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STD-Filtered, N-Pole Gaussian Filter [Loxx]This is a Gaussian Filter with Standard Deviation Filtering that works for orders (poles) higher than the usual 4 poles that was originally available in Ehlers Gaussian Filter formulas. Because of that, it is a sort of generalized Gaussian filter that can calculate arbitrary (order) pole Gaussian Filter and which makes it a sort of a unique indicator. For this implementation, the practical mathematical maximum is 15 poles after which the precision of calculation is useless--the coefficients for levels above 15 poles are so high that the precision loss actually means very little. Despite this maximal precision utility, I've left the upper bound of poles open-ended so you can try poles of order 15 and above yourself. The default is set to 5 poles which is 1 pole greater than the normal maximum of 4 poles. The purpose of the standard deviation filter is to filter out noise by and by default it will filter 1 standard deviation. Adjust this number and the filter selections (price, both, GMA, none) to reduce the signal noise. What is Ehlers Gaussian filter? This filter can be used for smoothing. It rejects high frequencies (fast movements) better than an EMA and has lower lag. published by John F. Ehlers in "Rocket Science For Traders". A Gaussian filter is one whose transfer response is described by the familiar Gaussian bell-shaped curve. In the case of low-pass filters, only the upper half of the curve describes the filter. The use of gaussian filters is a move toward achieving the dual goal of reducing lag and reducing the lag of high-frequency components relative to the lag of lower-frequency components. A gaussian filter with... One Pole: f = alpha*g + (1-alpha)f Two Poles: f = alpha*2g + 2(1-alpha)f - (1-alpha)2f Three Poles: f = alpha*3g + 3(1-alpha)f - 3(1-alpha)2f + (1-alpha)3f Four Poles: f = alpha*4g + 4(1-alpha)f - 6(1-alpha)2f + 4(1-alpha)3f - (1-alpha)4f and so on... For an equivalent number of poles the lag of a Gaussian is about half the lag of a Butterworth filters: Lag = N*P / pi^2, where, N is the number of poles, and P is the critical period Special initialization of filter stages ensures proper working in scans with as few bars as possible. From Ehlers Book: "The first objective of using smoothers is to eliminate or reduce the undesired high-frequency components in the eprice data. Therefore these smoothers are called low-pass filters, and they all work by some form of averaging. Butterworth low-pass filters can do this job, but nothing comes for free. A higher degree of filtering is necessarily accompanied by a larger amount of lag. We have come to see that is a fact of life." References John F. Ehlers: "Rocket Science For Traders, Digital Signal Processing Applications", Chapter 15: "Infinite Impulse Response Filters" Included Loxx's Expanded Source Types Signals Alerts Bar coloring Related indicators STD-Filtered, Gaussian Moving Average (GMA) STD-Filtered, Gaussian-Kernel-Weighted Moving Average One-Sided Gaussian Filter w/ Channels Fisher Transform w/ Dynamic Zones R-sqrd Adapt. Fisher Transform w/ D. Zones & Divs .
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Gaussian Filter MACD [Loxx]Gaussian Filter MACD is a MACD that uses an 1-4 Pole Ehlers Gaussian Filter for its calculations. Compare this with Ehlers Fisher Transform. What is Ehlers Gaussian filter? This filter can be used for smoothing. It rejects high frequencies (fast movements) better than an EMA and has lower lag. published by John F. Ehlers in "Rocket Science For Traders". First implemented in Wealth-Lab by Dr René Koch. A Gaussian filter is one whose transfer response is described by the familiar Gaussian bell-shaped curve. In the case of low-pass filters, only the upper half of the curve describes the filter. The use of gaussian filters is a move toward achieving the dual goal of reducing lag and reducing the lag of high-frequency components relative to the lag of lower-frequency components. A gaussian filter with... one pole is equivalent to an EMA filter. two poles is equivalent to EMA ( EMA ()) three poles is equivalent to EMA ( EMA ( EMA ())) and so on... For an equivalent number of poles the lag of a Gaussian is about half the lag of a Butterworth filters: Lag = N * P / (2 * ¶2), where, N is the number of poles, and P is the critical period Special initialization of filter stages ensures proper working in scans with as few bars as possible. From Ehlers Book: "The first objective of using smoothers is to eliminate or reduce the undesired high-frequency components in the eprice data. Therefore these smoothers are called low-pass filters, and they all work by some form of averaging. Butterworth low-pass filtters can do this job, but nothing comes for free. A higher degree of filtering is necessarily accompanied by a larger amount of lag. We have come to see that is a fact of life." References John F. Ehlers: "Rocket Science For Traders, Digital Signal Processing Applications", Chapter 15: "Infinite Impulse Response Filters" Included Loxx's Expanded Source Types Signals, zero or signal crossing, signal crossing is very noisy Alerts Bar coloring
