Introduction Remember that we can make filters by using convolution, that is summing the product between the input and the filter coefficients, the set of filter coefficients is sometime denoted "kernel", those coefficients can be a same value (simple moving average), a linear function (linearly weighted moving average), a gaussian function (gaussian filter), a...
This is Keltner Channel where I added Bull and Bear signals. It has a lot of settings to play around with. Have fun... For more information on Keltner Channel: www.investopedia.com
This is a potential solution to dealing with the inherent lag in most filters especially with instruments such as BTC and the effects of long periods of low volatility followed by massive volatility spikes as well as whipsaws/barts etc. We can try and solve these issues in a number of ways, adaptive lengths, dynamic weighting etc. This filter uses a non linear...
Introduction The estimation of a least squares moving average of any degree isn't an interesting goal, this is due to the fact that lsma of high degrees would highly overshoot as well as overfit the closing price, which wouldn't really appear smooth. However i proposed an estimate of an lsma of any degree using convolution and a new sine wave series, all the...
Introduction Envelopes indicators consist in displaying one upper and one lower extremity on the price chart. They are most of the time built by adding/subtracting a volatility estimator (rolling stdev, atr, range...etc) to a central tendency estimator (SMA, EMA, LSMA...etc) . Their interpretation is often subject to debate amongst technical analyst, some...
Introduction Channels indicators are widely used in technical analysis, they provide lot of information. In general, technical indicators giving upper/lower extremities are calculated by adding/subtracting a volatility component to a central tendency estimator. This is the case with Bollinger bands, using the rolling standard deviation as volatility estimator...
Introduction Who doesn't like smooth things? I'd like a smooth market price for christmas! But i can't get it, instead its so noisy...so you apply a filter to smooth it, such filters are called low-pass filters, they smooth and its great but they have lag, so nobody really use them, but they are pretty to look at. Its on a childish note that i will introduce...
Introduction Winning trades and gaining profits in trading is not impossible, however having gross profits superior to gross losses is what make trading challenging, it is logical to think that it is better to open a position when the probability of winning the trade is high, such probability can’t be measured with accuracy but a lot of metrics have been...
This is a scaled version of a Forecast Oscillator, which may be used as a standalone indicator or as a filter. Scaling allows to reduce data to a standard interval, say, 0..1 or -1..1. Oftentimes, it also makes data more contrastive.
This is a labelling module based on a range filter . Notice that the trick here is to use fibonachi numbers . Use smaller range multiplier for higher TFs. This module may serve as a signal generator to be passed through a signal filter. Quote from the original author: This is an experimental study designed to filter out minor price action for a clearer view...
Introduction Today i propose an hybrid filter that use a classical FIR architecture while using recursion. The proposed method aim to reduce the lag generated by fir filters. This particular filter is a sine weighted moving average, but you can change it since the indicator is built with the custom filter template (1). Even if it use recursion it still is a FIR...
Introduction FIR filters (finite impulse response) are widely used in technical analysis, there is the simple or arithmetic moving average, the triangular, the weighted, the least squares...etc. A FIR filter is characterized by the fact that its impulse response (the output of a filter using an impulse as input) is finite, this mean that the impulse response...
A Peek Into the Furure John F. Ehlers TASC Aug 2019
Introduction I already estimated the least-squares moving average numerous times, one of the most elegant ways was by rescaling a linear function to the price by using the z-score, today i will propose a new smoother (FLSMA) based on the line rescaling approach and the inverse fisher transform of a scaled moving average error with the goal to provide an...
Introduction Technical indicators often have parameters settings that the user must enter, those are inconvenient when the user must design a strategy because such settings must be optimized, it must also been noted that the optimal settings at time t could change at time t+n , this is why non parametric indicators are more efficient. Today i propose a moving...
Introduction Trailing stop are important indicators in technical analysis, today i propose a new trailing stop A2RTS based on my last published indicator A2RMA (1), this last indicator directly used an error measurement thus providing a way to create enveloppes, which provide a direct way to create trailing stops based on highest/lowest rules. The Indicator ...
Introduction Using conditions in filters is a way to make them adapt to those, i already used this methodology in one of my proposed indicators ARMA which gave a really promising adaptive filter, ARMA tried to have a flat response when dealing with ranging market while following the price when the market where trending or exhibiting volatile movements, the...
Introduction I already mentioned various problems associated with the lsma, one of them being overshoots, so here i propose to use an lsma using a developed and adaptive form of 1st order polynomial to provide several improvements to the lsma. This indicator will adapt to various coefficient of determinations while also using various recursions. More In Depth ...