗲 𝗠𝗼𝘀𝘁 𝗟𝗶𝗾𝘂𝗶𝗱 𝗨.𝗦. 𝗠𝗮𝗿𝗸𝗲𝘁 𝗦𝘆𝗺𝗯𝗼𝗹𝘀 (including 𝟬𝗗𝗧𝗘 𝗳𝗼𝗿 𝗦𝗣𝗫, SPY, QQQ, TLT, IWM, etc...)
🔃 Dynamic Updates : Receive precise GEX levels with auto-updating metrics up to 5 times a day throughout the trading session—no manual refresh needed!
🍒 Strategically Developed : Built by experienced options traders to meet the needs of serious options market participants.
🕒 0DTE? No Problem! : Designed with 0DTE traders in mind, our indicator keeps you updated with GEX levels and seamless auto-refresh to capture every crucial market shift.
📈 Optimized for Option Traders : See accurate GEX and NETGEX profiles for multiple expirations to maximize strategic potential.
🔶 Comprehensive GEX Levels
This indicator provides unparalleled insight into market dynamics with levels like Call/Put Support, Resistance, HVL (High Volatility Level), and Call/Put Walls. These levels are auto-updated based on live market movements and reflect gamma shifts and volatility signals essential for options traders.
🔶 Ideal for 0DTE and Multi-Leg Strategies
Track essential GEX levels across expirations with our unique Cumulative (⅀) and Selected Alone (⊙) calculation models. Customize your view to reveal high-impact levels across multiple expirations or focus on a specific expiration for a targeted strategy.
🔶 Coverage of 165+ Highly Liquid U.S. Symbols
Compatible with over 165 U.S. market symbols, including SP:SPX , AMEX:SPY , NASDAQ:QQQ , NASDAQ:TLT , AMEX:GLD , NASDAQ:NVDA , and more. The watchlist is expanding continuously to meet the needs of active traders. List of Compatible Symbols Available Here: www.tradingview.com
🔶How does the indicator work and why is it unique?
This is not just another GEX indicator. It incorporates 15min delayed option chain data from ORATS as data provider, processes and refines the delayed data package using pineseed, and sends it to TradingView, visualizing the key GEX levels using specific formulas (see detailed below). This method of incorporating options data into a visualization framework is unique and entirely innovative on TradingView.
Unlike other providers that only set GEX levels at market open, this indicator adjusts dynamically throughout the day, providing updated insights across the trading day and capturing gamma shifts as the market moves.
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🌑 𝗗 𝗢 𝗖 𝗨 𝗠 𝗘 𝗡 𝗧 𝗔 𝗧 𝗜 𝗢 𝗡 🌑
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🔶 Understanding GEX (Gamma Exposure) and Gamma Profiling
Gamma Exposure (GEX) is a crucial concept in options trading because it reveals how options market positions can influence the dynamics of asset prices. In essence, GEX measures the collective gamma exposure of options market participants, impacting overall market stability and price movements.
🔹 What is GEX?
At its core, GEX captures the aggregate impact of gamma, a key options Greek, which tells us how an option's delta changes in response to price movements in the underlying asset. Positive or negative GEX levels can reflect the collective bullish or bearish stance of the market:
Positive GEX (far above HVL) : Indicates a net bullish positioning by options holders. When GEX is strongly positive, it suggests that as the asset price increases, market participants might need to buy more of the asset to maintain their hedges. This behavior can fuel further upward momentum.
Negative GEX (far below HVL) : Implies a net bearish positioning. In a strongly negative GEX environment, declines in the asset's price might prompt participants to sell, potentially exacerbating the downward movement.
🔹 The Influence of GEX on Strike Prices and Expiration
A unique feature of GEX is its impact near expiration dates. As options approach expiration, GEX levels can “pin” the price to specific strike levels, where options positions are concentrated. This pinning effect arises as market makers adjust their hedging strategies, often causing the asset price to gravitate towards certain strike prices, where a large volume of options contracts sits.
🟨 Overview of our GEX Calculation Models for Options Traders 🟨
Our GEX indicator models were developed with serious options traders in mind, providing flexibility beyond typical GEX providers. We know that using GEX levels for multi-leg strategies, where the underlying doesn't need a strong trend to be profitable , calls for a nuanced approach that aligns with different trading horizons. Here’s a detailed breakdown of our GEX calculation models and how they support strategic trading across varying timeframes.
Thus, the HVL an orher CALL/PUT WALLS depends on the indicator's selected calculation mode and expiration. The NETGEX profile of the chosen expiration appears on the HVL line , which automatically updates five times during trading hours , except for 0DTE, which reflects the value set at market open.
🔶 Cumulative Expiration (⅀) Calculation Method
This method aggregates GEX data for all expirations up to the selected date , giving you a more comprehensive view of market dynamics. We recommend using this method, as it allows you to see how combined expirations impact GEX levels, which can be critical when setting up trades with a longer time horizon.
🔶 Selected Alone (⊙) Calculation Method
This option displays the GEX profile specific to only the chosen expiration , providing a unique, time-bound view. This approach is ideal for those seeking precise insight into how an individual expiration is performing without the broader context of other expirations.
🔶 Example of using calculation methods:
With options trading, especially for multi-leg strategies, choosing the right expiration and calculation model is crucial. Let’s break down an example:
Suppose you’re considering a Friday (4DTE) front-leg diagonal on the SPX at the start of the week. In this case, the focus isn’t strictly on any single expiration (like 0DTE or 4DTE individually), but rather on what might happen cumulatively by Friday across all expirations . Here, the Cumulative Expiration (⅀) model comes into play, as it shows you an aggregated view of the GEX profile, factoring in all strikes and legs for all expirations leading up to the selected date.
