Trump 0.0.2

Persistent Objectified Indexed Data

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.

Planning

See docs/planning.md for the direction of the project.

Basic Usage

This example dramatically understates the utility of Trump’s long term feature set.

Adding a Symbol

from trump.orm import SymbolManager
from trump.templating import QuandlFT, GoogleFinanceFT, YahooFinanceFT

sm = SymbolManager()

TSLA = sm.create(name = "TSLA",
                 description = "Tesla Closing Price USD")

TSLA.add_tags(["stocks","US"])

#Try Google First
#If Google's feed has a problem, try Quandl's backup
#If all else fails, use Yahoo's data...

TSLA.add_feed(GoogleFinanceFT("TSLA"))
TSLA.add_feed(QuandlFT("GOOG/NASDAQ_TSLA",fieldname='Close'))
TSLA.add_feed(YahooFinanceFT("TSLA"))

#Optional munging, validity checks and aggregation settings would be
#implemented here...

#All three feeds are cached...
TSLA.cache()

#But only a clean version of the data is served up...
print TSLA.df.tail()

              TSLA
dateindex
2015-03-20  198.08
2015-03-23  199.63
2015-03-24  201.72
2015-03-25  194.30
2015-03-26  190.40

sm.finish()

Using a Symbol

from trump.orm import SymbolManager

sm = SymbolManager()

TSLA = sm.get("TSLA")

#optional
TSLA.cache()

print TSLA.df.tail()

              TSLA
dateindex
2015-03-20  198.08
2015-03-23  199.63
2015-03-24  201.72
2015-03-25  194.30
2015-03-26  190.40

sm.finish()

Installation

See the latest Installation instructions on ReadTheDocs.org

Requirements

• Python 2.7; Support for Python 3.3 or 3.4 is do-able, if there is demand.

• A Relational Database Supported by SQLAlchemy should work, however the following is tested: * PostgreSQL 9.4 * Persistent SQLite (ie, file-based). Certain features of Trump, wouldn’t make sense with an in-memory implementation)

Dependencies

• Pandas (Tested with >= 15.2)

• SQLAlchemy (Tested with >= 0.9)

• Smuggle (Tested with >= 0.2.0)

Data Source Dependencies

• Quandl

Documentation

Read the latest on ReadTheDocs.org

Communication

• Questions, Bugs, Ideas, Requests or just say “Hi” -> GitHub Issues, [e-mail], or [e-mail]

• Contribute Code -> New Branch + GitHub Pull Request

• Chat -> Gitter

License

BSD-3 clause. See the actual License.

Background

The prototype for Trump was built at Equitable Life of Canada in 2014 by Jeffrey McLarty, CFA and Derek Vinke, CFA. Jeffrey McLarty currently leads the Open Source initiative.