DOC: I took more documentation from the pypi README page and added it to the

Sphinx documentation.

docs/contributing.rst

@@ -0,0 +1,40 @@
+***************************
+Contributing to the project
+***************************
+
+Style Guide
+===========
+
+To ensure that changes and patches are focused on behavior changes,
+the zipline codebase adheres to PEP-8,
+`<http://www.python.org/dev/peps/pep-0008/>`_.
+
+The maintainers check the code using the flake8 script,
+`<https://github.com/jcrocholl/pep8/>`_, which is included in the
+requirements_dev.txt.
+
+Before submitting patches or pull requests, please ensure that your
+changes pass 
+
+::
+
+    flake8 --ignore=E124,E125,E126 zipline tests
+
+Discussion and Help
+===================
+
+Discussion of the project is held at the Google Group,
+`<zipline@googlegroups.com>`_,
+`<https://groups.google.com/forum/#!forum/zipline>`_.
+
+Source
+======
+
+The source for Zipline is hosted at
+`<https://github.com/quantopian/zipline>`_.
+
+Contact
+=======
+
+For other questions, please contact `<opensource@quantopian.com>`_.
+

docs/index.rst

@@ -5,9 +5,9 @@
 
 .. module:: zipline
 
-*******************************************
-Zipline: The Open Source Backtesting Engine
-*******************************************
+****************************************************
+Zipline: Financial Backtester for Trading Algorithms
+****************************************************
 
 Python is quickly becoming the glue language which holds together data science
 and related fields like quantitative finance. Zipline is a new, BSD-licensed
@@ -22,10 +22,27 @@ Python eco-system. Furthermore, statistic and machine learning libraries like
 matplotlib, scipy, statsmodels, and sklearn support development, analysis and
 visualization of state-of-the-art trading systems.
 
-Zipline is currently used in production as the backtesting engine powering
-`<Quantopian.com>`_, a free, community-centered platform that allows
-development and real-time backtesting of trading algorithms in the web browser.
-Zipline was released at PyData NYC'12.
+Zipline is currently used in production as the backtesting engine
+powering `quantopian.com <https://app.quantopian.com>`_ -- a free, community-centered
+platform that allows development and real-time backtesting of trading
+algorithms in the web browser.
+
+Features
+========
+
+* Ease of use: Zipline tries to get out of your way so that you can focus on
+  algorithm development. See below for a code example.
+
+* Zipline comes "batteries included" as many common statistics like moving
+  average and linear regression can be readily accessed from within a
+  user-written algorithm.
+
+* Input of historical data and output of performance statistics is based on
+  Pandas DataFrames to integrate nicely into the existing Python eco-system.
+
+* Statistic and machine learning libraries like matplotlib, scipy, statsmodels,
+  and sklearn support development, analysis and visualization of
+  state-of-the-art trading systems.
 
 Contents
 ========
@@ -35,11 +52,12 @@ Contents
 
    manifesto.rst
    installation.rst
+   quickstart.rst
+   contributing.rst
    overview.rst
    modules.rst
    extensions.rst
 
-
 Indices and tables
 ==================
 

docs/quickstart.rst

@@ -0,0 +1,55 @@
+**********
+Quickstart
+**********
+
+Dual-Moving Average Example
+===========================
+
+The following code implements a simple dual moving average algorithm
+and tests it on data extracted from yahoo finance.
+
+.. code:: python
+
+    from zipline.algorithm import TradingAlgorithm
+    from zipline.transforms import MovingAverage
+    from zipline.utils.factory import load_from_yahoo
+
+    class DualMovingAverage(TradingAlgorithm):
+        """Dual Moving Average algorithm.
+        """
+        def initialize(self, short_window=200, long_window=400):
+            # Add 2 mavg transforms, one with a long window, one
+            # with a short window.
+            self.add_transform(MovingAverage, 'short_mavg', ['price'],
+                            market_aware=True,
+                            days=short_window)
+
+            self.add_transform(MovingAverage, 'long_mavg', ['price'],
+                            market_aware=True,
+                            days=long_window)
+
+            # To keep track of whether we invested in the stock or not
+            self.invested = False
+
+            self.short_mavg = []
+            self.long_mavg = []
+
+
+        def handle_data(self, data):
+            if (data['AAPL'].short_mavg['price'] > data['AAPL'].long_mavg['price']) and not self.invested:
+                self.order('AAPL', 100)
+                self.invested = True
+            elif (data['AAPL'].short_mavg['price'] < data['AAPL'].long_mavg['price']) and self.invested:
+                self.order('AAPL', -100)
+                self.invested = False
+
+            # Save mavgs for later analysis.
+            self.short_mavg.append(data['AAPL'].short_mavg['price'])
+            self.long_mavg.append(data['AAPL'].long_mavg['price'])
+
+    data = load_from_yahoo()
+    dma = DualMovingAverage()
+    results = dma.run(data)
+
+You can find other examples in the zipline/examples directory.
+