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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
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loxx tarafından
8
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
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loxx tarafından
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
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loxx tarafından
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
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loxx tarafından
1
Bull/Bear Candle % Oscillator█ OVERVIEW This script determines the proportion of bullish and bearish candles in a given sample size. It will produce an oscillator that fluctuates between 100 and -100, where values > 0 indicate more bullish candles in the sample and values < 0 indicate more bearish candles in the sample. Data produced by this oscillator is normalized around the 50% value, meaning that an even 50/50 split between bullish and bearish candles makes this oscillator produce 0; this oscillator indirectly represents the percent proportion of bullish and bearish candles in the sample (see HOW TO USE/INTERPRETATION OF DATA ). It has two overarching settings: 'classic' and 'range'. █ CONCEPTS This script will cover concepts related to candlestick analysis, volumetric analysis, and lower timeframes. Candlestick Analysis - The idea behind this script is to solely look at the candlesticks themselves and derive information from them in a given sample. It separates candles into two categories, bullish (close > open) and bearish (close < open). If the indicator's setting is set to 'classic', the size of candles do not matter and all are assigned a value of 1 or 0. If the indicator's setting is set to 'range', specific candle ranges modify the proportion of bullish/bearish values. Bullish candle values include all bullish candles in the set from their lows to the close, plus the lower wicks of all bearish candles. Bearish candle values include all bearish candles in the set from their highs to the close, plus the upper wicks of all bullish candles. Volumetric Analysis - One of this script's features allows the user to modify the bullish and bearish candle proportions by its 'weight' determined by its volume compared to the sample set's total volume. Volumetric analysis for the 'range' setting are more complex than 'classic' as described below. Lower Timeframes - For volumetric analysis to be done on candle wicks, there needed to be a way to determine how much volume had occurred in the wick by itself to find the weight of upper and lower wicks. To accomplish this, I employed PineScrypt's request.security_lower_tf function to grab OHLC values of lower timeframe candles (as well as volume) to determine how much volume had occurred in the wicks of the chart resolution's candle. The default OHLC values used here are the lows for upper wicks and highs for lower wicks. These OHLC values are then compared to the chart resolution candle's close to determine if the volume of that lower timeframe candle should be shifted to the wick weight or stay in the current weight of that candle. The reason 'low' and 'high' are used here is to guarantee that 100% of the volume of a lower timeframe candle had occurred in the wick of the candle at the current resolution (see LIMITATIONS ). Bullish candles will exclude volume of all lower timeframe candles whose lows were greater than that candle's close. Bearish candles will exclude volume of all lower timeframe candles whose highs were less than that candle's close. These wick volumes are then divided by the volume of the sample set, and wick sizes are then multiplied by this weight before being added to their specific bullish/bearish sums (lower wicks to bullish and upper wicks to bearish). █ FEATURES There are 13 inputs for the user to modify the behavior/visual representation of this script. Sample Length - This determines how many candles are in the sample set to find the proportion of bullish and bearish candles. Colors and Invert Colors - There are three colors set by the user: a bullish color, neutral color, and bearish color. The oscillator plots two lines, one at 0 and another that represents the proportion of bullish or bearish candles in the sample set (we'll call this the 'signal line'). If the oscillator is above 0, bullish color is used, bearish otherwise. This script generates a gradient to color a filled area between the 0 line and the signal line based on the historical values of the oscillator itself and the signal line. For bullish values, the closer the signal line is to the max (or restricted max described below) that the oscillator has experienced, the more colored toward bullish color the shaded area will be, using the neutral color as a starting point. The same is applied to the bearish values using the bearish color. There is an additional input to invert the colors so that the bearish color is associated with bullish values and vise-versa. Calculation Type - This determines the overarching behavior of the oscillator and has two settings: Classic - The weight of candles are either 1 if they occurred and 0 if not. Range - The weight of candles is determined by the size of specific sections as described in CONCEPTS - Candlestick Analysis . Volume Weighted - This enables modifying the weights of candles as described in CONCEPTS - Volumetric Analysis and Lower Timeframes based on which Calculation Type is used. Wick Slice Resolution - This is the lower timeframe resolution that will be used to slice the chart resolution's candle when determining the volumetric weight of wicks. Lower timeframe resolutions like '1 minute' will yield more precise results as they will give more data points to go off of (see LIMITATIONS ). Upper/Lower Wick Source - These two inputs allow the user to select which OHLC values to compare against the chart resolution's candle close when determining which lower timeframe candles will have their volumes associated with the wicks of candles being analyzed at the chart's resolution. Restrict Min/Max Data and Restriction - This will restrict the maximum and minimum values that will be used for the signal line when comparing its value to previous oscillator values and change how the color gradient is generated for the indicator. Restriction is the number of candles back that will determine these maximum and minimum values. Display Min/Max Guide - This will plot two lines that are colored the corresponding bullish and bearish colors which follow what the maximum and minimum values are currently for the oscillator. █ HOW TO USE/INTERPRETATION OF DATA As mentioned in the OVERVIEW section, this oscillator provides an indirect representation of the percent proportion of bullish or bearish candles in a given sample. If the oscillator reads 80, this does not mean that 80% of all candles in the sample were bullish . To find the percentage of candles that were bullish or bearish, the user needs to perform the following: 50% + ((|oscillator value| / 100) * 50)% If the oscillator value is negative, the value from above will represent the percentage of bearish candles in the sample. If it is positive, this value represents the percentage of bullish candles in the sample. Example 1 (oscillator value = 80): 50% + ((|80| / 100) * 50)% 50% + ((0.80) * 50)% 50% + 40% = 90% 90% of the candles in the sample were bullish. Example 2 (oscillator value = -43): 50% + ((|-43| / 100) * 50)% 50% + ((0.43) * 50)% 50% + 21.5% = 71.5% 71.5% of the candles in the sample were bearish. An example use of this indicator would be to put in a 'buy' order when its value shows a significant proportion of the sampled candles were bearish, and put in a 'sell' order when a significant proportion of candles were bullish. Potential divergences of this oscillator may also be used to plan trades accordingly such as bearish divergence - price continues higher as the oscillator decreases in value and vise-versa.