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Hilberttransform

Sine Wave Cycle Oscillator [HT]Overview: This oscillator identifies the dominant market cycle in real time using John Ehlers' Hilbert Transform and Homodyne Discriminator — a signal-processing technique adapted from radio engineering. Rather than using a fixed bar count to simulate a sine wave, the indicator measures the market's own oscillatory rhythm bar-by-bar, producing a phase-accurate sine wave that stays synchronized with actual price cycles as they expand and contract. How It Works: Adaptive Cycle Detection (Hilbert Transform) Price is passed through a multi-stage signal processing pipeline: 4-bar weighted smooth — reduces noise before analysis- Hilbert detrend — strips the DC trend component, isolating the cyclic portion In-phase (I1) / Quadrature (Q1) decomposition — constructs a phasor representation of price 90° phase advance — produces jI and jQ for phasor rotation Phasor addition — yields the corrected I2/Q2 complex pair Homodyne Discriminator — computes the instantaneous cycle period from the rate of phase change between consecutive bars Adaptive period — clamped between 6 and 50 bars, double-smoothed for stability The result is a live dominant cycle period that updates every bar and is displayed in the info table. Instantaneous Phase Engine From the I1/Q1 phasor, the indicator derives a continuous phase angle (0°–360°) that tracks where price currently sits within its cycle — no fixed-length assumption, no lag from windowing. Sine wave — sin(phase), EMA-smoothed for display clarity Lead sine — cos(phase) = exactly 90° ahead of the main wave, acting as an early-warning signal for turns Signals: Strong signals require both conditions to fire simultaneously (table) — the highest-conviction setup. Early warnings give traders advance notice of a potential turn before the zero-line confirms. Weak signals flag zero-line crossings that lack lead-sine confirmation and should be treated with caution. Visual Elements: Sine wave — colour-coded green above zero, red below, with a matching fill Lead sine — plotted in blue, always one quarter-cycle ahead ±0.8 bands — mark the 90° and 270° peak zones where cycle reversals are most likely Background shading — highlights three key phase zones: 🟢 80°–100° — approaching upside peak 🟡 170°–190° — mid-cycle reversal zone 🔴 260°–280° — approaching downside peak Label markers — L (Strong Buy) and S (Strong Sell) plotted directly on the oscillator panel; triangles mark early warnings Info Table (Top-Right) Field Description Dom Cycle Current adaptive period in bars (Homodyne Discriminator output) Angle Instantaneous phase angle in degrees Phase Named cycle stage: Cycle Start / Uptrend Peak / Reversal / Downtrend Peak Signal Current bar's signal classification Legend rows Cross / color change logic reference and phase reference guide Settings: Parameter Default Description Smoothing 3EMA length applied to the raw sine wave before plotting The cycle period requires no manual input — it is derived entirely from the market via the Hilbert Transform engine. Notes & Best Practices: Works on any symbol and timeframe; the adaptive period adjusts automatically Most reliable on liquid instruments with clear cyclic behavior (indices, major FX pairs, large-cap equities) Combine Strong signals with trend context from a higher timeframe for best results — this oscillator excels at timing entries within a known trend, not at predicting trend direction itself The lead sine can precede actual turns by several bars; wait for the zero-line confirmation on lower-conviction setups Ref: Based on the work of John F. Ehlers — Cybernetic Analysis for Stocks and Futures (2004) Disclaimer: This indicator is for educational purposes only. Always practice proper risk management and combine with your own analysis before making trading decisions. Happy trading.
