This program predicts a one month price change based off of 10 years of historic data for each stock in the SP500 using Python. Calculating at least a 90% success rate (9/10 years) between the same time periods. This produce a csv file that has a years worth of stock picks and their respective percentage change during that price change. This is a console Project
- Pandas
- Pandas DataReader
- Numpy
- Datetime
- BeautifulSoup4
- MatPlotLib
- MatPlotLib.pyplot
- os & shutil (for deleting and creating paths)
- Pickle
- Requests
| Ticker | Start Date | End Date | Avg % Change | Outlier Year |
|---|---|---|---|---|
| FISV | 06-08 | 07-08 | 3.25 | 2017 |
| AIZ | 06-08 | 07-08 | 4.629 | 2014 |
| JWN | 12-31 | 02-01 | -5.26 | 2015 |
- Note that start date and end data will be a datetime object, so when reading in will have to parse out the year because it does not matter.
- Percent Change and Year are Float objects
- Outlier Year is the year where during that time period the stock had an opposite price change. If Outlier Year is a NaN value, then the stock had 100% positive or negative price change during that time period.
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Email: ericsanderson333@gmail.com
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Please contact me and send me any questions/advice! Thanks!