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
>>
>>

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