Индикатор Pine Script®
от kingshukghosh71
CCI with Signals & Divergence [AIBitcoinTrend]👽 CCI with Signals & Divergence (AIBitcoinTrend) The Hilbert Adaptive CCI with Signals & Divergence takes the traditional Commodity Channel Index (CCI) to the next level by dynamically adjusting its calculation period based on real-time market cycles using Hilbert Transform Cycle Detection. This makes it far superior to standard CCI, as it adapts to fast-moving trends and slow consolidations, filtering noise and improving signal accuracy. Additionally, the indicator includes real-time divergence detection and an ATR-based trailing stop system, helping traders identify potential reversals and manage risk effectively. 👽 What Makes the Hilbert Adaptive CCI Unique? Unlike the traditional CCI, which uses a fixed-length lookback period, this version automatically adjusts its lookback period using Hilbert Transform to detect the dominant cycle in the market. ✅ Hilbert Transform Adaptive Lookback – Dynamically detects cycle length to adjust CCI sensitivity. ✅ Real-Time Divergence Detection – Instantly identifies bullish and bearish divergences for early reversal signals. ✅ Implement Crossover/Crossunder signals tied to ATR-based trailing stops for risk management 👽 The Math Behind the Indicator 👾 Hilbert Transform Cycle Detection The Hilbert Transform estimates the dominant market cycle length based on the frequency of price oscillations. It is computed using the in-phase and quadrature components of the price series: tp = (high + low + close) / 3 smooth = (tp + 2 * tp + 2 * tp + tp ) / 6 detrender = smooth - smooth quadrature = detrender - detrender inPhase = detrender + quadrature outPhase = quadrature - inPhase instPeriod = 0.0 deltaPhase = math.abs(inPhase - inPhase ) + math.abs(outPhase - outPhase ) instPeriod := nz(3.25 / deltaPhase, instPeriod ) dominantCycle = int(math.min(math.max(instPeriod, cciMinPeriod), 500)) Where: In-Phase & Out-Phase Components are derived from a detrended version of the price series. Instantaneous Frequency measures the rate of cycle change, allowing the CCI period to adjust dynamically. The result is bounded within a user-defined min/max range, ensuring stability. 👽 How Traders Can Use This Indicator 👾 Divergence Trading Strategy Bullish Divergence Setup: Price makes a lower low, while CCI forms a higher low. Buy signal is confirmed when CCI shows upward momentum. Bearish Divergence Setup: Price makes a higher high, while CCI forms a lower high. Sell signal is confirmed when CCI shows downward momentum. 👾 Trailing Stop & Signal-Based Trading Bullish Setup: ✅ CCI crosses above -100 → Buy signal. ✅ A bullish trailing stop is placed at Low - (ATR × Multiplier). ✅ Exit if the price crosses below the stop. Bearish Setup: ✅ CCI crosses below 100 → Sell signal. ✅ A bearish trailing stop is placed at High + (ATR × Multiplier). ✅ Exit if the price crosses above the stop. 👽 Why It’s Useful for Traders Hilbert Adaptive Period Calculation – No more fixed-length periods; the indicator dynamically adapts to market conditions. Real-Time Divergence Alerts – Helps traders anticipate market reversals before they occur. ATR-Based Risk Management – Stops automatically adjust based on volatility. Works Across Multiple Markets & Timeframes – Ideal for stocks, forex, crypto, and futures. 👽 Indicator Settings Min & Max CCI Period – Defines the adaptive range for Hilbert-based lookback. Smoothing Factor – Controls the degree of smoothing applied to CCI. Enable Divergence Analysis – Toggles real-time divergence detection. Lookback Period – Defines the number of bars for detecting pivot points. Enable Crosses Signals – Turns on CCI crossover-based trade signals. ATR Multiplier – Adjusts trailing stop sensitivity. Disclaimer: This indicator is designed for educational purposes and does not constitute financial advice. Please consult a qualified financial advisor before making investment decisions.
Индикатор Pine Script®
от aibitcointrend
1
Poly Cycle [Loxx]This is an example of what can be done by combining Legendre polynomials and analytic signals. I get a way of determining a smooth period and relative adaptive strength indicator without adding time lag. This indicator displays the following: The Least Squares fit of a polynomial to a DC subtracted time series - a best fit to a cycle. The normalized analytic signal of the cycle (signal and quadrature). The Phase shift of the analytic signal per bar. The Period and HalfPeriod lengths, in bars of the current cycle. A relative strength indicator of the time series over the cycle length. That is, adaptive relative strength over the cycle length. The Relative Strength Indicator, is adaptive to the time series, and it can be smoothed by increasing the length of decreasing the number of degrees of freedom. Other adaptive indicators based upon the period and can be similarly constructed. There is some new math here, so I have broken the story up into 5 Parts: Part 1: Any time series can be decomposed into a orthogonal set of polynomials . This is just math and here are some good references: Legendre polynomials - Wikipedia, the free encyclopedia Peter Seffen, "On Digital Smoothing Filters: A Brief Review of Closed Form Solutions and Two New Filter Approaches", Circuits Systems Signal Process, Vol. 5, No 2, 1986 I gave some thought to what should be done with this and came to the conclusion that they can be used for basic smoothing of time series. For the analysis below, I decompose a time series into a low number of degrees of freedom and discard the zero mode to introduce smoothing. That is: time series => c_1 t + c_2 t^2 ... c_Max t^Max This is the cycle. By construction, the cycle does not have a zero mode and more physically, I am defining the "Trend" to be the zero mode. The data for the cycle and the fit of the cycle can be viewed by setting ShowDataAndFit = TRUE; There, you will see the fit