* * Nothing in this script constitutes any form of financial advice. The user is solely responsible for their trading decisions and I will not be held liable for any losses or gains incurred with the use of this script. Please proceed with caution when using this script to assist with trading decisions. █ LIMITATIONS Range Volumetric Weights : Because of the conditions that must be met in order for volume to be considered part of wicks, it is possible that the default settings and their intended reasoning will not produce reliable results. If all lower timeframe candles have highs or lows that are within the body of the candle at the chart's resolution, the volume for the wicks will effectively be 0, which is not an accurate representation of those wicks. This is one of the reasons why I included the ability to change the source values used for these conditions as certain OHLC values may produce more reliable/intended results under these conditions. Wick Slice Resolution : PineScript restricts the number of intrabar references to 100,000 total. This script uses 3 separate request.security_lower_tf calls and has a default resolution of 1 minute. This means that if the user were to set the oscillator to the Range setting, enable volume weighted, and had the Wick Slice Resolution set to 1 minute, this script will exceed this 100,000 reference restriction within 24 days of data and will not produce any results beyond the previous 23.14 days. Below are example uses of all the different settings of this script, these are done on the 1D chart of COINBASE:BTCUSD : Default Settings: Classic - Volume Weighted: Range - no Volume Weight: Range - Volume Weighted (1 min slices): Range - Volume Weighted (1 hour slices): Display Min/Max Guide - No Restriction: Display Min/Max Guide - Restriction: Invert Colors:
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SolCollector tarafından
DCA After Downtrend v2 (by BHD_Trade_Bot)The purpose of the strategy is to identify the end of a short-term downtrend . So that you can easily to DCA certain amount of money for each month. ENTRY The buy orders are placed on a monthly basis for assets at the end of a short-term downtrend: - Each month condition: In 1-hour time frame, each month has 24 * 30 candles - The end of short-term downtrend condition: use MACD for less delay CLOSE The sell orders are placed when: - Is last bar The strategy use $1000 and trading fee is 1.1% for each order. Pro tip: The 1-hour time frame has the best results on average: - Total spent: $1000 x 33 = $33,000 - Total profit: $65,578
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BHD_Trade_Bot tarafından
1
Jathuko Boncu Serok Cendolserok brrooo.... Jakarta - Celsius Network LLC menggugat mantan manajer investasinya, Jason Stone pada selasa lalu. Perusahaan pemberi pinjaman kripto ini menuduh Stone mencuri aset puluhan juta dolar sebelum bangkrut bulan lalu. Dalam pengaduan yang diajukan di pengadilan kebangkrutan Manhattan, Celsius menuduh Stone dan perusahaannya KeyFi Inc melakukan kelalaian fatal. Stone juga dituduh tidak berkompeten dalam berinvestasi kripto. Dikutip dari Reuters, Rabu (24/8/2022), Celsius mengatakan Stone terbukti gagal memberikan untung dan menyebabkan kerugian puluhan juta dolar. Baca artikel detikfinance, "Diduga Tilep Kripto Puluhan Juta Dolar, Manajer Ini Digugat Perusahaannya" selengkapnya
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fachrudin99 tarafından
2
iBox, Initial Balance | IB High, Low, Midpoint | OpeningThis Indicator will print basically 4 lines. You have to understand the importance of the "Initial Balance" the first trading hour of Cash session is very important in trading stocks and indizes. So you will get 4 lines: Opening Initial Balance High Initial Balance Low Initial Balance Midpoint/Halfback Most indicators for Range Box, Opening Trade, Opening Range, Initial Balance, iBOX and "Ultimate Lines" will only use the timezone of the exchange or your own timezone. I'm living in europe and we have this daylight saving time change in Summer/Winter - which will cause problems with most of the existing indicators or at least will push you to change the times regularly. Another important point: I really like to switch the indizes when trading on my mobile. So what will happen when you set up opening range for DAX at 09:00 and then switch to S&P500? All indicators I tried failed here - they will just draw a wrong line for SPX, NDX, DJI, FTSE, ASX etc. I fixed that and hard coded stock exchanges and ticker symbols into 3 main groups Initial Balance EU/US/Asia For example we take DAX - XETRA DAX is opening at 09:00 MEZ (Europe/Berlin) The Initial Balance is set during the H1 Candle from 09:00 - 09:59 Please be aware, that some cash indizes only deliver data to TradingView after the "opening auction" - so for Xetra DAX you have to book live data and often you will only see data at 09:02/09:03 after the opening auction. So you will get different opening lines compared from cash to future or your CFD provider. Future and CFD should fit for 99% The Opening Line will be drawn at exactly 09:00 IB High on the highest price during the H1 candle IB Low on the lowest price during the H1 candle IB Halfback is simple: (IB High + IB Low) / 2 The lines will be drawn from cash start until next day and will end 2 minutes before the next cash session will start again. Please make your own experience! Activate the indicator for a CFD or Future switch to M15 and watch the wicks around the 4 lines. Even in the night or the next morning before the next Initial Balance will be set. For me the lines are valid for around 24 hours and often longer. That's why it's good to have the old lines on the chart too. To-do: Yesterday high / low / close / halfback (also for last week and month) Labels for the lines - sometimes only the colors will confuse you - a simple label should be a benefit (IB_h, IB_l, IB_1/2, 1D_o) Range Lines for Asia Range and Premarket Range - additionaly a parameter to disable premarket lines when premarket is trading in asia range. Add Alert condition to get alerts after IB is set If you have and thoughts, ideas or improvements, please send me an message or leave an comment! Like i said, the stock exchanges and ticker symbols are hard coded and can be extended for all relevant assets. Have fun and i really hope this indicator will help improving your trading experience!
