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Black Scholes Option Pricing Model w/ Greeks [Loxx]The Black Scholes Merton model If you are new to options I strongly advise you to profit from Robert Shiller's lecture on same . It combines practical market insights with a strong authoritative grasp of key models in option theory. He explains many of the areas covered below and in the following pages with a lot intuition and relatable anecdotage. We start here with Black Scholes Merton which is probably the most popular option pricing framework, due largely to its simplicity and ease in terms of implementation. The closed-form solution is efficient in terms of speed and always compares favorably relative to any numerical technique. The Black–Scholes–Merton model is a mathematical go-to model for estimating the value of European calls and puts. In the early 1970’s, Myron Scholes, and Fisher Black made an important breakthrough in the pricing of complex financial instruments. Robert Merton simultaneously was working on the same problem and applied the term Black-Scholes model to describe new generation of pricing. The Black Scholes (1973) contribution developed insights originally proposed by Bachelier 70 years before. In 1997, Myron Scholes and Robert Merton received the Nobel Prize for Economics. Tragically, Fisher Black died in 1995. The Black–Scholes formula presents a theoretical estimate (or model estimate) of the price of European-style options independently of the risk of the underlying security. Future payoffs from options can be discounted using the risk-neutral rate. Earlier academic work on options (e.g., Malkiel and Quandt 1968, 1969) had contemplated using either empirical, econometric analyses or elaborate theoretical models that possessed parameters whose values could not be calibrated directly. In contrast, Black, Scholes, and Merton’s parameters were at their core simple and did not involve references to utility or to the shifting risk appetite of investors. Below, we present a standard type formula, where: c = Call option value, p = Put option value, S=Current stock (or other underlying) price, K or X=Strike price, r=Risk-free interest rate, q = dividend yield, T=Time to maturity and N denotes taking the normal cumulative probability. b = (r - q) = cost of carry. (via VinegarHill-Financelab ) Things to know This can only be used on the daily timeframe You must select the option type and the greeks you wish to show This indicator is a work in process, functions may be updated in the future. I will also be adding additional greeks as I code them or they become available in finance literature. This indictor contains 18 greeks. Many more will be added later. Inputs Spot price: select from 33 different types of price inputs Calculation Steps: how many iterations to be used in the BS model. In practice, this number would be anywhere from 5000 to 15000, for our purposes here, this is limited to 300 Strike Price: the strike price of the option you're wishing to model % Implied Volatility: here you can manually enter implied volatility Historical Volatility Period: the input period for historical volatility ; historical volatility isn't used in the BS process, this is to serve as a sort of benchmark for the implied volatility , Historical Volatility Type: choose from various types of implied volatility , search my indicators for details on each of these Option Base Currency: this is to calculate the risk-free rate, this is used if you wish to automatically calculate the risk-free rate instead of using the manual input. this uses the 10 year bold yield of the corresponding country % Manual Risk-free Rate: here you can manually enter the risk-free rate Use manual input for Risk-free Rate? : choose manual or automatic for risk-free rate % Manual Yearly Dividend Yield: here you can manually enter the yearly dividend yield Adjust for Dividends?: choose if you even want to use use dividends Automatically Calculate Yearly Dividend Yield? choose if you want to use automatic vs manual dividend yield calculation Time Now Type: choose how you want to calculate time right now, see the tool tip Days in Year: choose how many days in the year, 365 for all days, 252 for trading days, etc Hours Per Day: how many hours per day? 24, 8 working hours, or 6.5 trading hours Expiry date settings: here you can specify the exact time the option expires The Black Scholes Greeks The Option Greek formulae express the change in the option price with respect to a parameter change taking as fixed all the other inputs. ( Haug explores multiple parameter changes at once .) One significant use of Greek measures is to calibrate risk exposure. A market-making financial institution with a portfolio of options, for instance, would want a snap shot of its exposure to asset price, interest rates, dividend fluctuations. It would try to establish impacts of volatility and time decay. In the formulae below, the Greeks merely evaluate change to only one input at a time. In reality, we might expect a conflagration of changes in interest rates and stock prices etc. (via VigengarHill-Financelab ) First-order Greeks Delta: Delta measures the rate of change of the theoretical option value with respect to changes in the underlying asset's price. Delta is the first derivative of the value Vega: Vegameasures sensitivity to volatility. Vega is the derivative of the option value with respect to the volatility of the underlying asset. Theta: Theta measures the sensitivity of the value of the derivative to the passage of time (see Option time value): the "time decay." Rho: Rho measures sensitivity to the interest rate: it is the derivative of the option value with respect to the risk free interest rate (for the relevant outstanding term). Lambda: Lambda, Omega, or elasticity is the percentage change in option value per percentage change in the underlying price, a measure of leverage, sometimes called gearing. Epsilon: Epsilon, also known as psi, is the percentage change in option value per percentage change in the underlying dividend yield, a measure of the dividend risk. The dividend yield impact is in practice determined using a 10% increase in those yields. Obviously, this sensitivity can only be applied to derivative instruments of equity products. Second-order Greeks Gamma: Measures the rate of change in the delta with respect to changes in the underlying price. Gamma is the second derivative of the value function with respect to the underlying price. Vanna: Vanna, also referred to as DvegaDspot and DdeltaDvol, is a second order derivative of the option value, once to the underlying spot price and once to volatility. It is mathematically equivalent to DdeltaDvol, the sensitivity of the option delta with respect to change in volatility; or alternatively, the partial of vega with respect to the underlying instrument's price. Vanna can be a useful sensitivity to