For most use cases, we recommend setting your indicator to the Cumulative (⅀) model , which provides a broad and insightful look at GEX levels across multiple expirations. However, you can always switch to Selected Alone (⊙) for targeted analysis of an individual expiration. Remember, 0DTE defaults to “Selected Alone”, and Every Expiry always shows a cumulative value by default.
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🟦 HVL (High Volatility Level) 🟦
Also known as the Gamma FLIP level or Zero Gamma , it represents the price level at which the gamma environment transitions from positive to negative or vice versa. The High Volatility Level (HVL) is a critical point for understanding gamma shifts and anticipating volatility. This shift influences how market makers hedge their positions, potentially increasing or dampening market volatility.
🔷 Understanding the Gamma Flip and HVL
At its core, the gamma flip represents the point where market makers may transition from a net positive to a net negative gamma position, or the reverse. When prices move above HVL, gamma is positive, often leading to lower volatility due to the stabilizing effects of market makers’ hedging. Conversely, when prices drop below HVL, gamma flips negative, and hedging by market makers can amplify volatility as they trade with the direction of price movements.
The HVL (High Volatility Level) is particularly important as it signals a shift in the impact of price movements on the GEX profile. Using the cumulative calculation mode, GEX values are aggregated across all strikes and expirations up to the selected expiration, helping to pinpoint the point where the GEX curve's slope changes from negative to positive.
🔷 Implications for Traders and Market Makers
For market makers, crossing below HVL into a negative gamma zone means that they hedge in the same direction as price movements, potentially amplifying volatility. For traders, understanding HVL's role is essential to choosing strategies that align with the prevailing volatility regime:
Positive GEX 🟢:
Above HVL, where GEX is positive, market makers hedge by buying stocks as prices fall and selling as prices rise. This has a stabilizing effect, creating a lower-volatility environment.
Negative GEX 🔴:
Below HVL, where GEX is negative, market makers' hedging aligns with price movements, increasing volatility. Here, they buy as prices rise and sell as they fall, reinforcing price direction.
🔷 HVL as a Momentum and Volatility Indicator
The HVL offers traders insight into potential shifts in market momentum. For example, above HVL, if the price increases, Net GEX also rises, which stabilizes prices as market makers hedge in opposition to price direction. Below HVL, however, a price rise decreases Net GEX, creating conditions where market makers’ hedging amplifies price movements, resulting in a more volatile environment.
HVL also acts as a significant support level, often preceding put supports. If the price falls below this level, traders may expect heightened volatility and increased bearish sentiment.
Knowing the location of HVL is vital for positioning yourself on the right side of volatility. By monitoring the HVL, traders can better anticipate shifts in sentiment and align strategies with prevailing market dynamics.
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🟩 Call Resistance and Call Wall Levels 🟩
In options trading, understanding GEX levels like Call Resistance and Call Wall levels is crucial for navigating potential price inflection points. Our indicator provides these levels directly on your chart, allowing you to customize and optimize your trading approach. Here’s a detailed guide to help you understand and use Call Resistance and additional Call Wall levels effectively.
🟢 Call Resistance Level
The Call Resistance Level is a key point where our model indicates heightened Call GEX concentration. This level serves as a potential resistance area where price movement may face a barrier, slowing or even reversing before a breakout. Here’s how the Call Resistance Level can influence market behavior:
Resistance and Price Reversal ⬇️ : Similar to the Put Support level, the Call Resistance acts as a "sticky" price level, where upward movement encounters resistance. When the price approaches this level, it’s common for market makers to begin shorting to maintain delta neutrality. This shorting activity, combined with the potential monetization of calls, introduces a technical bearish force in the short term, often causing the price to bounce downward.
Upside Acceleration Point ⬆️ : If investors reposition calls to higher strikes as the price reaches Call Resistance, this level can roll up, allowing the price to push upward and potentially accelerating the rally. This effect can drive the market to higher levels as market makers adjust their positions accordingly.
🟢 Additional Call Wall Levels
Our model identifies the second and third-highest Call GEX levels, known as additional Call Walls. These levels are often secondary resistance points but hold significance as they add layers of possible resistance or breakout points. They offer similar potential as the primary Call Resistance level, acting as either:
Resistance Zones: Slowing the price momentum as it approaches these levels.
Inflection Points for Upside Momentum: Allowing for a possible continuation of upward movement if prices break through.
🟢 How to Trade the Call Resistance Level
To use the Call Resistance level effectively, look for possible price rejections or consolidations as the price approaches this zone. Here are the main scenarios:
Bounce to Downside: As the price nears the Call Resistance level, market makers’ delta-hedging activity (through shorting) can turn this level into a short-term bearish force, leading to price pullbacks.
Rolling the Position: For bulls, a key objective at the Call Resistance level is to see investors roll their call positions higher, effectively moving the resistance up. This repositioning may lead to incremental price gains as the Call Resistance level rises with each roll.
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🟥 Put Support and Put Wall Levels 🟥
In options trading, understanding GEX levels like Put Support and secondary Put Wall levels is essential for managing potential price support points and gauging downside risk. Our indicator places these levels directly on your chart, allowing for customization to enhance your trading strategy. Here’s a detailed guide to help you leverage the Put Support and additional Put Wall levels effectively.
🔴 Put Support Level
The Put Support Level is a key zone where our model shows the highest concentration of negative GEX, representing an area with substantial put option interest. This level functions as a potential support zone, where price may stabilize or bounce upward, or as an inflection point, signaling increased downside momentum. Here’s how the Put Support Level can affect market behavior:
Support and Price Reversal🔺 : Similar to how Call Resistance operates on the upside, the Put Support Level often acts as a "sticky" level on the downside, where price finds support. As the asset price moves closer to this level, market makers begin adjusting their positions, frequently buying to maintain delta neutrality. This activity can create a temporary short squeeze, pushing prices back up.
