Indicator
Indicator
Motion Smoothness Index Introduction
Its holiday time for me, i have been working here a lot. But no leaving before publishing. Telling when market price is smooth or rough is not the easiest task, so today i present a trend metric indicator that allow you to give you this kind of information.
The Indicator
The indicator is in an approximate range of (0,1) with mean x̄ decaying for higher length's, when the indicator is below 0.5 the market is smooth, else rough, this is the simple interpretation. The indicator is simply the ratio of the price residual standard deviation and the price standard deviation.
Higher value of length will make the indicator less accurate when it comes to detect rough market price, you can still use the indicator direction or its running mean to give you insights but 0.5 is still the recommended detection threshold.
In More Depth
Even tho market is random by nature there can still be structures in the price (cycles and trends), the fractional BM model will tell you that market price is sometimes auto-correlated (trending) or non auto-correlated (ranging), knowing what is the current market state is therefore important, when price is rough it can means an excess in noise thus exhibiting an uncorrelated market at the contrary of a smoother price that can allow for auto-correlation.
Now, market is infected by noise, and thats really unfortunate but the noise posses various properties that can allow for all the structures we see in market price. So thinking about the market allowing for possible profits during auto-correlated states is encouraging.
Conclusion
Although the indicator measure smoothness/roughness it can still be interpreted as a trend/range state detector. I hope it provide to be useful.
I wish you all good holidays and see you next time ! Thanks for reading !
Indicator
Surface Roughness EstimatorIntroduction
Roughness of a signal is often non desired since smooth signals are easier to analyse, its logical to say that anything interacting with rough price is subject to decrease in accuracy/efficiency and can induce non desired effects such as whipsaws. Being able to measure it can give useful information and potentially avoid errors in an analysis.
It is said that roughness appear when a signal have high-frequencies (short wavelengths) components with considerable amplitudes, so its not wrong to say that "estimating roughness" can be derived into "estimating complexity".
Measuring Roughness
There are a lot of way to estimate roughness in a signal, the most well know method being the estimation of fractal dimensions. Here i will use a first order autocorrelation function.
Auto-correlation is defined by the linear relationship between a signal and a delayed version of itself, for exemple if the price goes on the same direction than the price i bars back then the auto-correlation will increase, else decrease. So what this have to do with roughness ? Well when the auto-correlation decrease it means that the dominant frequency is high, and therefore that the signal is rough.
Interpretation Of The Indicator
When the indicator is high it means that price is rough, when its low it indicate that price is smooth. Originally its the inverse way but i found that it was more convenient to do it this way. We can interpret low values of the indicator as a trending market but its not totally true, for example high values dont always indicate that the market is ranging.
Here the comparison with the indicator applied to price (orange) and a moving average (purple)
The average measurement applied to a moving average is way lower than the one using the price, this is because a moving average is smoother than price.
Its also interesting to see that some trend strength estimator like efficiency ratio can treat huge volatility signals as trend as shown below.
Here the efficiency ratio treat this volatile movement as a trending market, our indicator instead indicate that this movement is rough, such indication can avoid situation where price is followed by another huge volatile movement in the opposite direction.
Its important to make the distinction between volatility and trend strength, the trend is defined by low frequencies components of a signal, therefore measuring trend strength can be resumed as measuring the amplitude of such frequencies, but roughness estimation can do a great job as well.
Conclusion
I have showed how to estimate roughness in price and compared how our indicator behaved in comparison with a classic trend strength measurement tool. Filters or any other indicator can be way more efficient if they know how to filter according to a situation, more commonly smoothing more when price is rough and smoothing less when price is smooth. Its good to have a wider view of how market is behaving and not sticking with the binary view of "Trending" and "Ranging" .
I hope you find a use to this script :)
Best Regards
Indicator
