mirror of
https://github.com/wassname/catalyst.git
synced 2026-07-03 07:59:53 +08:00
Joe Jevnik
59c8e371a2
Adds the data bundle concept which makes it easy for users to register loading functions to build out minute and daily data along with an assets db and adjustments db. By default we have provided a `quandl` bundle which pulls from the public domain WIKI dataset. Users may register new bundles by decorating an ingest function with `zipline.data.bundles.register(<name>)`. This also provides a `yahoo_equities` function for creating an ingestion function that will load a static set of assets from yahoo. The cli is now structured as a couple of subcommands and has been changed to `python -m zipline`. The old behavior of `run_algo.py` has been moved to the `run` subcommand. This is almost entirely the same except that it now takes the name of the data bundle to use, defaulting to `quandl`. The next subcommand is `ingest` which takes the name of a data bundle to ingest. This will run the loading machinery and write the data to a specified location that `run` can find. There is also a `clean` subcommand which deletes the data that was written with `ingest`. Extensions have also been added to zipline. This is an experimental feature where users can provide an extra set of python files to run at the start of the process. These can be used to configure aspects of zipline. Right now the only thing that is supported in an extension file is the registration of a new data bundle.
109 lines
3.4 KiB
Python
109 lines
3.4 KiB
Python
#!/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_target, record, symbol
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def initialize(context):
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context.sym = symbol('AAPL')
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context.i = 0
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def handle_data(context, data):
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# Skip first 300 days to get full windows
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context.i += 1
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if context.i < 300:
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return
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# Compute averages
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# history() has to be called with the same params
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# from above and returns a pandas dataframe.
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short_mavg = data.history(context.sym, 'price', 100, '1d').mean()
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long_mavg = data.history(context.sym, 'price', 300, '1d').mean()
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# Trading logic
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if short_mavg > long_mavg:
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# order_target orders as many shares as needed to
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# achieve the desired number of shares.
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order_target(context.sym, 100)
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elif short_mavg < long_mavg:
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order_target(context.sym, 0)
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# Save values for later inspection
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record(AAPL=data.current(context.sym, "price"),
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short_mavg=short_mavg,
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long_mavg=long_mavg)
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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_mavg' in results and
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'long_mavg' in results):
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results['AAPL'].plot(ax=ax2)
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results[['short_mavg', 'long_mavg']].plot(ax=ax2)
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trans = results.ix[[t != [] for t in results.transactions]]
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buys = trans.ix[[t[0]['amount'] > 0 for t in
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trans.transactions]]
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sells = trans.ix[
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[t[0]['amount'] < 0 for t in trans.transactions]]
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ax2.plot(buys.index, results.short_mavg.ix[buys.index],
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'^', markersize=10, color='m')
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ax2.plot(sells.index, results.short_mavg.ix[sells.index],
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'v', markersize=10, color='k')
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plt.legend(loc=0)
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else:
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msg = 'AAPL, short_mavg & long_mavg 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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def _test_args():
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"""Extra arguments to use when zipline's automated tests run this example.
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"""
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import pandas as pd
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return {
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'start': pd.Timestamp('2011', tz='utc'),
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'end': pd.Timestamp('2013', tz='utc'),
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}
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