Downside Acceleration Point 🔻 : If the asset continues moving lower, triggering more hedging activity, this level can become a tipping point for accelerated downside momentum.
🔴 Additional Put Wall Levels
Our model also identifies the second and third-highest negative GEX levels, known as secondary Put Walls. These levels are often seen as secondary support points and hold significance by adding layers of support or potential downside inflection points. Like the primary Put Support Level, they can act in two ways:
Support Zones: Helping slow price declines as they approach these levels.
Downside Inflection Points: Allowing further price decline if the support fails.
🔴 How Investors Hedge with Put Options
Investors commonly use put options to hedge long positions and protect portfolios, especially during times of market stress when implied volatility rises. This demand for puts increases the Put Skew, as market makers short to remain delta hedged.
As prices approach the Put Support Level, the hedging activity often intensifies because more puts become At the Money (ATM) or In the Money (ITM). To realize the value of their hedges, investors typically monetize these puts at this level, triggering the closing of short positions by market makers and resulting in a price bounce.
🔴 The Role of Implied Volatility
Implied Volatility (IV) is also a critical factor since it directly influences market flows. If IV driving put flows decreases, market makers may buy back shorts, which contributes to the bounce at the Put Support Level. Additionally, another Greek, Vanna—representing changes in delta due to IV shifts—plays a vital role here. As IV changes, Vanna affects delta-hedging adjustments, adding a layer of complexity to understanding market makers' actions around these support levels.
🔴 Possible Price Scenarios at the Put Support Level
When the price reaches the Put Support Level, there are generally two scenarios:
Bounce to Upside🔺 : The Put Support Level is where substantial put hedging activity happens. As prices approach, market makers adjust their delta by buying, which can push prices back up.
Roll Positions🔻 : After monetizing puts, investors have two options: roll hedges to higher strikes if they expect a bullish move, or open new out-of-the-money puts at lower strikes. If new hedges are set at lower levels, the Put Support level may also shift lower, creating a new bearish force as market makers begin hedging these new positions.
🟨 Customizing Put Support/Call Resistance and Put/Call Wall Levels on Your Chart
Our indicator settings provide extensive customization options for displaying Put Support, Call Resistance, and Put/Call Wall levels.
You can:
adjust the depth to highlight the highest positive or negative NETGEX levels
choose to display relative data, show only the colored strike line
adjust the offset for enhanced visibility.
This flexibility helps you focus on the critical details that best align with your trading strategy, ensuring a clearer and more tailored view of the GEX levels on your chart.
Currently, we examine the top three levels with the highest positive and negative NETGEX values, allowing you to view seven key GEX levels on your chart (3 Call + 1 HVL + 3 Put). However, in the near future, we plan to expand this to seven levels per side, resulting in a total of up to 15 significant GEX levels on the chart instead of the current 7. This enhancement will cater to all needs, especially benefiting 0DTE traders.
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🔶 ADDITIONAL IMPORTANT COMMENTS
🔹- Why is there a slight difference between the displayed data and other GEX provider's data like MenthorQ, GammaEdge, SpotGamma, GEXBot, etc?
There are two reasons for this, and one is beyond our control:
🔹 (1) Option-data update frequency:
According to TradingView's regulations and guidelines, we can update external data a maximum of 5 times per day. We strive to use these updates in the most optimal way:
(1st update) 15 minutes after U.S. market open
(2nd, 3rd, 4th updates) 1.5–3 hours during U.S. market open hours
(5th update) 10 minutes before U.S. market close.
You don’t need to refresh your window; our latest refreshed data pack is always automatically applied to your indicator. You can see the time elapsed since the last update by hovering over the HVL.
🔹 (2) GEX Levels with Intraday Updates Based on Price Movements
The TanukiTrade Options GEX Indicator for TradingView provides open interest data with a 15-minute delay after the market opens. Using this data, we calculate and update the relevant levels throughout the trading day, reflecting almost real-time price changes and gamma values. Unlike other GEX providers, who set their GEX levels solely at market open without further updates, we dynamically adjust our levels intraday to capture significant price shifts.
🔹 Automatic & Seamless Intraday Updates and Special Cases
For our indicator, the HVL (High Volatility Level) reflects the selected calculation mode and expiration. We update these NETGEX profiles five times throughout the trading day, with one exception: 0DTE data, which is set at market open and does not update intraday due to the rapid narrowing of gamma levels . Note that similar to other GEX providers, our 0DTE remains fixed at open, while cumulative values update during the day based on almost real-time market movements.
🔹Consistent SPX 0DTE GEX Levels with Morning Open Interest Updates Only
For SPX, the 0DTE (Zero Days to Expiration) options and GEX levels are calculated based on openinterest data provided by the clearinghouse at market open. Due to the exponential narrowing of gamma levels throughout the day, we do not update these levels intraday, unlike other expirations. Therefore, if you select the expiring contract on that day, you’ll see the exact morning level, as it was calculated at market open. This status is also published the previous evening, based on the data available then, so you can already view the levels for the following day’s 1DTE (next day’s 0DTE) before market close. After market open, around 15 minutes later, this level is updated with the latest open interest data and remains unchanged for the rest of the day. Other providers take a similar approach. We do not support intraday volume-based GEX calculations, as our benchmarks show this can produce misleading results.
