Level: 2 Background John F. Ehlers introuced adding the Fisher Transform to the Adaptive RSI in his "Cycle Analytics for Traders" chapter 15 on 2013. Function The purpose of the Fisher transform is to take any indicator having a nominally zero mean and bounded between the limits of −1 to +1 and convert the amplitude so that the transformed indicator has an...
Level: 2 Background John F. Ehlers introuced Measuring the Dominant Cycle using the HomoDyne Discriminator in his "Cycle Analytics for Traders" chapter 14 on 2013. Function With Hilbert transformer, 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...
Level: 2 Background John F. Ehlers introuced Measuring the Dominant Cycle using the Phase Accumulation in his "Cycle Analytics for Traders" chapter 14 on 2013. Function With Hilbert transformer, 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...
Level: 2 Background John F. Ehlers introuced Measuring the Dominant Cycle using the Dual Differentiator in his "Cycle Analytics for Traders" chapter 14 on 2013. Function With Hilbert transformer, 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...
Level: 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...
Level: 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...
Level: 2 Background John F. Ehlers introduced Convolution Indicator in his "Cycle Analytics for Traders" chapter 13 on 2013. Function Since high correlation exists only at the market turning point, the convolution indicator is dependent on the lookback period used in the calculation. Assuming the two price segments have an equal time duration, the peak...
Level: 2 Background John F. Ehlers introduced Even Better sinwave Indicator in his "Cycle Analytics for Traders" chapter 12 on 2013. Function The original Sinewave Indicator was created by seeking the dominant cycle phase angle that had the best correlation between the price data and a theoretical dominant cycle sine wave. The Even Better Sinewave Indicator...
Level: 2 Background John F. Ehlers introduced Adaptive BandPass Filter in his "Cycle Analytics for Traders" chapter 11 on 2013. Function Adaptive band-pass filter was designed. It just makes since to tune that filter to the measured dominant cycle to eliminate all the other frequency components that are of no interest. Here, the adaptive band-pass indicator...
Level: 2 Background John F. Ehlers introduced Adaptive CCI 2013 in his "Cycle Analytics for Traders" chapter 11 on 2013. Function The time length to be used for the channel in the calculations is widely varied in the literature. In all cases, the length is rather arbitrarily established to fit the indicator to some preconceived event. It seems to me that it...
Level: 2 Background John F. Ehlers introduced Adaptive RSI 2013 in his "Cycle Analytics for Traders" chapter 11 on 2013. Function The adaptive RSI starts with the computation of the dominant cycle using the autocorrelation periodogram approach. The identification of the RSI indicator itself following the dominant cycle calculation is noted by the comment near...
Level: 2 Background John F. Ehlers introduced DFT Spectral Estimate in his "Cycle Analytics for Traders" chapter 9 on 2013. Function The DFT is accomplished by correlating the data with the cosine and sine of each period of interest over the selected window period. The sum of the squares of each of these correlated values represents the relative power at each...
Level: 2 Background John F. Ehlers introduced Autocorrelation Reversals in his "Cycle Analytics for Traders" chapter 8 on 2013. Function One of the distinctive characteristics of autocorrelation is that the autocorrelation shifts from yelow to red or from red to yellow at all values of lag at the cyclic reversals of the price. Therefore, all we need do to...
Level: 2 Background John F. Ehlers introduced Autocorrelation Periodogram in his "Cycle Analytics for Traders" chapter 8 on 2013. Function Construction of the autocorrelation periodogram starts with the autocorrelation function using the minimum three bars of averaging. The cyclic information is extracted using a discrete Fourier transform (DFT) of the...
Level: 2 Background John F. Ehlers introduced Autocorrelation Indicator in his "Cycle Analytics for Traders" chapter 8 on 2013. Function If we correlate a waveform composed of perfectly random numbers by itself, the correlation will be perfect. However, if we lag one of the data streams by just one bar, the correlation will be dramatically reduced. In a long...
Level: 2 Background John F. Ehlers create Synthetic Prices Using Random Numbers with Memory in his "Cycle Analytics for Traders" chapter 8 on 2013. Function Peter Swerling is best known for the class of statistically “fluctuating target” scattering models he developed in the early 1950s to characterize the performance of pulsed radar systems, referred to as...
Level: 2 Background John F. Ehlers introuced Modified RSI Indicator in his "Cycle Analytics for Traders" chapter 7 on 2013. Function The RSI is the percentage of the sum of the delta closes up to the sum of all the delta closes over the observation period. The only variable here is the observation period. To have maximum effectiveness the observation period...
Level: 2 Background John F. Ehlers introuced Modified Stochastic Indicator in his "Cycle Analytics for Traders" chapter 7 on 2013. Function Conventional indicators are not immune to the effects of spectral dilation. For example, a Stochastic indicator remains near its upper bound when the market is in an uptrend even though a relatively short lookback period...