#NIFTYIT
simple observation -Rally 2 consequtive years of negative return and current year is the first year in 2 year of cycle-so more pain ahead
#AhmedabadNest #analysi #India

More from NISHSHKUMAR JAANI
More from Cnxitlongterm
Parabolic move has been triggered. Let's have 27k. That could be the buying zone for IT names.
#CNXIT https://t.co/bJeKTMoCji
#CNXIT https://t.co/bJeKTMoCji

The current formation might look like a falling wedge, but the way moving averages are placed, it looks like a falling wedge which can lead to a parabolic downmove for the marked target. #CNXIT pic.twitter.com/GmXOI3HmUN
— Aakash Gangwar (@akashgngwr823) May 10, 2022
What happened to IEX can happen to CNXIT. Savdhaan rahe, satark rahe. Reiterating it again, every falling wedge/channel, RSI divergence is not a reversal signal.
#CNXIT
#CNXIT
Watching the marked zone to be tested. If it doesn't cross it, then most probably a parabolic downmove towards 24k. That would lead to even large caps cracking just like Small and Midcaps. #CNXIT https://t.co/FxbzP5vlBr pic.twitter.com/FSqcSqTQM9
— Aakash Gangwar (@akashgngwr823) June 21, 2022
We often hear that prediction in markets is a nasty game, but we don't really have to believe everything we read on social media. Do we? As long as you maintain good accuracy in your predictions, you will do absolutely great.
#CNXIT https://t.co/w3qedea7T6
#CNXIT https://t.co/w3qedea7T6

Almost there. Quick move. It can spend time over here before the next leg of fall. Let's see.#NIFTYIT https://t.co/GOB28HRvMp pic.twitter.com/6sNc7j8gEU
— Aakash Gangwar (@akashgngwr823) March 9, 2022
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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.
