# How do you measure noise? - page 5

sibirqk:

If we approach the question purely formally, then the noise can be defined as the difference between the data and some smoothing.

Exactly. If the curve is built optimally, the noise will be minimal. Now you need to measure the noise level, and see where the signal exceeds the noise.

Earlier, at the beginning of the thread, I posted a picture of one of the curves I used to measure noise. In the evening I will make and post the noise track.

sibirqk:
This is the difference between the opening prices of neighbouring bars.
And if you look under a magnifying glass i.e. open a 1m chart and look at the 4 hour period you will not see a straight line axis anywhere and the distribution will go to hell.

Владимир:
Bar to bar, in terms of its performance, the difference is enormous. There is no point in even comparing the 5-minute with the hour and even less so with the daily.
In principle there is no difference - you can draw the same exact images on 5-minute bars; the question is how to use them? For example, you can find noise between 5 minutes and a moving average with a period of 5, in which case the last value of the moving average will be shifted just 2 bars back and it would seem that we only need to extrapolate two values of the moving average and we can trade the return of deviations from the moving average. But everything is much sadder - prediction error turns out to be too big, as well as error of future price value calculated from forecasted moving average using inverse formula.

lilita bogachkova:
Do the same analysis with smoothing 1, because you have to measure the noise in one candle and not in 51
You have to measure in one candle by ticks.

sibirqk:

And this is what the difference between the smoothing and the closing prices looks like - the proverbial noise.

You can even see by eye that its character is changing all the time.

Your smoothing actually acts as a frequency filter. And what you call noise in this situation may simply be a high-frequency (relative to the smoothing period) component of the signal.

Your smoothing is in fact a frequency filter. And what you call noise in this situation may simply be the high-frequency (in relation to the smoothing period) component of the signal.
That's right, you need a signal model!

You can try to take a sine wave with a certain period, floating phase and amplitude as a signal model. Apply signal filtering algorithms to it and trade only the signal with a given period.
But I don't know how to describe such a model.

Do not trade if the noise is greater than the amplitude of the sinusoid and trade if the amplitude of the noise decreases

Maxim Romanov:
You can try to take a sine wave with a certain period, floating phase and amplitude as a signal model. Apply signal filtering algorithms to it and trade only the signal with a given period.
But I don't know how to describe such a model.
This approach is too simplistic and initially suggests high distortions of the signal.
A more rational approach is retrospective analysis with step-by-step determination of the signal components. It looks schematically like this
- It is initially assumed that the price movement in the future is influenced by several factors, e.g. day of the week, time of day, market condition (up/down trend, flat), important economic financial news, etc. The complexity of the model will depend on the number of influencing factors taken into account.
- We look for the dependence of price movement on each of the factors, which can be reduced to the form "Price vector=F{Factor(n)}". Factors, on the price dependence of which is not observed, are considered insignificant and are not considered further.
- We sum up the obtained dependencies in the chart and overlay it on the real signal. The obtained difference will be "noise" in our case.
But in its essence such "noise" is also a part of the signal, simply because of presence of significant influence factors not considered by us, we will be able to define but cannot predict neither the character of "noise" nor any of its characteristics.
So I don't see the point in measuring noise. But that is my personal opinion and my approach to the subject.

The question itself - how do you measure noise? -- is incorrect, illogical, wrong.

To begin with, it must be understood that the input is a mixture of "signal+noise".

If it was, the question would be: How do you separate the "signal" from the "signal+noise"? When you have solved this problem, identifying the "noise" would not be too difficult.

The problem is solved by methods of adaptive control theory.

For example.

The red line on the top graph is the "signal". The "noise" as such is not marked on the graph because it is not needed, but it is used to calculate the dispersion, in other words, the bandwidth, the propagation tube of the signal.