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Refactor interesting periods #163

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Oct 7, 2015
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Merged

Refactor interesting periods #163

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0c19304
MAINT Move interesting periods into a global variable so that users c…
twiecki Oct 7, 2015
e87cf25
ENH Add market stress period for Aug 2015 Closes #134.
twiecki Oct 7, 2015
d1ffb7c
MAINT Make PERIODS import global.
twiecki Oct 7, 2015
32f5c3b
MAINT Rename time period. Add missing pandas import.
twiecki Oct 7, 2015
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65 changes: 65 additions & 0 deletions pyfolio/interesting_periods.py
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Original file line number Diff line number Diff line change
@@ -0,0 +1,65 @@
#
# Copyright 2015 Quantopian, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""Generates a list of historical event dates that may have had
significant impact on markets. See extract_interesting_date_ranges."""

import pandas as pd

from collections import OrderedDict

PERIODS = OrderedDict()
# Dotcom bubble
PERIODS['Dotcom'] = (pd.Timestamp('20000310'), pd.Timestamp('20000910'))

# Lehmann Brothers
PERIODS['Lehmann'] = (pd.Timestamp('20080801'), pd.Timestamp('20081001'))

# 9/11
PERIODS['9/11'] = (pd.Timestamp('20010911'), pd.Timestamp('20011011'))

# 05/08/11 US down grade and European Debt Crisis 2011
PERIODS[
'US downgrade/European Debt Crisis'] = (pd.Timestamp('20110805'),
pd.Timestamp('20110905'))

# 16/03/11 Fukushima melt down 2011
PERIODS['Fukushima'] = (pd.Timestamp('20110316'), pd.Timestamp('20110416'))

# 01/08/03 US Housing Bubble 2003
PERIODS['US Housing'] = (
pd.Timestamp('20030108'), pd.Timestamp('20030208'))

# 06/09/12 EZB IR Event 2012
PERIODS['EZB IR Event'] = (
pd.Timestamp('20120910'), pd.Timestamp('20121010'))

# August 2007, March and September of 2008, Q1 & Q2 2009,
PERIODS['Aug07'] = (pd.Timestamp('20070801'), pd.Timestamp('20070901'))
PERIODS['Mar08'] = (pd.Timestamp('20080301'), pd.Timestamp('20070401'))
PERIODS['Sept08'] = (pd.Timestamp('20080901'), pd.Timestamp('20081001'))
PERIODS['2009Q1'] = (pd.Timestamp('20090101'), pd.Timestamp('20090301'))
PERIODS['2009Q2'] = (pd.Timestamp('20090301'), pd.Timestamp('20090601'))

# Flash Crash (May 6, 2010 + 1 week post),
PERIODS['Flash Crash'] = (
pd.Timestamp('20100505'), pd.Timestamp('20100510'))

# April and October 2014).
PERIODS['Apr14'] = (pd.Timestamp('20140401'), pd.Timestamp('20140501'))
PERIODS['Oct14'] = (pd.Timestamp('20141001'), pd.Timestamp('20141101'))

# Market down-turn in August/Sept 2015
PERIODS['Fall2015'] = (pd.Timestamp('20150815'), pd.Timestamp('20150930'))
61 changes: 2 additions & 59 deletions pyfolio/timeseries.py
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Original file line number Diff line number Diff line change
Expand Up @@ -27,6 +27,7 @@

from . import utils
from .utils import APPROX_BDAYS_PER_MONTH, APPROX_BDAYS_PER_YEAR
from .interesting_periods import PERIODS


def var_cov_var_normal(P, c, mu=0, sigma=1):
Expand Down Expand Up @@ -991,62 +992,6 @@ def cone_rolling(
return perf_ts_r


def gen_date_ranges_interesting():
"""Generates a list of historical event dates that may have had
significant impact on markets. See
extract_interesting_date_ranges.

Returns
-------
periods : OrderedDict
Significant events.

"""

periods = OrderedDict()
# Dotcom bubble
periods['Dotcom'] = (pd.Timestamp('20000310'), pd.Timestamp('20000910'))

# Lehmann Brothers
periods['Lehmann'] = (pd.Timestamp('20080801'), pd.Timestamp('20081001'))

# 9/11
periods['9/11'] = (pd.Timestamp('20010911'), pd.Timestamp('20011011'))

# 05/08/11 US down grade and European Debt Crisis 2011
periods[
'US downgrade/European Debt Crisis'] = (pd.Timestamp('20110805'),
pd.Timestamp('20110905'))

# 16/03/11 Fukushima melt down 2011
periods['Fukushima'] = (pd.Timestamp('20110316'), pd.Timestamp('20110416'))

# 01/08/03 US Housing Bubble 2003
periods['US Housing'] = (
pd.Timestamp('20030108'), pd.Timestamp('20030208'))

# 06/09/12 EZB IR Event 2012
periods['EZB IR Event'] = (
pd.Timestamp('20120910'), pd.Timestamp('20121010'))

# August 2007, March and September of 2008, Q1 & Q2 2009,
periods['Aug07'] = (pd.Timestamp('20070801'), pd.Timestamp('20070901'))
periods['Mar08'] = (pd.Timestamp('20080301'), pd.Timestamp('20070401'))
periods['Sept08'] = (pd.Timestamp('20080901'), pd.Timestamp('20081001'))
periods['2009Q1'] = (pd.Timestamp('20090101'), pd.Timestamp('20090301'))
periods['2009Q2'] = (pd.Timestamp('20090301'), pd.Timestamp('20090601'))

# Flash Crash (May 6, 2010 + 1 week post),
periods['Flash Crash'] = (
pd.Timestamp('20100505'), pd.Timestamp('20100510'))

# April and October 2014).
periods['Apr14'] = (pd.Timestamp('20140401'), pd.Timestamp('20140501'))
periods['Oct14'] = (pd.Timestamp('20141001'), pd.Timestamp('20141101'))

return periods


def extract_interesting_date_ranges(returns):
"""Extracts returns based on interesting events. See
gen_date_range_interesting.
Expand All @@ -1063,12 +1008,10 @@ def extract_interesting_date_ranges(returns):
Date ranges, with returns, of all valid events.

"""

periods = gen_date_ranges_interesting()
returns_dupe = returns.copy()
returns_dupe.index = returns_dupe.index.map(pd.Timestamp)
ranges = OrderedDict()
for name, (start, end) in periods.items():
for name, (start, end) in PERIODS.items():
try:
period = returns_dupe.loc[start:end]
if len(period) == 0:
Expand Down