In the first part, over here - Meditations on Screen-time - Part 1 we talked about what is it that one needs to do while looking at charts, the first aspect that we focused on was the difference between Seeing and Observing. 

In this post we will move further and see what is it that we can do with what we have observed.

By observation we collect data or information about whatever we are observing, we may be looking at a 1 min time frame chart or a 5 min time frame chart, or perhaps a footprint chart, it could be anything that is contextually relevant to you. Over time with note taking and reflection, you would have a lot of this information in your mind, what the psychologists call "Crystallized Intelligence". 

The next step is to put this observed information to use, by making inferences (Fluid Intelligence). There are two ways to look at the concept of "Inference" one is - as a plausible explanation about an observation (The Why), the other way to look at inference is the way Bayesians use it, for them Inference-ing is about thinking in terms of probability distributions, and updating the values we assign to the distribution as we come across new evidence. If that is too much to handle, let me try to simplify it with an example.

Let's say you have been observing the breakout in NIFTY the moment it crosses Day High, there are few ways to cognitively process this information -

a) You can think of "causes", meaning why is the price shooting at the point. You may deduce that perhaps stops are getting triggered etc. - This is an example of the first type of inference. No harm in doing this, the only limitation is, a lot of times its more difficult to find causes than correlations, and as a trader you know whats more important.

b) You can think in terms of "averages" - You may deduce that usually it shoots up by 10 points and keep that as a reference. This is good but then "averages" are not that precise as we know, and more importantly does for help build a framework/scaffold on which one could build a sort of model as more data/evidence is observed. That brings us to the third way.

c) You can think in terms of "Probability Distributions" and "Conditional Probabilities" - Every time you observe the breakout you could create an imaginary distribution.

For instance keep one can keep the following exponential distribution in mind (look at it more from a visual perspective rather than for it's mathematical accuracy)   

Now imagine, putting all your observations on the curve, perhaps adjusting the curve based on observations. That would get you to think about the outcomes more from precisely. You may be able to tell yourself, if Day High crosses we will get 2 points for sure, I am 90 % confident about it. 

Now add to this some "Conditional Probabilities" - Lets say you also observe that on days were you get a range beyond X in the first half of the day the above distribution no longer remains valid.  Now what you have done is you have found an exception to the above rule. Over time you would be able to "cognitively" add several such conditional exceptions as well to your inference. 

I hope the above example helps you visualize outcomes in a more structured way. If you are more interested in the Bayesian way of thinking about situations and outcomes you may want to start here. 

https://www.farnamstreetblog.com/2012/12/thomas-bayes-and-bayess-theorem/

So at the end of it, all the time you spend in front of the screen, watching price action and charts should help you create a probabilistic framework in your mind, and the more you gather evidence the more you should be able to refine your "confidence levels" of the outcomes.

As always happy to hear more form you.