monitor when maintaining a delta- or vega-hedged portfolio as vanna will help the trader to anticipate changes to the effectiveness of a delta-hedge as volatility changes or the effectiveness of a vega-hedge against change in the underlying spot price. Charm: Charm or delta decay measures the instantaneous rate of change of delta over the passage of time. Vomma: Vomma, volga, vega convexity, or DvegaDvol measures second order sensitivity to volatility. Vomma is the second derivative of the option value with respect to the volatility, or, stated another way, vomma measures the rate of change to vega as volatility changes. Veta: Veta or DvegaDtime measures the rate of change in the vega with respect to the passage of time. Veta is the second derivative of the value function; once to volatility and once to time. Vera: Vera (sometimes rhova) measures the rate of change in rho with respect to volatility. Vera is the second derivative of the value function; once to volatility and once to interest rate. Third-order Greeks Speed: Speed measures the rate of change in Gamma with respect to changes in the underlying price. Zomma: Zomma measures the rate of change of gamma with respect to changes in volatility. Color: Color, gamma decay or DgammaDtime measures the rate of change of gamma over the passage of time. Ultima: Ultima measures the sensitivity of the option vomma with respect to change in volatility. Dual Delta: Dual Delta determines how the option price changes in relation to the change in the option strike price; it is the first derivative of the option price relative to the option strike price Dual Gamma: Dual Gamma determines by how much the coefficient will changedual delta when the option strike price changes; it is the second derivative of the option price relative to the option strike price. Related Indicators Cox-Ross-Rubinstein Binomial Tree Options Pricing Model Implied Volatility Estimator using Black Scholes Boyle Trinomial Options Pricing Model
Penunjuk Pine Script®
oleh loxx
8
Cutlers RSICutlers' RSI is a variation of the original RSI Developed by Welles Wilder. This variation uses a simple moving average instead of an exponetial. Since a simple moving average is used by this variation, a longer length tends to give better results compared to a shorter length. CALCULATION Step1: Calculating the Gains and Losses within the chosen period. Step2: Calculating the simple moving averages of gains and losses. Step3: Calculating Cutler’s Relative Strength (RS). Calculated using the following: -> Cutler’s RS = SMA(gains,length) / SMA(losses,length) Step 4: Calculating the Cutler’s Relative Strength Index (RSI). Calculated used the following: -> RSI = 100 — I have added some signals and filtering options with moving averages: Trend OB/OS: Uptrend after above Overbought Level. Downtrend after below Oversold Level. OB/OS: When above Overbought, or below oversold 50-Cross: Above 50 line is uptrend, below is downtrend Direction: Moving up or down RSI vs MA: RSI above MA is an uptrend, RSI below MA is a downtrend The signals I added are just some potential ideas, always backtest your own strategies.
Penunjuk Pine Script®
oleh TanHef
Harris RSIThis is a variation of Wilder's RSI that was altered by Michael Harris. CALCULATION The average change of each of the length's source value is compared to the more recent source value. The average difference of both positive or negative changes is found. The range of 100 is divided by the divided result of the average incremented and decremented ratio plus one. This result of the above is subracted from the range value of 100 I have added some signals and filtering options with moving averages: Trend OB/OS: Uptrend after above Overbought Level. Downtrend after below Oversold Level\n(For the traditional RSI OB=60 and OS=40 is used) OB/OS: When above Overbought, or below oversold 50-Cross: Above 50 line is uptrend, below is downtrend Direction: Moving up or down RSI vs MA: RSI above MA is an uptrend, RSI below MA is a downtrend The signals I added are just some potential ideas, always backtest your own strategies.
Penunjuk Pine Script®
oleh TanHef
Boyle Trinomial Options Pricing Model [Loxx]Boyle Trinomial Options Pricing Model is an options pricing indicator that builds an N-order trinomial tree to price American and European options. This is different form the Binomial model in that the Binomial assumes prices can only go up and down wheres the Trinomial model assumes prices can go up, down, or sideways (shoutout to the "crab" market enjoyers). This method also allows for dividend adjustment. The Trinomial Tree via VinegarHill Finance Labs A two-jump process for the asset price over each discrete time step was developed in the binomial lattice. Boyle expanded this frame of reference and explored the feasibility of option valuation by allowing for an extra jump in the stochastic process. In keeping with Black Scholes, Boyle examined an asset (S) with a lognormal distribution of returns. Over a small time interval, this distribution can be approximated by a three-point jump process in such a way that the expected return on the asset is the riskless rate, and the variance of the discrete distribution is equal to the variance of the corresponding lognormal distribution. The three point jump process was introduced by Phelim Boyle (1986) as a trinomial tree to price options and the effect has been momentous in the finance literature. Perhaps shamrock mythology or the well-known ballad associated with Brendan Behan inspired the Boyle insight to include a third jump in lattice valuation. His trinomial paper has spawned a huge amount of ground breaking research. In the trinomial model, the asset price S is assumed to jump uS or mS or dS after one time period (dt = T/n), where u > m > d. Joshi (2008) point out that the trinomial model is characterized by the following five parameters: (1) the probability of an up move pu, (2) the probability of an down move pd, (3) the multiplier on the stock price for an up move u, (4) the multiplier on the stock price for a middle move m, (5) the multiplier on the stock price for a down move d. A recombining tree is computationally more efficient so we require: ud = m*m M = exp (r∆t), V = exp (σ 2∆t), dt or ∆t = T/N where where N is the total number of steps of a trinomial tree. For a tree to be risk-neutral, the mean and variance across each time steps must be asymptotically correct. Boyle (1986) chose the parameters to be: m = 1, u = exp(λσ√ ∆t), d = 1/u pu =( md − M(m + d) + (M^2)*V )/ (u − d)(u − m) , pd =( um − M(u + m) + (M^2)*V )/ (u − d)(m − d) Boyle suggested that the choice of value for λ should exceed 1 and the best results were obtained when λ is approximately 1.20. One approach to constructing trinomial trees is to develop two steps of a binomial in combination as a single step of a trinomial tree. This can be engineered with many binomials CRR(1979), JR(1979) and