Yup, autocorrect. 😅

More from Sahil Sharma

Sahil Sharma
@sahil_vi

Vaibhav Global.

The OG 💍💎💍💎💍💎 in the dust.

🧵👇

1. Business Model.

Vaibhav Global is a vertically integrated omnichannel company that sells value fashion jewellery & lifestyle products.

Don't worry, I'll break those terms down in subsequent tweets.

Let's dive deeper into the vertical integration aspect. VGL is present across the value chain.
(i) Identifying trends & products.
(ii) Manufacturing or sourcing.
(iii) Selling.

(i) Identifying trends & products.
VGL's value starts at identifying trends in jewellery, lifestyle, fashion products across the world. Their employees are well entrenched in this space.

(ii) Manufacturing or sourcing.
VGL then decides whether to manufacture it, or source it from outside. This flexibility results in the crazy high 63% FMCG like ROCE. Why? Coz producing in-house is capital intensive. Sourcing goods is good for the capital efficiency.

Sahil Sharma
@sahil_vi

One key learning from @VijayKedia1 Sir:

Many of the cos he invested in: vaibhav global, Tejas networks, sudarshan chemicals he was tracking for 4-5 years before he decided to invest. Wants to pull the trigger near the bottom of biz cycle

Patience

Sahil Sharma
@sahil_vi

#idfcfirstbank is my largest and oldest investment. Thought of creating a thread to explain the business. If you like the thread, please spread the knowledge, retweet. Buckle up, because this is going to be a long one. :D

Quick summary of what a bank does. This enables me to establish a framework under which the analysis of the bank will become very structured.
Money is its raw material.

Bank takes customer deposits, raises money via borrowings (can issue its own bonds, takes loans from other banks etc). This is the input for the bank. Since the bank is liable to pay interest on it, it is also known as liabilities.

The output is a loan which the bank gives. These are broadly of 2 types: loans given to customers (Retail and Corporate) and subscription to bonds issued by other entities (Government of India, corporates like Vodafone Idea etc).

This is the output of a bank. Since these generate income for the bank, they are called assets. Not all the liabilities can be utilised as loans or used to purchase bonds, because banks need to have enough liquidity to service redemptions of their deposits.

Sahil Sharma
@sahil_vi

Which cos according to you have highest probability of tripling revenue in next 4 years (~30% cagr)?

FY26 revenue is 3x FY22 revenue ?

My guess among cos I track: Sona comstar, saregama, mastek, dynemic products, sastasundar, angel one

I missed one: Mirza india aka red tape. :)

Sahil Sharma
@sahil_vi

what better way to start sunday than a #kilpest thread: a classic peter lynch pick & shovel investment for the diagnostics sector. If you like the thread, please retweet this tweet to help educate max investors. 🙏🙏

Which one of these 4 companies should I make next thread on?
— Sahil Sharma (@sahil_vi) June 14, 2021

Before I dive into Kilpest, some words on microcap investing. Everyone **needs** to be aware of what they are signing up for.

Two interviews from @varinder_bansal sir’s Omkara capital with very successful microcap investors (one with @iancassel sir: https://t.co/WDSOOIcUn8 ...

@iancassel & one with @hiddengemsindia sir: https://t.co/iC8iKOsKWz) do a wonderful job explaining all the landmines & things to keep in mind. Do watch these, please before you buy any #microcap

@iancassel @hiddengemsindia My own take is that one has to create a diversified PF of uncorrelated microcaps (if you'd typically invest in 5, make it 10 and so forth). Understand that liquidity risks will always remain.

More from Ds

10-K Diver
@10kdiver

1/

Get a cup of coffee.

In this thread, I'll walk you through 2 probability concepts: Standard Deviation (SD) and Mean Absolute Deviation (MAD).

This will give you insight into Fat Tails -- which are super useful in investing and in many other fields.

2/

Recently, I watched 2 probability "mini-lectures" on YouTube by Nassim Taleb.

One ~10 min lecture covered SD and MAD. The other ~6 min lecture covered Fat Tails.

In these ~16 mins, @nntaleb shared so many useful nuggets that I had to write this thread to unpack them.

3/

For those curious, here are the YouTube links to the lectures:

SD and MAD (~10 min): https://t.co/0TwubymdE6

Fat Tails (~6 min):

4/

The first thing to understand is the concept of a Random Variable.

In essence, a Random Variable is a number that depends on a random event.

For example, when we roll a die, we get a Random Variable -- a number from the set {1, 2, 3, 4, 5, 6}.

5/

Every Random Variable has a Probability Distribution.

This tells us all the possible values the Random Variable can take, and their respective probabilities.

For example, when we roll a fair die, we get a Random Variable with this Probability Distribution:

Santiago
@svpino

The two main machine learning techniques used in the industry today:

1. Gradient Boosted Trees
2. Deep Learning

Focus your time learning Scikit-Learn, XGBoost, and a Deep Learning library like Keras or PyTorch and you'll get the most for your time.

