I am actually pulling the data from a Bloomberg Data Terminal, Bank of Canada, ICE and NGX. All have various ways to access the data. Some providers have fairly nice API's (bloomberg) and others like NGX only provide Excel files.
I'm interested in this persistent dictionary concept and am curious if it at the least can be adapted to be used with the Bloomberg terminal data. -Trent On Wednesday, April 9, 2014 3:08:28 PM UTC-6, Massimo Di Pierro wrote: > > Are you getting from Yahoo finance? > Look into github.com/mdipierro/nlib > > from nlib import * > symbol = 'AAPL' > d = PersistentDictionary() > if symbol in d: > h = d[symbol] > else > h = d[symbol] = YStock(symbol).historical() > print d[0].adjusted_close > > PersistentDictionary() is like shelve but uses sqlite and therefore is > thread safe. > > > > > On Wednesday, 9 April 2014 10:29:14 UTC-5, Trent Telfer wrote: >> >> Brian M, >> >> Thanks for the reply. I am looking into doing the following with >> historical market information >> >> a) Base Table >> >> ID,Exchange,Description,Base Currency >> Char(30),Char(10),Varchar(256),Char(5) >> >> b) Market Data >> >> ID,Date,HighPrice,LowPrice,OpenPrice,ClosePrice,Volume >> Char(30),Date,Float,Float,Float,Float,Long >> >> I am unsure if that table setup is the best choice or if there is a >> better way to approach it? >> >> I've also been wondering if I should jump into the world of NoSQL >> (specifically cassandra) as I may need to have smaller pricing intervals >> than days in the future. >> >> -Trent >> >> >> On Tuesday, April 8, 2014 6:48:50 PM UTC-6, Brian M wrote: >>> >>> Assuming each source needs the same data fields, how about just using >>> one table and including an extra field to specify which source each record >>> came from? >>> Or if you really want a separate table for each timeseries, you could >>> look into using table inheritance. >>> http://web2py.com/books/default/chapter/29/06/the-database-abstraction-layer#Table-inheritance >>> >>> A little more about how you are planning to use or display the data >>> might help. 38 separate tables, one table with 38 columns or rows? As a >>> graph with each series being a line? >>> >>> ~Brian >>> >>> -- Resources: - http://web2py.com - http://web2py.com/book (Documentation) - http://github.com/web2py/web2py (Source code) - https://code.google.com/p/web2py/issues/list (Report Issues) --- You received this message because you are subscribed to the Google Groups "web2py-users" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. For more options, visit https://groups.google.com/d/optout.