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STD-Filtered, Gaussian Moving Average (GMA) [Loxx]STD-Filtered, Gaussian Moving Average (GMA) is a 1-4 pole Ehlers Gaussian Filter with standard deviation filtering. This indicator should perform similar to Ehlers Fisher Transform. The purpose of the standard deviation filter is to filter out noise by and by default it will filter 1 standard deviation. Adjust this number and the filter selections (price, both, GMA, none) to reduce the signal noise. What is Ehlers Gaussian filter? This filter can be used for smoothing. It rejects high frequencies (fast movements) better than an EMA and has lower lag. published by John F. Ehlers in "Rocket Science For Traders". First implemented in Wealth-Lab by Dr René Koch. A Gaussian filter is one whose transfer response is described by the familiar Gaussian bell-shaped curve. In the case of low-pass filters, only the upper half of the curve describes the filter. The use of gaussian filters is a move toward achieving the dual goal of reducing lag and reducing the lag of high-frequency components relative to the lag of lower-frequency components. A gaussian filter with... one pole is equivalent to an EMA filter. two poles is equivalent to EMA(EMA()) three poles is equivalent to EMA(EMA(EMA())) and so on... For an equivalent number of poles the lag of a Gaussian is about half the lag of a Butterworth filters: Lag = N * P / (2 * ¶2), where, N is the number of poles, and P is the critical period Special initialization of filter stages ensures proper working in scans with as few bars as possible. From Ehlers Book: "The first objective of using smoothers is to eliminate or reduce the undesired high-frequency components in the eprice data. Therefore these smoothers are called low-pass filters, and they all work by some form of averaging. Butterworth low-pass filtters can do this job, but nothing comes for free. A higher degree of filtering is necessarily accompanied by a larger amount of lag. We have come to see that is a fact of life." References John F. Ehlers: "Rocket Science For Traders, Digital Signal Processing Applications", Chapter 15: "Infinite Impulse Response Filters" Included Loxx's Expanded Source Types Signals Alerts Bar coloring Related indicators STD-Filtered, Gaussian-Kernel-Weighted Moving Average One-Sided Gaussian Filter w/ Channels Fisher Transform w/ Dynamic Zones R-sqrd Adapt. Fisher Transform w/ D. Zones & Divs.
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TheATR: Fisher Oscillator.Fisher Oscillator(FO). The Fisher Oscillator is inspired by John Ehlers "Fisher Transform". The oscillator highlights when prices have moved to an extreme, based on recent prices. The FO may help in spotting turning points, in the short-medium trends of an asset, also, it helps in recognizing the asset's trends themselves, giving a picture of mkt conditions affected by less noise. Fisher Oscillator Components. Fisher V1 -> Main FO. Fisher V2 -> Past Candle FO. 0-line threshold -> Directional Component. How to read the Fisher Oscillator. The FO is super easy to read by itself.. also, I coded some features which make it even easier to read. It's suggestions, which we can call "Signals", come from 2 different sources, accessible thanks to the variable "Signals Type". - 0-Line Crosses: When the "Fisher V1" upcrosses the oscillator 0-line, the oscillator suggests a Long scenario. When the "Fisher V1" downcrosses the oscillator 0-line, the oscillator suggests a Short scenario. - Classic Lines Crosses: When the "Fisher V1" upcrosses the "Fisher V2", the oscillator suggests a Long scenario. When the "Fisher V1" downcrosses the "Fisher V2", the oscillator suggests a Short scenario. Users will be able to recognise these Signals visually, thanks to some color customisation to the "Fisher V1" line, and thanks to the ability of the oscillator of plotting Signals. TheATR Documentation regarding TheATR: Fisher Oscillator. Researching and backtesting the FO, I noticed it's skill of being able to dynamically identify trend reversals with a nice degree of reliability. Also, the FO's able to keep up with trends up to their tops/bottoms, as it's very responsive. This makes the FO a trend-following oscillator in my personal view, because its nature of being very fast in detecting reversals will lead to many false reversals as well. On the other face of this coin, if we look at the FO as a source for confirmations for a trend-following strategy, may be very useful. To conclude, I would use the FO as a confirmation oscillator, in a trend-following strategy that needs to have other components. Thanks for reading, TheATR.
