Stocks to invest to play the EV⚡🔌 Theme:

Sona Comstar: EV Components
Fiem Industries: EV Components
Tata Power: Charging Infrastructure
Tata Chemicals: Lithium ION Cells
Tata Motors: EV Manufacturing
Kabra Extrusion: Battery Packs
Exide: Batteries, Solar, EV's

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Hey, for people interested in this stuff, here's a thread about a change we made to running our facebook account:

So, about 5 years ago, we got on facebook. Why? Well, facebook has a big audience and they like to consume on facebook. So, we started posting comics there, even though we got paid nothing and facebook made money off the free content.

Why? Because back then, once in a while, you would say "also hey, I sell books" or "hey, I'm going to be signing at this event" and your facebook followers would actually see it.

Effectively, the implicit old arrangement was "facebook lets me reach readers efficiently, and I supply facebook with free content they run ads on."

Over time, like a bad business partner, facebook basically made it impossible to reach your audience without *paying them*. Thus, you supply them free content on which they run ads, and you get nothing in exchange.

👇 Thread. New deck getting published this week: "Consumer startups are awesome, and here's what I'm looking to invest in at Andreessen Horowitz." If you want to read it, subscribe to my newsletter here: https://t.co/262t8eh0wf

1/ A lot of new consumer technologies have been introduced to US households in the last 100 years. But it's taken many of them - like the telephone - more than 50 years to get to the majority of the US. Why is that?

2/ We had to literally teach people how to use phone. Which end goes to your mouth, which goes to your ear. Say "hello" when people call. The motivation of consumers to talk to their friends has always been there, but we had to teach the behavior

3/ If you compare phones to the latest technologies, there's been a huge shift. Things are being picked up much faster.

4/ Even while there's been all this innovation recently, physically speaking, we are still the same human beings from 100,000 years ago.

Catch me if you can @Mitesh_Engr

Time for a🧵
Mitesh Sir's EXPIRY Option Selling 101:
• What to look for?
• Strike Selection & Ratios
• SL mgmt
• Avoiding freezes
• Monthy Expiry
• Event days
• How he would have traded last expiry?

In collaboration with @AdityaTodmal

Edward de Bono says intelligence is like a🚗 Automobile

“A powerful car may be driven badly. A less powerful car may be driven well. The skill of the car driver determines how the power of the car is used.”

What @Mitesh_Engr Sir looks at for Expiry Trading:

• Technicals of BNF
• Individual Heavyweight stocks of BNF
• Support/Resistance of BNF on weekly TF
• Also has Superb SL management
• When it comes to adjustments, he is always on toes

How to Select Strikes to Sell?

• You can't sell based on premium
• Follow only trend
• Price action is best to sell on expiry day
• Never wait for the options to go to zero

Using Ratios to Sell:

• If BNF is in a range then strangles
• If breakout/ breakdown from a range then directional
• Selling 70/30 or 80/20 ratios
• 2 examples on how he used ratios

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.