Disclaimer:
Our option indicator uses approximately 15min-3 hour delayed option market snapshot data to calculate the main option metrics. Exact realtime option contract prices are never displayed; only derived GEX metrics are shown to ensure accurate and consistent visualization. Due to the above, this indicator can only be used for decision support; exclusive decisions cannot be made based on this indicator. We reserve the right to make errors.This indicator is designed for options traders who understand what they are doing. It assumes that they are familiar with options and can make well-informed, independent decisions. We work with paid delayed data and we are not a data provider; therefore, we do not bear any financial or other liability.
GEX Profile [Lite] Real Auto-Updated Gamma Exposure LevelsReal GEX Levels with Seamless Auto-updates for 5 U.S. market symbols (AAPL, TSLA, ORCL, DIA, AMZN)
🔃 Dynamic Updates : Receive precise GEX levels with auto-updating metrics up to 5 times a day throughout the trading session—no manual refresh needed!
🍒 Strategically Developed : Built by experienced options traders to meet the needs of serious options market participants.
🕒 0DTE? No Problem! : Designed with 0DTE traders in mind, our indicator keeps you updated with GEX levels and seamless auto-refresh to capture every crucial market shift.
📈 Optimized for Option Traders : See accurate NETGEX profile for multiple expirations to maximize strategic potential.
🔶 Comprehensive GEX Levels
This indicator provides unparalleled insight into market dynamics with levels like Call/Put Support, Resistance, HVL (High Volatility Level), and Call/Put Walls. These levels are auto-updated based on live market movements and reflect gamma shifts and volatility signals essential for options traders.
🔶 Ticker Information:
This 'Lite' indicator is currently only available for 5 liquid U.S. market smbols:
NASDAQ:TSLA NASDAQ:AAPL NASDAQ:AMZN AMEX:DIA and NYSE:ORCL
🔶 Ideal for 0DTE and Multi-Leg Strategies
Track essential GEX levels across expirations with our unique Cumulative (⅀) and Selected Alone (⊙) calculation models. Customize your view to reveal high-impact levels across multiple expirations or focus on a specific expiration for a targeted strategy.
🔶How does the indicator work and why is it unique?
This is not just another GEX indicator. It incorporates 15min delayed option chain data from ORATS as data provider, processes and refines the delayed data package using pineseed, and sends it to TradingView, visualizing the key GEX levels using specific formulas (see detailed below). This method of incorporating options data into a visualization framework is unique and entirely innovative on TradingView.
Unlike other providers that only set GEX levels at market open, this indicator adjusts dynamically throughout the day, providing updated insights across the trading day and capturing gamma shifts as the market moves.
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🌑 𝗗 𝗢 𝗖 𝗨 𝗠 𝗘 𝗡 𝗧 𝗔 𝗧 𝗜 𝗢 𝗡 🌑
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🔶 Understanding GEX (Gamma Exposure) and Gamma Profiling
Gamma Exposure (GEX) is a crucial concept in options trading because it reveals how options market positions can influence the dynamics of asset prices. In essence, GEX measures the collective gamma exposure of options market participants, impacting overall market stability and price movements.
🔹 What is GEX?
At its core, GEX captures the aggregate impact of gamma, a key options Greek, which tells us how an option's delta changes in response to price movements in the underlying asset. Positive or negative GEX levels can reflect the collective bullish or bearish stance of the market:
Positive GEX (far above HVL) : Indicates a net bullish positioning by options holders. When GEX is strongly positive, it suggests that as the asset price increases, market participants might need to buy more of the asset to maintain their hedges. This behavior can fuel further upward momentum.
Negative GEX (far below HVL) : Implies a net bearish positioning. In a strongly negative GEX environment, declines in the asset's price might prompt participants to sell, potentially exacerbating the downward movement.
🔹 The Influence of GEX on Strike Prices and Expiration
A unique feature of GEX is its impact near expiration dates. As options approach expiration, GEX levels can “pin” the price to specific strike levels, where options positions are concentrated. This pinning effect arises as market makers adjust their hedging strategies, often causing the asset price to gravitate towards certain strike prices, where a large volume of options contracts sits.
🟨 Overview of our GEX Calculation Models for Options Traders 🟨
Our GEX indicator models were developed with serious options traders in mind, providing flexibility beyond typical GEX providers. We know that using GEX levels for multi-leg strategies, where the underlying doesn't need a strong trend to be profitable , calls for a nuanced approach that aligns with different trading horizons. Here’s a detailed breakdown of our GEX calculation models and how they support strategic trading across varying timeframes.
Thus, the HVL an orher CALL/PUT WALLS depends on the indicator's selected calculation mode and expiration. The NETGEX profile of the chosen expiration appears on the HVL line , which automatically updates five times during trading hours , except for 0DTE, which reflects the value set at market open.
🔶 Cumulative Expiration (⅀) Calculation Method
This method aggregates GEX data for all expirations up to the selected date , giving you a more comprehensive view of market dynamics. We recommend using this method, as it allows you to see how combined expirations impact GEX levels, which can be critical when setting up trades with a longer time horizon.
🔶 Selected Alone (⊙) Calculation Method
This option displays the GEX profile specific to only the chosen expiration , providing a unique, time-bound view. This approach is ideal for those seeking precise insight into how an individual expiration is performing without the broader context of other expirations.
🔶 Example of using calculation methods:
With options trading, especially for multi-leg strategies, choosing the right expiration and calculation model is crucial. Let’s break down an example:
Suppose you’re considering a Friday (4DTE) front-leg diagonal on the SPX at the start of the week. In this case, the focus isn’t strictly on any single expiration (like 0DTE or 4DTE individually), but rather on what might happen cumulatively by Friday across all expirations . Here, the Cumulative Expiration (⅀) model comes into play, as it shows you an aggregated view of the GEX profile, factoring in all strikes and legs for all expirations leading up to the selected date.