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Non-Lag Inverse Fisher Transform of RSX [Loxx]Non-Lag Inverse Fisher Transform of RSX is an Inverse Fisher Transform on the Non-Lagged Smoothing Filter of Jurik RSX. What is the Inverse Fisher Transform? The Inverse Fisher Transform was authored by John Ehlers. The IFT applies some math functions and constants to a moving average of the relative strength index (rsi) of the closing price to calculate its oscillator position. T read more here: www.mesasoftware.com What is RSX? RSI is a very popular technical indicator, because it takes into consideration market speed, direction and trend uniformity. However, the its widely criticized drawback is its noisy (jittery) appearance. The Jurk RSX retains all the useful features of RSI , but with one important exception: the noise is gone with no added lag. What is the Non-lag moving average? The Non Lag Moving average follows price closely and gives very quick signals as well as early signals of price change. As a standalone Moving Average, it should not be used on its own, but as an additional confluence tool for early signals. Included: Alerts Signals Bar coloring
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One-Sided Gaussian Support & Resistance Rate [Loxx]One-Sided Gaussian Support & Resistance Rate is a mean reversion oscillator much like Fisher Transform. This indicator is built using a one-sided Gaussian filter. If you pair this with Fisher Transform and line up the settings, you'll notice similar outcomes. You'll notice that as the oscillator levels out at around zero or one that this signifies a zone of resistance or support. See here for more details on calculating the OS Gaussian Filter: Included: Bar coloring Signals Alerts Ehlers 2-Pole Super Smoothing for smoothing source inputs
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R-sqrd Adapt. Fisher Transform w/ D. Zones & Divs. [Loxx]The full name of this indicator is R-Squared Adaptive Fisher Transform w/ Dynamic Zones and Divergences. This is an R-squared adaptive Fisher transform with adjustable dynamic zones, signals, alerts, and divergences. What is Fisher Transform? The Fisher Transform is a technical indicator created by John F. Ehlers that converts prices into a Gaussian normal distribution. The indicator highlights when prices have moved to an extreme, based on recent prices. This may help in spotting turning points in the price of an asset. It also helps show the trend and isolate the price waves within a trend. What is R-squared Adaptive? One tool available in forecasting the trendiness of the breakout is the coefficient of determination ( R-squared ), a statistical measurement. The R-squared indicates linear strength between the security's price (the Y - axis) and time (the X - axis). The R-squared is the percentage of squared error that the linear regression can eliminate if it were used as the predictor instead of the mean value. If the R-squared were 0.99, then the linear regression would eliminate 99% of the error for prediction versus predicting closing prices using a simple moving average . R-squared is used here to derive an r-squared value that is then modified by a user input "factor" What are Dynamic Zones? As explained in "Stocks & Commodities V15:7 (306-310): Dynamic Zones by Leo Zamansky, Ph .D., and David Stendahl" Most indicators use a fixed zone for buy and sell signals. Here’ s a concept based on zones that are responsive to past levels of the indicator. One approach to active investing employs the use of oscillators to exploit tradable market trends. This investing style follows a very simple form of logic: Enter the market only when an oscillator has moved far above or below traditional trading lev- els. However, these oscillator- driven systems lack the ability to evolve with the market because they use fixed buy and sell zones. Traders typically use one set of buy and sell zones for a bull market and substantially different zones for a bear market. And therein lies the problem. Once traders begin introducing their market opinions into trading equations, by changing the zones, they negate the system’s mechanical nature. The objective is to have a system automatically define its own buy and sell zones and thereby profitably trade in any market — bull or bear. Dynamic zones offer a solution to the problem of fixed buy and sell zones for any oscillator-driven system. An indicator’s extreme levels can be quantified using statistical methods. These extreme levels are calculated for a certain period and serve as the buy and sell zones for a trading system. The repetition of this statistical process for every value of the indicator creates values that become the dynamic zones. The zones are calculated in such a way that the probability of the indicator value rising above, or falling below, the dynamic zones is equal to a given probability input set by the trader. To better understand dynamic zones, let's first describe them mathematically and then explain their use. The dynamic zones definition: Find V such that: For dynamic zone buy: P{X <= V}=P1 For dynamic zone sell: P{X >= V}=P2 where P1 and P2 are the probabilities set by the trader, X is the value of the indicator for the selected period and V represents the value of the dynamic zone. The probability input P1 and P2 can be adjusted by the trader to encompass as much or as little data as the trader would like. The smaller the probability, the fewer data values above and below the dynamic zones. This translates into a wider range between the buy and sell zones. If a 10% probability is used for P1 and P2, only those data values that make up the top 10% and bottom 10% for an indicator are used in the construction of the zones. Of the values, 80% will fall between the two extreme levels. Because dynamic zone levels are penetrated so infrequently, when this happens, traders know that the market has truly moved into overbought or oversold territory. Calculating the Dynamic Zones The algorithm for the dynamic zones is a series of steps. First, decide the value of the lookback period t. Next, decide the value of the probability Pbuy for buy zone and value of the probability Psell for the sell zone. For i=1, to the last lookback period, build the distribution f(x) of the price during the lookback period i. Then find the value Vi1 such that the probability of the