It is simply a key/value store built on top of SQLite. Would you be able to share any example code about accessing Bloomberg Data Terminal and ICE?
Massimo On Wednesday, 9 April 2014 16:15:36 UTC-5, Trent Telfer wrote: > > 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 web2py+unsubscr...@googlegroups.com. For more options, visit https://groups.google.com/d/optout.
