#Asianpaint
Price & Time correction seems concluded and a fresh upmove has commenced.
Holding 2990, a LRHR trade possible here.

Ｇａｕ Ｍａｔａ Ｉｎ Ｖｅｄｉｃ
Ｃｕｌｔｕｒｅ

It is said that the one who bears the dust thrown from the hoof of Gaumata on his head, as if taking a bath in the water of the teerth sthalam and getting rid of all sins.

Ved and all other Hindu scriptures venerate the cow. The Vedic dictionary, Nighantu, gives amongst other synonyms of 'Gau' [ or cow] the words 'Aghnya', 'Ahi', and 'Aditi'.

Yaska the vedic decoder and commentator on Nighantu, defines these as-

Aghnya —— the one that ought not to be killed
Ahi —— the one that must not be slaughtered.
Aditi —— the one that ought not to be cut into pieces.

These three names of cow signify that this is an animal which should not to be put to tortures. These words appear frequently throughout the Veda in context of the cow.

But in due course the falsehood of killing cows spread far and wide and they became even more deep rooted when western scholars with their half baked knowledge of Sanskrit transliterated these interpretations of commentaries of Sayanacharya ,

How to trade on Expiry day with Nifty straddle?
(System trading on Expiry)

Triple straddle is one of the best and accurate methods for expiry day

If nifty trading at 15720 then will sell three straddles above 50 points from ATM, below 50 points and ATM.

Ex.
Sell straddle 15700(ATM), 15650(ATM-50), 15750 (ATM+50)

Stoploss of 80% on every leg and run your trade till 3 PM

Once SL hit in any Leg just keep holding other leg till expiry with SL (Never or less chance to hit both SL on expiry day)

Trading timing at 9:20 the Morning

1 There's a chasm between an NLP technology that works well in the research lab and something that works for applications that real people use. This was eye-opening when I started my career, and every time I talk to an NLP engineer at @textio, it continues to strike me even now.

2 Research conditions are theoretical and/or idealized. A huge problem for so-called NLP or AI startups with highly credentialed academic founders is that they bring limited knowledge of what it takes to build real products outside the lab.

3 A product is ultimately a thing that people pay for - not just cool technology or user experience. But I’m not even talking about knowledge gaps in go-to-market work. I'm talking purely technical gaps: how you go from science project to performant + delightful user experience.

4 Most commoditized NLP packages solve well-understood problems in standard ways that sacrifice either precision or performance. In a research lab, this is not usually a hard trade-off; in general, no one is using what you make, so performance is less important than precision.

5 In software, when you’re making something for real people to use, these tradeoffs are a big deal. Especially if you’re asking those people to pay for what you’ve made (can’t get away from that pesky GTM thinking). They expect quality, which includes precision AND performance.