#HAVELLS (weekly)
- Breakout from Supply zone
- Tarding All time high
- Strong Bullish trend
- Massive volume accumulations
- Buy above 1235
- Stoploss 1175
- Target 1267/1293/1347/1400+

In regards to the #CapitolCoup, I’m in a unique position. I monitored forums ahead of time and then was present for the coup itself.

These are my observations based on what I saw and the evidence that’s been collected so far. I reserve the right to adjust my analysis as needed.

Let’s talk about the organization of the coup first. Seeing some people insist that the coup must have been highly planned and coordinated.

Yes and no. This was a #StochasticCoup. We’ll see little evidence of direct communication between Trump and the insurrectionists.

Within the insurrectionists, there was not one group that executed the storming of the Capitol.

Rather, there were a number of smaller groups working independently towards the same goal. I expect you’ll see little evidence that these various groups communicated with each other.

What you will see is detailed planning and coordination within these small groups.

Then there were the joyriders, those Trump supporters who weren’t directly involved in any plotting but who were more than happy to go along for the ride once the coup was in motion.

After four years, the insurrectionists were carefully attuned to Trump’s messaging.

Every tweet and statement of his was treated as marching orders and carefully dissected for meaning.

It’s why shutting down Trump’s Twitter account was so important.

Important paper from Google on large batch optimization. They do impressively careful experiments measuring # iterations needed to achieve target validation error at various batch sizes. The main "surprise" is the lack of surprises. [thread]

https://t.co/7QIx5CFdfJ

The paper is a good example of lots of elements of good experimental design. They validate their metric by showing lots of variants give consistent results. They tune hyperparamters separately for each condition, check that optimum isn't at the endpoints, and measure sensitivity.

They have separate experiments where the hold fixed # iterations and # epochs, which (as they explain) measure very different things. They avoid confounds, such as batch norm's artificial dependence between batch size and regularization strength.

When the experiments are done carefully enough, the results are remarkably consistent between different datasets and architectures. Qualitatively, MNIST behaves just like ImageNet.

Importantly, they don't find any evidence for a "sharp/flat optima" effect whereby better optimization leads to worse final results. They have a good discussion of experimental artifacts/confounds in past papers where such effects were reported.

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.