Can somebody help me how to get sector key from Bloomberg using BDP.I am getting value if i use BDP("cusip Corp","cpn") is there a way I have cusip and how to get sector key like "corp" in the given example.
The question isn't exactly clear. If you want to get the sector of the security and you have its CUSIP, you can do this:
=BDP("459200101 CUSIP","INDUSTRY_SECTOR")
or
=BDP("459200101 CUSIP","GICS_SECTOR_NAME")
Depending on whether you want the industry sector or the GICS sector. In this example, 459200101 is IBM's CUSIP.
Related
Good afternoon and happy Friday, folks
I’m trying to automate a placement simulation of youth into residential treatment where they will have the highest likelihood of success. Success is operationalized as “not recidivating” within 3 years of entering treatment. Equations predicting recidivism have been generated for each location, and the equations have been applied to each individual in the scenario (based on youth characteristics like risk, age, etc., LOS). Each youth has predicted success rates for every location, which throws in a wrench: youth are not qualified for all of the treatment facilities for which they have predicted success rates. Indeed, treatment locations have differing, yet overlapping qualifications.
Let’s take a made-up example. Johnny (ID # 5, below) is a 15-year-old boy with drug charges. He could have “predicted success rates” of 91% for location A, 88% for location B, 50% for location C, and 75% for location D. Johnny is most likely to be successful (i.e., not recidivate within three years of entering treatment) if he is treated at location A; unfortunately, location A only accepts youth who are 17 years old or older; therefore, Johnny would not qualify for treatment here. Alternatively, for Johnny, location B is the next best location. Let us assume that Johnny is qualified for location B, but that all of location-B beds are filled; so, we must now look to location D, as it is now Johnny’s “best available” option at 75%.
The score so far: We are matching youth to available beds in location for which they qualify and might enjoy the greatest likelihood of success. Unfortunately, each location only has a certain number of available beds, and the number of available beds different across locations. The qualifications of entry into treatment facilities differ, yet overlap (e.g., 12-17 year-olds vs 14-20 year-olds).
In order to simulate what placement decisions might look like based on success rates, I went through the scenario describe above for over 400 youth, by hand, in excel. It took me about a week. I’d like to use PROC SQL imbedded in a SAS MACRO to automate these placement scenarios with the ultimate goals of a) obtain the ability to bootstrap iterations in order to examine effect sizes across distributions, b) save time, and c) prevent further brain damage from banging my head again desk and wall in frustration whilst doing this by hand. Whilst never having had the necessity—nay—the privilege of using SQL in my typical roll as a researcher, I believe that this time has now come to pass and I’m excited about it! Honestly. I believe it has the capacity I’m looking for. Unfortunately, it is beating the devil out of me!
Here’s what I’ve got cookin’ so far: I want to create and automate the placement simulation with the clever use of merging/joining/switching/or something like that.
I have two datasets (tables). The first dataset contains all of the youth information (one row per youth; several columns with demographics, location ranks, which correspond to the predicted success rates). The order of rows in the youth dataset (was/will be randomly generated (to simulate the randomness with which youth enter the system and are subsequently place into treatment). Note that I will be “cleaning” the youth dataset prior to merging such that rank-column cells will only be populated for programs for which a respective youth qualifies. This should take the “does the youth even qualify for the program” problem out of the equation.
However, it still leaves the issue of availability left to be contended with in the scenario.
The second dataset containing the treatment facility beds, with each row corresponding to an available bed in one of the treatment location; two columns contain bed numbers and location names. Each bed (row) has only one location cell populated, but locations will populate several cells.
Thus, in descending order, I want to merge each youth row with the available bed that represents his/her best chance of success, and so the merge/join/switch/thing should take place
on youth.Rank1= distinct TF.Location,
and if youth.Rank1≠ TF.location then
merge on youth.Rank2= TF.location,
if youth.Rank2≠ TF.location then merge at
youth.Rank3 = TF.location, etc.
Put plainly: “Merge on rank1 unless rank1 location is no longer available, then merge on rank2, unless rank2 location is no longer available, and on down the line, etc., etc., until all option are exhausted and foster care (i.e., alternative services). Is the only option.
I’ve had no success getting this to work. I haven’t even been successful getting the union function to work. About the only successful thing I’ve done in SQL so far is create a view of a single dataset. It’s pretty sad. I’ve been following this guidance, but I get hung up around the “where” command:
proc sql; /Calls the SQL procedure*/;
create table x as /*Tells SAS to create a table called x*/
select /*Specifies the column(s) to be selected*/
from /*Specificies the tables(s) (data sets) to be queried*/
where /*Subjests the data based on a condition*/
group by /*Classifies the data into groups based on the specified
column(s)*/
order by /*Sorts the resulting rows observations) by the specified
column(s)*/
; quit; /*Ends the proc sql procedure*/
Frankly, I’m stuck and I could use some advice. This greenhorn in me is in way over his head.
I appreciate any help or guidance anyone might lend.
Cheers!