Tian (1993) where the volatility is constant. Further reading: A Lattice Framework for Option Pricing with Two State Trinomial tree via wikipedia Inputs Spot price: select from 33 different types of price inputs Calculation Steps: how many iterations to be used in the Trinomial model. In practice, this number would be anywhere from 5000 to 15000, for our purposes here, this is limited to 220. Strike Price: the strike price of the option you're wishing to model Market Price: this is the market price of the option; choose, last, bid, or ask to see different results Historical Volatility Period: the input period for historical volatility ; historical volatility isn't used in the Trinomial model, this is to serve as a comparison, even though historical volatility is from price movement of the underlying asset where as implied volatility is the volatility of the option Historical Volatility Type: choose from various types of implied volatility , search my indicators for details on each of these Option Base Currency: this is to calculate the risk-free rate, this is used if you wish to automatically calculate the risk-free rate instead of using the manual input. this uses the 10 year bold yield of the corresponding country % Manual Risk-free Rate: here you can manually enter the risk-free rate Use manual input for Risk-free Rate? : choose manual or automatic for risk-free rate % Manual Yearly Dividend Yield: here you can manually enter the yearly dividend yield Adjust for Dividends?: choose if you even want to use use dividends Automatically Calculate Yearly Dividend Yield? choose if you want to use automatic vs manual dividend yield calculation Time Now Type: choose how you want to calculate time right now, see the tool tip Days in Year: choose how many days in the year, 365 for all days, 252 for trading days, etc Hours Per Day: how many hours per day? 24, 8 working hours, or 6.5 trading hours Expiry date settings: here you can specify the exact time the option expires Included Option pricing panel Loxx's Expanded Source Types Related indicators Implied Volatility Estimator using Black Scholes Cox-Ross-Rubinstein Binomial Tree Options Pricing Model
Penunjuk Pine Script®
oleh loxx
Implied Volatility Estimator using Black Scholes [Loxx]Implied Volatility Estimator using Black Scholes derives a estimation of implied volatility using the Black Scholes options pricing model. The Bisection algorithm is used for our purposes here. This includes the ability to adjust for dividends. Implied Volatility The implied volatility (IV) of an option contract is that value of the volatility of the underlying instrument which, when input in an option pricing model (such as Black–Scholes), will return a theoretical value equal to the current market price of that option. The VIX , in contrast, is a model-free estimate of Implied Volatility. The latter is viewed as being important because it represents a measure of risk for the underlying asset. Elevated Implied Volatility suggests that risks to underlying are also elevated. Ordinarily, to estimate implied volatility we rely upon Black-Scholes (1973). This implies that we are prepared to accept the assumptions of Black Scholes (1973). Inputs Spot price: select from 33 different types of price inputs Strike Price: the strike price of the option you're wishing to model Market Price: this is the market price of the option; choose, last, bid, or ask to see different results Historical Volatility Period: the input period for historical volatility ; historical volatility isn't used in the Bisection algo, this is to serve as a comparison, even though historical volatility is from price movement of the underlying asset where as implied volatility is the volatility of the option Historical Volatility Type: choose from various types of implied volatility , search my indicators for details on each of these Option Base Currency: this is to calculate the risk-free rate, this is used if you wish to automatically calculate the risk-free rate instead of using the manual input. this uses the 10 year bold yield of the corresponding country % Manual Risk-free Rate: here you can manually enter the risk-free rate Use manual input for Risk-free Rate? : choose manual or automatic for risk-free rate % Manual Yearly Dividend Yield: here you can manually enter the yearly dividend yield Adjust for Dividends?: choose if you even want to use use dividends Automatically Calculate Yearly Dividend Yield? choose if you want to use automatic vs manual dividend yield calculation Time Now Type: choose how you want to calculate time right now, see the tool tip Days in Year: choose how many days in the year, 365 for all days, 252 for trading days, etc Hours Per Day: how many hours per day? 24, 8 working hours, or 6.5 trading hours Expiry date settings: here you can specify the exact time the option expires *** the algorithm inputs for low and high aren't to be changed unless you're working through the mathematics of how Bisection works. Included Option pricing panel Loxx's Expanded Source Types Related Indicators Cox-Ross-Rubinstein Binomial Tree Options Pricing Model
Penunjuk Pine Script®
oleh loxx
1
Cox-Ross-Rubinstein Binomial Tree Options Pricing Model [Loxx]Cox-Ross-Rubinstein Binomial Tree Options Pricing Model is an options pricing panel calculated using an N-iteration (limited to 300 in Pine Script due to matrices size limits) "discrete-time" (lattice based) method to approximate the closed-form Black–Scholes formula. Joshi (2008) outlined varying binomial options pricing model furnishes a numerical approach for the valuation of options. Significantly, the American analogue can be estimated using the binomial tree. This indicator is the complex calculation for Binomial option pricing. Most folks take a shortcut and only calculate 2 iterations. I've coded this to allow for up to 300 iterations. This can be used to price American Puts/Calls and European Puts/Calls. I'll be updating this indicator will be updated with additional features over time. If you would like to learn more about options, I suggest you check out the book textbook Options, Futures and other Derivative by John C Hull. ***This indicator only works on the daily timeframe!