Pine Script® göstergesi
Roul_Charts tarafından
35
Rsi/W%R/Stoch/Mfi: HTF overlay mini-plotsOverlay mini-plots for various indicators. Shows current timeframe; and option to plot 2x higher timeframes (i.e. 15min and 60min on the 5min chart above). The idea is to de-clutter chart when you just want real-time snippets for an indicator. Useful for gauging overbought/oversold, across timeframes, at a glance. ~~Indicators~~ ~RSI: Relative strength index ~W%R: Williams percent range ~Stochastic ~MFI: Money flow index ~~Inputs~~ ~indicator length (NB default is set to 12, NOT the standard 14) ~choose 2x HTFs, show/hide HTF plots ~choose number of bars to show (current timeframe only; HTF plots show only 6 bars) ~horizontal position: offset (bars); shift plots right or left. Can be negative ~vertical position: top/middle/bottom ~other formatting options (color, line thickness, show/hide labels, 70/30 lines, 80/20 lines) ~~tips~~ ~should be relatively easy to add further indicators, so long as they are 0-100 based; by editing lines 9 and 11 ~change the vertical compression of the plots by playing around with the numbers (+100, -400, etc) in lines 24 and 25
Pine Script® göstergesi
twingall tarafından
7
Normalized, Variety, Fast Fourier Transform Explorer [Loxx]Normalized, Variety, Fast Fourier Transform Explorer demonstrates Real, Cosine, and Sine Fast Fourier Transform algorithms. This indicator can be used as a rule of thumb but shouldn't be used in trading. What is the Discrete Fourier Transform? In mathematics, the discrete Fourier transform (DFT) converts a finite sequence of equally-spaced samples of a function into a same-length sequence of equally-spaced samples of the discrete-time Fourier transform (DTFT), which is a complex-valued function of frequency. The interval at which the DTFT is sampled is the reciprocal of the duration of the input sequence. An inverse DFT is a Fourier series, using the DTFT samples as coefficients of complex sinusoids at the corresponding DTFT frequencies. It has the same sample-values as the original input sequence. The DFT is therefore said to be a frequency domain representation of the original input sequence. If the original sequence spans all the non-zero values of a function, its DTFT is continuous (and periodic), and the DFT provides discrete samples of one cycle. If the original sequence is one cycle of a periodic function, the DFT provides all the non-zero values of one DTFT cycle. What is the Complex Fast Fourier Transform? The complex Fast Fourier Transform algorithm transforms N real or complex numbers into another N complex numbers. The complex FFT transforms a real or complex signal x in the time domain into a complex two-sided spectrum X in the frequency domain. You must remember that zero frequency corresponds to n = 0, positive frequencies 0 < f < f_c correspond to values 1 ≤ n ≤ N/2 −1, while negative frequencies −fc < f < 0 correspond to N/2 +1 ≤ n ≤ N −1. The value n = N/2 corresponds to both f = f_c and f = −f_c. f_c is the critical or Nyquist frequency with f_c = 1/(2*T) or half the sampling frequency. The first harmonic X corresponds to the frequency 1/(N*T). The complex FFT requires the list of values (resolution, or N) to be a power 2. If the input size if not a power of 2, then the input data will be padded with zeros to fit the size of the closest power of 2 upward. What is Real-Fast Fourier Transform? Has conditions similar to the complex Fast Fourier Transform value, except that the input data must be purely real. If the time series data has the basic type complex64, only the real parts of the complex numbers are used for the calculation. The imaginary parts are silently discarded. What is the Real-Fast Fourier Transform? In many applications, the input data for the DFT are purely real, in which case the outputs satisfy the symmetry X(N-k)=X(k) and efficient FFT algorithms have been designed for this situation (see e.g. Sorensen, 1987). One approach consists of taking an ordinary algorithm (e.g. Cooley–Tukey) and removing the redundant parts of the computation, saving roughly a factor of two in time and memory. Alternatively, it is possible to express an even-length real-input DFT as a complex DFT of half the length (whose real and imaginary parts are the even/odd elements of the original real data), followed by O(N) post-processing operations. It was once believed that real-input DFTs could be more efficiently computed by means of the discrete Hartley transform (DHT), but it was subsequently argued that a specialized real-input DFT algorithm (FFT) can typically be found that requires fewer operations than the corresponding DHT algorithm (FHT) for the same number of inputs. Bruun's algorithm (above) is another method that was initially proposed to take advantage of real inputs, but it has not proved popular. There are further FFT specializations for the cases of real data that have even/odd symmetry, in which case one can gain another factor of roughly two in time and memory and the DFT becomes the discrete cosine/sine transform(s) (DCT/DST). Instead of directly modifying an FFT algorithm for these cases, DCTs/DSTs can also be computed via FFTs of real data combined with O(N) pre- and post-processing. What is the Discrete Cosine Transform? A discrete cosine transform ( DCT ) expresses a finite sequence of data points in terms of a sum of cosine functions oscillating at different frequencies. The DCT , first proposed by Nasir Ahmed in 1972, is a widely used transformation technique in signal processing and data compression. It is used in most digital media, including digital images (such as JPEG and HEIF, where small high-frequency components can be discarded), digital video (such as MPEG and H.26x), digital audio (such as Dolby Digital, MP3 and AAC ), digital television (such as SDTV, HDTV and VOD ), digital radio (such as AAC+ and DAB+), and speech coding (such as AAC-LD, Siren and Opus). DCTs are also important to numerous other applications in science and engineering, such as digital signal processing, telecommunication devices, reducing network bandwidth usage, and spectral methods for the numerical solution of partial differential equations. The use of cosine rather than sine functions is critical for compression, since it turns out (as described below) that fewer cosine functions are needed to approximate a typical signal, whereas for differential equations the cosines express a particular choice of boundary conditions. In particular, a DCT is a