#HDFC futures on short term charts - strike back pattern is a good pullback pattern gives low risk entries
(for more details on strike back pattern - check @Definedge newsletter dated 30-May-2021) There is also a twitter thread by @Prashantshah267

A Thread on the Boss himself @Mitesh_Engr

Mitesh Sir's Positional Option Selling 101:

• How to find direction
• Which options to sell
• How to deploy capital
• Exit criteria
• What ROI he targets weekly
• What % risk he takes

Done with the help of @niki_poojary

How @Mitesh_Engr Sir finds the direction?

• Daily charts S/R
• 75 min charts S/R
• Intraday trend
• Always play directional
• Never trades in strategies

Which options to sell in weekly expiry according to @Mitesh_Engr?

• Weekly candle High/Low
• Sell 1% away options from those
• Exit when levels breached

Position Sizing by @Mitesh_Engr Sir.

How to deploy your capital?

• First sell 20%
• Pyramid the next day
• When to exit
• What to do when view goes wrong
• What to do with idle capital

Some important tweets of @Mitesh_Engr Sir

• What Data to look at
• ROI for safe players per month
• Max risk per day
• Gather knowledge and play directional
• Never play strategies

Observing the public conversation around FB, and the private ones happening among techies and ex-FBers, I think the mutual misunderstanding is worse than when I set out two years (and 500 pages) ago to (in a small way) bridge that gulf.

We're basically fucked.

The tech world has gotten so huge, self-reinforcing, and insulated from reality they can no longer even vaguely look at themselves (and their actions) as others do. They just live on a different planet than most people.

Conversely, the average tech consumer doesn't understand the technology that has slowly taken over their lives, and their designated emissaries to figure it out--politicians, pundits, regulators, journalists--understand it barely better than they do, and have their own agendas.

To say more than generalities for a moment, here's what I think is likely the core problem.

Techies take weird, improbable visions, and make them realities: some BS pitch deck to a VC, mixed with money and people, really does turn into some novel thing.

Most people work inside a legacy industry that's evolved that way over time (usually for good reasons), and they think about the future via some analogy with their present (which is a function of a long-ago past). The interruption that tech will introduce is often hard to grasp.

A #story from #Thiruvilayadal puranam-one of divine leelas of Bhagwan Shiva

#threadseries #Madurai

Story 16- it is about how Bhagwan Shiva explained the meaning of Vedas to the Rishis.

வேதத்துக்குப் பொருள் அருளிச் செய்த படலம்

Once there was a great deluge,everything in the world was destroyed.All lifeforms that existed got perished.Afterwards ,due to Bhagwan Shiva creation started.Then from Bhagwan Shiv ji's mouth-birth of 🕉 OM- Pranavam happened.From Pranavam,4 vedas appeared. Rishis learned them.

Many Rishis in Naimisharanya learned Vedas&recited them but they didn't understand the meaning&essence of Vedas. They were worried as they couldn't find any guru who can teach them the meaning of Vedas.That time,Rishi Arabhatar visited them.Rishis told him about need their worry.

Rishi Arabhatar told them that Bhagwan Shiva has given Vedas &he would be grateful enough to teach the meaning as well.But for that rishis have to rigorous penance at a sacred place.Also told them to go to Sundareswarar temple in Madurai to do tapas in front of Dakshinamurthy.

Assured them that Dakshinamurthy himself will come & teach them.Rishis proceeded to Madurai & started their tapas in front of Dakshinamurthy sannidhi for almost a year starting from Karthigai month pournami to next year Karthigai month pournami following all rituals..annadan

Hello Dear Tweeples!

It’s Siddhars time of the week! Last week I shared story of Guru (Machamuni or Yogi Matsyendranath), this week I bring you information on his Shishya.

#Thread on Gorakanathar Siddhar.

@LostTemple7

His origin story is interesting, though I couldn’t verify it’s authenticity, so not sharing the story here. He’s shishya of Machamuni Siddhar and also learnt from Agasthiya Siddhar.

@HelloNNewman @RajiIndustani @Kishoreciyer1

Unlike other Siddhars, he is depicted as either with a Tiger or seated & wearing Tiger skin. While I couldn’t find any explanation for this. My interpretation is as follows:

Tiger is a predator that lurks around and attacks unexpectedly.

Our 5 indriyas and 5 vikitis also similar to Tiger, they lurk around and attacks our thoughts when we’re least expecting. Gorakkar is said to have mastered these aspects, thus to denote this, he shown with Tiger.

@Itishree001 @VarierSangitha @jananisampath

Gorakanathar or Gorakshnath (as he’s known in the Nath Sampraday) is celebrated Guru in both Northern and Southern part of India. He’s one of the NavNath in Nath Sampraday and also 18 Siddhars of Tamil tradition. During his life, he’s travelled Indian subcontinent extensively +

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.