*** A quick graphic of what this all means: In the graphic, "n" are the steps, in this case we can do up to 300, in production we'd need to do 5-15K. That's a lot of steps! You can see here how the binomial tree fans out. As I said previously, most folks only calculate 2 steps, here we are calculating up to 300. Want to learn more about Simple Introduction to Cox, Ross Rubinstein (1979) ? Watch this short series "Introduction to Basic Cox, Ross and Rubinstein (1979) model." Limitations of Black Scholes options pricing model This is a widely used and well-known options pricing model, factors in current stock price, options strike price, time until expiration (denoted as a percent of a year), and risk-free interest rates. The Black-Scholes Model is quick in calculating any number of option prices. But the model cannot accurately calculate American options, since it only considers the price at an option's expiration date. American options are those that the owner may exercise at any time up to and including the expiration day. What are Binomial Trees in options pricing? A useful and very popular technique for pricing an option involves constructing a binomial tree. This is a diagram representing different possible paths that might be followed by the stock price over the life of an option. The underlying assumption is that the stock price follows a random walk. In each time step, it has a certain probability of moving up by a certain percentage amount and a certain probability of moving down by a certain percentage amount. In the limit, as the time step becomes smaller, this model is the same as the Black–Scholes–Merton model. What is the Binomial options pricing model ? This model uses a tree diagram with volatility factored in at each level to show all possible paths an option's price can take, then works backward to determine one price. The benefit of the Binomial Model is that you can revisit it at any point for the possibility of early exercise. Early exercise is executing the contract's actions at its strike price before the contract's expiration. Early exercise only happens in American-style options. However, the calculations involved in this model take a long time to determine, so this model isn't the best in rushed situations. What is the Cox-Ross-Rubinstein Model? The Cox-Ross-Rubinstein binomial model can be used to price European and American options on stocks without dividends, stocks and stock indexes paying a continuous dividend yield, futures, and currency options. Option pricing is done by working backwards, starting at the terminal date. Here we know all the possible values of the underlying price. For each of these, we calculate the payoffs from the derivative, and find what the set of possible derivative prices is one period before. Given these, we can find the option one period before this again, and so on. Working ones way down to the root of the tree, the option price is found as the derivative price in the first node. Inputs Spot price: select from 33 different types of price inputs Calculation Steps: how many iterations to be used in the Binomial model. In practice, this number would be anywhere from 5000 to 15000, for our purposes here, this is limited to 300 Strike Price: the strike price of the option you're wishing to model % Implied Volatility: here you can manually enter implied volatility Historical Volatility Period: the input period for historical volatility; historical volatility isn't used in the CRRBT process, this is to serve as a sort of benchmark for the implied volatility, Historical Volatility Type: choose from various types of implied volatility, search my indicators for details on each of these Option Base Currency: this is to calculate the risk-free rate, this is used if you wish to automatically calculate the risk-free rate instead of using the manual input. this uses the 10 year bold yield of the corresponding country % Manual Risk-free Rate: here you can manually enter the risk-free rate Use manual input for Risk-free Rate? : choose manual or automatic for risk-free rate % Manual Yearly Dividend Yield: here you can manually enter the yearly dividend yield Adjust for Dividends?: choose if you even want to use use dividends Automatically Calculate Yearly Dividend Yield? choose if you want to use automatic vs manual dividend yield calculation Time Now Type: choose how you want to calculate time right now, see the tool tip Days in Year: choose how many days in the year, 365 for all days, 252 for trading days, etc Hours Per Day: how many hours per day? 24, 8 working hours, or 6.5 trading hours Expiry date settings: here you can specify the exact time the option expires Take notes: Futures don't risk free yields. If you are pricing options of futures, then the risk-free rate is zero. Dividend yields are calculated using TradingView's internal dividend values This indicator only works on the daily timeframe Included Option pricing panel Loxx's Expanded Source Types
Penunjuk Pine Script®
oleh loxx
1
Filtered, N-Order Power-of-Cosine, Sinc FIR Filter [Loxx]Filtered, N-Order Power-of-Cosine, Sinc FIR Filter is a Discrete-Time, FIR Digital Filter that uses Power-of-Cosine Family of FIR filters. This is an N-order algorithm that allows up to 50 values for alpha, orders, of depth. This one differs from previous Power-of-Cosine filters I've published in that it this uses Windowed-Sinc filtering. I've also included a Dual Element Lag Reducer using Kalman velocity, a standard deviation filter, and a clutter filter. You can read about each of these below. Impulse Response What are FIR Filters? In discrete-time signal processing, windowing is a preliminary signal shaping technique, usually applied to improve the appearance and usefulness of a subsequent Discrete Fourier Transform. Several window functions can be defined, based on a constant (rectangular window), B-splines, other polynomials, sinusoids, cosine-sums, adjustable, hybrid, and other types. The windowing operation consists of multipying the given sampled signal by the window function. For trading purposes, these FIR filters act as advanced weighted moving averages. A finite impulse response (FIR) filter is a filter whose impulse response (or response to any finite length input) is of finite duration, because it settles to zero in finite time. This is in contrast to infinite impulse response (IIR) filters, which may have internal feedback and may continue to respond indefinitely (usually decaying). The impulse response (that is, the output in response to a Kronecker delta input) of an Nth-order discrete-time FIR filter lasts exactly {\displaystyle N+1}N+1 samples (from first nonzero element through last nonzero element) before it then settles to zero. FIR filters can be discrete-time or continuous-time, and digital or analog. A FIR filter is (similar to, or) just a weighted moving average filter, where (unlike a typical equally weighted moving average filter) the weights of each delay tap are not constrained to be identical or even of the same sign. By changing various values in the array of weights (the impulse response, or time shifted and sampled version of the same), the frequency response of a FIR filter can be completely changed. An FIR filter simply CONVOLVES the input time series (price data) with its IMPULSE RESPONSE. The impulse response is just a set of weights (or "coefficients") that multiply each data point. Then you just add up all the products and divide by the sum of the weights and that is it; e.g., for a 10-bar SMA you just add up 10 bars of price data (each multiplied by 1) and divide by 10. For a