Fourier-related transform similar to the discrete Fourier transform (DFT), but using only real numbers. The DCTs are generally related to Fourier Series coefficients of a periodically and symmetrically extended sequence whereas DFTs are related to Fourier Series coefficients of only periodically extended sequences. DCTs are equivalent to DFTs of roughly twice the length, operating on real data with even symmetry (since the Fourier transform of a real and even function is real and even), whereas in some variants the input and/or output data are shifted by half a sample. There are eight standard DCT variants, of which four are common. The most common variant of discrete cosine transform is the type-II DCT , which is often called simply "the DCT". This was the original DCT as first proposed by Ahmed. Its inverse, the type-III DCT , is correspondingly often called simply "the inverse DCT" or "the IDCT". Two related transforms are the discrete sine transform ( DST ), which is equivalent to a DFT of real and odd functions, and the modified discrete cosine transform (MDCT), which is based on a DCT of overlapping data. Multidimensional DCTs ( MD DCTs) are developed to extend the concept of DCT to MD signals. There are several algorithms to compute MD DCT . A variety of fast algorithms have been developed to reduce the computational complexity of implementing DCT . One of these is the integer DCT (IntDCT), an integer approximation of the standard DCT ,: ix, xiii, 1, 141–304  used in several ISO /IEC and ITU-T international standards. What is the Discrete Sine Transform? In mathematics, the discrete sine transform (DST) is a Fourier-related transform similar to the discrete Fourier transform (DFT), but using a purely real matrix. It is equivalent to the imaginary parts of a DFT of roughly twice the length, operating on real data with odd symmetry (since the Fourier transform of a real and odd function is imaginary and odd), where in some variants the input and/or output data are shifted by half a sample. A family of transforms composed of sine and sine hyperbolic functions exists. These transforms are made based on the natural vibration of thin square plates with different boundary conditions. The DST is related to the discrete cosine transform (DCT), which is equivalent to a DFT of real and even functions. See the DCT article for a general discussion of how the boundary conditions relate the various DCT and DST types. Generally, the DST is derived from the DCT by replacing the Neumann condition at x=0 with a Dirichlet condition. Both the DCT and the DST were described by Nasir Ahmed T. Natarajan and K.R. Rao in 1974. The type-I DST (DST-I) was later described by Anil K. Jain in 1976, and the type-II DST (DST-II) was then described by H.B. Kekra and J.K. Solanka in 1978. Notable settings windowper = period for calculation, restricted to powers of 2: "16", "32", "64", "128", "256", "512", "1024", "2048", this reason for this is FFT is an algorithm that computes DFT (Discrete Fourier Transform) in a fast way, generally in 𝑂(𝑁⋅log2(𝑁)) instead of 𝑂(𝑁2). To achieve this the input matrix has to be a power of 2 but many FFT algorithm can handle any size of input since the matrix can be zero-padded. For our purposes here, we stick to powers of 2 to keep this fast and neat. read more about this here: Cooley–Tukey FFT algorithm SS = smoothing count, this smoothing happens after the first FCT regular pass. this zeros out frequencies from the previously calculated values above SS count. the lower this number, the smoother the output, it works opposite from other smoothing periods Fmin1 = zeroes out frequencies not passing this test for min value Fmax1 = zeroes out frequencies not passing this test for max value barsback = moves the window backward Inverse = whether or not you wish to invert the FFT after first pass calculation Related indicators Real-Fast Fourier Transform of Price Oscillator STD-Stepped Fast Cosine Transform Moving Average Real-Fast Fourier Transform of Price w/ Linear Regression Variety RSI of Fast Discrete Cosine Transform Additional reading A Fast Computational Algorithm for the Discrete Cosine Transform by Chen et al. Practical Fast 1-D DCT Algorithms With 11 Multiplications by Loeffler et al. Cooley–Tukey FFT algorithm Ahmed, Nasir (January 1991). "How I Came Up With the Discrete Cosine Transform". Digital Signal Processing. 1 (1): 4–5. doi:10.1016/1051-2004(91)90086-Z. DCT-History - How I Came Up With The Discrete Cosine Transform Comparative Analysis for Discrete Sine Transform as a suitable method for noise estimation
Pine Script® göstergesi
loxx tarafından
3
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
Pine Script® göstergesi
loxx tarafından
vol_boxA simple script to draw a realized volatility forecast, in the form of a box. The script calculates realized volatility using the EWMA method, using a number of periods of your choosing. Using the "periods per year", you can adjust the script to work on any time frame. For example, if you are using an hourly chart with bitcoin, there are 24 periods * 365 = 8760 periods per year. This setting is essential for the realized volatility figure to be accurate as an annualized figure, like VIX. By default, the settings are set to mimic CBOE volatility indices. That is, 252 days per year, and 20 period window on the daily timeframe (simulating a 30 trading day period). Inside the box are three figures: 1. The current realized volatility. 2. The rank. E.g. "10%" means the current realized volatility is less than 90% of realized volatility measures. 3. The "accuracy": how often price has closed within the box, historically. Inputs: stdevs: the number of standard deviations for the box periods to project: the number of periods to forecast window: the number of periods for calculating realized volatility periods per year: the number of periods in one year (e.g. 252 for the "D" timeframe)
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taylor_o tarafından
8
FOMC AnnouncementsThis indicator plots vertical lines at the scheduled times of US Federal Reserve's FOMC Meeting Announcements. Usually, that time or the 24 hours before and after could see big moves in markets. You can change those dates and times in the settings, and could use the indicator option "Add this indicator to entire layout" if you want to easily reflect that across all panes of a layout. Those lines will show on any symbol you switch to, saving you time and effort of drawing them manually.