price less than or equal to Vi1 during the lookback period i is equal to Pbuy. Find the value Vi2 such that the probability of the price greater or equal to Vi2 during the lookback period i is equal to Psell. The sequence of Vi1 for all periods gives the buy zone. The sequence of Vi2 for all periods gives the sell zone. In the algorithm description, we have: Build the distribution f(x) of the price during the lookback period i. The distribution here is empirical namely, how many times a given value of x appeared during the lookback period. The problem is to find such x that the probability of a price being greater or equal to x will be equal to a probability selected by the user. Probability is the area under the distribution curve. The task is to find such value of x that the area under the distribution curve to the right of x will be equal to the probability selected by the user. That x is the dynamic zone. Included: Bar coloring 4 signal variations w/ alerts Divergences w/ alerts Loxx's Expanded Source Types
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Fisher Transform of MACD w/ Quantile Bands [Loxx]Fisher Transform of MACD w/ Quantile Bands is a Fisher Transform indicator with Quantile Bands that takes as it's source a MACD. The MACD has two different source inputs for fast and slow moving averages. What is Fisher Transform? The Fisher Transform is a technical indicator created by John F. Ehlers that converts prices into a Gaussian normal distribution. The indicator highlights when prices have moved to an extreme, based on recent prices. This may help in spotting turning points in the price of an asset. It also helps show the trend and isolate the price waves within a trend. What is Quantile Bands? In statistics and the theory of probability, quantiles are cutpoints dividing the range of a probability distribution into contiguous intervals with equal probabilities, or dividing the observations in a sample in the same way. There is one less quantile than the number of groups created. Thus quartiles are the three cut points that will divide a dataset into four equal-size groups (cf. depicted example). Common quantiles have special names: for instance quartile, decile (creating 10 groups: see below for more). The groups created are termed halves, thirds, quarters, etc., though sometimes the terms for the quantile are used for the groups created, rather than for the cut points. q-Quantiles are values that partition a finite set of values into q subsets of (nearly) equal sizes. There are q − 1 of the q-quantiles, one for each integer k satisfying 0 < k < q. In some cases the value of a quantile may not be uniquely determined, as can be the case for the median (2-quantile) of a uniform probability distribution on a set of even size. Quantiles can also be applied to continuous distributions, providing a way to generalize rank statistics to continuous variables. When the cumulative distribution function of a random variable is known, the q-quantiles are the application of the quantile function (the inverse function of the cumulative distribution function) to the values {1/q, 2/q, …, (q − 1)/q}. What is MACD? Moving average convergence divergence ( MACD ) is a trend-following momentum indicator that shows the relationship between two moving averages of a security’s price. The MACD is calculated by subtracting the 26-period exponential moving average ( EMA ) from the 12-period EMA . Included: Zero-line and signal cross options for bar coloring, signals, and alerts Alerts Signals Loxx's Expanded Source Types 35+ moving average types
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Fisher Transform w/ Dynamic Zones [Loxx]What is Fisher Transform? The Fisher Transform is a technical indicator created by John F. Ehlers that converts prices into a Gaussian normal distribution. The indicator highlights when prices have moved to an extreme, based on recent prices. This may help in spotting turning points in the price of an asset. It also helps show the trend and isolate the price waves within a trend. What are Dynamic Zones? As explained in "Stocks & Commodities V15:7 (306-310): Dynamic Zones by Leo Zamansky, Ph .D., and David Stendahl" Most indicators use a fixed zone for buy and sell signals. Here’ s a concept based on zones that are responsive to past levels of the indicator. One approach to active investing employs the use of oscillators to exploit tradable market trends. This investing style follows a very simple form of logic: Enter the market only when an oscillator has moved far above or below traditional trading lev- els. However, these oscillator- driven systems lack the ability to evolve with the market because they use fixed buy and sell zones. Traders typically use one set of buy and sell zones for a bull market and substantially different zones for a bear market. And therein lies the problem. Once traders begin introducing their market opinions into trading equations, by changing the zones, they negate the system’s mechanical nature. The objective is to have a system automatically define its own buy and sell zones and thereby profitably trade in any market — bull or bear. Dynamic zones offer a solution to the problem of fixed buy and sell zones for any oscillator-driven system. An indicator’s extreme levels can be quantified using statistical methods. These extreme levels are calculated for a certain period and serve as the buy and sell zones for a trading system. The repetition of this statistical process for every value of the indicator creates values that become the dynamic zones. The zones are calculated in such a way that the probability of the indicator value rising above, or falling below, the dynamic zones is equal to a given probability input set by the trader. To better understand dynamic zones, let's first describe them mathematically and then explain their use. The dynamic zones definition: Find V such that: For dynamic zone buy: P{X <= V}=P1 For dynamic zone sell: P{X >= V}=P2 where P1 and P2 are the probabilities set by the trader, X is the value of the indicator for the selected period and V represents the value of the dynamic zone. The probability input P1 and P2 can be adjusted by the trader to encompass as much or as little data as the trader would like. The smaller the probability, the fewer data values above and below the dynamic zones. This translates into a wider range