of the last bar as well as the time series of the leading edge of the fits. If you don't know what I mean by the "leading edge", please see some of the postings in . The leading edges are in grayscale, and the fit of the last bar is in color. I have chosen Length = 17 and Degree = 4 as the default. I am simply making sure by eye that the fit is reasonably good and degree 4 is the lowest polynomial that can represent a sine-like wave, and 17 is the smallest length that lets me calculate the Phase Shift (Part 3 below) using the Hilbert Transform of width=7 (Part 2 below). Depending upon the fit you make, you will capture different cycles in the data. A fit that is too "smooth" will not see the smaller cycles, and a fit that is too "choppy" will not see the longer ones. The idea is to use the fit to try to suppress the smaller noise cycles while keeping larger signal cycles. Part 2: Every time series has an Analytic Signal, defined by applying the Hilbert Transform to it. You can think of the original time series as amplitude * cosine(theta) and the transformed series, called the quadrature, can be thought of as amplitude * sine(theta). By taking the ratio, you can get the angle theta, and this is exactly what was done by John Ehlers in . It lets you get a frequency out of the time series under consideration. Amazon.com: Rocket Science for Traders: Digital Signal Processing Applications (9780471405672): John F. Ehlers: Books It helps to have more references to understand this. There is a nice article on Wikipedia on it. Read the part about the discrete Hilbert Transform: en.wikipedia.org If you really want to understand how to go from continuous to discrete, look up this article written by Richard Lyons: www.dspguru.com In the indicator below, I am calculating the normalized analytic signal, which can be written as: s + i h where i is the imagery number, and s^2 + h^2 = 1; s= signal = cosine(theta) h = Hilbert transformed signal = quadrature = sine(theta) The angle is therefore given by theta = arctan(h/s); The analytic signal leading edge and the fit of the last bar of the cycle can be viewed by setting ShowAnalyticSignal = TRUE; The leading edges are in grayscale fit to the last bar is in color. Light (yellow) is the s term, and Dark (orange) is the quadrature (hilbert transform). Note that for every bar, s^2 + h^2 = 1 , by construction. I am using a width = 7 Hilbert transform, just like Ehlers. (But you can adjust it if you want.) This transform has a 7 bar lag. I have put the lag into the plot statements, so the cycle info should be quite good at displaying minima and maxima (extrema). Part 3: The Phase shift is the amount of phase change from bar to bar. It is a discrete unitary transformation that takes s + i h to s + i h explicitly, T = (s+ih)*(s -ih ) , since s *s + h *h = 1. writing it out, we find that T = T1 + iT2 where T1 = s*s + h*h and T2 = s*h -h*s and the phase shift is given by PhaseShift = arctan(T2/T1); Alas, I have no reference for this, all I doing is finding the rotation what takes the analytic signal at bar to the analytic signal at bar . T is the transfer matrix. Of interest is the PhaseShift from the closest two bars to the present, given by the bar and bar since I am using a width=7 Hilbert transform, bar is the earliest bar with an analytic signal. I store the phase shift from bar to bar as a time series called PhaseShift. It basically gives you the (7-bar delayed) leading edge the amount of phase angle change in the series. You can see it by setting ShowPhaseShift=TRUE The green points are positive phase shifts and red points are negative phase shifts. On most charts, I have looked at, the indicator is mostly green, but occasionally, the stock "retrogrades" and red appears. This happens when the cycle is "broken" and the cycle length starts to expand as a trend occurs. Part 4: The Period: The Period is the number of bars required to generate a sum of PhaseShifts equal to 360 degrees. The Half-period is the number of bars required to generate a sum of phase shifts equal to 180 degrees. It is usually not equal to 1/2 of the period. You can see the Period and Half-period by setting ShowPeriod=TRUE The code is very simple here: Value1=0; Value2=0; while Value1 < bar_index and math.abs(Value2) < 360 begin Value2 = Value2 + PhaseShift ; Value1 = Value1 + 1; end; Period = Value1; The period is sensitive to the input length and degree values but not overly so. Any insight on this would be appreciated. Part 5: The Relative Strength indicator: The Relative Strength is just the current value of the series minus the minimum over the last cycle divided by the maximum - minimum over the last cycle, normalized between +1 and -1. RelativeStrength = -1 + 2*(Series-Min)/(Max-Min); It therefore tells you where the current bar is relative to the cycle. If you want to smooth the indicator, then extend the period and/or reduce the polynomial degree. In code: NewLength = floor(Period + HilbertWidth+1); Max = highest(Series,NewLength); Min = lowest(Series,NewLength); if Max>Min then Note that the variable NewLength includes the lag that comes from the Hilbert transform, (HilbertWidth=7 by default). Conclusion: This is an example of what can be done by combining Legendre polynomials and analytic signals to determine a smooth period without adding time lag. ________________________________ Changes in this one : instead of using true/false options for every single way to display, use Type parameter as following : 1. The Least Squares fit of a polynomial to a DC subtracted time series - a best fit to a cycle. 2. The normalized analytic signal of the cycle (signal and quadrature). 3. The Phase shift of the analytic signal per bar. 4. The Period and HalfPeriod lengths, in bars of the current cycle. 5. A relative strength indicator of the time series over the cycle length. That is, adaptive relative strength over the cycle length.
