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Ok here is the explanation. Grab a cup of coffee and read on. If you have not read/noticed this, you will see intraday options movement in a new light.
Say we have two options, one 50 delta ATM options and another 30 delta OTM option. Normally for a 100 point move, the ATM option will move 50 points and the OTM option will move 30 points. But in a high volatile environment, the OTM option will also move nearly 50 points
To understand why this happens, first understand why an ATM option is 50 delta. An ATM option has the probability of 50% of expiring as ITM. The price just has to close a rupee above the strike for the CE to be ITM and vice versa for PEs
Now think of a highly volatile day like today. If someone is asked where the BNF will close for the day or expiry, no one can answer. BNF can close freakin anywhere, That makes every option of an equal probability of being ITM. So all options have a 50% probability of being ITM
Hence, when a huge volatile move starts, all OTM options behave like ATM options. This phenomenon was first observed in the Black Monday crash of 1987 at Wall Street, which also gave rise to the volatility skew/smirk
In a high IV environment or when the market is very volatile
— Subhadip Nandy (@SubhadipNandy16) January 21, 2022
" OTM options will behave like ATM options", one will get almost the same delta movement
Say we have two options, one 50 delta ATM options and another 30 delta OTM option. Normally for a 100 point move, the ATM option will move 50 points and the OTM option will move 30 points. But in a high volatile environment, the OTM option will also move nearly 50 points
To understand why this happens, first understand why an ATM option is 50 delta. An ATM option has the probability of 50% of expiring as ITM. The price just has to close a rupee above the strike for the CE to be ITM and vice versa for PEs
Now think of a highly volatile day like today. If someone is asked where the BNF will close for the day or expiry, no one can answer. BNF can close freakin anywhere, That makes every option of an equal probability of being ITM. So all options have a 50% probability of being ITM
Hence, when a huge volatile move starts, all OTM options behave like ATM options. This phenomenon was first observed in the Black Monday crash of 1987 at Wall Street, which also gave rise to the volatility skew/smirk
Two year back thread on MFI, someone liked this so came up in notifications . Rather than running around 100s of indicators, I have made this my go to indicator under any circumstances and have been using this for years
This thread actually had some great answers , one can learn a lot about the thought processes of different traders from the answers. Please go thru them
What do you think/use as the most robust leading indicator if following technical analysis ? Please answer with reason , I will provide my answer after 2 hours
— Subhadip Nandy (@SubhadipNandy16) August 12, 2019
( At Delhi airport , bored as hell )
This thread actually had some great answers , one can learn a lot about the thought processes of different traders from the answers. Please go thru them
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Nano Course On Python For Trading
==========================
Module 1
Python makes it very easy to analyze and visualize time series data when you’re a beginner. It's easier when you don't have to install python on your PC (that's why it's a nano course, you'll learn python...
... on the go). You will not be required to install python in your PC but you will be using an amazing python editor, Google Colab Visit https://t.co/EZt0agsdlV
This course is for anyone out there who is confused, frustrated, and just wants this python/finance thing to work!
In Module 1 of this Nano course, we will learn about :
# Using Google Colab
# Importing libraries
# Making a Random Time Series of Black Field Research Stock (fictional)
# Using Google Colab
Intro link is here on YT: https://t.co/MqMSDBaQri
Create a new Notebook at https://t.co/EZt0agsdlV and name it AnythingOfYourChoice.ipynb
You got your notebook ready and now the game is on!
You can add code in these cells and add as many cells as you want
# Importing Libraries
Imports are pretty standard, with a few exceptions.
For the most part, you can import your libraries by running the import.
Type this in the first cell you see. You need not worry about what each of these does, we will understand it later.
==========================
Module 1
Python makes it very easy to analyze and visualize time series data when you’re a beginner. It's easier when you don't have to install python on your PC (that's why it's a nano course, you'll learn python...
... on the go). You will not be required to install python in your PC but you will be using an amazing python editor, Google Colab Visit https://t.co/EZt0agsdlV
This course is for anyone out there who is confused, frustrated, and just wants this python/finance thing to work!
In Module 1 of this Nano course, we will learn about :
# Using Google Colab
# Importing libraries
# Making a Random Time Series of Black Field Research Stock (fictional)
# Using Google Colab
Intro link is here on YT: https://t.co/MqMSDBaQri
Create a new Notebook at https://t.co/EZt0agsdlV and name it AnythingOfYourChoice.ipynb
You got your notebook ready and now the game is on!
You can add code in these cells and add as many cells as you want
# Importing Libraries
Imports are pretty standard, with a few exceptions.
For the most part, you can import your libraries by running the import.
Type this in the first cell you see. You need not worry about what each of these does, we will understand it later.
