Master marketing psychology, copywriting, close sales, and get customers for your startup.

10 threads to make more money and grow your business:

“Why are Tumblr and Facebook cracking down on sex so much? And why is Facebook in so over zealous about it? It’s all Apples fault!” Etc etc

I can explain all. It is largely the fault of a set of US laws called SESTA and FOSTA.

Technical Analysis Masterclass- Part 2

'Entering the trade'

Entering a trade without ascertaining a certain things is gambling.

In this masterclass we will learn the pre-requisites to enter a trade.

DON'T ENTER A TRADE WITHOUT DETERMINNG THE FOLLOWING.

Please share.

We understand what reward to risk (popularly called risk to reward) is.
It will be denoted by R:R.

We will also try to bust a few myths about R:R and how to avoid losing trades.

Before entering a trade, you need to determine 3 things.

1. Entry trigger
2. Stop loss
3. Target

1. Entry trigger = Reasons for entering a trade. There could be multiple reasons or a single reason for entry.

Generally a set of reasons AKA confluence is a higher probability trade and a generally a safer entry.

Example of an entry trigger.

2. Stop Loss.

The price in the opposite direction of the trade where the trade is exited, at a loss.

At this level, the reason for the entry becomes invalidated according to TA and the price can then move in the opposite direction, probabilistically.

3. Target is the possible price level that the asset might touch based on previous trends or confluence AND where a possible reversal could occur.

Target is the next path of least resistance from where the price might reverse.

We will always ONLY use TA to determine all 3.

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.