Индикатор Pine Script®
от loxx
4
Adaptivity: Measures of Dominant Cycles and Price Trend [Loxx]Adaptivity: Measures of Dominant Cycles and Price Trend is an indicator that outputs adaptive lengths using various methods for dominant cycle and price trend timeframe adaptivity. While the information output from this indicator might be useful for the average trader in one off circumstances, this indicator is really meant for those need a quick comparison of dynamic length outputs who wish to fine turn algorithms and/or create adaptive indicators. This indicator compares adaptive output lengths of all publicly known adaptive measures. Additional adaptive measures will be added as they are discovered and made public. The first released of this indicator includes 6 measures. An additional three measures will be added with updates. Please check back regularly for new measures. Ehers: Autocorrelation Periodogram Band-pass Instantaneous Cycle Hilbert Transformer Dual Differentiator Phase Accumulation (future release) Homodyne (future release) Jurik: Composite Fractal Behavior (CFB) Adam White: Veritical Horizontal Filter (VHF) (future release) What is an adaptive cycle, and what is Ehlers Autocorrelation Periodogram Algorithm? From his 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 this Hilbert Transformer? An analytic signal allows for time-variable parameters and is a generalization of the phasor concept, which is restricted to time-invariant amplitude, phase, and frequency. The analytic representation of a real-valued function or signal facilitates many mathematical manipulations of the signal. For example, computing the phase of a signal or the power in the wave is much simpler using analytic signals. The Hilbert transformer is the technique to create an analytic signal from a real one. The conventional Hilbert transformer is theoretically an infinite-length FIR filter. Even when the filter length is truncated to a useful but finite length, the induced lag is far too large to make the transformer useful for trading. From his Ehlers' book Cycle Analytics for Traders Advanced Technical Trading Concepts by John F. Ehlers , 2013, pages 186-187: "I want to emphasize that the only reason for including this section is for completeness. Unless you are interested in research, I suggest you skip this section entirely. To further emphasize my point, do not use the code for trading. A vastly superior approach to compute the dominant cycle in the price data is the autocorrelation periodogram. The code is included because the reader may be able to capitalize on the algorithms in a way that I do not see. All the algorithms encapsulated in the code operate reasonably well on theoretical waveforms that have no noise component. My conjecture at this time is that the sample-to-sample noise simply swamps the computation of the rate change of phase, and therefore the resulting calculations to find the dominant cycle are basically worthless.The imaginary component of the Hilbert transformer cannot be smoothed as was done in the Hilbert transformer indicator because the smoothing destroys the orthogonality of the imaginary component." What is the Dual Differentiator, a subset of Hilbert Transformer? From his Ehlers' book Cycle Analytics for Traders Advanced Technical Trading Concepts by John F. Ehlers , 2013, page 187: "The first algorithm to compute the dominant cycle is called the dual differentiator. In this case, the phase angle is computed from the analytic signal as the arctangent of the ratio of the imaginary component to the real component. Further, the angular frequency is defined as the rate change of phase. We can use these facts to derive the cycle period." What is the Phase Accumulation, a subset of Hilbert Transformer? From his Ehlers' book Cycle Analytics for Traders Advanced Technical Trading Concepts by John F. Ehlers , 2013, page 189: "The next algorithm to compute the dominant cycle is the phase accumulation method. 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 the Homodyne, a subset of Hilbert Transformer? From his Ehlers' book Cycle Analytics for Traders Advanced Technical Trading Concepts by John F. Ehlers , 2013, page 192: "The third algorithm for computing the dominant cycle is the homodyne approach. Homodyne means the signal is multiplied by itself. More precisely, we want to multiply the signal of the current bar with the complex value of the signal one bar ago. The complex conjugate is, by definition, a complex number whose sign of the imaginary component has been reversed." What is the Instantaneous Cycle? The Instantaneous Cycle Period Measurement was authored by John Ehlers; it is built upon his Hilbert Transform Indicator. From his Ehlers' book Cybernetic Analysis for Stocks and Futures: Cutting-Edge DSP Technology to Improve Your Trading by John F. Ehlers, 2004, page 107: "It is obvious that cycles exist in the market. They can be found on any chart by the most casual observer. What is not so clear is how to identify those cycles in real time and how to take advantage of their existence. When Welles Wilder first introduced the relative strength index (rsi), I was curious as to why he selected 14 bars as the basis of his calculations. I reasoned that if i knew the correct market conditions, then i could