# asset-class

### Description

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

rolling_weights = asset_class.asset_class_and_subclass_by_interval(fgtix, 'quarterly')
```

And that's it. Let's see the subclass attributions that adjusted r-squared 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()) 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()
``` sub_classes

## 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 <https://github.com/benjaminmgross/wealth-viz>`__ > 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

asset-class

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### Maintainers Public

latest

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