DOC: Updated example in readme and added python-dateutils dependency. Closes #139.

README.md

@@ -73,6 +73,7 @@ Dependencies
 * requests
 * delorean
 * iso8601
+* [python-dateutil](https://pypi.python.org/pypi/python-dateutil) (>= 2.1)
 
 Quickstart
 ==========
@@ -81,46 +82,87 @@ The following code implements a simple dual moving average algorithm
 and tests it on data extracted from yahoo finance.
 
 ```python
-from zipline.algorithm import TradingAlgorithm
+from zipline import TradingAlgorithm
 from zipline.transforms import MovingAverage
 from zipline.utils.factory import load_from_yahoo
 
+from datetime import datetime
+import pytz
+import matplotlib.pyplot as plt
+
 class DualMovingAverage(TradingAlgorithm):
-    """Dual Moving Average algorithm.
+    """Dual Moving Average Crossover algorithm.
+
+    This algorithm buys apple once its short moving average crosses
+    its long moving average (indicating upwards momentum) and sells
+    its shares once the averages cross again (indicating downwards
+    momentum).
+
     """
-    def initialize(self, short_window=200, long_window=400):
+    def initialize(self, short_window=100, 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,
                            window_length=short_window)
 
         self.add_transform(MovingAverage, 'long_mavg', ['price'],
-                           market_aware=True,
                            window_length=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:
+        short_mavg = data['AAPL'].short_mavg['price']
+        long_mavg = data['AAPL'].long_mavg['price']
+        buy = False
+        sell = False
+
+	# Has short mavg crossed long mavg?
+        if short_mavg > long_mavg 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:
+            buy = True
+        elif short_mavg < long_mavg and self.invested:
             self.order('AAPL', -100)
             self.invested = False
+            sell = True
 
-        # Save mavgs for later analysis.
-        self.short_mavg.append(data['AAPL'].short_mavg['price'])
-        self.long_mavg.append(data['AAPL'].long_mavg['price'])
+	# Record state variables. A column for each
+	# variable will be added to the performance
+	# DataFrame returned by .run()
+        self.record(short_mavg=short_mavg,
+                    long_mavg=long_mavg,
+                    buy=buy,
+                    sell=sell)
 
-data = load_from_yahoo()
+# Load data
+start = datetime(1990, 1, 1, 0, 0, 0, 0, pytz.utc)
+end = datetime(2002, 1, 1, 0, 0, 0, 0, pytz.utc)
+data = load_from_yahoo(stocks=['AAPL'], indexes={}, start=start,
+       		       end=end, adjusted=False)
+
+# Run algorithm
 dma = DualMovingAverage()
-results = dma.run(data)
+perf = dma.run(data)
+
+# Plot results
+fig = plt.figure()
+ax1 = fig.add_subplot(211,  ylabel='Price in $')
+data['AAPL'].plot(ax=ax1, color='r', lw=2.)
+perf[['short_mavg', 'long_mavg']].plot(ax=ax1, lw=2.)
+
+ax1.plot(perf.ix[perf.buy].index, perf.short_mavg[perf.buy],
+         '^', markersize=10, color='m')
+ax1.plot(perf.ix[perf.sell].index, perf.short_mavg[perf.sell],
+         'v', markersize=10, color='k')
+
+ax2 = fig.add_subplot(212, ylabel='Portfolio value in $')
+perf.portfolio_value.plot(ax=ax2, lw=2.)
+
+ax2.plot(perf.ix[perf.buy].index, perf.portfolio_value[perf.buy],
+         '^', markersize=10, color='m')
+ax2.plot(perf.ix[perf.sell].index, perf.portfolio_value[perf.sell],
+         'v', markersize=10, color='k')
 ```
 
 You can find other examples in the zipline/examples directory.