For most use cases, we recommend setting your indicator to the Cumulative (⅀) model , which provides a broad and insightful look at GEX levels across multiple expirations. However, you can always switch to Selected Alone (⊙) for targeted analysis of an individual expiration. Remember, 0DTE defaults to “Selected Alone”, and Every Expiry always shows a cumulative value by default.
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🟦 HVL (High Volatility Level) 🟦
Also known as the Gamma FLIP level or Zero Gamma , it represents the price level at which the gamma environment transitions from positive to negative or vice versa. The High Volatility Level (HVL) is a critical point for understanding gamma shifts and anticipating volatility. This shift influences how market makers hedge their positions, potentially increasing or dampening market volatility.
🔷 Understanding the Gamma Flip and HVL
At its core, the gamma flip represents the point where market makers may transition from a net positive to a net negative gamma position, or the reverse. When prices move above HVL, gamma is positive, often leading to lower volatility due to the stabilizing effects of market makers’ hedging. Conversely, when prices drop below HVL, gamma flips negative, and hedging by market makers can amplify volatility as they trade with the direction of price movements.
The HVL (High Volatility Level) is particularly important as it signals a shift in the impact of price movements on the GEX profile. Using the cumulative calculation mode, GEX values are aggregated across all strikes and expirations up to the selected expiration, helping to pinpoint the point where the GEX curve's slope changes from negative to positive.
🔷 Implications for Traders and Market Makers
For market makers, crossing below HVL into a negative gamma zone means that they hedge in the same direction as price movements, potentially amplifying volatility. For traders, understanding HVL's role is essential to choosing strategies that align with the prevailing volatility regime:
Positive GEX 🟢:
Above HVL, where GEX is positive, market makers hedge by buying stocks as prices fall and selling as prices rise. This has a stabilizing effect, creating a lower-volatility environment.
Negative GEX 🔴:
Below HVL, where GEX is negative, market makers' hedging aligns with price movements, increasing volatility. Here, they buy as prices rise and sell as they fall, reinforcing price direction.
🔷 HVL as a Momentum and Volatility Indicator
The HVL offers traders insight into potential shifts in market momentum. For example, above HVL, if the price increases, Net GEX also rises, which stabilizes prices as market makers hedge in opposition to price direction. Below HVL, however, a price rise decreases Net GEX, creating conditions where market makers’ hedging amplifies price movements, resulting in a more volatile environment.
HVL also acts as a significant support level, often preceding put supports. If the price falls below this level, traders may expect heightened volatility and increased bearish sentiment.
Knowing the location of HVL is vital for positioning yourself on the right side of volatility. By monitoring the HVL, traders can better anticipate shifts in sentiment and align strategies with prevailing market dynamics.
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🟩 Call Resistance and Call Wall Levels 🟩
In options trading, understanding GEX levels like Call Resistance and Call Wall levels is crucial for navigating potential price inflection points. Our indicator provides these levels directly on your chart, allowing you to customize and optimize your trading approach. Here’s a detailed guide to help you understand and use Call Resistance and additional Call Wall levels effectively.
🟢 Call Resistance Level
The Call Resistance Level is a key point where our model indicates heightened Call GEX concentration. This level serves as a potential resistance area where price movement may face a barrier, slowing or even reversing before a breakout. Here’s how the Call Resistance Level can influence market behavior:
Resistance and Price Reversal ⬇️ : Similar to the Put Support level, the Call Resistance acts as a "sticky" price level, where upward movement encounters resistance. When the price approaches this level, it’s common for market makers to begin shorting to maintain delta neutrality. This shorting activity, combined with the potential monetization of calls, introduces a technical bearish force in the short term, often causing the price to bounce downward.
Upside Acceleration Point ⬆️ : If investors reposition calls to higher strikes as the price reaches Call Resistance, this level can roll up, allowing the price to push upward and potentially accelerating the rally. This effect can drive the market to higher levels as market makers adjust their positions accordingly.
🟢 Additional Call Wall Levels
Our model identifies the second and third-highest Call GEX levels, known as additional Call Walls. These levels are often secondary resistance points but hold significance as they add layers of possible resistance or breakout points. They offer similar potential as the primary Call Resistance level, acting as either:
Resistance Zones: Slowing the price momentum as it approaches these levels.
Inflection Points for Upside Momentum: Allowing for a possible continuation of upward movement if prices break through.
🟢 How to Trade the Call Resistance Level
To use the Call Resistance level effectively, look for possible price rejections or consolidations as the price approaches this zone. Here are the main scenarios:
Bounce to Downside: As the price nears the Call Resistance level, market makers’ delta-hedging activity (through shorting) can turn this level into a short-term bearish force, leading to price pullbacks.
Rolling the Position: For bulls, a key objective at the Call Resistance level is to see investors roll their call positions higher, effectively moving the resistance up. This repositioning may lead to incremental price gains as the Call Resistance level rises with each roll.
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🟥 Put Support and Put Wall Levels 🟥
In options trading, understanding GEX levels like Put Support and secondary Put Wall levels is essential for managing potential price support points and gauging downside risk. Our indicator places these levels directly on your chart, allowing for customization to enhance your trading strategy. Here’s a detailed guide to help you leverage the Put Support and additional Put Wall levels effectively.
🔴 Put Support Level
The Put Support Level is a key zone where our model shows the highest concentration of negative GEX, representing an area with substantial put option interest. This level functions as a potential support zone, where price may stabilize or bounce upward, or as an inflection point, signaling increased downside momentum. Here’s how the Put Support Level can affect market behavior:
Support and Price Reversal🔺 : Similar to how Call Resistance operates on the upside, the Put Support Level often acts as a "sticky" level on the downside, where price finds support. As the asset price moves closer to this level, market makers begin adjusting their positions, frequently buying to maintain delta neutrality. This activity can create a temporary short squeeze, pushing prices back up.