between the buy and sell zones. If a 10% probability is used for P1 and P2, only those data values that make up the top 10% and bottom 10% for an indicator are used in the construction of the zones. Of the values, 80% will fall between the two extreme levels. Because dynamic zone levels are penetrated so infrequently, when this happens, traders know that the market has truly moved into overbought or oversold territory. Calculating the Dynamic Zones The algorithm for the dynamic zones is a series of steps. First, decide the value of the lookback period t. Next, decide the value of the probability Pbuy for buy zone and value of the probability Psell for the sell zone. For i=1, to the last lookback period, build the distribution f(x) of the price during the lookback period i. Then find the value Vi1 such that the probability of the price less than or equal to Vi1 during the lookback period i is equal to Pbuy. Find the value Vi2 such that the probability of the price greater or equal to Vi2 during the lookback period i is equal to Psell. The sequence of Vi1 for all periods gives the buy zone. The sequence of Vi2 for all periods gives the sell zone. In the algorithm description, we have: Build the distribution f(x) of the price during the lookback period i. The distribution here is empirical namely, how many times a given value of x appeared during the lookback period. The problem is to find such x that the probability of a price being greater or equal to x will be equal to a probability selected by the user. Probability is the area under the distribution curve. The task is to find such value of x that the area under the distribution curve to the right of x will be equal to the probability selected by the user. That x is the dynamic zone. Included 3 signal types Bar coloring Alerts Channels fill Loxx's Expanded Source Types
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VHF Adaptive Fisher Transform [Loxx]VHF Adaptive Fisher Transform is an adaptive cycle Fisher Transform using a Vertical Horizontal Filter to calculate the volatility adjusted period. What is VHF Adaptive Cycle? Vertical Horizontal Filter (VHF) was created by Adam White to identify trending and ranging markets. VHF measures the level of trend activity, similar to ADX DI. Vertical Horizontal Filter does not, itself, generate trading signals, but determines whether signals are taken from trend or momentum indicators. Using this trend information, one is then able to derive an average cycle length. What is Fisher Transform? The Fisher Transform is a technical indicator created by John F. Ehlers that converts prices into a Gaussian normal distribution. The indicator highlights when prices have moved to an extreme, based on recent prices. This may help in spotting turning points in the price of an asset. It also helps show the trend and isolate the price waves within a trend. Included: Zero-line and signal cross options for bar coloring Customizable overbought/oversold thresh-holds Alerts Signals
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CFB Adaptive Fisher Transform [Loxx]CFB Adaptive Fisher Transform is an adaptive cycle Fisher Transform using Jurik's Composite Fractal Behavior Algorithm to calculate the price-trend cycle period. What is Composite Fractal Behavior (CFB)? All around you mechanisms adjust themselves to their environment. From simple thermostats that react to air temperature to computer chips in modern cars that respond to changes in engine temperature, r.p.m.'s, torque, and throttle position. It was only a matter of time before fast desktop computers applied the mathematics of self-adjustment to systems that trade the financial markets. Unlike basic systems with fixed formulas, an adaptive system adjusts its own equations. For example, start with a basic channel breakout system that uses the highest closing price of the last N bars as a threshold for detecting breakouts on the up side. An adaptive and improved version of this system would adjust N according to market conditions, such as momentum, price volatility or acceleration. Since many systems are based directly or indirectly on cycles, another useful measure of market condition is the periodic length of a price chart's dominant cycle, (DC), that cycle with the greatest influence on price action. The utility of this new DC measure was noted by author Murray Ruggiero in the January '96 issue of Futures Magazine. In it. Mr. Ruggiero used it to adaptive adjust the value of N in a channel breakout system. He then simulated trading 15 years of D-Mark futures in order to compare its performance to a similar system that had a fixed optimal value of N. The adaptive version produced 20% more profit! This DC index utilized the popular MESA algorithm (a formulation by John Ehlers adapted from Burg's maximum entropy algorithm, MEM). Unfortunately, the DC approach is problematic when the market has no real dominant cycle momentum, because the mathematics will produce a value whether or not one actually exists! Therefore, we developed a proprietary indicator that does not presuppose the presence of market cycles. It's called CFB (Composite Fractal Behavior) and it works well whether or not the market is cyclic. CFB examines price action for a particular fractal pattern, categorizes them by size, and then outputs a composite fractal size index. This index is smooth, timely and accurate Essentially, CFB reveals the length of the market's trending action time frame. Long trending activity produces a large CFB index and short choppy action produces a small index value. Investors have found many applications for CFB which involve scaling other existing technical indicators adaptively, on a bar-to-bar basis. What is Jurik Volty used in the Juirk Filter? One of the lesser known qualities of Juirk smoothing is that the Jurik smoothing process is adaptive. "Jurik Volty" (a sort of market volatility ) is what makes Jurik smoothing adaptive. The Jurik Volty calculation can be used as both a standalone indicator and to smooth other indicators that you wish to make adaptive. What is the Jurik Moving Average? Have you noticed how moving averages add some lag (delay) to your signals? ... especially when price gaps up or down in a big move, and you are waiting for your moving average to catch up? Wait no more! JMA eliminates this problem forever and gives you the best of both worlds: low lag and smooth lines. Ideally, you would like a filtered signal to be both smooth and lag-free. Lag causes delays in your trades, and increasing lag in your indicators typically result in lower profits. In other words, late comers get what's left on the table after the feast has already begun. What is Fisher Transform? The Fisher Transform is a technical indicator created by John F. Ehlers that converts prices into a Gaussian normal distribution. The indicator highlights when prices have moved to an extreme, based on recent prices. This may help in spotting turning points in the price of an asset. It also helps show the trend and isolate the price waves within a trend. Included: Zero-line and signal cross options for bar coloring Customizable overbought/oversold thresh-holds Alerts Signals
