ENH: Add example that uses talib transform.

The new example is almost identical to the dual_moving_average one.
However, instead of our in-house mavg transform it uses the new
talib exponential moving average (EMA).

zipline/examples/dual_ema_talib.py

@@ -0,0 +1,92 @@
+#!/usr/bin/python
+#
+# Copyright 2013 Quantopian, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import matplotlib.pyplot as plt
+
+from zipline.algorithm import TradingAlgorithm
+from zipline.utils.factory import load_from_yahoo
+
+# Import exponential moving average from talib wrapper
+from zipline.transforms.ta import EMA
+
+from datetime import datetime
+import pytz
+
+
+class DualEMATaLib(TradingAlgorithm):
+    """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=20, long_window=40):
+        # Add 2 mavg transforms, one with a long window, one
+        # with a short window.
+        self.short_ema_trans = EMA('AAPL', window_length=short_window)
+        self.long_ema_trans = EMA('AAPL', window_length=long_window)
+
+        # To keep track of whether we invested in the stock or not
+        self.invested = False
+
+    def handle_data(self, data):
+        self.short_ema = self.short_ema_trans.handle_data(data)
+        self.long_ema = self.long_ema_trans.handle_data(data)
+        if self.short_ema is None or self.long_ema is None:
+            return
+
+        self.buy = False
+        self.sell = False
+
+        if self.short_ema > self.long_ema and not self.invested:
+            self.order('AAPL', 100)
+            self.invested = True
+            self.buy = True
+        elif self.short_ema < self.long_ema and self.invested:
+            self.order('AAPL', -100)
+            self.invested = False
+            self.sell = True
+
+        self.record(AAPL=data['AAPL'].price,
+                    short_ema=self.short_ema,
+                    long_ema=self.long_ema,
+                    buy=self.buy,
+                    sell=self.sell)
+
+if __name__ == '__main__':
+    start = datetime(1990, 1, 1, 0, 0, 0, 0, pytz.utc)
+    end = datetime(1991, 1, 1, 0, 0, 0, 0, pytz.utc)
+    data = load_from_yahoo(stocks=['AAPL'], indexes={}, start=start,
+                           end=end)
+
+    dma = DualEMATaLib()
+    results = dma.run(data).dropna()
+
+    fig = plt.figure()
+    ax1 = fig.add_subplot(211, ylabel='portfolio value')
+    results.portfolio_value.plot(ax=ax1)
+
+    ax2 = fig.add_subplot(212)
+    results[['AAPL', 'short_ema', 'long_ema']].plot(ax=ax2)
+
+    ax2.plot(results.ix[results.buy].index, results.short_ema[results.buy],
+             '^', markersize=10, color='m')
+    ax2.plot(results.ix[results.sell].index, results.short_ema[results.sell],
+             'v', markersize=10, color='k')
+    plt.legend(loc=0)
+    plt.show()