Thanks @bobwest. The deviations plotted n the chart are more about setting expectations and visualising what the market has been doing. If I were to drill down and try to determine probabilities for those deviations to be achieved I would use measured moves and basic price action principles.

What does that mean in practice?

If you take a look at the chart in my previous post do you notice the size of each of those swings? Thats where you determine probabilities. So you extract all those swing numbers for the past 14 days. RTH only - Im not interested in overnight or European hours.
You group the numbers, and find the percentages as in the screenshot below:




What that breakdown tells you is that swing sizes between 20-29 points occur the most often at 24% of the time. The next most frequent ranges fall between 10-19 points at 21% of the time. ANd so on. You can use these statistics to find high probability trade locations. So for instance if the market surges 100 points and through your analysis you have determined that a swing size between 100-110 occur 1.3% of the time and that this has only happened 4 times in the last 289 samples, then you might have a reason to look for signs that price is going to revert back to value.
Any trade using this reasoning would be a high probability trade. But you cannot simply rely solely on the stats. You still have to use common sense and basic price action principles when you make your decision. For instance the footprint charts which I believe holds key information (and which I am still learning about) with regards to volume, order flow and trader positioning. These things all have to come together and the pieces have to fit together in a logical way where you aren't thinking too hard about it.

@Grantx, May I ask what you are using to draw these statistics? I know there was a function in NT7 PAS that did not move to NT8. Would you share this indicator?

May I know whether you have seen this webinar and not considering approach discussed by FT71 for some reason?

Sent using the nexusfi.com mobile app

Webinar: FuturesTrader71 (FT71) Trade Management Techniques This was an excellent webinar, so much good advice. Thanks for reposting that Big Mike. I found the probability graphs on the scale out method very interesting. I am trying to figure out how he managed to do that distribution profile in excel. If anyone …

@Grantx May I know how did you export ZigZag indicator data? I know export is possible with IRT but I guess for other platforms it needs to be coded?

well... you just have to look... and understand the chart

the Gaussian distribution is wider...
meaning that there are more values or samples away from the center
the Laplace shows that these values drop off fast...
meaning the farther you get from zero, the drastically fewer values or samples exist

remember... these are charts of distributions..

in both cases, there are just as many up moves as down moves
there are a lot more 10 cent moves in the normal/Gaussian distribution than in the Laplace

What is the Laplace distribution?
https://rss.onlinelibrary.wiley.com/doi/pdf/10.1111/j.1740-9713.2018.01185.x

The Laplace (or double exponential) distribution, like the normal, has a distinguished history in statistics. It has applications in image and speech recognition, ocean engineering, hydrology, and finance.

What does it look like?
The plot in Figure 1a depicts the Laplace probability density function centered at zero and, for comparison, the normal one.



To explain what these fatter tails mean, consider that the cumulative probability of the standard normal at 3 is approximately 99.9%, which justifies the common assertion that events 3 standard deviations or more above (or below) the mean are unlikely. However, the corresponding cumulative probability of the Laplace with the same median and variance is about 99.3%. Therefore the occurrence of an “extreme” event in a Laplace population is more than five times as likely as in a normal population

Maybe someone else can explain it more simply...

A practical gaussian example from today. First the daily prep:


Then the distribution (right skew ) falling within the gaussian distribution framework calculated from the most recent days of volatility. On the right is the TPO chart on the left are deviation price markers showing higher probability price reversal levels or targets depending on your style. Today is not over yet, but if you go back over the past few days you will see very similar behavior.

Any data can be plotted into different distributions, it doesn't make the data actual distribution that way
the same data you have you can also apply a Laplace distribution and many others...
However its the fit that matters... not what you apply to the data...


Predicting Stock Market Returns—Lose the Normal and Switch to Laplace
https://sixfigureinvesting.com/2016/03/modeling-stock-market-returns-with-laplace-distribution-instead-of-normal/
[fair warning, since the prior time, i have actually taken the time to talk to the author]

and from another on finance
Against the Norm: Modeling Daily Stock Returns with the Laplace Distribution
Many introductory-level courses teach students to use the normal approximation for daily stock returns when modeling over a long period of time, but even some of the most influential names in mathematical finance agree that daily returns fall outside the realm of the normal distribution. 1 While it is true in many cases that the
distributions of daily stock returns tend to be symmetrical, the distributions also have fat-tails, meaning
there is a greater likelihood of observing extreme cases


how to apply this? i dont know...
i also cant truly tell you with confidence what would happen if you are using gaussian distribution for prediction on laplace shaped data... though my guess would be that due to the fatter tails and the higher center, the suprises would be more often and more detrimental than in a normal distribution.