Trump 0.0.1

Persistent Objectification of Indexed Data

Trump is a framework for objectifying data, with the goal of centralizing the responsibility of managing feeds, munging, calculating and validating data, upstream of any application or user requirement.

With a focus on business processes, Trump’s long run goals enable data feeds to be:

• Prioritized, flexibly - a symbol can be associated with multiple data source for a variety of reasons including redundancy, calculations, or optionality.

• Modified, reliably - a symbol’s data feeds can be changed out, without any changes requiring testing to the downstream application or user.

• Verified, systematically - a variety of common data processing checks are performed as the symbol’s data is cached.

• Audited, quickly - alerts and reports all become possible to assess integrity or inspect where manual over-rides have been performed.

• Aggregated, intelligently - on a symbol by symbol basis, feeds can be combined and used in an extensible number of ways.

• Customized, dynamically - extensibility is possible at the templating, munging, aggregation, and validity steps.

from trump.orm import SymbolManager
from trump.templating import fQuandl, fSQL

sm = SymbolManager()

oil = sm.create(name = "oil_front_month",
                description = "Crude Oil",
                freq = 'D',
                units = '$ / barrel')

oil.addTags(['commodity','oil','futures'])

f1 = fQuandl(r"CHRIS/CME_CL2",fieldname='Settle')
f2 = fSQL("SELECT date,data FROM test_oil_data;")

oil.addFeed(f1)
oil.addFeed(f2)

oil.cache()

print oil.df.tail()

from trump.orm import SymbolManager

sm = SymbolManager()

oil = sm.get("oil_front_month")

#optional
oil.cache()

print oil.df.tail()