text: #!/usr/bin/env 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.finance import trading
  from zipline.transforms import MovingAverage
  from zipline.utils.factory import load_from_yahoo
  
  from datetime import datetime
  import pytz
  
  
  class DualMovingAverage(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.add_transform(MovingAverage, 'short_mavg', ['price'],
                             window_length=short_window)
  
          self.add_transform(MovingAverage, 'long_mavg', ['price'],
                             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_mavg = data['AAPL'].short_mavg['price']
          self.long_mavg = data['AAPL'].long_mavg['price']
          self.buy = False
          self.sell = False
  
          if self.short_mavg > self.long_mavg and not self.invested:
              self.order('AAPL', 5000)
              self.invested = True
              self.buy = True
          elif self.short_mavg < self.long_mavg and self.invested:
              self.order('AAPL', -5000)
              self.invested = False
              self.sell = True
  
          self.record(short_mavg=self.short_mavg,
                      long_mavg=self.long_mavg,
                      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 = DualMovingAverage()
      results = dma.run(data)
  
      br = trading.environment.benchmark_returns
      bm_returns = br[(br.index >= start) & (br.index <= end)]
      results['benchmark_returns'] = (1 + bm_returns).cumprod().values
      results['algorithm_returns'] = (1 + results.returns).cumprod()
      fig = plt.figure()
      ax1 = fig.add_subplot(211, ylabel='cumulative returns')
  
      results[['algorithm_returns', 'benchmark_returns']].plot(ax=ax1,
                                                               sharex=True)
  
      ax2 = fig.add_subplot(212)
      data['AAPL'].plot(ax=ax2, color='r')
      results[['short_mavg', 'long_mavg']].plot(ax=ax2)
  
      ax2.plot(results.ix[results.buy].index, results.short_mavg[results.buy],
               '^', markersize=10, color='m')
      ax2.plot(results.ix[results.sell].index, results.short_mavg[results.sell],
               'v', markersize=10, color='k')
      plt.legend(loc=0)
  
      sharpe = [risk['sharpe'] for risk in dma.risk_report['one_month']]
      print "Monthly Sharpe ratios:", sharpe
      plt.gcf().set_size_inches(18, 8)
  