===== Trump

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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 <https://github.com/Equitable/trump/blob/master/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

.. code-block:: python

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

.. code-block:: python

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()

Contributing

If you're interested in contributing to Trump, we would love for you to do so! The best place to start is cloning the project, then use the latest commit from the master branch to install the package. After that, follow the configuration instructions, in the installation instructions linked below. While you do so, please make notes about any unclear parts or errors you get, during the installation instructions. Please post an issue on GitHub with ANY notes, or if you're ambitious, feel free to submit a pull request yourself. Don't hesitate, doing either.

If it's not working, or unclear, it's our fault. And, we really want it to be easy for people to get started. It's really hard for the creator of the project, to assess their own instructions.

After installation, there are many paths to take; each one can be addressed by posting an issue, or a pull request. Exploring the docs, you'll inevitably find areas that need improving. Explore the open issues, ones tagged with "Good First Pull Request" are the low hanging fruit. Often, current issues won't have a ton of information. If you want to work on one, just add a comment, asking for more info and mention that you're going to try to tackle it. Also, just posting an issue to "Say Hi", and ask for recommended issues to start on, is a great way to get started too.

Installation

See the latest Installation instructions on ReadTheDocs.org <http://trump.readthedocs.org/en/latest/install.html>_

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 <http://pandas.pydata.org/>_ (Tested with >= 0.16.2)
• SQLAlchemy <http://sqlalchemy.org/>_ (Tested with >= 0.9)
• smuggle <https://pypi.python.org/pypi/smuggle>_ (Tested with >= 0.2.0)
• validada <https://pypi.python.org/pypi/validada>_ (Tested with >= 0.0.1)

Data Source Dependencies

• Quandl <https://pypi.python.org/pypi/Quandl>_

Documentation

Read the latest on ReadTheDocs.org <http://trump.readthedocs.org>_

Communication

• Questions, Bugs, Ideas, Requests or just say "Hi" -> GitHub Issues, InvTech@equitable.ca, or jeffrey.mclarty@gmail.com
• Contribute Code -> New Branch + GitHub Pull Request
• Chat -> Gitter <https://gitter.im/Equitable/trump>_

License

BSD-3 clause. See the actual License <https://raw.githubusercontent.com/Equitable/trump/master/LICENSE.txt>_.

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.