Gann's square of 9 from the all time high for #hdfcbank

Don't even need a computer actually. Print the chart, fetch a pencil and a ruler and you are good to go.

See if you can spot #support and #resistance not to mention the time anchors.

You want to know something about how bullshit insane our brains are?
OK, so there's a physical problem with our eyes: We move them in short fast bursts called "saccades", right? very quick, synchronized movements.
The only problem is: they go all blurry and useless during this

having your vision turn into a blurry mess every time you move your eyes is obviously not a good idea, so our brains hide it from us. Now, imagine you're an engineer and you have this problem.

You've got some obvious solutions you could do.
1. make the vision go black during movement. (Some VR games do this!)
2. just keep showing the last thing we saw prior to movement

both are good options with different downsides, but OH NO. this is assuming everything makes sense and is chronological and (regular) logical.
Your brain does neither of these options, really.

first, it basically puts your visual system on "pause". You're not seeing blackness or even nothing, you're just not seeing period.
then when you finish your saccade, it shows you what you now see at the new position. and then it pretends it can time travel.

This post is pretty bizarre, but it manages to hit on so many false beliefs that I've seen hurt junior data scientists that it deserves some explicit

(1) The notion that R is well-suited to "building web applications" seems totally out of left field. I don't feel like most R loyalists think this is a good idea, but it's worth calling out that no normal company will be glad you wrote your entire web app in R.

(2) It is true that Python had some issues historically with the 2-to-3 transition, but it's not such a big deal these days. On the flip side, I have found interesting R code that doesn't run in modern R interpreters because of changes in core operations (e.g. assignment syntax).

(3) "Most of the time we only need a latest, working interpreter with the latest packages to run the code" -- this is where things get real and reveal some things that hurt data scientists. If this sentence is true, it's likely because you don't share code with coworkers.

(3) Really is a broader issue in data science: people only think of what they need to do their work if no one else existed and code was never maintained. Junior data scientists almost always operate on projects they start from scratch and don't have to maintain for long.