Downside Acceleration Point 🔻 : If the asset continues moving lower, triggering more hedging activity, this level can become a tipping point for accelerated downside momentum.
🔴 Additional Put Wall Levels
Our model also identifies the second and third-highest negative GEX levels, known as secondary Put Walls. These levels are often seen as secondary support points and hold significance by adding layers of support or potential downside inflection points. Like the primary Put Support Level, they can act in two ways:
Support Zones: Helping slow price declines as they approach these levels.
Downside Inflection Points: Allowing further price decline if the support fails.
🔴 How Investors Hedge with Put Options
Investors commonly use put options to hedge long positions and protect portfolios, especially during times of market stress when implied volatility rises. This demand for puts increases the Put Skew, as market makers short to remain delta hedged.
As prices approach the Put Support Level, the hedging activity often intensifies because more puts become At the Money (ATM) or In the Money (ITM). To realize the value of their hedges, investors typically monetize these puts at this level, triggering the closing of short positions by market makers and resulting in a price bounce.
🔴 The Role of Implied Volatility
Implied Volatility (IV) is also a critical factor since it directly influences market flows. If IV driving put flows decreases, market makers may buy back shorts, which contributes to the bounce at the Put Support Level. Additionally, another Greek, Vanna—representing changes in delta due to IV shifts—plays a vital role here. As IV changes, Vanna affects delta-hedging adjustments, adding a layer of complexity to understanding market makers' actions around these support levels.
🔴 Possible Price Scenarios at the Put Support Level
When the price reaches the Put Support Level, there are generally two scenarios:
Bounce to Upside🔺 : The Put Support Level is where substantial put hedging activity happens. As prices approach, market makers adjust their delta by buying, which can push prices back up.
Roll Positions🔻 : After monetizing puts, investors have two options: roll hedges to higher strikes if they expect a bullish move, or open new out-of-the-money puts at lower strikes. If new hedges are set at lower levels, the Put Support level may also shift lower, creating a new bearish force as market makers begin hedging these new positions.
🟨 Customizing Put Support/Call Resistance and Put/Call Wall Levels on Your Chart
Our indicator settings provide extensive customization options for displaying Put Support, Call Resistance, and Put/Call Wall levels.
You can:
adjust the depth to highlight the highest positive or negative NETGEX levels
choose to display relative data, show only the colored strike line
adjust the offset for enhanced visibility.
This flexibility helps you focus on the critical details that best align with your trading strategy, ensuring a clearer and more tailored view of the GEX levels on your chart.
Currently, we examine the top three levels with the highest positive and negative NETGEX values, allowing you to view seven key GEX levels on your chart (3 Call + 1 HVL + 3 Put). However, in the near future, we plan to expand this to seven levels per side, resulting in a total of up to 15 significant GEX levels on the chart instead of the current 7. This enhancement will cater to all needs, especially benefiting 0DTE traders.
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🔶 ADDITIONAL IMPORTANT COMMENTS
🔹- Why is there a slight difference between the displayed data and other GEX provider's data like MenthorQ, GammaEdge, SpotGamma, GEXBot, etc?
There are two reasons for this, and one is beyond our control:
🔹 (1) Option-data update frequency:
According to TradingView's regulations and guidelines, we can update external data a maximum of 5 times per day. We strive to use these updates in the most optimal way:
(1st update) 15 minutes after U.S. market open
(2nd, 3rd, 4th updates) 1.5–3 hours during U.S. market open hours
(5th update) 10 minutes before U.S. market close.
You don’t need to refresh your window; our latest refreshed data pack is always automatically applied to your indicator. You can see the time elapsed since the last update by hovering over the HVL.
🔹 (2) GEX Levels with Intraday Updates Based on Price Movements
The TanukiTrade Options GEX Indicator for TradingView provides open interest data with a 15-minute delay after the market opens. Using this data, we calculate and update the relevant levels throughout the trading day, reflecting almost real-time price changes and gamma values. Unlike other GEX providers, who set their GEX levels solely at market open without further updates, we dynamically adjust our levels intraday to capture significant price shifts.
🔹 Automatic & Seamless Intraday Updates and Special Cases
For our indicator, the HVL (High Volatility Level) reflects the selected calculation mode and expiration. We update these NETGEX profiles five times throughout the trading day, with one exception: 0DTE data, which is set at market open and does not update intraday due to the rapid narrowing of gamma levels . Note that similar to other GEX providers, our 0DTE remains fixed at open, while cumulative values update during the day based on almost real-time market movements.
Disclaimer:
Our option indicator uses approximately 15min-3 hour delayed option market snapshot data to calculate the main option metrics. Exact realtime option contract prices are never displayed; only derived GEX metrics are shown to ensure accurate and consistent visualization. Due to the above, this indicator can only be used for decision support; exclusive decisions cannot be made based on this indicator. We reserve the right to make errors.This indicator is designed for options traders who understand what they are doing. It assumes that they are familiar with options and can make well-informed, independent decisions. We work with paid delayed data and we are not a data provider; therefore, we do not bear any financial or other liability.
Crypto Options Greeks & Volatility Analyzer [BackQuant]Crypto Options Greeks & Volatility Analyzer
Overview
The Crypto Options Greeks & Volatility Analyzer is a comprehensive analytical tool that calculates Black-Scholes option Greeks up to the third order for Bitcoin and Ethereum options. It integrates implied volatility data from VOLMEX indices and provides multiple visualization layers for options risk analysis.