weighted-MA you add up the product of the price data with triangular-number weights and divide by the total weight. What is a Standard Deviation Filter? If price or output or both don't move more than the (standard deviation) * multiplier then the trend stays the previous bar trend. This will appear on the chart as "stepping" of the moving average line. This works similar to Super Trend or Parabolic SAR but is a more naive technique of filtering. What is a Clutter Filter? For our purposes here, this is a filter that compares the slope of the trading filter output to a threshold to determine whether to shift trends. If the slope is up but the slope doesn't exceed the threshold, then the color is gray and this indicates a chop zone. If the slope is down but the slope doesn't exceed the threshold, then the color is gray and this indicates a chop zone. Alternatively if either up or down slope exceeds the threshold then the trend turns green for up and red for down. Fro demonstration purposes, an EMA is used as the moving average. This acts to reduce the noise in the signal. What is a Dual Element Lag Reducer? Modifies an array of coefficients to reduce lag by the Lag Reduction Factor uses a generic version of a Kalman velocity component to accomplish this lag reduction is achieved by applying the following to the array: 2 * coeff - coeff The response time vs noise battle still holds true, high lag reduction means more noise is present in your data! Please note that the beginning coefficients which the modifying matrix cannot be applied to (coef whose indecies are < LagReductionFactor) are simply multiplied by two for additional smoothing . Whats a Windowed-Sinc Filter? Windowed-sinc filters are used to separate one band of frequencies from another. They are very stable, produce few surprises, and can be pushed to incredible performance levels. These exceptional frequency domain characteristics are obtained at the expense of poor performance in the time domain, including excessive ripple and overshoot in the step response. When carried out by standard convolution, windowed-sinc filters are easy to program, but slow to execute. The sinc function sinc (x), also called the "sampling function," is a function that arises frequently in signal processing and the theory of Fourier transforms. In mathematics, the historical unnormalized sinc function is defined for x ≠ 0 by sinc x = sinx / x In digital signal processing and information theory, the normalized sinc function is commonly defined for x ≠ 0 by sinc x = sin(pi * x) / (pi * x) For our purposes here, we are used a normalized Sinc function Included Bar coloring Loxx's Expanded Source Types Signals Alerts Related indicators Variety, Low-Pass, FIR Filter Impulse Response Explorer STD-Filtered, Variety FIR Digital Filters w/ ATR Bands STD/C-Filtered, N-Order Power-of-Cosine FIR Filter STD/C-Filtered, Truncated Taylor Family FIR Filter STD/Clutter-Filtered, Kaiser Window FIR Digital Filter STD/Clutter Filtered, One-Sided, N-Sinc-Kernel, EFIR Filt
Penunjuk Pine Script®
oleh loxx
1
STD/Clutter Filtered, One-Sided, N-Sinc-Kernel, EFIR Filt [Loxx]STD/Clutter Filtered, One-Sided, N-Sinc-Kernel, EFIR Filt is a normalized Cardinal Sine Filter Kernel Weighted Fir Filter that uses Ehler's FIR filter calculation instead of the general FIR filter calculation. This indicator has Kalman Velocity lag reduction, a standard deviation filter, a clutter filter, and a kernel noise filter. When calculating the Kernels, the both sides are calculated, then smoothed, then sliced to just the Right side of the Kernel weights. Lastly, blackman windowing is used for our purposes here. You can read about blackman windowing here: Blackman window Advantages of Blackman Window over Hamming Window Method for designing FIR Filter The Kernel amplitudes are shown below with their corresponding values in yellow: This indicator is intended to be used with Heikin-Ashi source inputs, specially HAB Median. You can read about this here: Moving Average Filters Add-on w/ Expanded Source Types What is a Finite Impulse Response Filter? In signal processing, a finite impulse response (FIR) filter is a filter whose impulse response (or response to any finite length input) is of finite duration, because it settles to zero in finite time. This is in contrast to infinite impulse response (IIR) filters, which may have internal feedback and may continue to respond indefinitely (usually decaying). The impulse response (that is, the output in response to a Kronecker delta input) of an Nth-order discrete-time FIR filter lasts exactly {\displaystyle N+1}N+1 samples (from first nonzero element through last nonzero element) before it then settles to zero. FIR filters can be discrete-time or continuous-time, and digital or analog. A FIR filter is (similar to, or) just a weighted moving average filter, where (unlike a typical equally weighted moving average filter) the weights of each delay tap are not constrained to be identical or even of the same sign. By changing various values in the array of weights (the impulse response, or time shifted and sampled version of the same), the frequency response of a FIR filter can be completely changed. An FIR filter simply CONVOLVES the input time series (price data) with its IMPULSE RESPONSE. The impulse response is just a set of weights (or "coefficients") that multiply each data point. Then you just add up all the products and divide by the sum of the weights and that is it; e.g., for a 10-bar SMA you just add up 10 bars of price data (each multiplied by 1) and divide by 10. For a weighted-MA you add up the product of the price data with triangular-number weights and divide by the total weight. Ultra Low Lag Moving Average's weights are designed to have MAXIMUM possible smoothing and MINIMUM possible lag compatible with as-flat-as-possible phase response. Ehlers FIR Filter Ehlers Filter (EF) was authored, not surprisingly, by John Ehlers. Read all about them here: Ehlers Filters What is Normalized Cardinal Sine? The sinc function sinc (x), also called the "sampling function," is a function that arises frequently in signal processing and the theory of Fourier transforms. In mathematics, the historical unnormalized sinc function is defined for x ≠ 0 by sinc x = sinx / x In digital signal processing and information theory, the normalized sinc function is commonly defined for x ≠ 0 by sinc x = sin(pi * x) / (pi * x) What is a Clutter Filter? For our purposes here, this is a filter that compares the slope of the trading filter output to a threshold to determine whether to shift trends. If the slope is up but the slope doesn't exceed the threshold, then the color is gray and this indicates a chop zone. If the slope is down but the slope doesn't exceed the threshold, then the color is gray and this indicates a chop zone. Alternatively if either up or down slope exceeds the threshold then the trend turns green for up and red for down. Fro demonstration purposes, an EMA is used as the moving average. This acts to reduce the noise in the signal. What is a Dual Element Lag Reducer? Modifies an array of coefficients to reduce lag by the Lag Reduction Factor uses a generic version of a Kalman velocity component to accomplish this lag reduction is achieved by applying the following to the array: 2 * coeff - coeff The response time vs noise battle still holds true, high lag reduction means more noise is present in your data! Please note that the beginning coefficients which the modifying matrix cannot be applied to (coef whose indecies are < LagReductionFactor) are simply multiplied by two for additional smoothing . Included Bar coloring Loxx's Expanded Source Types Signals Alerts
