Download facebook favfriends ticker

Column value, you can manually confirm the value appearing in the shortPercentOfFloat column.The shortPercentOfFloat row value is about five times larger for Tesla than for Facebook. By this measure, Tesla has a more negative sentiment among stock traders than Facebook. The “Summary of the use case” section in this tip describes the rationale for why trader sentiment is worse for Tesla than Facebook.Confirming whether the shortPercentOfFloat and the sharesShort indicate a likely decline in a ticker’s future closing price is beyond the scope of this tip. Nevertheless, some modeling and validation research may be able to uncover a relationship between shortPercentOfFloat and the likelihood of a future price decline. This type of research has the potential to provide useful guidelines about whether it might be worth taking the risk of shorting a company’s stock when the shortPercentOfFloat rises above a criterion value.Next StepsThis tip’s download file contains the Python and T-SQL scripts presented in this tip. If you re-run the Python script, the values for key-value pairs are likely to change from those reported in the tip because key values change on a regular if not daily basis.There are really two parts to this tip.The first part collects with a Python script some fundamental data from Yahoo Finance about a pair of stock tickers. You can easily modify the scope of the scriptby replacing the two ticker symbols used in this tip with another pair of ticker symbols orby including more than two ticker symbolsThe second part imports into and displays in SQL Server a subset of the key-value pairs exported as an ascii file by the first part. Next steps for this second part can include the tracking of different fundamental measures than those reported on this tip.The “Summary of the use case” and “Python steps for exporting JSON

Consists of two rows – one for each ticker column value.-- declare @json_tsla local variable as a nvarchar data typedeclare @json_tsla nvarchar(max) = (select * from #tsla)-- declare @json_fb local variable as a nvarchar data typedeclare @json_fb nvarchar(max) = (select * from #fb) -- display a select set of json data -- using a custom schemaselect 'tsla' as ticker,*from openjson(@json_tsla) with ( shortName nvarchar(20), fullTimeEmployees int, sector nvarchar (50), industry nvarchar (50), floatShares bigint, sharesShort bigint, shortPercentOfFloat real ) union -- display a select set of json data -- using a custom schemaselect 'fb' as ticker,*from openjson(@json_fb) with ( shortName nvarchar(20), fullTimeEmployees int, sector nvarchar (50), industry nvarchar (50), floatShares bigint, sharesShort bigint, shortPercentOfFloat real );Here is the results set from running the preceding script. The with clause in each select statement returns just a small subset of key-value pairs.As you can see in the screen shot below, there are two rows in the results set – the first one is for the fb ticker and the second one is for the tsla ticker.One key reason for selecting a subset or key-value pairs is to help add multiple context indicators for each row in the results set and to make it easy to contrast the tickers in terms of investor sentiment as indicated by the shortPercentOfFloat key.The row with a ticker column value of fb is for Facebook and the other row is for Tesla.As can see, the shortName column indicates the name of the company represented by the ticker value.The number of employees is very roughly the same (with about 1000 more full time employees for Tesla).Facebook is in the Internet Content & Information industry, but Tesla is in the Auto Manufacturers industry.The shortPercentOfFloat column reveals consumer sentiment towards each ticker.By dividing the sharesShort column value by the floatShares

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