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hadialaddin tarafından
16
Weight Gain 4000 - (Adjustable Volume Weighted MA) - [mutantdog]Short Version: This is a fairly self-contained system based upon a moving average crossover with several unique features. The most significant of these is the adjustable volume weighting system, allowing for transformations between standard and weighted versions of each included MA. With this feature it is possible to apply partial weighting which can help to improve responsiveness without dramatically altering shape. Included types are SMA, EMA, WMA, RMA, hSMA, DEMA and TEMA. Potentially more will be added in future (check updates below). In addition there are a selection of alternative 'weighted' inputs, a pair of Bollinger-style deviation bands, a separate price tracker and a bunch of alert presets. This can be used out-of-the-box or tweaked in multiple ways for unusual results. Default settings are a basic 8/21 EMA cross with partial volume weighting. Dev bands apply to MA2 and are based upon the type and the volume weighting. For standard Bollinger bands use SMA with length 20 and try adding a small amount of volume weighting. A more detailed breakdown of the functionality follows. Long Version: ADJUSTABLE VOLUME WEIGHTING In principle any moving average should have a volume weighted analogue, the standard VWMA is just an SMA with volume weighting for example. Actually, we can consider the SMA to be a special case where volume is a constant 1 per bar (the value is somewhat arbitrary, the important part is that it's constant). Similar principles apply to the 'elastic' EVWMA which is the volume weighted analogue of an RMA. In any case though, where we have standard and weighted variants it is possible to transform one into the other by gradually increasing or decreasing the weighting, which forms the basis of this system. This is not just a simple multiplier however, that would not work due to the relative proportions being the same when set at any non zero value. In order to create a meaningful transformation we need to use an exponent instead, eg: volume^x , where x is a variable determined in this case by the 'volume' parameter. When x=1, the full volume weighting applies and when x=0, the volume will be reduced to a constant 1. Values in between will result in the respective partial weighting, for example 0.5 will give the square root of the volume. The obvious question here though is why would you want to do this? To answer that really it is best to actually try it. The advantages that volume weighting can bring to a moving average can sometimes come at the cost of unwanted or erratic behaviour. While it can tend towards much closer price tracking which may be desirable, sometimes it needs moderating especially in markets with lower liquidity. Here the adjustability can be useful, in many cases i have found that adding a small amount of volume weighting to a chosen MA can help to improve its responsiveness without overpowering it. Another possible use case would be to have two instances of the same MA with the same length but different weightings, the extent to which these diverge from each other can be a useful indicator of trend strength. Other uses will become apparent with experimentation and can vary from one market to another. THE INCLUDED MODES At the time of publication, there are 7 included moving average types with plans to add more in future. For now here is a brief explainer of what's on offer (continuing to use x as shorthand for the volume parameter), starting with the two most common types. SMA: As mentioned above this is essentially a standard VWMA, calculated here as sma(source*volume^x,length)/sma(volume^x,length). In this case when x=0 then volume=1 and it reduces to a standard SMA. RMA: Again mentioned above, this is an EVWMA (where E stands for elastic) with constant weighting. Without going into detail, this method takes the 1/length factor of an RMA and replaces it with volume^x/sum(volume^x,length). In this case again we can see that when x=0 then volume=1 and the original 1/length factor is restored. EMA: This follows the same principle as the RMA where the standard 2/(length+1) factor is replaced with (2*volume^x)/(sum(volume^x,length)+volume^x). As with an RMA, when x=0 then volume=1 and this reduces back to the standard 2/(length+1). DEMA: Just a standard Double EMA using the above. TEMA: Likewise, a standard Triple EMA using the above. hSMA: This is the same as the SMA except it uses harmonic mean calculations instead of arithmetic. In most cases the differences are negligible however they can become more pronounced when volume weighting is introduced. Furthermore, an argument can be made that harmonic mean calculations are better suited to downtrends or bear markets, in principle at least. WMA: Probably the most contentious one included. Follows the same basic calculations as for the SMA except uses a WMA instead. Honestly, it makes little sense to combine both linear and volume weighting in this manner, included only for completeness and because it can easily be done. It may be the case that a superior composite could be created with some more complex calculations, in which case i may add that later. For now though this will do. An additional 'volume filter' option is included, which applies a basic filter to the volume prior to calculation. For types based around the SMA/VWMA system, the volume filter is a WMA-4, for types based around the RMA/EVWMA system the filter is a RMA-2. As and when i add more they will be listed in the updates at the bottom. WEIGHTED INPUTS The ohlc method of source calculations is really a leftover from a time when data was far more limited. Nevertheless it is still the method used in charting and for the most part is sufficient. Often the only important value is 'close' although sometimes 'high' and 'low' can be relevant also. Since we are volume weighting however, it can be useful to incorporate as much information as possible. To that end either 'hlc3' or 'hlcc4' tend to be the best of the defaults (in the case of 24/7 charting like crypto or intraday trading, 'ohlc4' should be avoided as it is effectively the same as a lagging version of 'hlcc4'). There are many other (infinitely many, in fact) possible combinations that can be created, i have included a few here. The premise is fairly straightforward, by subtracting one value from another, the remaining difference can act as a kind of weight. In a simple case consider 'hl2' as simply the midrange ((high+low)/2), instead of this using 'high+low-open' would give more weight to the value furthest from the open, providing a good estimate of the median. An even better estimate can be achieved by combining that with 'high+low-close' to give the included result 