This is the repository for some sample mySQL Yahoo Finance code done using Jupyter on Visual Studio Code.
What I aim to achieve is pull finance API data from Yahoo Finance and visualize the data. I use PandasDB as a visualization tool and am quite lucky that Yahoo Finance is already in Pandas format so no need to convert data.
I also put the data in a mySQL database so that we can build a database so that we do not have to rely on Yahoo Finance for historical data (Though Realtime data still using Yahoo Finance).
This here is an image of AAPL stock from 1980-2023. Here, however, we are not using PandasDB but rather the data that twas inserted into the mySQL database and table is being visualized using a software called PopSQL.
PopSQL is quite handy as it not only allows me to change mySQL database info quite easily, it's intuitive GUI allows me to do so without breaking a sweat. Not only that, it has a built in tool to create graphs and visualize the data and can change the visuals pretty easily. Very intuitive. Although it is easy to use PopSQL to visualize data, I want to visualize the data on my own.
This image shows the AAPL historical stock data inside the mySQL database. It shows the amount of bytes and amount of data inside the database as well.
What the StockInput code does is it:
- Asks for a ticker Symbol
- For example: AAPL, GOOG, M, TM (Apple, Google, Microsoft, Toyota Motor Corp.)
- Asks for a start and end date
- For example: 1980-09-08 & 2023-04-03 (September 8th 1980 to April 3rd 2023)
- Adds it into the mySQL database in a table called stock FYI: If you are using this code, then remember to change the mySQL information
What Stock Visualization does is instead of adding to the mySQL database, it just requests the data from Yahoo Finance and outputs a graph and table. The graph it outputs is the PandasDB chart from above:
The output of the table is:
[*********************100%***********************] 1 of 1 completed
Open High Low Close \
Datetime
2023-03-31 09:30:00-04:00 162.440002 162.550003 161.910004 162.520004
2023-03-31 09:31:00-04:00 162.520004 162.600006 162.380005 162.410004
2023-03-31 09:32:00-04:00 162.410004 162.500000 162.210007 162.490005
2023-03-31 09:33:00-04:00 162.500000 162.764999 162.470001 162.690002
2023-03-31 09:34:00-04:00 162.699997 163.029999 162.699997 163.020004
... ... ... ... ...
2023-03-31 15:55:00-04:00 164.660004 165.000000 164.660004 164.919998
2023-03-31 15:56:00-04:00 164.919998 164.970001 164.860001 164.934998
2023-03-31 15:57:00-04:00 164.940002 164.940002 164.809998 164.889999
2023-03-31 15:58:00-04:00 164.889999 164.970001 164.809998 164.940002
2023-03-31 15:59:00-04:00 164.940002 165.000000 164.699997 164.839996
Adj Close Volume
Datetime
2023-03-31 09:30:00-04:00 162.520004 1514404
2023-03-31 09:31:00-04:00 162.410004 207438
2023-03-31 09:32:00-04:00 162.490005 199147
2023-03-31 09:33:00-04:00 162.690002 260253
2023-03-31 09:34:00-04:00 163.020004 395121
... ... ...
2023-03-31 15:55:00-04:00 164.919998 1047744
2023-03-31 15:56:00-04:00 164.934998 662830
2023-03-31 15:57:00-04:00 164.889999 618646
2023-03-31 15:58:00-04:00 164.940002 895679
2023-03-31 15:59:00-04:00 164.839996 2330398
[390 rows x 6 columns]
mydb = mysql.connector.connect(
host="localhost",
user="root",
password="Saitanujk1234",
database="Cyan"
)In this code, remember host is the localhost if you are hosting the mySQL database on your computer, user is usually root if you did not change it. Enter your password and the name of the database that you are making changes to.