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Thread on
"Intraday Banknifty Strangle based on OI data"

(System already shared, today just share few examples)
(1/n)

-Trade timing after 9:45

-Check Banknifty change in OI data at 9:45, and identify the highest OI built up in call side and put side

-We will sell both strikes with combined premium as SL on both legs.
(2/n)

28th June 2021

BN open-35440
At 9:45 trading at 35350
Highest OI in call side at 35500 CE and 35000 at put side
Strike selection 35500 CE- 35000 PE
(3/n)

29th June 2021

BN open-35323

At 9:45 trading at 35112

Highest OI in call side at 35300 CE and 35000 at put side

Strike selection 35300 CE (232) - 35000 PE (150) Combined premium 382 , Both legs SL 382
(4/n)

30th June 2021

BN open-35030

At 9:45 trading at 35002

Highest OI in call side at 35000 CE and 35000 at put side

Strike selection 35000 CE(176)- 35000 PE(213) Combined premium 389 , Both legs SL 389
(5/n)

@FionaPearman @ScottMorrisonMP Morrison is our statue of unempathic neoliberalism - erected by evangelical and corporate Australia to celebrate their victory over compassion, equality and secular humanism. 

Hopefully it gets pulled down like the statues of dubious heroes of oppression from an earlier age.

@ScottMorrisonMP The two-party-preferred system in 
Australia politics isn’t working for people, and it sure isn’t working for the planet.

@ScottMorrisonMP My collapse of faith in our democracies isn’t an accident. It’s one of the central victories of the neoliberal project, delivered through two generations of privatisation, outsourcing and deregulation, underfunding of government services (locked in by tax cuts),...

@ScottMorrisonMP restricting freedom of information, targeting whistleblowers and raiding the media who report on them, delegitimising and gagging public interest advocacy, and criminalising protest.

@ScottMorrisonMP Politics in Australia today relies hugely on the fact that most people don’t know most of what’s going on so as to get away with stuff that couldn’t be done under real scrutiny we have seen a clear example of this with the National Coronvirus Cabinet.

Low-risk, High-return stocks are what we look for relentlessly

But many of them we know but ignore
some of them don't grab financial headlines

Here are 10 low-risk, high return stocks (that we ignore):

What I mean by “high-return”?

These stocks have OUTPERFORMED the market:

💹Chart with upwards trend Since IPO
💹Chart with upwards trend Over the last 5 Years or since IPO

These are all long-term winners that will keep on winning

What do We Mean by “low-risk”?

A combination of:

❣️Great management
❣️Broad moat
❣️Financially strong
❣️High ROCE
❣️Low Disruption/Dilution/Volatility
❣️Consistent Organic Growth
❣️Assorted Customers/Suppliers
❣️Low debt

1-#CDSL

A depository is a big business and will grow continuously, Its a sustainable business
cause "Market hai toh CDSL bhi hai or rahega"

2- #Dixon

High Management Efficiency with a high ROE of 19.30%
Healthy long-term growth as Net Sales has grown by an annual rate of more than 30%
High Institutional Holdings

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.

one of the things i like about Batman is that it's fundamentally about being orphaned, and being an orphan — how this guy stays tethered to the world in his own weird, abstract, extrajudicial way when he feels that his link to it has been violently severed

Batman does this by permanently recapitulating his own trauma: solving crimes for other people, protecting them. But that's actually just the beginning; he also recapitulates his own experience of orphanness by essentially raising several orphans

Dick is an orphan in the traditional sense: dead parents. Batman sees it happen. Jason, less so: missing dad, known mom who eventually dies. Tim, even less so: parents are around a while, and involved, before both dying. Carrie, even less than that: negligent parents.

Steph: evil father, who openly works against her. And then there's Damian, whose parents are both alive and yet both have identities that totally obliterate their role as his parents. He doesn't relate to them as a mother and father; he can't, and neither can they.

In all these different cases, Batman tries all these different ways of making orphanness for them not what it was for him: Either by trying to teach them the (arguably poor) coping skills early that he learned over a very long time, or by literally legally adopting them, etc.