#asset_class

A simple library that uses r-squared maximization techniques and asset sub class ETFs (that I personally chose) to determine asset class information, as well as historical asset subclass information for a given asset

##Installation

$git clone https://github.com/benjaminmgross/asset_class
$ cd asset_class
$python setup.py install

##Quickstart

Let's say we had some fund, for instance the Franklin Templeton Growth Allocation Fund A -- ticker FGTIX -- against which we we wanted to do historical attribution.

In just a couple of key strokes, we can come up with quarterly attribution analysis to see where returns were coming from

import pandas.io.data as web
import asset_class

fgtix = web.DataReader('FGTIX', 'yahoo', start = '01/01/2000')['Adj Close']
rolling_weights = asset_class.asset_class_and_subclass_by_interval(fgtix, 'quarterly')

And that's it. Let's see the subclass attributions that the adjusted r-squared optimization algorithm came up with.

import matplotlib.pyplot as plt

#create the stacked area graph
fig = plt.figure()
ax = plt.subplot2grid((1,1), (0,0))
stack_coll = ax.stackplot(rolling_attr.index, rolling_attr.values.transpose())
ax.set_ylim(0, 1.)
proxy_rects = [plt.Rectangle( (0,0), 1, 1, 
	fc = pc.get_facecolor()[0]) for pc in stack_coll]
ax.legend(proxy_rects, rolling_attr.columns.values.tolist(), ncol = 3, 
	loc = 8, bbox_to_anchor = (0.5, -0.15))
plt.title("Asset Subclass Attribution Over Time", fontsize = 16)
plt.show()

##Dependencies

###Obvious Ones:

pandas numpy scipy.optimize (uses the TNC method to optimize the objective function of r-squared)

###Not So Obvious: Another one of my open source repositories visualize_wealth

But that's just for adjusted r-squared functionality, you could easily clone and hack it yourself without that library

##Status

Still very much a WIP, although I've added [Sphinx]http://sphinx-doc.org/) docstrings to auto generate documentation

##To Do:

• Given a pandas.DataFrame of asset prices, and asset price weights, return an aggregated asset class pandas.DataFrame on a quarterly basis

• Write the Best Fitting Benchmark algorithm, either for use in this library or from the private strat_check repository that uses this module