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APA Adaptive Fisher Transform [Loxx]APA Adaptive Fisher Transform is an adaptive cycle Fisher Transform using Ehlers Autocorrelation Periodogram Algorithm to calculate the dominant cycle period. What is an adaptive cycle, and what is Ehlers Autocorrelation Periodogram Algorithm? From Ehlers' book Cycle Analytics for Traders Advanced Technical Trading Concepts by John F. Ehlers , 2013, page 135: "Adaptive filters can have several different meanings. For example, Perry Kaufman’s adaptive moving average ( KAMA ) and Tushar Chande’s variable index dynamic average ( VIDYA ) adapt to changes in volatility . By definition, these filters are reactive to price changes, and therefore they close the barn door after the horse is gone.The adaptive filters discussed in this chapter are the familiar Stochastic , relative strength index ( RSI ), commodity channel index ( CCI ), and band-pass filter.The key parameter in each case is the look-back period used to calculate the indicator. This look-back period is commonly a fixed value. However, since the measured cycle period is changing, it makes sense to adapt these indicators to the measured cycle period. When tradable market cycles are observed, they tend to persist for a short while.Therefore, by tuning the indicators to the measure cycle period they are optimized for current conditions and can even have predictive characteristics. The dominant cycle period is measured using the Autocorrelation Periodogram Algorithm. That dominant cycle dynamically sets the look-back period for the indicators. I employ my own streamlined computation for the indicators that provide smoother and easier to interpret outputs than traditional methods. Further, the indicator codes have been modified to remove the effects of spectral dilation.This basically creates a whole new set of indicators for your trading arsenal." What is Fisher Transform? The Fisher Transform is a technical indicator created by John F. Ehlers that converts prices into a Gaussian normal distribution. The indicator highlights when prices have moved to an extreme, based on recent prices. This may help in spotting turning points in the price of an asset. It also helps show the trend and isolate the price waves within a trend. Included: Zero-line and signal cross options for bar coloring Customizable overbought/oversold thresh-holds Alerts Signals
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Phase Accumulation Adaptive Fisher Transform [Loxx]Phase Accumulation Adaptive Fisher Transform is an adaptive Fisher Transform using a modified version of Ehlers Phase Accumulation Cycle Period. This version of Phase Accumulation Cylce Period accepts as inputs: 1) total number of cycles you wish to inject into the calculation, this works as a multiplier so the higher this number, the longer the period output; 2) filter is to change the alpha value of the final smother before returning the period output. What is the Phase Accumulation Cycle? The phase accumulation method of computing the dominant cycle is perhaps the easiest to comprehend. In this technique, we measure the phase at each sample by taking the arctangent of the ratio of the quadrature component to the in-phase component. A delta phase is generated by taking the difference of the phase between successive samples. At each sample we can then look backwards, adding up the delta phases.When the sum of the delta phases reaches 360 degrees, we must have passed through one full cycle, on average.The process is repeated for each new sample. The phase accumulation method of cycle measurement always uses one full cycle’s worth of historical data.This is both an advantage and a disadvantage.The advantage is the lag in obtaining the answer scales directly with the cycle period.That is, the measurement of a short cycle period has less lag than the measurement of a longer cycle period. However, the number of samples used in making the measurement means the averaging period is variable with cycle period. longer averaging reduces the noise level compared to the signal.Therefore, shorter cycle periods necessarily have a higher out- put signal-to-noise ratio. What is Fisher Transform? The Fisher Transform is a technical indicator created by John F. Ehlers that converts prices into a Gaussian normal distribution. The indicator highlights when prices have moved to an extreme, based on recent prices. This may help in spotting turning points in the price of an asset. It also helps show the trend and isolate the price waves within a trend. Included: Zero-line and signal cross options for bar coloring Customizable overbought/oversold thresh-holds Alerts Signals
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Goertzel Cycle Period Adaptive Fisher Transform [Loxx]Goertzel Cycle Period Adaptive Fisher Transform is an adaptive Fisher Transform using the Goertzel Cycle Algorithm to derive length inputs. What is Goertzel Cycle Algorithm? Read here: What is Fisher Transform? The Fisher Transform is a technical indicator created by John F. Ehlers that converts prices into a Gaussian normal distribution. The indicator highlights when prices have moved to an extreme, based on recent prices. This may help in spotting turning points in the price of an asset. It also helps show the trend and isolate the price waves within a trend. Included: Zero-line and signal cross options for bar coloring Customizable overbought/oversold thresh-holds Alerts Signals ***Please note, the Goertzel Cycle Algorithm is processor heavy, so this indicator will take some time to load.