Penunjuk Pine Script®
oleh loxx
2
STD- and Clutter-Filtered, Non-Lag Moving Average [Loxx]STD- and Clutter-Filtered, Non-Lag Moving Average is a Weighted Moving Average with a minimal lag using a damping cosine wave as the line of weight coefficients. The indicator has two filters. They are static (in points) and dynamic (expressed as a decimal). They allow cutting the price noise giving a stepped shape to the Moving Average. Moreover, there is the possibility to highlight the trend direction by color. This also includes a standard deviation and clutter filter. This filter is a FIR filter. What is a Generic or Direct Form FIR Filter? In signal processing, a finite impulse response (FIR) filter is a filter whose impulse response (or response to any finite length input) is of finite duration, because it settles to zero in finite time. This is in contrast to infinite impulse response (IIR) filters, which may have internal feedback and may continue to respond indefinitely (usually decaying). The impulse response (that is, the output in response to a Kronecker delta input) of an Nth-order discrete-time FIR filter lasts exactly {\displaystyle N+1}N+1 samples (from first nonzero element through last nonzero element) before it then settles to zero. FIR filters can be discrete-time or continuous-time, and digital or analog. A FIR filter is (similar to, or) just a weighted moving average filter, where (unlike a typical equally weighted moving average filter) the weights of each delay tap are not constrained to be identical or even of the same sign. By changing various values in the array of weights (the impulse response, or time shifted and sampled version of the same), the frequency response of a FIR filter can be completely changed. An FIR filter simply CONVOLVES the input time series (price data) with its IMPULSE RESPONSE. The impulse response is just a set of weights (or "coefficients") that multiply each data point. Then you just add up all the products and divide by the sum of the weights and that is it; e.g., for a 10-bar SMA you just add up 10 bars of price data (each multiplied by 1) and divide by 10. For a weighted-MA you add up the product of the price data with triangular-number weights and divide by the total weight. What is a Clutter Filter? For our purposes here, this is a filter that compares the slope of the trading filter output to a threshold to determine whether to shift trends. If the slope is up but the slope doesn't exceed the threshold, then the color is gray and this indicates a chop zone. If the slope is down but the slope doesn't exceed the threshold, then the color is gray and this indicates a chop zone. Alternatively if either up or down slope exceeds the threshold then the trend turns green for up and red for down. Fro demonstration purposes, an EMA is used as the moving average. This acts to reduce the noise in the signal. What is a Dual Element Lag Reducer? Modifies an array of coefficients to reduce lag by the Lag Reduction Factor uses a generic version of a Kalman velocity component to accomplish this lag reduction is achieved by applying the following to the array: 2 * coeff - coeff The response time vs noise battle still holds true, high lag reduction means more noise is present in your data! Please note that the beginning coefficients which the modifying matrix cannot be applied to (coef whose indecies are < LagReductionFactor) are simply multiplied by two for additional smoothing . Included Bar coloring Loxx's Expanded Source Types Signals Alerts
Penunjuk Pine Script®
oleh loxx
3
Clutter-Filtered, D-Lag Reducer, Spec. Ops FIR Filter [Loxx]Clutter-Filtered, D-Lag Reducer, Spec. Ops FIR Filter is a FIR filter moving average with extreme lag reduction and noise elimination technology. This is a special instance of a static weight FIR filter designed specifically for Forex trading. This is not only a useful indictor, but also a demonstration of how one would create their own moving average using FIR filtering weights. This moving average has static period and weighting inputs. You can change the lag reduction and the clutter filtering but you can't change the weights or the numbers of bars the weights are applied to in history. Plot of weighting coefficients used in this indicator These coefficients were derived from a smoothed cardinal sine weighed SMA on EURUSD in Matlab. You can see the coefficients in the code. What is Normalized Cardinal Sine? The sinc function sinc (x), also called the "sampling function," is a function that arises frequently in signal processing and the theory of Fourier transforms. In mathematics, the historical unnormalized sinc function is defined for x ≠ 0 by sinc x = sinx / x In digital signal processing and information theory, the normalized sinc function is commonly defined for x ≠ 0 by sinc x = sin(pi * x) / (pi * x) What is a Generic or Direct Form FIR Filter? In signal processing, a finite impulse response (FIR) filter is a filter whose impulse response (or response to any finite length input) is of finite duration, because it settles to zero in finite time. This is in contrast to infinite impulse response (IIR) filters, which may have internal feedback and may continue to respond indefinitely (usually decaying). The impulse response (that is, the output in response to a Kronecker delta input) of an Nth-order discrete-time FIR filter lasts exactly {\displaystyle N+1}N+1 samples (from first nonzero element through last nonzero element) before it then settles to zero. FIR filters can be discrete-time or continuous-time, and digital or analog. A FIR filter is (similar to, or) just a weighted moving average filter, where (unlike a typical equally weighted moving average filter) the weights of each delay tap are not constrained to be identical or even of the same sign. By changing various values in the array of weights (the impulse response, or time shifted and sampled version of the same), the frequency response of a FIR filter can be completely changed. An FIR filter simply CONVOLVES the input time series (price data) with its IMPULSE RESPONSE. The impulse response is just a set of weights (or "coefficients") that