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Ehlers

Low Pass Channel [DW]This is an experimental study designed to attenuate higher frequency oscillations in price and volatility with minimal lag. In this study, a single pole low pass filter is used. The low pass filter's cutoff period is determined either by a fixed user input, or by using an Instantaneous Frequency Measurement (IFM) algorithm. Most radar warning, electronic countermeasures, and electronic intelligence systems employ IFM to identify threats, map the electronic battlefield, and implement deceptive countermeasures. The IFM technique used for this study was devised by John Ehlers. It calculates In Phase and Quadrature (IQ) components using the Hilbert Transform and uses them to determine the dominant price cycle. To generate the channel, the same filter approach is applied to true range then added to and subtracted from the price filter. Custom bar colors are included for simple wave and trend indication.
Pine Script® indicator
by DonovanWall
11
Ehlers Super Passband FilterAs someone hidden my old script that is just what one guy copied from a book from John Ehlers. I rewrite what i read in the book. I also found this. So i rewrite this to the guys that was using it. If it hiddes again i will just keep it to myself and downgrade my plan in this TV // Ehlers Super Passband Filter script may be freely distributed under the MIT license. // Ehlers Super Passband Filter script may be freely distributed under the MIT license. // Ehlers Super Passband Filter script may be freely distributed under the MIT license. // Ehlers Super Passband Filter script may be freely distributed under the MIT license. // Ehlers Super Passband Filter script may be freely distributed under the MIT license. I wrote 5 times to be clear. If you guys dont understand portuguese go to the translator to understand what i am explaining in // inside the code.
Pine Script® indicator
by FLeite28
6
MTF IQ IFM Moving AverageMTF ready adaptive MA using Ehler's IQ IFM ( In Phase - Quadrature Instantaneous Frequency Measurement ). Ehler's formula is a method of quantitatively measuring the length of a market cycle. In this case it is used to calculate the "optimal" adaptive EMA. Theoretically the length generated by Ehler's formula could be used in many indicators and it's been placed within it's own function so you should be able to simply copy/paste it. HOWEVER pine will not accept series variables for the length input used in built-in functions. You will have to manually code (or find) a version of your indicator that doesn't use the pine built in. Options : type : optionally add volume weighting range : historical range used in IQ IFM cycle length mult : method to create faster/slower MAs. eg 0.5 is half the length of a cycle and a faster EMA ie EMA10 vs EMA20 low sat fix : some cryptocurrencies with low satoshi values cause an issue with the calculation, if you get no/nonsensical lines, enable this. Shouldn't affect other instruments, but can be disabled just in case. MTF options: run the calculation on an alternative timeframe -------------------------------------- If you find it useful please consider a tip/donation : BTC - 3BMEXEDyWJ58eXUEALYPadbn1wwWKmf6sA
Pine Script® indicator
by rumpypumpydumpy
2
Center of Gravity Oscillator - Ehlers by KIVANC fr3762Center of Gravity OSCILLATOR by JOHN EHLERS Converted the original code from his book "Cybernetic Analysis for Stocks and Futures" This article describes a new oscillator that is unique because it is both smoothed and has essentially zero lag. The smoothing enables clear identification of turning points and the zero lag aspect enables action to be taken early in the move. This oscillator is the serendipitous result of my research into adaptive filters. While the filters have not yet produced the result I seek, this oscillator has substantial advantages over conventional oscillators used in technical analysis . The “CG” in the name of the oscillator stands for the Center of Gravity of the prices over the window of observation. The Center of Gravity ( CG ) of a physical object is its balance point. For example, if you balance a 12 inch ruler on your finger, the CG will be at its 6 inch point. If you change the weight distribution of the ruler by putting a paper clip on one end, then the balance point (e.g. the CG ) shifts toward the paper clip. Moving from the physical world to the trading world, we can substitute the prices over our window of observation for the units of weight along the ruler. With this analogy, we see that the CG of the window moves to the right when prices increase sharply. Correspondingly, the CG of the window moves to the left when prices decrease. For further information: www.mesasoftware.com Here's the link to a complete list of all my indicators: t.co Şimdiye kadar paylaştığım indikatörlerin tam listesi için: t.co