'hl-oc2'. Similarly, 'hlc3' can be considered the basic mean of the three significant values, an included weighted version 'hlc2-o2' combines a sum with subtraction of open to give an estimated mean that may be more accurate. Finally we can apply a similar principle to the close, by subtracting the other values, this one potentially gets more complex so the included 'cc-ohlc4' is really the simplest. The result here is an overbias of the close in relation to the open and the midrange, while in most cases not as useful it can provide an estimate for the next bar assuming that the trend continues. Of the three i've included, hlc2-o2 is in my opinion the most useful especially in this context, although it is perhaps best considered to be experimental in nature. For that reason, i've kept 'hlcc4' as the default for both MAs. Additionally included is an 'aux input' which is the standard TV source menu and, where possible, can be set as outputs of other indicators. THE SYSTEM This one is fairly obvious and straightforward. It's just a moving average crossover with additional deviation (bollinger) bands. Not a lot to explain here as it should be apparent how it works. Of the two, MA1 is considered to be the fast and MA2 is considered to be the slow. Both can be set with independent inputs, types and weighting. When MA1 is above, the colour of both is green and when it's below the colour of both is red. An additional gradient based fill is there and can be adjusted along with everything else in the visuals section at the bottom. Default alerts are available for crossover/crossunder conditions along with optional marker plots. MA2 has the option for deviation bands, these are calculated based upon the MA type used and volume weighted according to the main parameter. In the case of a unweighted SMA being used they will be standard Bollinger bands. An additional 'source direct' price tracker is included which can be used as the basis for an alert system for price crossings of bands or MAs, while taking advantage of the available weighted inputs. This is displayed as a stepped line on the chart so is also a good way to visualise the differences between input types. That just about covers it then. The likelihood is that you've used some sort of moving average cross system before and are probably still using one or more. If so, then perhaps the additional functionality here will be of benefit. Thanks for looking, I welcome any feedack
Pine Script® göstergesi
mutantdog tarafından
1
ka66: Auto-Guppy Multiple Moving Average (GMMA)This implements a Guppy Multiple Moving Average (GMMA) with the following twists, which may be a feature or a bug, depending on your perspective: For both fast and slow group of MAs, only a starting MA (the fastest in that group) is specified. For either group, a configurable factor is set, which will be used to calculate subsequent MAs. Automatically selects colours as gradients within a configurable colour range, clearly differentiating between the short-term and long-term groups of averages. Use Weighted Moving Average (WMAs) as the averaging mechanism. More on this later. For example, if in the fast group, we start with MA 3, and a factor of 2, then the 6 MAs in the group will be: 3, 6, 12, 24, 48, 96. The calculated lookbacks are displayed on a table on the top-right, in case further indicators need to be calculated based on these values. Use of WMAs : This is an annoyance of the implementation: As I use arrays to store lookback calculations (12 of them, individual variables would be a pani to work with!), getting these back out of the array returns a series rather than a simple value. For some unfathomable reason, PineScript doesn't allow copying/conversion of these into a simple value. To add insult to the injury, a bunch of moving averaging functions (e.g.: ta.ema, ta.hma) only work with simple int lookback values. Go figure. SMAs and WMAs are the two that allow series lookback values, and WMAs are less laggy than SMAs but remain smooth, so WMAs it is!
Pine Script® göstergesi
ka66 tarafından
SigmaSpikes Background Highlight [vnhilton]SigmaSpikes is an indicator created by Adam H Grimes. It's a volatility indicator which applies a standard deviation measure on candles for a set period of time, in order to find big candles/moves relative to the other candles. These big moves could be the outcome of setups being traded by market players, large market orders put in by big money players, &/or HFT algorithms reacting to events (usually fundamental events). These big moves can also be seen as inefficient as it doesn't fit in with the mostly efficient market - this is very similar to gaps of which price would want to fill as they're inefficient, in order to "restore order" to the market. This indicator attempts to give better information at a glance, by highlighting the background of candles that have sigma spikes over the set standard deviation threshold. In the chart snapshot image above featuring EURUSD, we can see at 24/06/22 3PM BST, a big move has occurred (highlighted in green showing upward move) leaving an inefficiency area that needs to be filled. The high end of the inefficiency area was reached in the following candle as there was no gap between that candle's open & the previous big candle's close. The low end of the inefficiency area was finally reached almost 4 hours later, at 6:55PM BST.
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vnhilton tarafından
CRYPTO MARKET SESSION ANALYZER INDICATORCrypto Market Session Analyzer is an easy-to-use yet powerful analysis tool that helps the trader visualize and analyze price movements over three different trading sessions: 1) European Session 2) US session 3) Asian session Automatically tracks the corresponding levels for each market session. This indicator can be used on all timeframes equal to or less than 15 minutes. Although this is a simple indicator to use, some care must be taken when using it. The trader must be careful to set the correct times for each session according to his UTC timezone. By default the indicator uses UTC. If your console is set to UTC + 2 for example, you will need to take this into account and align the times correctly. You can adjust the time for each session from the user interface. Following the example, if the opening of the UE session is set to 9 and UTC of your console is set to UTC + 2, the script will proceed to create the level at opening time 11. HOW IT WORK The indicator automatically draws a horizontal line at the open and a horizontal line at the close of each session. The indicator clears past support and resistance every 24 hours to provide a clean and easy-to-read chart, updating new levels session after session. Blue indicates the EU session. Orange indicates the US session. Purple indicates the Asian session.