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由loxx提供
3
Fancy Bars v.02.07Hello Lads, this is the mandatory description to not get this indicator banned by tradingview. :-) So what you can basically see are different types of colored candles and some weird signs below and above the candles. In this picture you can see all features that the indicator is providing at the moment of the relase. More to come soon anon. As first we got different color settings for different candles. There are two different types of color candles. Casual Extensions of the source you have chosen in the settings are either green or red by default. If the market extends a little more, the bars are being colored either blue or purple. These candles work well to identifiy local exhaustion points after a rallye to the up or the down side. But you can also take them as an entry. For Example: A green or blue extension appears after a significant drop in price. Candles like these are usually a first sign of a local bottom to form or a bounce to appear. In order to view green and blue bars at the opposite, you can take them as an exit for your long after a rally, because the market tends to be exhausted afterwards. It works the same way for the red and purple bars as you can see here. I hope the screenshot can make that clear aswell. :-) So on the screenshots you can see aswell many different signs below and above the candles. Candles with an "F" below or above the candle are showing you that the Fisher Transform indicator has made a turnover. So this basically means that if there is a green "F" below a bar the value of the Fischer Oscillator is smaller then - 3 and the fast line is crossing the slow line, which is bullish. If you see a "F" above a candle, this means that the slow line is crossing the fast line and the value of the fisher transform is greater then 3. If you see an "H" above or below the candle this means that the Indicator "Hull Suite by InSilico" is turning over. If the "H" is red and above the candle it´s a bearish sign, if it´s green and below the candle, it is a bullish sign. Well as next we have these white colored "X" appearing form time to time. Well X stands basically for EXIT. We have calculated the difference from the current price to a selectable EMA / MA and if the percentage difference in market is bigger then the selected value in the menu, the market will simply print an "X" where you can exit your long or short position. We recommand to use this function, espacially as all other functions aswell, only for scalping purposes, to catch a possible reversion of the price. Here you can see the menu at it´s full beauty. You can select every mode and adjust your settings for your personal preferences. gyazo.com gyazo.com You can even chose different modes for the extension bars, which are currently being tested. If you got some feedback, please let us know! The "Vanilla" mode of the extension bars takes the volume from the current chart that you are looking at. So this mode basically works for all in TradingView existing markets where chart provides you volume. The second mode aggregates Spot and Perpetual Volume from the biggest exchanges. We recommand to use this and the other modes (except vanilla ofc) only for BTC and ETH. Furthermore we took the provided Open Interest from Binance to calculate the bars. It is as all the other things aswell highly experimental and hasn´t been tested a lot yet. Last but not least, we calculated from the given Open Interest by Binance a CVD for BTC and ETH, which expansions are also being plotted as the previous explained candles. gyazo.com You can also change the length and the type of the AVG that is being calculated for the bar coloring. As well as for the Exit Bars and the Hull Suite. gyazo.com gyazo.com gyazo.com For this Project we have used Public Source Code by @InSilico for the HullSuite and ressources by Tradingview for the Fisher Turnover feature.
Pine Script™指标
由FynnJL提供
7
RAVI FX Fisher [Loxx]RAVI FX Fisher is a special implementation of RAVI using WMA moving averages and ATR and then normalized like Fisher Transform. If the histogram falls between the white lines, the market is too choppy to trade. This indicator is tuned for Forex. What is RAVI? The Range Action Verification Index (RAVI) indicator shows the percentage difference between current prices and past prices to identify market trends. It is calculated based on moving averages of different lengths. Included: -Change bar colors
Pine Script™指标
由loxx提供
1
Fisher Transform, clone of MT4 "Fisher_no_repainting" [Loxx]The Fisher Transform is a technical indicator created by John F. Ehlers that converts prices into a Gaussian normal distribution.1 The indicator highlights when prices have moved to an extreme, based on recent prices. This may help in spotting turning points in the price of an asset. It also helps show the trend and isolate the price waves within a trend. Included: - Oversold and overbought regions This is an exact clone of the "Fisher_no_repainting" MT4 indicator
Pine Script™指标
由loxx提供
2
Directional Movement Index + Fisher Price Action With LabelsDIRECTIONAL MOVEMENT INDEX + FISHER PRICE ACTION WITH LABELS Directional Movement Index shows buy and sell pressure. Fisher transform shows price action trending bullish or bearish. Caution dots notify you of conflicting trends. ***HOW TO USE*** The top lines are the fisher transform showing you the price action trend. The bottom lines filled with color shows the DMI directional movement index. The yellow dots at the bottom tell you if these two indicators are currently giving conflicting signals. DMI If the green line is above the red line and the background is colored green, there is more market buying than selling. If the red line is above the green line and the background is colored red, there is more market selling than buying. FISHER TRANSFORM If the lines are painted green, the price action is trending up. If the lines are painted red, the price action is trending down. CAUTION DOTS If a yellow dot shows up at the bottom of the chart, it is notifying you that the DMI and Fisher Transform are currently giving opposite signals…. so use caution. ***BULLISH/BEARISH LABEL*** There is also a label on the right side that tells you whether there is more buying or selling. This table updates in real time and changes colors so you can get an easy, quick interpretation of the current buy/sell pressure without having to look at the indicator data so you can make faster decisions on whether to enter or exit a trade. Green means more market buying than selling. Red means more market selling than buying. Blue means an equal amount of market buying and selling. If buying pressure is bullish but below the 20 level, a second label will show up in purple letting you know there is weak buying pressure so use caution. If selling pressure is bearish but below the 20 level, a second label will show up in purple letting you know there is weak selling pressure so use caution. There is a third label showing the current trend of the fisher transform. Green means bullish price action. Red means bearish price action. The fourth label is orange and only shows up when the DMI and Fisher Transform are currently giving opposite signals, so make sure you use caution during those times. ***MARKETS*** This indicator can be used as a signal on all markets, including stocks, crypto, futures and forex. ***TIMEFRAMES*** This directional movement index + fisher transform indicator can be used on all timeframes. ***TIPS*** Try using numerous indicators of ours on your chart so you can instantly see the bullish or bearish trend of multiple indicators in real time without having to analyze the data. Some of our favorites are our Auto Fibonacci, Volume Profile, Momentum, Auto Support And Resistance and Money Flow Index in combination with this Directional Movement Index + Fisher Transform. They all have real time Bullish and Bearish labels as well so you can immediately understand each indicator's trend.