make indicators such as the rsi adaptive to those conditions. Cycles were the answer. I knew cycles could be measured. Once i had the cyclic measurement, a host of automatically adaptive indicators could follow. Measurement of market cycles is not easy. The signal-to-noise ratio is often very low, making measurement difficult even using a good measurement technique. Additionally, the measurements theoretically involve simultaneously solving a triple infinity of parameter values. The parameters required for the general solutions were frequency, amplitude, and phase. Some standard engineering tools, like fast fourier transforms (ffs), are simply not appropriate for measuring market cycles because ffts cannot simultaneously meet the stationarity constraints and produce results with reasonable resolution. Therefore i introduced maximum entropy spectral analysis (mesa) for the measurement of market cycles. This approach, originally developed to interpret seismographic information for oil exploration, produces high-resolution outputs with an exceptionally short amount of information. A short data length improves the probability of having nearly stationary data. Stationary data means that frequency and amplitude are constant over the length of the data. I noticed over the years that the cycles were ephemeral. Their periods would be continuously increasing and decreasing. Their amplitudes also were changing, giving variable signal-to-noise ratio conditions. Although all this is going on with the cyclic components, the enduring characteristic is that generally only one tradable cycle at a time is present for the data set being used. I prefer the term dominant cycle to denote that one component. The assumption that there is only one cycle in the data collapses the difficulty of the measurement process dramatically." What is the Band-pass Cycle? From his Ehlers' book Cycle Analytics for Traders Advanced Technical Trading Concepts by John F. Ehlers , 2013, page 47: "Perhaps the least appreciated and most underutilized filter in technical analysis is the band-pass filter. The band-pass filter simultaneously diminishes the amplitude at low frequencies, qualifying it as a detrender, and diminishes the amplitude at high frequencies, qualifying it as a data smoother. It passes only those frequency components from input to output in which the trader is interested. The filtering produced by a band-pass filter is superior because the rejection in the stop bands is related to its bandwidth. The degree of rejection of undesired frequency components is called selectivity. The band-stop filter is the dual of the band-pass filter. It rejects a band of frequency components as a notch at the output and passes all other frequency components virtually unattenuated. Since the bandwidth of the deep rejection in the notch is relatively narrow and since the spectrum of market cycles is relatively broad due to systemic noise, the band-stop filter has little application in trading." From his Ehlers' book Cycle Analytics for Traders Advanced Technical Trading Concepts by John F. Ehlers , 2013, page 59: "The band-pass filter can be used as a relatively simple measurement of the dominant cycle. A cycle is complete when the waveform crosses zero two times from the last zero crossing. Therefore, each successive zero crossing of the indicator marks a half cycle period. We can establish the dominant cycle period as twice the spacing between successive zero crossings." 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 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.
Индикатор Pine Script®
от loxx
8
Adaptive Trend Cipher loxx]Adaptive Trend Cipher Highly experimental! Features: -Implements 5 different Dominant Adaptive Cycle Measures to determine optimal inputs for correlation functions. These cycle calculations include the following: ** * Ehler's Autocorrelation Dominant Cycle * Ehler's Instantaneous Dominant Cycle * Ehler's Band-pass Dominant Cycle * Ehler's Hilbert Period Dominant Cycle * Ehler's Dual Differentiator Dominant Cycle **additional cycle measures to be added in future releases -Uses price to time correlation with look-back periods determined by the dominant cycle measures -Allows users to manipulate the range of Dominant Cycle inputs, also allows the user to change the size % of the the output Dominant cycle to be used to determine correlation lengths -Bars are colored according to correlation extremes. Green bars are uptrend, Red bars are downtrend; Yellow bars are high correlation, Fuchsia bars are low correlation Uses -Trend cipher is a novel approach to teasing out macro trends in the market. This version is geared to be used on the daily time frame only -Reversals at yellow and fuchsia bars when they appear, it shows price exhaustion using Warning: This may not work on certain assets due to the high processing power required to calculate cycle dominance. This also uses a custom correlation function since the data being input intot he correlation function is not constant but variable based on cycle dominance at every bar. To correct this in most circumstances you must change the max_bars_back constant in the indicator method call If you use parts of the code, please let me know, I would love to hear what you do with it. Happy trading!