Pine Script® indicator
by KivancOzbilgic
9
Ghosty's Modded Super Bandpass Filter [DasanC]Very cool Indicator from Ehlers and published originally by @DasanC I made minor modifications, and added a zero line and changed some values. I use this indicator differently then it is intended to be used for scalping shorter time frames (15 min - 1 hour). I use it like a cross over, either from the zeroline or when it passes the RMS, for 5-10 pips. While no indicator is 100% this one does a nice job for small scalps. try it out on a demo and see if you like it. enjoy. original Indy -
Pine Script® indicator
by greyghost7
1
Fourier series Model Of The Market█ OVERVIEW The Fourier Series Model of the Market (FSMM) decomposes price action into harmonic components using bandpass filtering, then reconstructs a composite wave weighted by rolling energy ratios. This approach isolates cyclical market behavior at multiple frequencies, emphasizing dominant cycles for cleaner signal generation. The energy-adaptive weighting is the key differentiator from simple harmonic summation: cycles that dominate current price action contribute more to the output. Based on Fourier analysis principles applied to financial markets, the indicator extracts harmonics (fundamental, 2nd, 3rd, etc.) using second-order IIR bandpass filters, then weights each harmonic's contribution by its relative energy compared to adjacent harmonics. This energy-adaptive weighting naturally emphasizes the cycles that are most prominent in current market conditions. █ CONCEPTS Fourier Decomposition Fourier analysis represents any periodic signal as a sum of sine waves at different frequencies. In market analysis, price action can be decomposed into a fundamental cycle (the base period) plus harmonics at integer multiples of that frequency (period/2, period/3, etc.). Each harmonic captures oscillations at a specific frequency band, and their sum reconstructs the original cyclical behavior. Bandpass Filtering Each harmonic is extracted using a second-order IIR (Infinite Impulse Response) bandpass filter tuned to that harmonic's frequency. The filter isolates price activity within a narrow frequency range while rejecting both higher-frequency noise and lower-frequency trend drift. Before filtering, the source is debiased via 2-bar momentum to remove DC offset, ensuring each bandpass operates around true zero. Energy-Weighted Reconstruction Rather than simply summing all harmonics equally, FSMM weights each harmonic by its rolling energy relative to the previous harmonic. The energy score combines the current harmonic value with its rate of change, so it reflects both amplitude and momentum. Higher harmonics that hold comparatively more energy therefore contribute more to the composite wave, while weaker harmonics fade out. This adaptive weighting allows the model to respond to changing market cyclicality. Quadrature Component (Rate of Change) The rate of change output represents the 90°-phase-shifted (quadrature) component of the wave. When the wave is at zero and rising, the rate of change is at maximum positive. This provides complementary information about cycle phase and can be used for timing entries relative to cycle position. █ INTERPRETATION Wave Output The composite wave oscillates around zero, representing the sum of all extracted harmonic components weighted by energy: • Above zero : Net bullish cyclical momentum across harmonics • Below zero : Net bearish cyclical momentum across harmonics • Zero crossings : Cycle phase transitions - potential reversal points • Wave amplitude : Strength of cyclical behavior; larger swings indicate cleaner cycles Rate of Change The quadrature component (90° phase-shifted) provides cycle phase information: • Maximum rate of change : Wave is near zero and accelerating - early cycle phase • Zero rate of change : Wave is at peak or trough - cycle extremes • Rate/Wave divergence : When wave makes new highs/lows but rate of change does not confirm (lower momentum), suggests cycle exhaustion or impending phase shift Combined Analysis • Wave crossing above zero with positive rate of change: Strong bullish cycle initiation • Wave crossing below zero with negative rate of change: Strong bearish cycle initiation • Wave at extreme with rate of change reversing: Potential cycle peak/trough Threshold Bands When enabled, threshold bands define statistically