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127 lines
4.0 KiB
Python
Executable File
127 lines
4.0 KiB
Python
Executable File
#!/usr/bin/env python
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#
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# Copyright 2014 Quantopian, Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Dual Moving Average Crossover algorithm.
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This algorithm buys apple once its short moving average crosses
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its long moving average (indicating upwards momentum) and sells
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its shares once the averages cross again (indicating downwards
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momentum).
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"""
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from zipline.api import order, record, symbol
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# Import exponential moving average from talib wrapper
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from zipline.transforms.ta import EMA
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def initialize(context):
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context.asset = symbol('AAPL')
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# Add 2 mavg transforms, one with a long window, one with a short window.
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context.short_ema_trans = EMA(timeperiod=20)
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context.long_ema_trans = EMA(timeperiod=40)
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# To keep track of whether we invested in the stock or not
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context.invested = False
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def handle_data(context, data):
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short_ema = context.short_ema_trans.handle_data(data)
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long_ema = context.long_ema_trans.handle_data(data)
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if short_ema is None or long_ema is None:
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return
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buy = False
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sell = False
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if (short_ema > long_ema).all() and not context.invested:
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order(context.asset, 100)
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context.invested = True
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buy = True
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elif (short_ema < long_ema).all() and context.invested:
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order(context.asset, -100)
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context.invested = False
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sell = True
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record(AAPL=data[context.asset].price,
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short_ema=short_ema[context.asset],
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long_ema=long_ema[context.asset],
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buy=buy,
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sell=sell)
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# Note: this function can be removed if running
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# this algorithm on quantopian.com
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def analyze(context=None, results=None):
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import matplotlib.pyplot as plt
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import logbook
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logbook.StderrHandler().push_application()
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log = logbook.Logger('Algorithm')
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fig = plt.figure()
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ax1 = fig.add_subplot(211)
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results.portfolio_value.plot(ax=ax1)
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ax1.set_ylabel('Portfolio value (USD)')
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ax2 = fig.add_subplot(212)
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ax2.set_ylabel('Price (USD)')
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# If data has been record()ed, then plot it.
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# Otherwise, log the fact that no data has been recorded.
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if 'AAPL' in results and 'short_ema' in results and 'long_ema' in results:
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results[['AAPL', 'short_ema', 'long_ema']].plot(ax=ax2)
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ax2.plot(results.ix[results.buy].index, results.short_ema[results.buy],
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'^', markersize=10, color='m')
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ax2.plot(results.ix[results.sell].index,
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results.short_ema[results.sell],
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'v', markersize=10, color='k')
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plt.legend(loc=0)
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plt.gcf().set_size_inches(18, 8)
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else:
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msg = 'AAPL, short_ema and long_ema data not captured using record().'
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ax2.annotate(msg, xy=(0.1, 0.5))
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log.info(msg)
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plt.show()
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# Note: this if-block should be removed if running
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# this algorithm on quantopian.com
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if __name__ == '__main__':
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from datetime import datetime
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import pytz
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from zipline.algorithm import TradingAlgorithm
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from zipline.utils.factory import load_from_yahoo
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# Set the simulation start and end dates.
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start = datetime(2014, 1, 1, 0, 0, 0, 0, pytz.utc)
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end = datetime(2014, 11, 1, 0, 0, 0, 0, pytz.utc)
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# Load price data from yahoo.
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data = load_from_yahoo(stocks=['AAPL'], indexes={}, start=start,
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end=end)
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# Create and run the algorithm.
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algo = TradingAlgorithm(initialize=initialize, handle_data=handle_data,
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identifiers=['AAPL'])
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results = algo.run(data).dropna()
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# Plot the portfolio and asset data.
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analyze(results=results)
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