Stock Price of Top USA Banks Finance Data Project
In this project, I used Numpy, Pandas, Seaborn, Matplotlib, Pandas_datareader and Datetime to collect stock price data from Yahoo Finance, analyzed the data and obtain the visualization between the price and datetime of banks. Since the data was collected from Yahoo Finance, it is reliable and authenticated. However, the purpose of this project is only for study and analytics purpose, not for the realistic stock purchase purpose. The date of stock price data collected was from 1/1/2006 to 12/31/2016.
The imports:
Import the finance data from Yahoo Finance
Create the column names and concatenate these banks stock price tables
I used .xs function to find out the maximum close price for each bank's stock throughout the entire 2006 - 2016 period.
I used pct_change() function to create the new columns to show the return value of each bank's
Imports Seaborn and made a pairplot
Calculate the standard deviation of the returns for each bank's stock and made the plots
Plot 2008 returns for CitiGroup in 2008 only
Imports plotply and cufflinks
Plot all stocks for all banks during 2006 - 2016
Plot a heatmap of the correlation between the stocks close price
Plot a clustermap for the correlations of close price
This is the display for part of my project, including coding and visualization. If you would like to see the full picture of my project, please click here Bank Stock Python Project.
Thank you for watching, and I would really appreciate your feedback and comment.
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