significant wave deviations: • Breach above +threshold : Unusually strong bullish cyclical behavior • Breach below -threshold : Unusually strong bearish cyclical behavior • Return inside thresholds : Normalizing behavior, potential mean reversion ahead Alert Conditions Four built-in alerts trigger on bar close (no repainting): • Above +Threshold : Strong bullish cycle behavior • Below -Threshold : Strong bearish cycle behavior • Above Zero : Bullish cycle phase shift • Below Zero : Bearish cycle phase shift █ SETTINGS & PARAMETER TUNING Fourier Series Model • Source : Price series to decompose into harmonic components. • Period (6-100): Base period for the fundamental harmonic. Higher harmonics divide this period (harmonic 2 = period/2, harmonic 3 = period/3). Match to the dominant market cycle for best results. Default 20. • Bandwidth (0.05-0.5): Bandpass filter selectivity. Lower values create narrower passbands that isolate harmonics more precisely but may miss slightly off-frequency cycles. Higher values capture broader ranges but reduce harmonic separation. Default 0.1 balances precision and robustness. • Harmonics (1-20): Number of harmonic components to extract. More harmonics capture finer cyclical detail but increase computation. For most applications, 3-5 harmonics suffice. The fundamental alone (1 harmonic) functions as a simple bandpass filter. Display Settings • Wave Outputs : Toggle visibility and color of the composite Fourier wave. • Rate of Change : Toggle visibility and color of the quadrature component (90° phase-shifted wave). • Zero Line : Reference line for oscillator neutrality. Diagnostics - Dynamic Thresholds Optional significance bands that identify when wave readings indicate strong cyclical behavior: • Dynamic Threshold : Toggle threshold bands and set colors. • Threshold Mode : Select calculation method: - MAD (Median Absolute Deviation) : Robust, outlier-resistant measure using k * MAD where MAD ≈ 0.6745 * stdev. - Standard Deviation : Volatility-sensitive, calculated as k * stdev of wave over the lookback period. - Percentile Rank : Fixed probability bands using percentile of |wave| (90% means only 10% of values exceed threshold). • Period (2-200): Lookback for threshold calculations. Default 50. • Multiplier (k) : Scaling for MAD/Standard Deviation modes. Default 1.5. • Percentile (%) (0-100): For Percentile Rank mode only. Default 90%. Parameter Interactions • Shorter periods respond faster to cycle changes but may capture noise. • Lower bandwidth + more harmonics = more precise decomposition but requires accurate period setting. • Higher bandwidth is more forgiving of period mismatches. • For strongly trending markets, restrict harmonics to 1-2 so the model tracks the dominant cycle with fewer higher-frequency components. • For ranging/oscillating markets, more harmonics (4-6) capture complex cycles. █ LIMITATIONS Inherent Characteristics • Period dependency : Effectiveness depends on correctly matching the Period parameter to actual market cycles. Use cycle measurement tools (autocorrelation, FFT, dominant cycle indicators) to identify appropriate periods. • Stationarity assumption : The indicator assumes cycle frequencies remain relatively stable within the lookback window. Rapidly shifting dominant cycles (regime transitions) may produce inconsistent results until the buffer adapts. • Filter lag : Despite bandpass design, some lag remains inherent to causal filtering. Higher harmonics have less lag but more noise sensitivity. • Energy weighting artifacts : During regime changes when harmonic energy ratios shift rapidly, weighting may produce transient anomalies. Market Conditions to Avoid • Strong trending markets : Pure trends with no cyclicality produce weak, meandering signals. The indicator assumes cyclical market behavior. • News events/gaps : Large discontinuities disrupt filter continuity. Requires 1-2 full periods to stabilize. • Period mismatch : If the Period parameter doesn't match actual market cycles, harmonic extraction produces noise rather than signal. Parameter Selection Pitfalls • Too many harmonics : Beyond 5-6 harmonics, additional components often capture noise rather than meaningful cycles. • Bandwidth too narrow : Very low bandwidth (< 0.05) requires extremely precise period matching; slight mismatches cause signal loss. • Over-optimization : Perfect historical parameter fits typically fail forward. Use robust defaults across multiple instruments. █ NOTES Credits This indicator applies Fourier analysis principles to financial market data, building on the extensive work of Dr. John F. Ehlers in applying digital signal processing to trading. The bandpass filter implementation and harmonic decomposition approach draw from DSP fundamentals as presented in Ehlers' publications. For those interested in the underlying mathematics and DSP concepts: • Ehlers, J.F. (2001). Rocket Science for Traders: Digital Signal Processing Applications . John Wiley & Sons. • Ehlers, J.F. (2013). Cycle Analytics for Traders . John Wiley & Sons. • Various TASC articles by John Ehlers on bandpass filters, cycle analysis, and harmonic decomposition. by ♚@e2e4
