Comments on: Yet Another Improved RSI

By: Yet Another Improved RSI - QuantInfo - Empowering Algorithmic Trading Insights

Yet Another Improved RSI - QuantInfo - Empowering Algorithmic Trading Insights — Wed, 11 Oct 2023 01:41:50 +0000

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By: Quantocracy's Daily Wrap for 12/15/2021 - Quantocracy

Quantocracy's Daily Wrap for 12/15/2021 - Quantocracy — Fri, 28 Jan 2022 06:46:35 +0000

[…] Yet Another Improved RSI [Financial Hacker] […]

By: Giles Penny

Giles Penny — Wed, 22 Dec 2021 19:42:42 +0000

Now I realise there are two ways to calculate the Hann coefficients:
Symmetric: 0.5 cos(2πn/(N-1))

Periodic: cos(2πn/(N+1))
“This option is useful for spectral analysis because it enables a windowed signal to have the perfect periodic extension implicit in the discrete Fourier transform.”

By: Giles Penny

Giles Penny — Wed, 22 Dec 2021 12:32:58 +0000

Thanks for this interesting and very useful blog.

The Hann Function to generate an array of smoothing coefficients is widely published and implemented, eg;
Matlab https://uk.mathworks.com/help/signal/ref/hann.html
Scipy https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.windows.hann.html

Here’s the formula:
0.5 cos(2πn/(N-1))
N = length of window

However the formula Ehlers uses is different:
1 – cos(2πn/(N+1))

In Python an 8 element, standard Hann Window is :

import numpy as np
N = 8
n = np.arange(N)

0.5 * (1 – np.cos(2*math.pi*(n/(N-1))))
0. , 0.1882551 , 0.61126047, 0.95048443, 0.95048443, 0.61126047, 0.1882551, 0.
(this matches the output from the Matlab and Scipy implementations)

If we use Ehlers formula, again in Python, the output is quite different
N=8
X = np.arange(1, N+1)
1 – np.cos(2*math.pi*(n/(N+1)))
0, 0.23395556, 0.82635182, 1.5, 1.93969262, 1.93969262, 1.5, 0.82635182

So what’s going on here ?
I have read through Ehler’s two articles in TASC (Sep 2021 and Jan 2022), have spent several hours coding away and feel none the wiser!

By: Henry Stern

Henry Stern — Wed, 15 Dec 2021 07:41:01 +0000

Adding a windowing function to an indicator smooths the output, but at the cost of additional phase delay (lag). Window functions, such as Hann, Hamming, and Blackman, are effectively low pass finite impulse response (FIR) filters, which, like all low pass filters, result in the smoothing. The longer the window length, the more the smoothing, but also the more the phase delay, an unavoidable tradeoff. So, while the additional smoothing can help avoid some of the whipsaws, the added delay also slows the response to rapid changes in longer-term trends, which most likely is responsible for the worsened performance. The smoothed windowed output looks nice on a chart, though!

By: Arnis Lapsa

Arnis Lapsa — Tue, 14 Dec 2021 20:13:33 +0000

> I can only say what you cannot do: replacing the RSI in a working strategy with RSIH.

Not much luck here either.

Somehow I find RSIS most useful although not a huge fan of RSI concept to begin with.

And still failing to find a use for windowing.