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thequantscience tarafından
wnG - VWAP MOD Modified version of VWAP : Classic VWAP with 6 levels based on the Average True Range to identify the distance and distribution of the prices around the VWAP. There are 2 calcul methodologies for the bands - Last 24 Hours Average True Range - Progressive Average True Range starting from 00:00 As prices tend to move around the VWAP level, favor LONG positions in the GREEN ZONE (and SHORT in the RED ZONE). How to use it : Avoid taking long position when price is in the RED ZONE Avoid taking short position when price is in the GREEN ZONE ==> Adjust the settings depending on your timeframe and asset
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wingoutoulouse tarafından
2
Momentum CloudThis is a modified Ichimoku Cloud: -The default Lookback-Length and Displacement settings have been modified to operate optimally on 24/7 markets - which is popular among Crypto analysts. -The Lagging Span, Base Line, and Conversion Line have been removed - leaving just the bare cloud. -Additionally, the Cloud's color will shift blue when it is compressed. (More specifically - when Leading Span A retreats to Leading Span B, the color changes.) This allows the user to easily identify when the Cloud is "thinning", either to the upside, or the downside. Being that the "spread" or "width" of an Ichimoku Cloud generally gauges it's efficacy as potential Support or Resistance, this tool is particularly useful for highlighting when momentum is weakening. *This script will be updated in the future to allow the user to view the Momentum Cloud of alternate time-frames! (e.g, Viewing the 1D Momentum Cloud on the 1H timeframe)
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TheRiskChair tarafından
HighTimeframeTimingLibrary "HighTimeframeTiming" @description Library for sampling high timeframe (HTF) historical data at an arbitrary number of HTF bars back, using a single security() call. The data is fixed and does not alter over the course of the HTF bar. It also behaves consistently on historical and elapsed realtime bars. ‼ LIMITATIONS: This library function depends on there being a consistent number of chart timeframe bars within the HTF bar. This is almost always true in 24/7 markets like crypto. This might not be true if the chart doesn't produce an update when expected, for example because the asset is thinly traded and there is no volume or price update from the feed at that time. To mitigate this risk, use this function on liquid assets and at chart timeframes high enough to reliably produce updates at least once per bar period. The consistent ratio of bars might also break down in markets with irregular sessions and hours. I'm not sure if or how one could mitigate this. Another limitation is that because we're accessing a multiplied number of chart bars, you will run out of chart bars faster than you would HTF bars. This is only a problem if you use a large historical operator. If you call a function from this library, you should probably reproduce these limitations in your script description. However, all of this doesn't mean that this function might not still be the best available solution, depending what your needs are. If a single chart bar is skipped, for example, the calculation will be off by 1 until the next HTF bar opens. This is certainly inconsistent, but potentially still usable. @function f_offset_synch(float _HTF_X, int _HTF_H, int _openChartBarsIn, bool _updateEarly) Returns the number of chart bars that you need to go back in order to get a stable HTF value from a given number of HTF bars ago. @param float _HTF_X is the timeframe multiplier, i.e. how much bigger the selected timeframe is than the chart timeframe. The script shows a way to calculate this using another of my libraries without using up a security() call. @param int _HTF_H is the historical operator on the HTF, i.e. how many bars back you want to go on the higher timeframe. If omitted, defaults to zero. @param int _openChartBarsIn is how many chart bars have been opened within the current HTF bar. An example of calculating this is given below. @param bool _updateEarly defines whether you want to update the value at the closing calculation of the last chart bar in the HTF bar or at the open of the first one. @returns an integer that you can use as a historical operator to retrieve the value for the appropriate HTF bar. 🙏 Credits: This library is an attempt at a solution of the problems in using HTF data that were laid out by Pinecoders in "security() revisited" - Thanks are due to the authors of that work for an understanding of HTF issues. In addition, the current script also includes some of its code. Specifically, this script reuses the main function recommended in "security() revisited", for the purposes of comparison. And it extends that function to access historical data, again just for comparison. All the rest of the code, and in particular all of the code in the exported function, is my own. Special thanks to LucF for pointing out the limitations of my approach. ~~~~~~~~~~~~~~~~| EXPLANATION ~~~~~~~~~~~~~~~~| Problems with live HTF data: Many problems with using live HTF data from security() have been clearly explained by Pinecoders in "security() revisited" In short, its behaviour is inconsistent between historical and elapsed realtime bars, and it changes in realtime, which can cause calculations and alerts to misbehave. Various unsatisfactory solutions are discussed in "security() revisited", and understanding that script is a prerequisite to understanding this library. PineCoders give their own solution, which is to fix the data by essentially using the previous HTF bar's data. Importantly, that solution is consistent between historical and realtime bars. This library is an attempt to provide an alternative to that solution. Problems with historical HTF data: In addition to the problems with live HTF data, there are different issues when trying to access historical HTF data. Why use historical HTF data? Maybe you want to do custom calculations that involve previous HTF bars. Or to use HTF data in a function that has mutable variables and so can't be done in a security() call. Most obviously, using the historical operator (in this description, represented using { } because the square brackets don't render) on variables already retrieved from a security() call, e.g. HTF_Close{1}, is not very useful: it retrieves the value from the previous *chart* bar, not the previous HTF bar. Using {1} directly in the security() call instead does get data from the previous HTF bar, but it behaves inconsistently, as we shall see. This library addresses these concerns as well. Housekeeping: To follow what's going on with the example and comparisons, turn line labels on: Settings > Scales > Indicator Name Label. The following explanation assumes "close" as the source, but you can change it if you want. To quickly see the difference between historical and realtime bars, set the HTF to something like 3 minutes on a 15s chart. The bars at the top of the screen show the status. Historical bars are grey, elapsed realtime bars are red, and the realtime bar is green. A white vertical line shows the open of a HTF bar. A: This library function f_offset_synch(): When supplied with an input offset of 0, it plots a stable value of the close of the *previous* HTF bar. This value is thus safe to use for calculations and alerts. For a historical operator of {1}, it gives the close of the *last-but-one* bar. Sounds simple enough. Let's look at the other options to see its advantages. B: Live HTF data: Represented