Pine Script® indicator
by e2e4
12
Voss Predictive Filter█ OVERVIEW The Voss Predictive Filter (VPF) is a negative group delay (NGD) filter that anticipates cyclical price movement through phase compensation. The VPF isolates band-limited cyclical components via a bandpass filter, then applies negative group delay to shift the signal's phase forward, causing the output to lead the input by a fraction of the cycle period. Based on Dr. John F. Ehlers' "Voss Predictive Filter" article in Technical Analysis of Stocks & Commodities (TASC) magazine, the VPF displays a predictive oscillator with optional dynamic threshold bands for identifying significant cycle behavior. The indicator is timeframe-agnostic - the mathematics work identically from tick charts to monthly bars, though shorter timeframes require more careful parameter selection due to noise. █ CONCEPTS Bandpass Filtering A bandpass filter isolates price activity within a specific frequency range, removing both high-frequency noise and low-frequency trend drift. The VPF uses a second-order IIR (Infinite Impulse Response) bandpass filter characterized by the center frequency (the Bandpass Period input) and bandwidth. The center frequency determines which cycle period the filter emphasizes, while bandwidth controls the damping coefficient - how tightly the filter focuses around that frequency. Before filtering, the source is debiased via 2-bar momentum to remove DC offset, ensuring the filter operates around a true zero centerline. Negative Group Delay Filtering The predictive capability stems from negative group delay (NGD) - a filter characteristic where output appears to "lead" the input. Most causal filters introduce lag (positive group delay), but by combining the bandpass filter output with appropriately weighted past values, the VPF achieves negative group delay characteristics. This is a universal NGD filter application for band-limited signals: the bandpass filter isolates the cyclical component of interest, then the NGD stage advances the phase within this limited frequency range to create an anticipatory output. This isn't statistical forecasting; it's phase compensation that shifts the signal's timing forward, causing peaks and troughs to appear before they occur in the bandpass output. Negative Group Delay Stage The NGD stage combines the current bandpass output with weighted historical values to produce an output that leads the input. By subtracting a weighted average of past deviations from a scaled version of the current filter value, the algorithm advances the signal's phase: peaks and zero-crossings in the voss output appear before the corresponding events in the bandpass filter. The prediction order (`3 * Prediction Multiplier`) controls how many past values contribute to the phase advance. Higher orders provide smoother output but reduce the leading effect; lower orders maximize anticipation at the cost of stability. █ INTERPRETATION Zero-Line Crossovers Crossings above zero suggest bullish momentum in the filtered cycle; below zero suggests bearish momentum. Crossings from near-zero regions are most reliable, as extreme excursions need time to return to equilibrium. Threshold Bands Threshold bands define "significant" deviation. Breaches indicate unusually strong behavior and can serve as: • Trend confirmation when aligned with price direction • Overbought/oversold warnings at extremes • Trade entry filters (requiring threshold breach in the intended direction) Threshold Mode affects sensitivity: MAD (outlier-resistant), Standard Deviation (volatility-sensitive), Percentile Rank (fixed probability bands). Alert Conditions Four built-in alerts trigger on bar close (no repainting): Above +Threshold (strong bullish cycle), Below -Threshold (strong bearish cycle), Above Zero (bullish phase shift), Below Zero (bearish phase shift). █ SETTINGS & PARAMETER TUNING Voss Predictive Filter • Source : Price series to filter. • Bandpass Period (1-100): Primary tuning parameter determining which cycle length the filter emphasizes. Short periods (8-15) are more responsive but noisier; medium periods (16-30) balance responsiveness and smoothness; long periods (31-100) focus on longer cycles with more smoothing. • Bandwidth (0.01-0.45): Controls filter selectivity. Narrow bandwidths (0.01-0.15) isolate specific cycle periods precisely; medium (0.16-0.30) tolerate cycle irregularity; wide (0.31-0.45) capture broader cycle ranges. Shorter periods pair well with narrower bandwidths. • Prediction Multiplier (2-10): Controls how many past values contribute to the phase advance. Higher values provide smoother output but reduce the leading effect; lower values maximize anticipation at the cost of stability. Display Settings Control visibility and colors of the Voss output, bandpass filter, and zero reference lines. Diagnostics - Dynamic Thresholds Three methods identify significant signal deviation: • MAD (Median Absolute Deviation) : Robust, outlier-resistant measure using `k * MAD` where `MAD ≈ 0.6745 * stdev`. • Standard Deviation : Volatility-sensitive, calculated as `k * stdev` of Voss over the lookback period. • Percentile Rank : Fixed probability bands using the percentile of |Voss| (e.g., 90% means only 10% of values exceed threshold). Settings: • Dynamic Threshold : Toggle threshold bands and set colors. • Threshold Mode : Select MAD, Standard Deviation, or Percentile Rank. • Period (2-200): Lookback for threshold calculations. Default 50. • Multiplier (k) : Scaling for MAD/Standard Deviation modes. Default 1.5. • Percentile (%) (0-100): For Percentile Rank mode only. Default 90%. █ LIMITATIONS Inherent Characteristics • Residual lag : Despite negative group delay design, some lag remains relative to price action. • Cyclical markets required : Performs best on instruments with clear cyclical components. Strongly trending markets with little cyclicality produce less useful signals. • Signal interpretation : Absolute Voss values are instrument-specific. Always interpret relative to adaptive threshold bands, not fixed levels. Market Conditions to Avoid • Sudden news events/gaps : Major discontinuities disrupt cycle continuity, causing erratic signals. Requires 1-2 full cycle periods to re-stabilize. • Low volume/illiquid markets : Sporadic trading produces false cycles from liquidity artifacts. Use only on actively traded instruments during liquid hours. • Regime changes : During cyclical ↔ trending transitions, watch for persistent extremes without mean reversion, increasing price/indicator divergence, or unresolved threshold breaches. Parameter Selection Pitfalls • Mismatched period : If Bandpass Period doesn't match actual market cycles, the filter produces weak signals. Use cycle measurement tools (FFT, autocorrelation, Dominant Cycle) to identify appropriate periods first. • Overoptimization : Perfect historical fits typically fail forward. Choose robust parameters that work across multiple instruments and timeframes. █ NOTES Credits This indicator is based on concepts from Dr. John F. Ehlers' work on predictive filters and bandpass techniques for technical analysis. Dr. Ehlers has published extensively on applying digital signal processing methods to financial markets in Technical Analysis of Stocks & Commodities (TASC) magazine. His articles on bandpass filters and predictive techniques, particularly the Voss Predictive Filter concept, provided the theoretical foundation for this implementation. For those interested in the underlying mathematics and DSP concepts: • Ehlers, J.F. (2001). Rocket Science for Traders: Digital Signal Processing Applications . John Wiley & Sons. • Various TASC articles by John Ehlers on bandpass filters, cycle analysis, and predictive filtering techniques. • Ehlers, J.F. "Voss Predictive Filter" - Technical Analysis of Stocks & Commodities magazine. by ♚@e2e4
Pine Script® indicator
by e2e4
23
Ehler's Super Smoother 2 and 3 pole (properly initialized)John Ehlers' Super Smoother 2 and 3 pole - properly initialized www.stockspotter.com Failure to properly initialize early values of the super smoother will result in misleading values early in the output. Because the SS is an IIR ( infinite impulse response) filter, this error can ring in the filter for a long time, but is extremely evident in the first 2*len bars. This is an implementation if the 2 and 3 pole SS filter, with special attention to initializing the early values. It uses (src+scr)/2 per Ehlers but contains code to just use src if you prefer to calculate that outside the function as everget does in his SS here: there is code included to make that change. Many thanks to everget for his terrific implementations of much of John Ehlers' work. It has been tremendously helpful to me.