Индикатор Pine Script®
от loxx
2
Ehlers Hilbert Transform [CC]The Hilbert Transform was created by John Ehlers (Stocks & Commodities V. 18:3 (16-27)) and this indicator can work pretty well as a trend confirmation. This essentially transforms the underlying price data into a soundwave and when you compare the two (blue is positive and red is negative) then it provides fairly clear buy and sell signals. Ehlers did warn in his original article that this indicator has a lag of 4 bars so you have to keep that in mind. I have found that this indicator works pretty well when you buy when the blue line goes over the red line and sell when the blue lines hits the zero line. You could also ignore the red line and buy when the blue line crosses over the zero line and sell when it crosses under. Let me know how you wind up using this indicator in your trading. Let me know if there are any other scripts you would like to see me publish!
Индикатор Pine Script®
от cheatcountry
3
Advance AMA with Sylvain BandsMany traders believe that the moving averages are favorite tools and analysts have spent decades trying to improve moving averages partiularly the simple moving average. One way to address the disadvantages of moving averages is to multiply the weighting factor by a volatility ratio which is called Adaptive moving averages. This indicator uses an special adaptive moving averages which is developed by John Ehlers. The model adapts to price movement “based on the rate change of phase as measured by the Hilbert Transform Discriminator”. This method of adaptation features a fast and a slow moving average so that the composite moving average swiftly responds to price changes and holds the average value until the next bars close. In addition, the smoothed Volatility Bands were created by Sylvain Vervoort is included.
Индикатор Pine Script®
от master-in-chart
7
[blackcat] L2 Ehlers Hilbert Channel Breakout Trading SystemLevel: 2 Background John F. Ehlers introuced Hilbert Channel Breakout Trading System in Nov, 2000. Function This indicator will show how the adaptive filter is being applied to a trading strategy. After the Hilbert Channel Breakout Signal is optimized, set the inputs for this indicator to match the corresponding inputs for the signal. In the March 2000 STOCKS & COMMODITIES, John Ehlers published a algorithm for the Hilbert cycle period, an indicator that plots the length of the current market cycle. The Hilbert transform achieved computational efficiency by using a two-dimensional numbering system. Unfortunately, this introduces amplitude error in calculating the quadrature component. Dr. Ehlers compensated for this error. He have updated the method of compensating for the amplitude error by applying a straight-line compensation term using the frequency calculation from one bar ago. This is possible because the cycle period cannot change drastically from bar to bar. The slowly varying cycle period is adequate to do a good job of amplitude compensation. In addition, Dr. Ehlers have used a different way to compute the cycle period. He used a homodyne discriminator because it exhibits superior performance in a low signal-to-noise environment. Homodyne means he used the signal multiplied by itself one bar ago to produce a zero-frequency beat note. This beat note carries the phase angle of the one-bar change. Still using the basic definition of a cycle, the one-bar rate of change of phase is exactly the cycle period. Here is the pine v4 code to generate the signals in the Hilbert channel breakout trading system, as discussed in Dr. Ehlers article in this issue, "Optimizing With Hilbert Indicators." The signal itself is a simple channel breakout system that generates buy and exit signals, that shows whether the system is long or flat; the high of the bar and the value of the entry channel; and the low of the bar and the value of the exit channel. This helps you see on a bar-by-bar basis exactly how the system is behaving. Key Signal longcond--> when high breakouts EntryChannel to long shortcond--> when low breakouts ExitChannel to short Pros and Cons 100% John F. Ehlers definition translation, even variable names are the same. This help readers who would like to use pine to read his book. Remarks The 66th script for Blackcat1402 John F. Ehlers Week publication. I tested it and believe it work better in small time frame e.g. 15m than large time frames. Readme In real life, I am a prolific inventor. I have successfully applied for more than 60 international and regional patents in the past 12 years. But in the past two years or so, I have tried to transfer my creativity to the development of trading