The purpose of this project is to help Steve's parents make the best decision about which stocks to buy.
To simplified the analysis, I have filtered out all the stocks that performed below average.
- Only 3 stocks have a 'Total Daily Volume' above the average in both years (2017 and 2018).
- Out of the 3 stocks, only 'RUN' shows a volume increase from 2017 to 2018.
- Only 2 stocks have a 'Return' above the average in both years (2017 and 2018).
- From the 2 stocks with Return above average, only 'ENPH' has a positive return in both years (2017 and 2018).
- The price tag trend for the top three stocks (RUN, ENPH, SEDG) look very similar.
- A deepest investigation is needed to select the best option.
The execution times have improved by 400% in the 2017 data set, and by 500% in the 2018 data set. This is because the refactored script loops over the entire data set only once, rather than as many tickers there are.
2017
2018
- The advantage of refactoring the code is to reduce the running time. This allows us to analyze greater amount of data without sacrificing the waiting time.
- The only disadvantage on this specific refactoring case if that the data needs to be sorted before running the code.
- One of the limitations of the data set is that the values are recorded by day, instead of by minute. Stocks prices changes a lot during the day.
- One of the challenges was to convert the yy-mm-dd format to yy/mm/dd format to be recognize by the pivot table. After spending a lot of time playing with the date formating option, the solution was as simple as replacing the '-' with '/' using the Find and Replace option