Pine Script® indicator
by yatrader2
Ehlers Decycler OscillatorThis indicator was originally developed by John F. Ehlers (Stocks & Commodities , V.33:10 (September, 2015): "Decyclers"). The idea is still the same as for the Simple Decycler. Mr. Ehlers suggested to virtually eliminate lag by getting rid of the very low-frequency components. So, he applied the high-pass filter to the simple decycler. Mr. Ehlers recommended to use two instances of the Decycler Oscillator with different parameters (high-pass filter period and multiplier). As a result, he got the Decycler Oscillator pair. The first oscillator (red line) has a period of 125 bars, the second one (yellow line) has a period of 100 bars. The interpretation is straightforward: When the yellow line crosses over the red line, a trend reversal to the upside is indicated. When the yellow line crosses under the red line, a trend reversal to the downside is indicated.
Pine Script® indicator
by everget
8
Ehlers Simple DecyclerThis indicator was originally developed by John F. Ehlers (Stocks & Commodities, V.33:10 (September, 2015): "Decyclers"). Mr. Ehlers suggested a way to improve trend identification using high-pass filters. The basic smoothers like SMA, low-pass filters, have considerable lag in their display. Mr. Ehlers applied the high-pass filter and subtracted the high-pass filter output from the time series input. Doing these steps he removed high-frequency short-wavelength components (the ones causing the wiggles) from the time series. As a result he got a special series of the low-frequency components with virtually no lag - the Decycler. The Decycler is plotted with two additional lines (the percent-shifts of Decycler) and together they form a hysteresis band. If the prices are above the upper hysteresis line, then the market is in an uptrend . If the prices are below the low hysteresis line, then the market is in a downtrend . Prices within the hysteresis band are trend-neutral .
Pine Script® indicator
by everget
4
Ehlers Triple Delay-Line DetrenderThis indicator was originally developed by John F. Ehlers (Stocks & Commodities , V.18:7 (July, 2000): "Optimal Detrending"). Mr. Ehlers applied the ideas of the radar systems for the financial time series detrending. Mr. Ehlers constructed the Triple Delay-Line Canceller first, then smoothed it with the Modified Optimum Elliptic Filter with minimal lag. The smoothed detrended signal is smoothed again with the Modified Optimum Elliptic Filter to obtain signal line. As result, the crossings of the two indicator lines catch every major cyclic move and the detrender itself can be used as the first step in more sophisticated analyses.
Pine Script® indicator
by everget
3
Signal to Noise Ratio [SNR]Intro This script measures the Signal to Noise ratio of a security and plots it in deciBels scale! Usage Ideally, you would want the ratio to be above 10 dB, meaning the Signal strength is 10x the noise strength. As a baseline, you should not rely on indicators that use any kind of moving average if the SNR is below 6 dB - meaning Signal strength is only 4x noise strength. I've written the SNR as a functional block so you may simply copy and paste, then call getSNR() to get the ratio in dB. Principle I consider a bar's High and Low to be the range of that period and (High + Low)/2 to be the "real" value of the signal. This script compares a bars range (noise) to the perceived signal using a Hilbert Transform. Cheers, DasanC
Pine Script® indicator
by DasanC
5