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https://github.com/wassname/catalyst.git
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twiecki
f5086e4b0e
When zipline is imported it checks whether it runs in the IPython notebook. If it does, it registers a %%zipline magic that takes the same arguments as the CLI with the addition of a -o for specifying the output variable to store the performance frame in. The algo code in the cell is, as of yet, executed in its own environment rather than that of the IPython NB which is probably what we want. Also adds cli option to save the perf dataframe to a pickle file. Also adds an IPython notebook buyapple example.
57 lines
1.8 KiB
Python
Executable File
57 lines
1.8 KiB
Python
Executable File
#!/usr/bin/env python
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#
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# Copyright 2013 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_target, record, symbol
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from collections import deque as moving_window
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import numpy as np
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def initialize(context):
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# Add 2 windows, one with a long window, one
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# with a short window.
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# Note that this is bound to change soon and will be easier.
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context.short_window = moving_window(maxlen=100)
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context.long_window = moving_window(maxlen=300)
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def handle_data(context, data):
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# Save price to window
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context.short_window.append(data[symbol('AAPL')].price)
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context.long_window.append(data[symbol('AAPL')].price)
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# Compute averages
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short_mavg = np.mean(context.short_window)
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long_mavg = np.mean(context.long_window)
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# Trading logic
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if short_mavg > long_mavg:
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order_target(symbol('AAPL'), 100)
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elif short_mavg < long_mavg:
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order_target(symbol('AAPL'), 0)
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# Save values for later inspection
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record(AAPL=data[symbol('AAPL')].price,
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short_mavg=short_mavg,
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long_mavg=long_mavg)
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