Fishertrasnform — Indicators and Strategies

Inverse Fisher Oscillator [BigBeluga]The Inverse Fisher Oscillator is a powerful tool for identifying market trends and potential reversal points by applying the Inverse Fisher Transform to normalized price data. This indicator plots multiple smoothed oscillators, each color-coded to signify their relation to dynamic volatility bands. Additionally, the Butterworth filter is incorporated to further refine trend signals. The Inverse Fisher Oscillator offers traders a visually appealing and insightful approach to trend analysis and market direction detection. 🔵 KEY FEATURES ● Inverse Fisher Oscillator Visualization Multiple Oscillators : The indicator calculates and plots six different Inverse Fisher Oscillators, each smoothed at increasing levels to provide a layered view of price momentum. Color-Coded Signals : The oscillator lines are color-coded based on their relation to the volatility bands—green for bullish momentum, red for bearish momentum, and yellow for neutral movements. ● Butterworth Filter Integration Filtering : The Butterworth filter is applied to mid-line Bands to reduce noise, allowing for clearer trend detection. // Calculate constants for the Butterworth filter float piPrd = math.pi / mid_len float g = math.sqrt(2) float a1 = math.exp(-g * piPrd) float b1 = 2 * a1 * math.cos(g * piPrd) float coef2 = b1 float coef3 = -a1 * a1 float coef1 = (1 - b1 + a1 * a1) / 4 // Source data for the Butterworth filter float source = ifish // The first inverse Fisher Oscillator is used as the source // Previous source and butter filter values var float butter = na // Initialize the 'butter' variable // Handle null values using the nz function float prevB1 = nz(butter , source) // Use 'source' as a fallback if butter is null float prevB2 = nz(butter , source) // Use 'source' as a fallback if butter is null // Calculate the Butterworth filter value butter := coef1 * (source + (2 * source ) + source ) + (coef2 * prevB1) + (coef3 * prevB2) ● Numbered Signal Marks Signal Markers : The indicator plots numbered signals on the chart when an oscillator crosses above the upper volatility band or below the lower volatility band. Numbered Lines : Numbers correspond to the different oscillators (1-6), helping traders easily identify which smoothing level generated the signal. Visual Cues : The signals are color-coded—green for bullish crossovers and red for bearish crossunders—providing clear visual cues for trend accumulation phases. Mid-Line Option : Traders can choose between plotting the Butterworth filter as a dynamic mid-line or simply displaying it as part of the bands. Volatility Bands : Dynamic volatility bands provide additional context for interpreting the strength and sustainability of trends. ● Dashboard Display Real-Time Market Trend Overview : The dashboard in the bottom-right corner of the chart displays the market trend based on the Inverse Fisher Oscillator for six different smoothing levels, providing a clear visual summary of market direction. Direction Symbols : Directional symbols (up, down, or neutral) are displayed in the dashboard, color-coded to represent bullish, bearish, or neutral momentum. Current Price Display : The dashboard also shows the current price and highlights whether it is above or below the opening price. 🔵 HOW TO USE ● Identifying Trend Reversals Bullish Reversals : When the oscillators short period lines start to cross above the upper volatility band (green), it indicates potential bullish momentum. Bearish Reversals : When the oscillator crosses below the lower volatility band (red), it signals potential bearish momentum. Neutral Signals : When the oscillator remains within the bands (yellow), it suggests that the market is in a neutral or consolidating state. Traders may choose to wait for a clearer trend signal. ● Using the Dashboard for Trend Overview Market Trend Summary : The dashboard provides a quick overview of market direction across six different smoothing levels. Green arrows indicate bullish momentum, red arrows indicate bearish momentum, and wavy lines suggest neutrality. Price Context : The dashboard also displays the current price, helping traders quickly assess whether the price is moving in the expected direction relative to their trend analysis. ● Volatility Band Interpretation Volatility-Based Signals : Pay attention to how the oscillators interact with the volatility bands. Strong trends will often result in oscillators staying above or below the bands, while weaker trends or consolidations will see oscillators hovering within the bands. 🔵 CUSTOMIZATION Length and Smoothing : Adjust the length and smoothing parameters to fit different market conditions and timeframes. Bands Multiplier : Customize the multiplier for the volatility bands to make them more or less sensitive to price changes. Mid-Line Type : Choose whether to display the Butterworth filter as a mid-line or incorporate it into the volatility bands. Signal Markers : Toggle on or off the number markers for signal crossovers, making it easier to identify key entry and exit points. 🔵 CONCLUSION The Inverse Fisher Oscillator combines the power of the Inverse Fisher Transform and the Butterworth filter to provide a sophisticated approach to trend and reversal detection. By leveraging volatility-based analysis and visually intuitive signals, this indicator helps traders spot potential entry and exit points with greater clarity. The customizable dashboard display adds further value, offering a real-time summary of market conditions to enhance decision-making. Use this tool in conjunction with other technical analysis methods to develop a well-rounded trading strategy.