Quick Introduction to Options Trading
Options are financial derivatives that give the holder the right, but not the obligation, to buy or sell an underlying asset at a predetermined price (strike price) within a specific time period (expiration date). Understanding options requires grasping two fundamental concepts:
Call Options : Give the right to buy the underlying asset at the strike price. Calls increase in value when the underlying price rises above the strike price.
Put Options : Give the right to sell the underlying asset at the strike price. Puts increase in value when the underlying price falls below the strike price.
The Language of Options: Greeks
Options traders use "Greeks" - mathematical measures that describe how an option's price changes in response to various factors:
Delta : How much the option price moves for each $1 change in the underlying
Gamma : How fast delta changes as the underlying moves
Theta : Daily time decay - how much value erodes each day
Vega : Sensitivity to implied volatility changes
Rho : Sensitivity to interest rate changes
These Greeks are essential for understanding risk. Just as a pilot needs instruments to fly safely, options traders need Greeks to navigate market conditions and manage positions effectively.
Why Volatility Matters
Implied volatility (IV) represents the market's expectation of future price movement. High IV means:
Options are more expensive (higher premiums)
Market expects larger price swings
Better for option sellers
Low IV means:
Options are cheaper
Market expects smaller moves
Better for option buyers
This indicator helps you visualize and quantify these critical concepts in real-time.
Back to the Indicator
Key Features & Components
1. Complete Greeks Calculations
The indicator computes all standard Greeks using the Black-Scholes-Merton model adapted for cryptocurrency markets:
First Order Greeks:
Delta (Δ) : Measures the rate of change of option price with respect to underlying price movement. Ranges from 0 to 1 for calls and -1 to 0 for puts.
Vega (ν) : Sensitivity to implied volatility changes, expressed as price change per 1% change in IV.
Theta (Θ) : Time decay measured in dollars per day, showing how much value erodes with each passing day.
Rho (ρ) : Interest rate sensitivity, measuring price change per 1% change in risk-free rate.
Second Order Greeks:
Gamma (Γ) : Rate of change of delta with respect to underlying price, indicating how quickly delta will change.
Vanna : Cross-derivative measuring delta's sensitivity to volatility changes and vega's sensitivity to price changes.
Charm : Delta decay over time, showing how delta changes as expiration approaches.
Vomma (Volga) : Vega's sensitivity to volatility changes, important for volatility trading strategies.
Third Order Greeks:
Speed : Rate of change of gamma with respect to underlying price (∂Γ/∂S).
Zomma : Gamma's sensitivity to volatility changes (∂Γ/∂σ).
Color : Gamma decay over time (∂Γ/∂T).
Ultima : Third-order volatility sensitivity (∂²ν/∂σ²).
2. Implied Volatility Analysis
The indicator includes a sophisticated IV ranking system that analyzes current implied volatility relative to its recent history:
IV Rank : Percentile ranking of current IV within its 30-day range (0-100%)
IV Percentile : Percentage of days in the lookback period where IV was lower than current
IV Regime Classification : Very Low, Low, High, or Very High
Color-Coded Headers : Visual indication of volatility regime in the Greeks table
Trading regime suggestions based on IV rank:
IV Rank > 75%: "Favor selling options" (high premium environment)
IV Rank 50-75%: "Neutral / Sell spreads"
IV Rank 25-50%: "Neutral / Buy spreads"
IV Rank < 25%: "Favor buying options" (low premium environment)
3. Gamma Zones Visualization
Gamma zones display horizontal price levels where gamma exposure is highest:
Purple horizontal lines indicate gamma concentration areas
Opacity scaling : Darker shading represents higher gamma values
Percentage labels : Shows gamma intensity relative to ATM gamma
Customizable zones : 3-10 price levels can be analyzed
These zones are critical for understanding:
Pin risk around expiration
Potential for explosive price movements
Optimal strike selection for gamma trading
Market maker hedging flows
4. Probability Cones (Expected Move)
The probability cones project expected price ranges based on current implied volatility:
1 Standard Deviation (68% probability) : Shown with dashed green/red lines
2 Standard Deviations (95% probability) : Shown with dotted green/red lines
Time-scaled projection : Cones widen as expiration approaches
Lognormal distribution : Accounts for positive skew in asset prices
Applications:
Strike selection for credit spreads
Identifying high-probability profit zones
Setting realistic price targets
Risk management for undefined risk strategies
5. Breakeven Analysis
The indicator plots key price levels for options positions:
White line : Strike price
Green line : Call breakeven (Strike + Premium)
Red line : Put breakeven (Strike - Premium)
These levels update dynamically as option premiums change with market conditions.
6. Payoff Structure Visualization
Optional P&L labels display profit/loss at expiration for various price levels:
Shows P&L at -2 sigma, -1 sigma, ATM, +1 sigma, and +2 sigma price levels
Separate calculations for calls and puts
Helps visualize option payoff diagrams directly on the chart
Updates based on current option premiums
Configuration Options
Calculation Parameters
Asset Selection : BTC or ETH (limited by VOLMEX IV data availability)
Expiry Options : 1D, 7D, 14D, 30D, 60D, 90D, 180D
Strike Mode : ATM (uses current spot) or Custom (manual strike input)
Risk-Free Rate : Adjustable annual rate for discounting calculations
Display Settings
Greeks Display : Toggle first, second, and third-order Greeks independently
Visual Elements : Enable/disable probability cones, gamma zones, P&L labels
Table Customization : Position (6 options) and text size (4 sizes)
Price Levels : Show/hide strike and breakeven lines
Technical Implementation
Data Sources
Spot Prices : INDEX:BTCUSD and INDEX:ETHUSD for underlying prices
Implied Volatility : VOLMEX:BVIV (Bitcoin) and VOLMEX:EVIV (Ethereum) indices
Real-Time Updates : All calculations update with each price tick
Mathematical Framework
The indicator implements the full Black-Scholes-Merton model:
Standard normal distribution approximations using Abramowitz and Stegun method
Proper annualization factors (365-day year)
Continuous compounding for interest rate calculations
Lognormal price distribution assumptions
Alert Conditions
Four categories of automated alerts:
Price-Based : Underlying crossing strike price
Gamma-Based : 50% surge detection for explosive moves
Moneyness : Deep ITM alerts when |delta| > 0.9
Time/Volatility : Near expiration and vega spike warnings
Practical Applications
For Options Traders
Monitor all Greeks in real-time for active positions
Identify optimal entry/exit points using IV rank
Visualize risk through probability cones and gamma zones
Track time decay and plan rolls
For Volatility Traders
Compare IV across different expiries
Identify mean reversion opportunities
Monitor vega exposure across strikes
Track higher-order volatility sensitivities
Conclusion
The Crypto Options Greeks & Volatility Analyzer transforms complex mathematical models into actionable visual insights. By combining institutional-grade Greeks calculations with intuitive overlays like probability cones and gamma zones, it bridges the gap between theoretical options knowledge and practical trading application.