multiply each data point. Then you just add up all the products and divide by the sum of the weights and that is it; e.g., for a 10-bar SMA you just add up 10 bars of price data (each multiplied by 1) and divide by 10. For a weighted-MA you add up the product of the price data with triangular-number weights and divide by the total weight. Ultra Low Lag Moving Average's weights are designed to have MAXIMUM possible smoothing and MINIMUM possible lag compatible with as-flat-as-possible phase response. What is a Clutter Filter? For our purposes here, this is a filter that compares the slope of the trading filter output to a threshold to determine whether to shift trends. If the slope is up but the slope doesn't exceed the threshold, then the color is gray and this indicates a chop zone. If the slope is down but the slope doesn't exceed the threshold, then the color is gray and this indicates a chop zone. Alternatively if either up or down slope exceeds the threshold then the trend turns green for up and red for down. Fro demonstration purposes, an EMA is used as the moving average. This acts to reduce the noise in the signal. What is a Dual Element Lag Reducer? Modifies an array of coefficients to reduce lag by the Lag Reduction Factor uses a generic version of a Kalman velocity component to accomplish this lag reduction is achieved by applying the following to the array: 2 * coeff - coeff The response time vs noise battle still holds true, high lag reduction means more noise is present in your data! Please note that the beginning coefficients which the modifying matrix cannot be applied to (coef whose indecies are < LagReductionFactor) are simply multiplied by two for additional smoothing . Things to note Due to the computational demands of this indicator, there is a bars back input modifier that controls how many bars back the indicator is calculated on. Because of this, the first few bars of the indicator will sometimes appear crazy, just ignore this as it doesn't effect the calculation. Related Indicators STD-Filtered, Ultra Low Lag Moving Average Included Bar coloring Loxx's Expanded Source Types Signals Alerts
Penunjuk Pine Script®
oleh loxx
4
Simple LevelsSImple levels is a clean way to automatically plot important daily levels including: Yesterday's High Yesterday's Low 50% level between Prior High/Low Today's Open Premarket Low Premarket High This Daily Levels indicator is unique in its ability to: -Plot all of the daily level PLUS premarket high/low levels (extended hours must be turned ON) -Can hide past days levels, only plotting levels on the current day, to keep chart cleaner -Can extend line levels right or fullscreen -Plots the level price at each level on the chart -Can show/hide price levels labels -Can add supplemental premarket levels plot to show levels being formed during the premarket time period -Coded with line.new vs plot so dashed lines are available as a style -Automatically hides the indicator if the timeframe selected is Daily or greater
Penunjuk Pine Script®
oleh simplemmxm
BTMM|TDIThis is the trader's dynamic index inspired by Steve Mauro's BTMM strategy. In addition to the RSI, Trendline, Baseline, Volatility Bands I have also included additional trend biases that are painted in the background to provide more confluence when the markets break out in either direction. For convenience, a position size calculator is included for all users to quickly calculate lot sizes on forex pairs with difference account balance currencies. The calculator works accurately on forex pairs. DO NOT USE for crypto or indices as some brokers have unique contract sizes that could not be fully incorporated into the tool. There is also data table that displays historical values of the RSI, Trendline, Baseline, and an EMA vs Price scoring procedure that covers the current candle (t0) and up to 3 candles back. The table is meant to provide a snapshot view of either bullish or bearish dominance that can be deciphered with a quick glance.
Penunjuk Pine Script®
oleh The_Trading_Jedi
11
Helme-Nikias Weighted Burg AR-SE Extra. of Price [Loxx]Helme-Nikias Weighted Burg AR-SE Extra. of Price is an indicator that uses an autoregressive spectral estimation called the Weighted Burg Algorithm, but unlike the usual WB algo, this one uses Helme-Nikias weighting. This method is commonly used in speech modeling and speech prediction engines. This is a linear method of forecasting data. You'll notice that this method uses a different weighting calculation vs Weighted Burg method. This new weighting is the following: w = math.pow(array.get(x, i - 1), 2), the squared lag of the source parameter and w += math.pow(array.get(x, i), 2), the sum of the squared source parameter This take place of the rectangular, hamming and parabolic weighting used in the Weighted Burg method Also, this method includes Levinson–Durbin algorithm. as was already discussed previously in the following indicator: Levinson-Durbin Autocorrelation Extrapolation of Price What is Helme-Nikias Weighted Burg Autoregressive Spectral Estimate Extrapolation of price? In this paper a new stable modification of the weighted Burg technique for autoregressive (AR) spectral estimation is introduced based on data-adaptive weights that are proportional to the common power of the forward and backward AR process realizations. It is shown that AR spectra of short length sinusoidal signals generated by the new approach do not exhibit phase dependence or line-splitting. Further, it is demonstrated that improvements in resolution may be so obtained relative to other weighted Burg algorithms. The method suggested here is shown to resolve two closely-spaced peaks of dynamic range 24 dB whereas the modified Burg schemes employing rectangular, Hamming or "optimum" parabolic windows fail. Data inputs Source Settings: -Loxx's Expanded Source Types. You typically use "open" since open has already closed on the current active bar LastBar - bar where to start the prediction PastBars - how many bars back to model LPOrder - order of linear prediction model; 0 to 1 FutBars - how many bars you want to forward predict Things to know Normally, a simple moving average is calculated on source data. I've expanded this to 38 different averaging methods using Loxx's Moving Avreages. This indicator repaints Further reading A high-resolution modified Burg algorithm for spectral estimation Related Indicators Levinson-Durbin Autocorrelation Extrapolation of Price Weighted Burg AR Spectral Estimate Extrapolation of Price
Penunjuk Pine Script®
oleh loxx
