This is a Time Series Analysis Project involving ( Stock Market Case-Study)
The Stocks to be Analysed are AAPL,GOOG,MSFT and AMZN.
Covert the date column to datetime before ploting, it's a object initially
To archieve the daily price change create a column containing the change in df['close'] and df['open'].
1day % return = ((df['close']-df['open'])/df['close'])*100
you can Choose a date range optional, set_index of any key your using before Resampling the data according to the month (M) or Year (Y).
Problem Statment => Analyse Whether Stock Prices of these Tech companies (Amazon,Google,Microsolft,Apple) are correlated or not.
Create a new df to contiain informations of [close] columns from Amzn,Goog,Msft and Appl Dataframe.
Stocks to be analysed are Amzn,Goog,Msft and Appl create a new df and store changes in % daily return in the new df.
use displot from seaborn to visualized the change in percentage daily return of each stock and use std to analyse the value Risk on the stocks. Also you can use quantile() on the percentage daily return to discover the minimum daily return.
