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https://github.com/wassname/catalyst.git
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Freddie Vargus
a12c34c39c
I'm not sure what the raw csv pulled from the federal reserve looked like before, but when trying to download fresh treasure data (data not stored in `./zipline`), there is an error that says "Time Period not in list". After checking the raw csv now, it looks like there are 5 header rows rather than just 1, so skipping those rows removes that error.
62 lines
1.9 KiB
Python
62 lines
1.9 KiB
Python
#
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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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import numpy as np
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import pandas as pd
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import pandas_datareader.data as pd_reader
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def get_benchmark_returns(symbol, first_date, last_date):
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"""
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Get a Series of benchmark returns from Google associated with `symbol`.
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Default is `SPY`.
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Parameters
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----------
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symbol : str
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Benchmark symbol for which we're getting the returns.
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first_date : pd.Timestamp
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First date for which we want to get data.
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last_date : pd.Timestamp
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Last date for which we want to get data.
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The furthest date that Google goes back to is 1993-02-01. It has missing
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data for 2008-12-15, 2009-08-11, and 2012-02-02, so we add data for the
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dates for which Google is missing data.
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We're also limited to 4000 days worth of data per request. If we make a
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request for data that extends past 4000 trading days, we'll still only
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receive 4000 days of data.
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first_date is **not** included because we need the close from day N - 1 to
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compute the returns for day N.
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"""
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data = pd_reader.DataReader(
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symbol,
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'google',
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first_date,
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last_date
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)
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data = data['Close']
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data[pd.Timestamp('2008-12-15')] = np.nan
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data[pd.Timestamp('2009-08-11')] = np.nan
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data[pd.Timestamp('2012-02-02')] = np.nan
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data = data.fillna(method='ffill')
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return data.sort_index().tz_localize('UTC').pct_change(1).iloc[1:]
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