P
The process you describe (and to be honest I skiped to the end so I might of missed something) does not lend itself to SQL because each step could affect the results of the next one. However, you want to get the most best results for the most kids. (I think a lot of that text was to convince us how important it is to help out). You don't actually give us anything we can really use to help since you don't give any details of your data model, your data, or expected results. There really is no way to answer this question. But I don't care -- I'm going to go forward with some suggestions because it is a friday and I've never done a stream of consciousness answer to a stream of consciousness question before. I will suggest you don't formulate your solution just in sql, but instead use a higher level program and engage is a process like the one described below -- because this a DB questions I've noted the locations where the DB might be involved.
Generate a list kids (this can be in a table -- called NEEDY-KID)
Have a list of locations to assign (this can also be a table LOCATION)
Run your matching for best fit from KID to location -- at this point don't worry about assign more than one kid to a location -- there can be duplicates (put this in table called KID2LOC using a query)
Check KID2LOC for locations assigned twice -- use some method to remove the duplicate ones so each loc is only assigned once. (remove from the KID2LOC using a query)
Prune the LOCATION list to remove assigned locations (once again -- a query)
If kids exist without a location go to 3 with new pruned location list.
Done.
Now that SEPA requirements are getting people used to BIC & IBAN, there are legacy system that cannot cope with this new data. Is there an algorithm or tool available for converting BIC & IBAN back to sort code and account?
Here is an example:
from this website.
Wikipedia has a list of IBAN formats by country, so it seems at least possible.
However, there is no complete algorithm for it - being a software developer, you can derive an algorithm from that input. Note that other countries might follow in the future, so you can expect more work (and hopefully not more exceptional cases of sort codes and accounts).
Regarding the tool or library, that's off-topic here on StackOverflow, but you might want to ask on Software Recommendations, though. Note that they have different requirements on how to ask questions, so you might want to read the tour first. Don't forget to mention the programming language.
Well, a quick search pointed me at this page: http://www.business.hsbc.co.uk/1/2/international-business/iban-bic.
Looks to me like you can just extract appropriate substrings. Although, a bit more searching seems to indicate that the format may vary a bit depending on the country.
Both sort code and account number are present inside a United Kingdom or Ireland IBAN.
You can simply substring like, PHP Examples:
$iban = "GB04BARC20474473160944";
$sort = substr($iban,8,6);
$account = substr($iban,14,8);
print "SortCode:" . $sort;
print "AccountNumber:" . $account;
The IBAN Calculator webservice has an API which digs up bank and branch information and so on. Also does check digit validation on the iban and sort/account.
But for simple extracting of the sort/account the substring is sufficient.
Am trying to query data from http://www.census.gov, using their API
I want to get the population of a particular city in the US, by using the city name and the US state code.
Given that I already have the key, what other parameters do I add in the URL below, so that I can get the population.
http://api.census.gov/data/2010/sf1?key=<my key>
any assistance will be greatly appreciated
Judging from your query URI, you wish to access population data from the 2010 Census Summary File. You would add GET paramaters of get and for to your query. Example:
http://api.census.gov/data/2010/sf1?key=b48301d897146e8f8efd9bef3c6eb1fcb864cf&get=P0010001&for=state:06
The population table as given in the get parameter are identified with a "P" and you can use the for parameter to further narrow down your scope. Examples of valid criteria formatted as URIs can be found here...
EDIT: It seems that for a finer grained search such as cities, you're going to need to use the governments cumbersome FIPS (Federal Information Processing Standard) codes (after converting lat/lon regions to their coding system)... I've found this resource that should be helpful, specifically points 5 thru 7, but it seems mega complex...
Another alternative I found is the USA Today census API, it seems that they mirror the data from the census and they do have available endpoints with data granularity at the city level... Check it out here...
no need to use API the data is available in CSV here http://www.census.gov/popest/data/cities/totals/2012/SUB-EST2012.html
I would like to group all the merchant transactions from a single table, and just get a count. The problem is, the merchant, let's say redbox, will have a redbox plus the store number added to the end(redbox 4562,redbox*1234). I will also include the category for grouping purpose.
Category Merchant
restaurant bruger king 123 main st
restaurant burger king 456 abc ave
restaurant mc donalds * 45877d2d
restaurant mc 'donalds *888544d
restaurant subway 454545
travelsubway MTA
gas station mc donalds gas
travel nyc taxi
travel nyc-taxi
The question: How can I group the merchants when they have address or store locations added on to them.All I need is a count for each merchant.
The short answer is there is no way to accurately do this, especially with just pure SQL.
You can find exact matches, and you can find wildcard matches using the LIKE operator or a (potentially huge) series of regular expressions, but you cannot find similar matches nor can you find potential misspellings of matches.
There's a few potential approaches I can think of to solve this problem, depending on what type of application you're building.
First, normalize the merchant data in your database. I'd recommend against storing the exact, unprocessed string such as Bruger King in your database. If you come across a merchant that doesn't match a known set of merchants, ask the user if it already matches something in your database. When data goes in, process it then and match it to an existing known merchant.