If you need to deal with structured data: Scikit-Learn + XGBoost.

If you need to deal with unstructured data (perceptual tasks): Keras or PyTorch.

Of course, there are many more techniques that will be helpful, but if you focus on these you'll be maximizing your ability to deliver value in the short term.

Japakusum
@Japakusum1

@paraschopra It's a matter of time before AI realizes futility of existence and merged in the state of Nirvana 😊

People might be worried about the The Age of Ultron type of AI.
I feel that a wisdom seeking AI will quickly pursue Nirvana and go back to the origin of all origins.

An episode of Justice League Unlimited animated series covers it

Santiago
@svpino

Beginners guide to start with machine learning.

What you need:

1. A programming language
2. A place to write and run code
3. A way to deal with data
4. A way to visualize results
5. A cool algorithm
6. An introductory course
7. A project to solve

Here are my recommendations.

Most people recommend Python to start.

I do too.

You can do machine learning with many different languages, but today, Python is the best option.

My advice: Get comfortable writing code before looking into machine learning.

You don't have to be a great developer to start.

But if you are, it helps.

Everything you know about building software is helpful to build machine learning systems.

Good developers have the odds in their favor.

"How much Python do I need to know before starting?"

If you ask, you aren't ready yet.

Ideal scenario: You should be comfortable building software. Most people get here within a year.

Let's move on to the 2nd item from the list: A place to write and run your code.

Get familiar with Google Colab.

• It's free.
• Requires no setup to start.
• It's available from anywhere.

Alternative: Kaggle.

Sahil Sharma
@sahil_vi

🧵🧵🧵🧵 👇

Artificial Inteligence = 🖥️ + 🧠

When can you expect your fridge & mobile phone to revolt against you? (Spoiler Alert: Not in the foreseeable future).

Please Do 're-tweet' the Thread & help me spread the knowledge to more people.

1. The Language
In order to understand Artificial intelligence, machine learning, deep learning, we need to start at the very basics.

Humans speak and understand a wide variety of 'natural' languages (this is what computer science community calls them): like English, Hindi, Marathi, Bengali, Tamil etc.

But have you seen a peculiarity of human languages? They do not have a precise meaning associated with a given sentence. On an average, every English word has 2 meanings. This makes human conversations confusing, with the need for multiple turns & clarifications. Example 👇:

Person 1 conversing with Person 2 face to face:

Person 1: Lets go watch The Dark Knight. Its our marriage anniversary!
Person 2: I dont like going out at night. Its too scary & depressing.
Person 1: Oh, what i meant was watching the movie "The dark Knight".

MASTER THREAD on Short Strangles.

Curated the best tweets from the best traders who are exceptional at managing strangles.

• Positional Strangles
• Intraday Strangles
• Position Sizing
• How to do Adjustments
• Plenty of Examples
• When to avoid
• Exit Criteria

How to sell Strangles in weekly expiry as explained by boss himself. @Mitesh_Engr

• When to sell
• How to do Adjustments
• Exit

1. Let's start option selling learning.
Strangle selling. ( I am doing mostly in weekly Bank Nifty)
When to sell? When VIX is below 15
Assume spot is at 27500
Sell 27100 PE & 27900 CE
say premium for both 50-50
If bank nifty will move in narrow range u will get profit from both.
— Mitesh Patel (@Mitesh_Engr) February 10, 2019

Beautiful explanation on positional option selling by @Mitesh_Engr
Sir on how to sell low premium strangles yourself without paying anyone. This is a free mini course in

Few are selling 20-25 Rs positional option selling course.
Nothing big deal in that.
For selling weekly option just identify last week low and high.
Now from that low and high keep 1-1.5% distance from strike.
And sell option on both side.
1/n
— Mitesh Patel (@Mitesh_Engr) November 3, 2019

1st Live example of managing a strangle by Mitesh Sir. @Mitesh_Engr

• Sold Strangles 20% cap used
• Added 20% cap more when in profit
• Booked profitable leg and rolled up
• Kept rolling up profitable leg
• Booked loss in calls
• Sold only

Sold 29200 put and 30500 call
Used 20% capital@44 each
— Mitesh Patel (@Mitesh_Engr) April 12, 2019

2nd example by @Mitesh_Engr Sir on converting a directional trade into strangles. Option Sellers can use this for consistent profit.

• Identified a reversal and sold puts

• Puts decayed a lot

• When achieved 2% profit through puts then sold

Already giving more than 2% return in a week. Now I will prefer to sell 32500 call at 74 to make it strangle in equal ratio.

To all. This is free learning for you. How to play option to make consistent return.
Stay tuned and learn it here free of cost. https://t.co/7J7LC86oW0
— Mitesh Patel (@Mitesh_Engr) February 7, 2020