Crude Oil forming bottom at US$ 100 for now, which is a big psychological level.

Needs to break below this level for real impact. https://t.co/r5KoExzFKc

Time for a Thread🧵On yesterday’s “F&O Pe Charcha - Diary Of An Option Seller” with @Mitesh_Engr by @Paytm Money

Why Gain/loss ratio is important that the batting average
• More than the Win Loss ratio check the total Gain /loss ratio
• If your avg loss is 1% and avg gain is +5% then even if you are losing in 7 positions and winning in 3 out of 10 still you will be net profitable

Treat Trading as a business:
• Stick to a single strategy. Eg: A restaurateur usually wouldn’t be changing his recipe on a daily basis
• Mitesh Sir only works on BO or BD strategy, as he is a versatile trader

• Any trader should atleast experience 3 scenarios i.e. bull, bear & consolidation
• Target 4% per month i.e. 48% p.a.
• Option buying requires skill, everyday there wont be huge momentum, so buying options won’t yield good results, but high chances of falling into a bad habit

Stock Selection:
• In F&O segment there are ~150 stocks
• Not everyday all stocks will move
• Only 5-7 stocks move upside or downside
• Focus only these momentum stocks
• Check which one is moving
• Range BO/BD never disappoints. BO/BD from a rectangle (consolidation)

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.

MUST READ TILL END:
FB POST BY MR PRABEER BASU,
IAS-1976(BIHAR)
" Received a hurtful post from an old friend and colleague. He was on my daily Broadcast list of WhatsApp. He requested that I do not send him any of my posts on 'political' issues. I gave him a long explaination
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2/29
on why I write and what I write. But he was not convinced.

Being alerted by him thus, I felt I should clarify to all who read my posts on what exactly I think and do.

The charge against me is that I wrongly support our current Prime Minister, Shri Narendra Modi..
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and that it establishes I am not rational and impartial anymore.
Let me try and explain.First and foremost,I do not write on 'political' issues. I write on 'national' issues. For example, I have no opinion on whether Rahul Gandhi should be President of his party or not.
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Similarly, I have no views whether BJP is a better party than Congress or vice versa.
Even after 36 years in the IAS I have no MLA, MP or anyone involved in politics whom I can classify as my friend or even acquaintance.
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So, naturally I don't care which party wins or loses as long as the winning party runs an honest government doing good to my nation.
Now, why I speak in favour of Shri Modi:

1. Ever since the beginning of my service I realised that Planning Commission was not serving..

>6

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.