Difference between revisions of "Stock Analysis Log"
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(2 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) | (2 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. | |
| + | Possible Issues: _Setup second AWS EC2 instance in a way such that all of us can ssh into it. Remembering python | ||
[[Category:Logs]] | [[Category:Logs]] | ||
[[Category:Fall 2018 Logs]] | [[Category:Fall 2018 Logs]] | ||
Revision as of 12:14, 25 September 2018
Link to Project Page: Stock Analysis
Contents
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.
(2 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.
Possible Issues: _Setup second AWS EC2 instance in a way such that all of us can ssh into it. Remembering python
