mirror of
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.
102 lines
3.3 KiB
Python
102 lines
3.3 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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from operator import itemgetter
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import re
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import numpy as np
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import pandas as pd
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get_unit_and_periods = itemgetter('unit', 'periods')
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def parse_treasury_csv_column(column):
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"""
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Parse a treasury CSV column into a more human-readable format.
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Columns start with 'RIFLGFC', followed by Y or M (year or month), followed
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by a two-digit number signifying number of years/months, followed by _N.B.
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We only care about the middle two entries, which we turn into a string like
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3month or 30year.
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"""
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column_re = re.compile(
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r"^(?P<prefix>RIFLGFC)"
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"(?P<unit>[YM])"
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"(?P<periods>[0-9]{2})"
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"(?P<suffix>_N.B)$"
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)
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match = column_re.match(column)
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if match is None:
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raise ValueError("Couldn't parse CSV column %r." % column)
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unit, periods = get_unit_and_periods(match.groupdict())
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# Roundtrip through int to coerce '06' into '6'.
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return str(int(periods)) + ('year' if unit == 'Y' else 'month')
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def earliest_possible_date():
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"""
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The earliest date for which we can load data from this module.
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"""
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# The US Treasury actually has data going back further than this, but it's
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# pretty rare to find pricing data going back that far, and there's no
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# reason to make people download benchmarks back to 1950 that they'll never
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# be able to use.
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return pd.Timestamp('1980', tz='UTC')
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def get_treasury_data(start_date, end_date):
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return pd.read_csv(
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"https://www.federalreserve.gov/datadownload/Output.aspx"
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"?rel=H15"
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"&series=bf17364827e38702b42a58cf8eaa3f78"
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"&lastObs="
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"&from=" # An unbounded query is ~2x faster than specifying dates.
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"&to="
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"&filetype=csv"
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"&label=include"
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"&layout=seriescolumn"
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"&type=package",
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skiprows=5, # First 5 rows are useless headers.
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parse_dates=['Time Period'],
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na_values=['ND'], # Presumably this stands for "No Data".
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index_col=0,
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).loc[
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start_date:end_date
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].dropna(
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how='all'
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).rename(
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columns=parse_treasury_csv_column
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).tz_localize('UTC') * 0.01 # Convert from 2.57% to 0.0257.
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def dataconverter(s):
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try:
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return float(s) / 100
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except:
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return np.nan
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def get_daily_10yr_treasury_data():
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"""Download daily 10 year treasury rates from the Federal Reserve and
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return a pandas.Series."""
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url = "https://www.federalreserve.gov/datadownload/Output.aspx?rel=H15" \
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"&series=bcb44e57fb57efbe90002369321bfb3f&lastObs=&from=&to=" \
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"&filetype=csv&label=include&layout=seriescolumn"
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return pd.read_csv(url, header=5, index_col=0, names=['DATE', 'BC_10YEAR'],
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parse_dates=True, converters={1: dataconverter},
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squeeze=True)
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