on the line label as "security(){0}". Note: this is the source that f_offset_synch() samples. The raw HTF data is very different on historical and realtime bars: + On historical bars, it uses a flat value from the end of the previous HTF bar. It updates at the close of the HTF bar. + On realtime bars, it varies between and within each chart bar. There might be occasions where you want to use live data, in full knowledge of its drawbacks described above. For example, to show simple live conditions that are reversible after a chart bar close. This library transforms live data to get the fixed data, thus giving you access to both live and fixed data with only one security() call. C: Historical data using security(){H}: To see how this behaves, set the {H} value in the settings to 1 and show options A, B, and C. + On historical bars, this option matches option A, this library function, exactly. It behaves just like security(){0} but one HTF bar behind, as you would expect. + On realtime bars, this option takes the value of security(){0} at the end of a HTF bar, but it takes it from the previous *chart* bar, and then persists that. The easiest way to see this inconsistency is on the first realtime bar (marked red at the top of the screen). This option suddenly jumps, even if it's in the middle of a HTF bar. Contrast this with option A, which is always constant, until it updates, once per HTF bar. D: PineCoders' original function: To see how this behaves, show options A, B, and D. Set the {H} value in the settings to 0, then 1. The PineCoders' original function (D) and extended function (E) do not have the same limitations as this library, described in the Limitations section. This option has all of the same advantages that this library function, option A, does, with the following differences: + It cannot access historical data. The {H} setting makes no difference. + It always updates at the open of the first chart bar in a new HTF bar. By contrast, this library function, option A, is configured by default to update at the close of the last chart bar in a HTF bar. This little frontrunning is only a few seconds but could be significant in trading. E.g. on a 1D HTF with a 4H chart, an alert that involves a HTF change set to trigger ON CLOSE would trigger 4 hours later using this method - but use exactly the same value. It depends on the market and timeframe as to whether you could actually trade this. E.g. at the very end of a tradfi day your order won't get executed. This behaviour mimics how security() itself updates, as is easy to see on the chart. If you don't want it, just set in_updateEarly to false. Then it matches option D exactly. E: PineCoders' function, extended to get history: To see how this behaves, show options A and E. Set the {H} value in the settings to 0, then 1. I modified the original function to be able to get historical values. In all other respects it is the same. Apart from not having the option to update earlier, the only disadvantage of this method vs this library function is that it requires one security() call for each historical operator. For example, if you wanted live data, and fixed data, and fixed data one bar back, you would need 3 security() calls. My library function requires just one. This is the essential tradeoff: extra complexity and less robustness in certain circumstances (the PineCoders function is simple and universal by comparison) for more flexibility with fewer security() calls.
Pine Script® kütüphanesi
SimpleCryptoLife tarafından
7
CDOI ProfileCumulative Delta of Open Interest Profile This script lets you visualize where there were Open Interest build-ups and discharges on a price basis. It only supports pairs where TradingView added the appropriate Open Interest data (at the time of posting that is only Binance and Kraken perpetual contracts) The script uses my own functions to poll lower timeframe data and compile it into a higher timeframe profile. And as such, it needs some tweaking to adjust it to your timeframe until Tradingview lets me do it codewise (hopefully one day) The instructions for using the Indicators are as follows: Condition: How often a new profile should be generated Sampling Rate and 1/Nth of the TF: These have to be calculated together to have a product that should correspond to the current timeframe in minutes. A few examples below ----------- Sampling - 1Nth of the TF 5 min ------- 5 --------------- 1 10 min ------ 10 ------------- 1 15 min ------ 5 --------------- 3 20 min ------ 10 ------------- 2 30 min ------ 10 -------------- 3 45 min ------- 9 -------------- 5 1 hour ------- 10 ------------- 6 4 hours ----- 10 -------------- 24 1 day -------- 10 ------------- 144 Transparency: This one is pretty self-explanatory but only applies to the Profile bars % change for a bar: This one indicates how precise each bar will be, but if you go too low the script becomes too heavy and stop running Bar limit: Limits the amounts of bars the script is run for (ae for the last 1000 bars). Lower = faster loading, too high will stop running UI color: Color and transparency of the center line and the box surrounding the whole profile
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Dereek69 tarafından
20
CHOPORSI CHOPORSI is a multiindicator. This indicator help You to recognize potential in or out singal. Base singals are from Choppines, RSI, AND DMI indicators. It is a combination of 3 separate indicators like choppines RSI and DMI. Then our new indicator see like bellow on next image. Yellow line is sum of CHOP index and RSI , in this case we can say its a CHOPORSI Index. Green line is DMI- line , this show us strength of sell position on the market. We schould use other signals, like LSMA 50/100 to improve trend changing. Like on next picture. Now how this indicator works? Yellow line is the sum oF Chop and RSI value - 50. Max and minimum value of CHOP and RSI are the same from 0 to 100. We have sum of them. Our minimum signal is 0+0-50=-50 maximum signal is 100+100-50= 150 Most times if both of tem are on top level ( then we have 150) the trend is chanhing from bullish to bearish. The same way if the RSI ist on 0 and chop is over 50 ( then we have index 0 ) wee changing the tren from bearish to bullish. Off course it not every time. We see other signals, to take our risk self not sugested by some art of indicators. But if we are abowe topline, witch is set to 85 we can sey, we have have oversold signal. Underline 30 isour potentialy buy signal. Midrange 50 is mostly trand changin line. This valu of top, mid bottom line you can change on the setting. Every Coin have another level of this lines, and need to be checked individual to the coin. Standard, settings are set fo timeframe : 12 min. 24 min, 1H and 4 H > Blue crosses signalize possibilities trend changing. This picture shou us how this indicator works. Buy long signal : If yellow line is mostly at the bottom and green mostly on the top. Sell long signal l. Yellow -top , green -bottom. The Green line is from Directional Movement Index and is - DI line. Its show us selling trend. even higher position then mor sell of . Standard value of CHOPPINES is 14 , works fin on 1H and abowe also wit the value of 28 Standard value for RSI AND -DI unchanging 14. I tjink this is a simplu helpfull indycator. WARNING!!! IF YOU AT THIS POINT CANT UNDERSUD THIS INDICATOR, PLEASE DONT USE THEM . Signal, schould be confirmed with other indicators like MA, EMA even better with LSMA . Please try it an make only paper trading, to undertand how its realy works. Thank You!
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ChartsHussars tarafından
8