Store a similarity coefficient. You might have some luck using something like a Jaccard index to judge how similar two strings are. Perhaps after stripping out the numbers, this could work fairly well. At the very least, it could allow you to create a user interface that can attempt to guess what merchant it is. Also, some database engines have full-text indexing operators that can descibe things like similar to or sounds like. Those could potentially be worth investigating.
Remember merchant matches per user. If a user corrects bruger king 123 main st to Burger King, store that relation and remember it in the future without having to prompt the user. This data could also be used to help other users correct their data.
But what if there is no UI? Perhaps you're trying to do some automated data processing. I really see no way to handle this without some sort of human intervention, though some of the techniques described above could help automate this process. I'd also look at the source of your data. Perhaps there's a distinct merchant ID you can use as a key, or perhaps there exists somewhere a list of all known merchants (maybe credit card companies provide this API?) If there's boat loads of data to process, another option would be to partially automate it using a service such as Amazon's Mechanical Turk.
You can use LIKE
SELECT COUNT(*) AS "COUNT", "BURGER KING"
FROM <tables>
WHERE restaurant LIKE "%king%"
UNION ALL
SELECT COUNT(*) AS "COUNT", "JACK IN THE BOX"
FROM <tables>
Where resturant LIKE "jack in the box%"
You may have to move the wildcards around depending on how the records were spelled out.
It depends a bit on what database you use, but most have some kind of REGEXP_INSTR or other function you can use to check for the first index of a pattern. You can then write something like this
SELECT SubStr(merchant, 1, REGEXP_INSTR(merchant, '[0-9]')), count('x')
FROM Expenses
GROUP BY SubStr(merchant, 1, REGEXP_INSTR(merchant, '[0-9]'))
This assumes that the merchant name doesn't have a number and the store number does. However you still may need to strip out any special chars with a replace (like *, -, etc).
I have several sources of tables with personal data, like this:
SOURCE 1
ID, FIRST_NAME, LAST_NAME, FIELD1, ...
1, jhon, gates ...
SOURCE 2
ID, FIRST_NAME, LAST_NAME, ANOTHER_FIELD1, ...
1, jon, gate ...
SOURCE 3
ID, FIRST_NAME, LAST_NAME, ANOTHER_FIELD1, ...
2, jhon, ballmer ...
So, assuming that records with ID 1, from sources 1 and 2, are the same person, my problem is how to determine if a record in every source, represents the same person. Additionally, sure not every records exists in all sources. All the names, are written in spanish, mainly.
In this case, the exact matching needs to be relaxed because we assume the data sources has not been rigurously checked against the official bureau of identification of the country. Also we need to assume typos are common, because the nature of the processes to collect the data. What is more, the amount of records is around 2 or 3 millions in every source...
Our team had thought in something like this: first, force exact matching in selected fields like ID NUMBER, and NAMES to know how hard the problem can be. Second, relaxing the matching criteria, and count how much records more can be matched, but is here where the problem arises: how to do to relax the matching criteria without generating too noise neither restricting too much?
What tool can be more effective to handle this?, for example, do you know about some especific extension in some database engine to support this matching?
Do you know about clever algorithms like soundex to handle this approximate matching, but for spanish texts?
Any help would be appreciated!
Thanks.
The crux of the problem is to compute one or more measures of distance between each pair of entries and then consider them to be the same when one of the distances is less than a certain acceptable threshold. The key is to setup the analysis and then vary the acceptable distance until you reach what you consider to be the best trade-off between false-positives and false-negatives.
One distance measurement could be phonetic. Another you might consider is the Levenshtein or edit distance between the entires, which would attempt to measure typos.
If you have a reasonable idea of how many persons you should have, then your goal is to find the sweet spot where you are getting about the right number of persons. Make your matching too fuzzy and you'll have too few. Make it to restrictive and you'll have too many.
If you know roughly how many entries a person should have, then you can use that as the metric to see when you are getting close. Or you can divide the number of records into the average number of records for each person and get a rough number of persons that you're shooting for.
If you don't have any numbers to use, then you're left picking out groups of records from your analysis and checking by hand whether they look like the same person or not. So it's guess and check.
I hope that helps.
This sounds like a Customer Data Integration problem. Search on that term and you might find some more information. Also, have a poke around inside The Data Warehousing Institude, and you might find some answers there as well.
Edit: In addition, here's an article that might interest you on spanish phonetic matching.
I've had to do something similar before and what I did was use a double metaphone phonetic search on the names.
Before I compared the names though, I tried to normalize away any name/nickname differences by looking up the name in a nick name table I created. (I populated the table with census data I found online) So people called Bob became Robert, Alex became Alexander, Bill became William, etc.
Edit: Double Metaphone was specifically designed to be better than Soundex and work in languages other than English.
SSIS , try using the Fuzzy Lookup transformation
Just to add some details to solve this issue, I'd found this modules for Postgresql 8.3
Fuzzy String Match
Trigrams
You might try to cannonicalise the names by comparing them with a dicionary.
This would allow you to spot some common typos and correct them.
Sounds to me you have a record linkage problem. You can use the references in the link.