Stock Analysis Log

Link to Project Page: Stock Analysis

Week 1: (Aug. 27 -- Sept. 2)

(1.5 hours) All group members discussed how to target what data we should find and what property of a home we find important.

Week 2: (Sept. 3 -- Sept 9)

(1.0 hours) Brandt, Keith, and Jessica met with Prof. Fehr to review the project and receive further guidance.

Week 3: (Sept. 10 -- Sept. 16)

(3.0 hours) Brandt and Keith further refined the project: investigated data scraping, how to add data to a database, and sites which are easily scrappable and have the data were looking for. Keith downloaded PyCharm so that we could begin experimenting with our ideas using python.

(9/15 Keith: 2 hrs.) Worked towards scraping data from google finance. Google protects financial information which will be a challenge. Currently working on a work around.

Week 4: (Sept. 17 -- Sept. 23)

(2 hours) Brandt, Keith, and Jessica met to discuss and refine the project idea as well as flesh out the wiki.

(2 hours) Jessica created and polished the presentation.

Week 4: (Sept. 24 -- Sept. 30)

(3 hours) Brandt compiled data and began to join the data in excel. The result was uploaded to excel. I investigated uploading these tables of data to a SQL server, and created accounts on AWS and Microsoft AZURE, however had issues figuring out how to upload the data.

(3 hours) Keith researched data scraping, ran code which successfully scrapped data through both Yahoo and Quandl, and discussed with Brandt on how we can load our data into a cloud SQL server.

(2 hours) Keith setup MySQL and SQLite and worked towards figuring out a way to make data accessible to everyone.

(3 hours) Brandt Used Pandas to read-in the excel data, and MatPlotLib to display the results graphically in a plot. The code was uploaded to GitHub to enable the group to view and edit. I also Identified another type of data that could be used later on in the project for further analysis; technical data on SP 500 such as Simple moving average, Stochastic RSI, and bollinger bands. This data can be obtained statically for free via our Bloomberg terminals in the business library. Problems; how to handle data that is N/A (unavailable at the time like EUR before it was a thing or Libor before it existed)

(1 Hour) Keith played around with Numpy and matrix manipulations and how to set up functions and define the main method.

(2 hours) Jessica figured out how to use Python and got part of a GUI set up.

(2.5 hours) Jessica worked on GUI. Got a rough version done.

(1.5 hours) Keith set up AWS EC2 instance and configured apache, so now the instance is connected to internet. Had to overcome Permission denied (public key) error. Check it out here

(1 hour) Keith and Brandt talked to Madjid Zeggane about how to attack this problem and how to obtain data from bloomberg. We need to finalize which tickers, industries and indexes we need download. He advised us to narrow the scope of the project and make out make our goals more reachable for the scope of our course.

Possible Issues: _Setup second AWS EC2 instance in a way such that all of us can ssh into it. Remembering python

Week 5: (Sept. 30 -- Oct. 6)

(4 hours) Brandt completed intro/intermediate python module found on Kaggle and DataCamp. Now I'm more familiar with pandas and matplotlib. They have lessons over sci-kit learn which I will be going over as well. Check out my github Here

(45 min) Keith configured mysql database, created a non-root user for accessing information online and configured phpmyadmin to have a user interface with mysql.

(1 hour) Keith downloaded anaconda and python and configured to AWS EC2 instance. Then set up IPython and jupyter notebook. The link to the notebook is password protected and your browser may have security issues with the link. To overcome just proceed to advanced settings and add this as a security exception. This is just to see the python files in our notebook.

(1 hour) Keith, Jessica, and Brandt met.

(1 hour) Brandt applied what was learned from and Kaggle and DataCamp to produce visualizations of the data using MatPlotLib. Then I utilized Seaborn in order to create a visualization of the correlation matrix of the features, and a second matrix with an OLS regression line draw through the graph.

(2 hours) Keith fixed the phpmyadmin issue (took more research than anticipated), so you an now see the the databases, structure and content. Content may be hard to view in browser since there are over 17,000 entries. Additionally loaded data into SQL.