Pick Right Trading Strategy with the Hurst Exponent

The Hurst exponent is a measure of long-term memory or self-similarity in a time series or signal.
The Hurst exponent is used to quantify the statistical properties of a time series. It provides information about the persistence or trendiness of the data.
I explained why we need the Hurst exponent and demonstrated how to calculate it in Python.
You are welcome to provide your comments and subscribe to my UA-cam channel.
The Python code is uploaded into github.com/AIMLModeling/Hurst...

[@vogldata3246]

As a matter of fact, the Hurst exponent is not measuring long memory effects. Long memory is a slowly decaying autocorrelation function, existing in parallel to tradable trends.
The Hurst exponent is measuring fractal trends (patterns) instead if H < 0.5. This is the reason why momentum trading is working at all. If you calculate time varying Hurst exponents you see momentum crashes tied to vanishing (fractal or scaling) trends during crises.
If you regard multifractal analysis and calculate the generalised Hurst exponent (GHE), you can see the scaling features directly (MFDFA).
For trading this means that you need to adjust your strategy according to the regime the Hurst exponents dictate, because you cannot trend follow a mean-reverting process and not loose money.

[@vistorgonzales9778]

thank you :)

[@EV4UTube]

Would it be possible to get a copy of the PowerPoint presentation?
I have an idea that involves taking the text of the PPTX and reconfiguring it (re-formatting it) in order to produce set of notes that would be a little easier for me to understand, to track, and to remember.
I tend to be a visual learner, so I derive a lot of benefit from (sometimes minor but important) formatting enhancements.
I mean, I could just pause the video and re-type everything, but that seems inefficient.
If you want, I can show you what it is I produced after I'm done.

[@hkrish26]

actually there are apps that will convert video to ppt or pdf. you can use it