strategies. Tradingview is the ideal platform for me. I am selecting and contributing some of the hundreds of scripts to publish in Tradingview community. Welcome everyone to interact with me to discuss these interesting pine scripts. The scripts posted are categorized into 5 levels according to my efforts or manhours put into these works. Level 1 : interesting script snippets or distinctive improvement from classic indicators or strategy. Level 1 scripts can usually appear in more complex indicators as a function module or element. Level 2 : composite indicator/strategy. By selecting or combining several independent or dependent functions or sub indicators in proper way, the composite script exhibits a resonance phenomenon which can filter out noise or fake trading signal to enhance trading confidence level. Level 3 : comprehensive indicator/strategy. They are simple trading systems based on my strategies. They are commonly containing several or all of entry signal, close signal, stop loss, take profit, re-entry, risk management, and position sizing techniques. Even some interesting fundamental and mass psychological aspects are incorporated. Level 4 : script snippets or functions that do not disclose source code. Interesting element that can reveal market laws and work as raw material for indicators and strategies. If you find Level 1~2 scripts are helpful, Level 4 is a private version that took me far more efforts to develop. Level 5 : indicator/strategy that do not disclose source code. private version of Level 3 script with my accumulated script processing skills or a large number of custom functions. I had a private function library built in past two years. Level 5 scripts use many of them to achieve private trading strategy.
Индикатор Pine Script®
от blackcat1402
2
[blackcat] L2 Ehlers Hilbert Transformer IndicatorLevel: 2 Background John F. Ehlers introuced Hilbert Transformer Indicator in his "Cycle Analytics for Traders" chapter 14 on 2013. Function Basically, the real component moves with the general direction of the prices, and the imaginary component is a predictive indicator for the real component in the same sense that a cosine wave is a predictor of a sine wave. Although the default LPPeriod input is set to 20 bars in an attempt to smooth the indicator the imaginary component is still too erratic to be useful. Key Signal Imag --> Quadrature phase output signal Real --> In-phase output signal Pros and Cons 100% John F. Ehlers definition translation, even variable names are the same. This help readers who would like to use pine to read his book. Remarks The 58th script for Blackcat1402 John F. Ehlers Week publication. Readme In real life, I am a prolific inventor. I have successfully applied for more than 60 international and regional patents in the past 12 years. But in the past two years or so, I have tried to transfer my creativity to the development of trading strategies. Tradingview is the ideal platform for me. I am selecting and contributing some of the hundreds of scripts to publish in Tradingview community. Welcome everyone to interact with me to discuss these interesting pine scripts. The scripts posted are categorized into 5 levels according to my efforts or manhours put into these works. Level 1 : interesting script snippets or distinctive improvement from classic indicators or strategy. Level 1 scripts can usually appear in more complex indicators as a function module or element. Level 2 : composite indicator/strategy. By selecting or combining several independent or dependent functions or sub indicators in proper way, the composite script exhibits a resonance phenomenon which can filter out noise or fake trading signal to enhance trading confidence level. Level 3 : comprehensive indicator/strategy. They are simple trading systems based on my strategies. They are commonly containing several or all of entry signal, close signal, stop loss, take profit, re-entry, risk management, and position sizing techniques. Even some interesting fundamental and mass psychological aspects are incorporated. Level 4 : script snippets or functions that do not disclose source code. Interesting element that can reveal market laws and work as raw material for indicators and strategies. If you find Level 1~2 scripts are helpful, Level 4 is a private version that took me far more efforts to develop. Level 5 : indicator/strategy that do not disclose source code. private version of Level 3 script with my accumulated script processing skills or a large number of custom functions. I had a private function library built in past two years. Level 5 scripts use many of them to achieve private trading strategy.