Pine Script™指标
由FriendOfTheTrend提供
4
Fisher Cycle Adaptive, Fisher Transform [loxx]Fisher Cycle Adaptive, Fisher Transform Things to know -Experimental, not to be used in trading Calculation -Uses a measurement where the dominant, raw Fisher Transform position is measured and then used as the length input for the next bar -This is based on raw recursive look backs, not based on any sine wave or signal processing measure of cycle dominance How to use -Change from Fixed to Fisher Cycle, adjust the wave cycle percent look back % Features -Bar coloring -Thresholds
Pine Script™指标
由loxx提供
7
ADX Screener// Identify potential trend reversals using ADX on up to 40 crypto assets. // ADX shows the strength of a trend, not the direction. // By monitoring the difference of ADX values between candles, you can potentially identify reversals before they happen. // A strong trend has a 14 period ADX slope increasing .5 or more from the prior candle. // At a minimum, a weak trend has a 14 period ADX slope of +.25 or less, and strictly it would be decreasing. // This indicator prints one row for each asset with three columns for ADX differences one candle back each. // The asset name is colored green or red dependent on whether +DI > -DI or not. The market was in a down trend (-DI > +DI). A bullish wave moved price up to EMA 8 resistance with strong ADX momentum (ADX diff of prior candle >= 0.5). A Spinning Top showed that the trend was losing momentum (ADX diff lower than previous candle, showed the bullish wave losing momentum). A Morning Star bearish reversal pattern draws resistance at the high of the pattern. (ADX Bullish momentum exhausted). Symbol: -DI > +DI 2: Strong momentum 1: Losing momentum (spinning top) 0: Trend reversal (bearish engulfing pattern)
Pine Script™指标
由zerocashcool提供
6
Inverse Fisher Transform ScreenerThis is a Screener for Inverse Fisher Transform on multiple oscillators This tool is intended to aid you to visually analyse reasonable buy and sell IFT-signal thresholds across multiple tokens and different sources. It will help you speed up the tedious and manual work of trying out different IFT-settings when you're able to review the chart visually to find which values are present the most often and the location of that signal on the chart. What does it bring to the table, why is it useful? - Invese Fisher Transform on multiple oscillators such as RSI, MFI, CCI, Stochastic - Combine multiple sources into one signal, select how it's calculated - Visually review the chart for good IFT-settings - 4 dynamic buy labels with different thresholds - 4 dynamic sell labels with different thresholds - Buy and sell on signal reversal - Create your own trade alerts for automation - Backtester compatible (plots 1 and 2 for buy and sell signals) - A dynamic table counting amount of signals for each setting - The labels are automatically updated when you change the values in the settings How to use? 1. Change signal source and method 2. Change buy and sell thresholds 3. Show/hide additional labels 4. Review chart 5. Change trade settings 6. Backtest 7. Create alerts Description With this indicator you're able to visually review the signal strength of one or multiple oscillators processed with Inverse Fisher Transform ( IFT ), combine them and choose the method of calculation when they are combined. This produces a signal which strongly fluctuates between -1 and +1 instead of the more common 0-100 most oscillators uses. The intention is to make the decision making clearer and easier when you decide when to enter or exit a trade. This aims to help you remove the feelings from your trading. Inverse Fisher Transform was first presented by John Ehlers in Stocks & Commodities V. 22:5 where he proposes to process RSI with the formula for hyperbolic tangent, tanh (x). This transforms the oscillator into a smoother and more compressed version with quicker oscillations.
Pine Script™指标
由asterbots提供