Point of Control V2 The genesis of this project was to create a POC library that would be available to deliver volume profile information via pine to other scripts of indicators and strategies. This is a republish of an invite only script to open access This is the indicator version of the library function. A few points of significance: - Allows the choice of reset of the study period, day/week or bars. This is simple enough to expand to other conditions - Bar count resets starting from the beginning of the data set (bar index =0) vs bars back from the end of the data set - A 'period' in this context is the time between resets - the start of the POC (eg. start of Day or Week) until it resets (for example at the beginning of a next day or week) - Automates the determination of the increment level rather than the user specifying ticks or price brackets - Does not allow for setting the # of rows and then calculating the implied price increment levels - When a period is complete it is often useful to look back at the POCs of historical periods, or extend them forward. - This script will find the historical POCs around the current price and display them rather than extend all the historical POC lines to the right - This script also looks across all the period POCs and identifies the master POC or what I call the Grand POC, and also the next 3 runner up POCs This indicator is also available as a library. BINANCE:BTCUSDT NSE:NIFTY OANDA:XAUUSD NASDAQ:AAPL TVC:USOIL
Penunjuk Pine Script®
oleh JohnBaron
30
PointofControlLibrary "PointofControl" POC_f() The genesis of this project was to create a POC library that would be available to deliver volume profile information via pine to other scripts of indicators and strategies. This is the indicator version of the library function. A few things that would be unique with the built in - it allows you to choose the kind of reset of the period, day/week or bars. This is simple enough to expand to other conditions - it resets on bar count starting from the beginning of the data set (bar index =0) vs bars back from the end of the data set - A 'period' in this context is the time between resets - the start of the POC until it resets (for example at the beginning of a new day or week) - it will calculate an increment level rather than the user specifying ticks or price brackets - it does not allow for setting the # of rows and then calculating the implied price levels - When a period is complete it is often useful to look back at the POCs of historical periods, or extend them forward. - This script will find the historical POCs around the current price and display them rather than extend all the historical POC lines to the right - This script also looks across all the period POCs and identifies the master POC or what I call the Grand POC, and also the next 3 runner up POCs There is a matching indicator to this library
Perpustakaan Pine Script®
oleh JohnBaron
7
EPS & SalesHi everyone, I just adapted a little utility script to visualise EPS % increase (quarters vs Year -1) and sales. I used the code from @ARUN_SAXENA and modified it to fix what I saw as issues. (Using base 3M instead of 1M + request.earnings(syminfo.tickerid, earnings.actual, ignore_invalid_symbol=true) instead of request.financial(syminfo.tickerid, "EARNINGS_PER_SHARE", "FQ") Data will differ from MarketSmith because they use sometimes actual EPS sometimes standard, but think we can at least trust what we see in term of % The tool is far from being perfect !
Penunjuk Pine Script®
oleh Fred6724
272
Trigonometric compare close vs obvTrigonometric compare This is copy and mod of a script from alexgrower which did this great trigonometric math. As there was this idea floating around from some unicorn doing it instead of close also with the ta.obv, why not compare them. from a first idea: green=bullish trend red=baserish trend blue=deciding and acceleration zone or maybe SL hunting of whales Plot1: trigonometrics for obv Plot2: trigonometrics for close Plot3: trigonometrics for obv-close what to trade or how to trade no idea, just hat do post the basic idea of this compare. have fun
Penunjuk Pine Script®
oleh djmad
Candle Strength IndicatorThe candle strength indicator depicts the average strength of the price action by evaluating bullish vs bearish candles. The scale is relative to price fluctuation and the size of the candles for the particular ticker / market, so there are no significant levels. A cross on the zero line would generally indicate a change in trend / sentiment. This indicator may be useful as a filter for entries and use in confluence with other indicators.
Penunjuk Pine Script®
oleh PtGambler
2
Historical US Bond Yield CurvePreface: I'm just the bartender serving today's freshly blended concoction; I'd like to send a massive THANK YOU to all the coders and PineWizards for the locally-sourced ingredients. I am simply a code editor, not a code author. Many thanks to these original authors! Source 1 (Aug 8, 2019): Source 2 (Aug 11, 2019): About the Indicator: The term yield curve refers to the yields of U.S. treasury bills, notes, and bonds in order from shortest to longest maturity date. The yield curve describes the shapes of the term structures of interest rates and their respective terms to maturity in years. The slope of the yield curve tells us how the bond market expects short-term interest rates to move in the future based on bond traders' expectations about economic activity and inflation. The best use of the yield curve is to get a sense of the economy's direction rather than to try to make an exact prediction. This indicator plots the U.S. yield curve as maturity (x-axis/time) vs yield (y-axis/price) in addition to historical yield curves and advanced data tickers . The visual array of historical yield curves helps investors visualize shifts in the yield curve that are useful when identifying & forecasting economic conditions. The bond market can help predict the direction of the economy which can be useful in crafting your investment strategy. An inverted 10y/2y yield curve for durations longer than 5 consecutive trading days signals an almost certain recession on the horizon. An inversion happens when short-term bonds pay better than longer-term bonds. There is Federal Reserve Board data that suggests the 10y3m may be a better predictor of recessions. Features: Advanced dual data ticker that performs curve & important spread analysis, plus additional hover info. Advanced yield curve data labels with additional hover info. Customizable historical curves and color theme. ‼ IMPORTANT: Hover over labels/tables for advanced information. Chart asset and timeframe may affect the yield curve results; I have found consistently accurate results using BINANCE:BTCUSDT on 1d timeframe. Historical curve lookbacks will have an effect on whether the curve analysis says the curve is bull/bear steepening/flattening, so please use appropriate lookbacks. ⚠ DISCLAIMER: Not financial advice. Not a trading system. DYOR. I am not affiliated with the original authors, TradingView, Binance, or the Federal Reserve Board. About the Editor: I am a former FINRA Registered Representative, inventor/patent holder, futures trader, and hobby PineScripter.
Penunjuk Pine Script®
oleh BarefootJoey