Индикатор Pine Script®
от blackcat1402
[blackcat] L2 Ehlers Classic Hilbert TransformerLevel: 2 Background John F. Ehlers introuced Classic Hilbert Transform in his "Cycle Analytics for Traders" chapter 14 on 2013. The Hilbert Transform is a procedure to create complex signals from the simple chart data familiar to all traders. Once we have the complex signals, we can compute indicators and signals that are more accurate and responsive than those computed using conventional techniques. In fact, some of the indicators we will discuss cannot be calculated at all without the Hilbert Transform. Function blackcat L2 Ehlers Hilbert Transform indicator performs basic Hilbert Transform ( HT ) from chart data source and generate complex IQ data. Key Signal Imag --> Quadrature phase output signal Real --> In-phase output signal Pros and Cons 100% John F. Ehlers definition translation, even variable names are the same. This help readers who would like to use pine to read his book. Remarks The 57th script for Blackcat1402 John F. Ehlers Week publication. Readme In real life, I am a prolific inventor. I have successfully applied for more than 60 international and regional patents in the past 12 years. But in the past two years or so, I have tried to transfer my creativity to the development of trading strategies. Tradingview is the ideal platform for me. I am selecting and contributing some of the hundreds of scripts to publish in Tradingview community. Welcome everyone to interact with me to discuss these interesting pine scripts. The scripts posted are categorized into 5 levels according to my efforts or manhours put into these works. Level 1 : interesting script snippets or distinctive improvement from classic indicators or strategy. Level 1 scripts can usually appear in more complex indicators as a function module or element. Level 2 : composite indicator/strategy. By selecting or combining several independent or dependent functions or sub indicators in proper way, the composite script exhibits a resonance phenomenon which can filter out noise or fake trading signal to enhance trading confidence level. Level 3 : comprehensive indicator/strategy. They are simple trading systems based on my strategies. They are commonly containing several or all of entry signal, close signal, stop loss, take profit, re-entry, risk management, and position sizing techniques. Even some interesting fundamental and mass psychological aspects are incorporated. Level 4 : script snippets or functions that do not disclose source code. Interesting element that can reveal market laws and work as raw material for indicators and strategies. If you find Level 1~2 scripts are helpful, Level 4 is a private version that took me far more efforts to develop. Level 5 : indicator/strategy that do not disclose source code. private version of Level 3 script with my accumulated script processing skills or a large number of custom functions. I had a private function library built in past two years. Level 5 scripts use many of them to achieve private trading strategy.
Индикатор Pine Script®
от blackcat1402
2
[blackcat] L2 Ehlers Hilbert TransformLevel: 2 Background John F. Ehlers introuced Hilbert Transform in his "Rocket Science for Traders" chapter 6. The Hilbert Transform is a procedure to create complex signals from the simple chart data familiar to all traders. Once we have the complex signals, we can compute indicators and signals that are more accurate and responsive than those computed using conventional techniques. In fact, some of the indicators we will discuss cannot be calculated at all without the Hilbert Transform. Function blackcat L2 Ehlers Hilbert Transform indicator performs basic Hilbert Transform (HT) from chart data source and generate complex IQ data. Key Signal Smooth --> 4 bar wma() w/ 1 bar lag Detrender --> The amplitude response of a minimum-length HT can be improved by adjusting the filter coefficients by trial and error. HT does not allow DC component at zero frequency for transformation. So, Detrender is used to remove DC component/ trend component. Q1 --> Quadrature phase output signal I1 --> In-phase output signal Pros and Cons 100% John F. Ehlers definition translation, even variable names are the same. This help readers who would like to use pine to read his book. Remarks The 1st script for Blackcat1402 John F. Ehlers Week publication. Readme In real life, I am a prolific inventor. I have successfully applied for more than 60 international and regional patents in the past 12 years. But in the past two years or so, I have tried to transfer my creativity to the development of trading strategies. Tradingview is the ideal platform for me. I am selecting and contributing some of the hundreds of scripts to publish in Tradingview community. Welcome everyone to interact with me to discuss these interesting pine scripts. The scripts posted are categorized into 5 levels according to my efforts or manhours put into these works. Level 1 : interesting script snippets or distinctive improvement from classic indicators or strategy. Level 1 scripts can usually appear in more complex indicators as a function module or element. Level 2 : composite indicator/strategy. By selecting or combining several independent or dependent functions or sub indicators in proper way, the composite script exhibits a resonance phenomenon which can filter out noise or fake trading signal to enhance trading confidence level. Level 3 : comprehensive indicator/strategy. They are simple trading systems based on my strategies. They are commonly containing several or all of entry signal, close signal, stop loss, take profit, re-entry, risk management, and position sizing techniques. Even some interesting fundamental and mass psychological aspects are incorporated. Level 4 : script snippets or functions that do not disclose source code. Interesting element that can reveal market laws and work as raw material for indicators and strategies. If you find Level 1~2 scripts are helpful, Level 4 is a private version that took me far more efforts to develop. Level 5 : indicator/strategy that do not disclose source code. private version of Level 3 script with my accumulated script processing skills or a large number of custom functions. I had a private function library built in past two years. Level 5 scripts use many of them to achieve private trading strategy.
Индикатор Pine Script®
от blackcat1402
1