- This project delivers on creating an analysis for stock trading data from 2017 and 2018. The analysis gives the total daily volume for the yearly data while also giving the return based on the starting and ending price. The purpose of this project is to help Steve refactor the previous written code for efficiency and also make sure that the code can be run with any amount of data,number of tickets.
| Ticker | Total Daily Volume | Return |
|---|---|---|
| AY | 309,500 | 0.0% |
| CSIQ | 310,592,800 | 33.1% |
| DQ | 35,796,200 | 199.4% |
| ENPH | 221,772,100 | 129.5% |
| FSLR | 684,181,400 | 101.3% |
| HASI | 80,949,300 | 25.8% |
| JKS | 191,632,200 | 53.9% |
| RUN | 267,681,300 | 5.5% |
| SEDG | 206,885,200 | 184.5% |
| SPWR | 782,187,000 | 23.1% |
| TERP | 139,402,800 | -7.2% |
| VSLR | 109,487,900 | 50.0% |
Looking at the above results the majority of the tickets in 2017 had an increase in return with DQ having the greatest increase at 199.4%.
| Ticker | Total Daily Volume | Return |
|---|---|---|
| AY | 83,079,900 | -7.3% |
| CSIQ | 200,879,900 | -16.3% |
| DQ | 107,873,900 | -62.6% |
| ENPH | 607,473,500 | 81.9% |
| FSLR | 478,113,900 | -39.7% |
| HASI | 104,340,600 | -20.7% |
| JKS | 158,309,000 | -60.5% |
| RUN | 502,757,100 | 84.0% |
| SEDG | 237,212,300 | -7.8% |
| SPWR | 538,024,300 | -44.6% |
| TERP | 151,434,700 | -5.0% |
| VSLR | 136,539,100 | -3.5% |
Looking at the 2018 results the majority of the interested stocks had a decrese in return, but only JKS, SPWR, and TERP had net decrease over the span of 2017 and 2018.
- https://github.com/ssyed21/stocks-analysis/blob/main/resources/VBA_Challenge_2017.png https://github.com/ssyed21/stocks-analysis/blob/main/resources/VBA_Challenge_2018.png
Looking at the resulting screenshots the 2017 runtime was barely more efficient.
- The biggest challenge for myself was learning the VBA language and steer away from switching between python and java while I was coding. A big problem was that I constanly was using != instead of <>.
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What are the advantages or disadvantages of refactoring code? The advantages of refactorign code is that by doing so you can make it more efficient and also add more functionality to it. Refactoring can make code run in less steps, use less steps, and improve the logic. The disadvantages to refactoring is that if not careful you can break the code and waste a good amount of time. Also refactoring could cause future bugs to occur if proper test cases aren't given.
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How do these pros and cons apply to refactoring the original VBA script? For refactoring the original VBA script into it's current commit, we added major functionality to it so that we can use the script with any amount of sorted stock data. The only cons that I see, could be from the amount of time spent writing the new code could have been used for a different analysis of the DQ Analysis results and that a bug may occur with data not properly sorted.