Fisher+ [OSC]The Fisher Transform Indicator is classified as an oscillator, meaning that its value swings above and below a central point. This characteristic allows traders to identify overbought and oversold conditions, providing potential clues about market reversals. As mentioned previously, it is an oscillator so the strength of the move is displayed by how long the fisher line stays above/below zero. Indicator can be used to aid in confluence near supply/demand zones. White Line = Fisher Red/Blue Line = Moving Average --Changes color whether fisher line is above/below the MA Red/Blue Shaded Line = Moving Average --Changes color based on a smoothing factor Red/Blue Shaded Fill = Asset in Overbought/Oversold Conditions Red/Blue Circles = Asset in Extreme Overbought/Oversold Conditions Red/Blue Triangles = MACD Signals Below/Above "0" Divergence Labels = Asset Signaling Divergence The moving average line will turn red/blue as long as the fisher line is below/above the moving average. The shaded MA line will switch colors based on if it is moving in an up/down trend. The MA can also be used as a signal and treated similar to an oscillator. Market trending conditions will either keep the MA below/above the dashed zero line. MACD code credited to LazyBear's MACD Leader indicator. It is used to filter out/confirm any signals such as divergences. As long as the MACD Leader line is above both the MACD line and signal lines then it'll signal with with a triangle. MACD divergences will be added at a later time.

Moonhub Index Moonhub Index combines several popular technical indicators to create an aggregated index that aims to give a clearer overall picture of the market. The index takes into account the current market condition (trending, ranging, or volatile) to adjust its calculations accordingly. The indicators used in this composite index are: Hull Moving Average (HMA) Fisher Transform (FT) Williams Alligator Moving Average Convergence Divergence (MACD) Average True Range (ATR) On-Balance Volume (OBV) Money Flow Index (MFI) Accumulation/Distribution (AD) Pivot Points True Strength Index (TSI) Volume-Weighted Average Price (VWAP) The script calculates the values of each indicator and then normalizes and weighs them according to predefined weights. The composite index is formed by summing the weighted values of each indicator. The final Moon Index is plotted on the chart, along with several other related lines like the exponential moving averages (EMA) and simple moving averages (SMA) of the index. This custom index can be used by traders to get a more comprehensive view of the market and make better-informed trading decisions based on the combined insights of multiple indicators.

Limited Fisher Transform what is Limited Fisher Transform? This indicator is a compressed version of the Fisher transform indicator between 100 and 0 values. what it does? It allows us to define overbought and oversold zones by compressing the values of the "fisher transform" indicator between 0 and 100. also these zones are the same for every timeframe and trading pair, just like RSI. how it does it? it use this formula: x = fisher transform values a = average how to use it? its use is indistinguishable from the standard fisher. You can use it to set alarms for overbought and oversold zones. so you will be notified when a possible opportunity arises in the market.

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 .

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

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

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

LNL Squeeze Arrows If you struggle with the entries, low % win rate or trading the squeeze setup overall, this indicator is for you! If you look closely at your losing trades, chances are the losers have one thing in common = inverse momentum. I created this tool after I found out that Stacked EMAs and picture perfect trend is not the only thing you need for a squeeze setup. Squeeze arrows pinpoint the exact moment where the squeeze momentum change happens (momentum change is absolutely crucial for the squeeze setup). These arrows will help you stay out of "everything was aligned but still failed" type of setups. Squeeze Arrows: 1. Momentum Arrows (cyan blue/red) - Showing the best possible moment for an entry during the squeeze (after you see one, you can expect the squeeze to fire soon). 2. Slingshot Arrows (yellow) - Even though you can trade off of them, these arrows work mostly as a confirmation & caution tool. If an inverse slingshot arrow is plotted during a squeeze that means caution = you should wait because momentum is not on your side thus there there is a quite high probability that the squeeze can fire the other direction. Squeeze Dots Trigger: Represents the number of red dots (squeeze) after which the arrows should plot. Default = 5 (only after 5 red dots, arrows will appear), some traders like to set it on 3 or even 1. Tips & Tricks: 1.Breakout or Bailout Mentality - The big advantage of the arrows is the fact that they either work straight away or they don't. This is where you can apply the breakout or bailout mentality and really focus exclusively on the breakout part of the whole squeeze move. You can minimize the risk by putting mental stops just a few points below the last low of the candle where the arrows appeared. That way you can be stopped out even during the squeeze = won't hurt as much as when the squeeze fire the opposite direction. Reward may be the same but the risk is lower. 2. Yellow Flags - Use the slingshot arrows as a caution tool. Even if all your squeeze criteria are met. Yellow inverse arrow = caution (wait for the true momentum change). Once the slingshot arrow appears in the conext of the trend, you are good to go. 3. Last Arrow Rule - Sometimes you will see a lot of arrows during the longer squeezes. This is where the last arrow rule come in handy. The last arrow you see on chart can be canceled anytime by a new one. The last arrow is the valid one! Hope you can squeeze from these squeeze arrows as much as there is to squeeze so you can finally trade the squeeze with ease. Hope it helps.