Whether you're:
A directional trader using options for leverage
A volatility trader capturing IV mean reversion
A hedger managing portfolio risk
Or simply learning about options mechanics
This tool provides the quantitative foundation needed for informed decision-making in cryptocurrency options markets.
Remember that options trading involves substantial risk and complexity. The Greeks and visualizations provided by this indicator are tools for analysis - they should be combined with proper risk management, position sizing, and a thorough understanding of options strategies.
As crypto options markets continue to mature and grow, having professional-grade analytics becomes increasingly important. This indicator ensures you're equipped with the same analytical capabilities used by institutional traders, adapted specifically for the unique characteristics of 24/7 cryptocurrency markets.
VolatilityCone by ImpliedVolatilityThis volatility cone draws the implied volatility as standard deviations from a measurement date.
For best results set measurement date to high volume bars.
How to use:
1) Select VolatilityCone from Indicators
2) Click to the chart to set the measurement date
3) Determine the impliedvolatility for the measurement date of your symbol
e.g.
For S&P500 use VIX value at measurement date for implied volatility
Intellxis Premium InsightUnderstanding the Intellxis - Premium Insight Indicator
This guide provides a way to understand the output of the Premium Insight plugin for TradingView. Its core feature is the "Premium Status" column, which analyzes how an option's premium behaves relative to the underlying asset's price. Use the below guide to decode every status message and leverage this powerful plugin in your trading.
Call Option Statuses
Strong (Spot 🡅): The Call premium is increasing as the underlying asset price rises. This confirms a bullish trend and indicates the option is behaving as expected.
Down (Spot 🡇): The Call premium is decreasing as the underlying asset price falls. This is the normal, expected behavior for a call option in a downtrend.
Down (Spot ⟷): The Call premium is decreasing while the underlying asset price is flat. This erosion of value is due to the passage of time and is an expected behavior.
Weak (Spot 🡅): The Call premium is decreasing slightly even though the underlying asset price is rising. This is an anomaly and suggests weakness in the bullish move.
Flat (Spot 🡅): The Call premium is not changing despite a rise in the underlying asset price. This indicates the premium is not responding to a favorable move, which is a sign of weakness.
Strong (Spot 🡇): The Call premium is increasing even though the underlying asset price is falling. This is a highly counter-intuitive signal and could point to a sharp increase in implied volatility.
MELTDOWN (Spot 🡅): The Call premium is collapsing significantly while the underlying asset price is RISING. This contradicts normal option behavior and may signal an imminent reversal or volatility crush.
MELTDOWN (Spot ⟷): The Call premium is collapsing significantly while the underlying is flat. This suggests a massive drop in implied volatility or other strong selling pressure not related to price direction.
Down Significantly (Spot 🡇): The Call premium is dropping significantly as the underlying spot price is moving down.
Up (Spot ⟷): The Call premium is increasing while the underlying spot price is flat. This is likely due to a sudden increase in volatility.
Flat (Spot ⟷): Normal: The Call premium is flat and the underlying spot price is also flat.
Put Option Statuses
Strong (Spot 🡇): The Put premium is increasing as the underlying asset price falls. This confirms a bearish trend and indicates the option is behaving as expected.
Down (Spot 🡅): The Put premium is decreasing as the underlying asset price rises. This is the normal, expected behavior for a put option in an uptrend.
Down (Spot ⟷): The Put premium is decreasing while the underlying asset price is flat. This erosion of value is due to the passage of time and is an expected behavior.
Weak (Spot 🡇): The Put premium is dropping slightly even though the underlying asset price is falling. This is an anomaly and suggests weakness in the bearish move.
Flat (Spot 🡇): The Put premium is not changing despite a fall in the underlying asset price. This indicates the premium is not responding to a favorable move, which is a sign of weakness.
Strong (Spot 🡅): The Put premium is increasing even though the underlying asset price is rising. This is a highly counter-intuitive signal and could point to a sharp increase in implied volatility.
MELTDOWN (Spot 🡇): The Put premium is collapsing significantly while the underlying asset price is FALLING. This contradicts normal option behavior and may signal an imminent reversal or volatility crush.
MELTDOWN (Spot ⟷): The Put premium is collapsing significantly while the underlying is flat. This suggests a massive drop in implied volatility or other strong selling pressure not related to price direction.
Down Significantly (Spot 🡅): The Put premium is dropping significantly as the underlying spot price is moving up.
Up (Spot ⟷): The Put premium is increasing while the underlying spot price is flat. This is likely due to a sudden increase in volatility.
Flat (Spot ⟷): The Put premium is flat and the underlying spot price is also flat.
