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Historical Market Event Windows and Regime Dates for Analysis

Code pyfolio

Summary

This document provides a predefined catalog of date ranges associated with notable market events and broader market regimes. The event windows include the dot-com period, the September 11 attacks, the global financial crisis, the Flash Crash, Fukushima, the US credit downgrade and European debt crisis, and the 2015 market downturn. It also labels longer spans as a low volatility bull market, crash, recovery, and new normal.

The date ranges can serve as a reference for examining strategy behavior across event windows or contrasting market conditions. However, the file only records labels and start and end dates; it does not provide asset-specific returns, causal analysis, or evidence that the selected periods had the stated effects on every market. The ranges are historical conventions, so researchers should verify dates and tailor them to the instruments and question being studied before drawing conclusions.

Key ideas

  • The catalog pairs market event labels with historical start and end dates.
  • It includes both short event windows and longer market regime intervals.
  • The labeled periods can help organize historical strategy analysis by context.
  • The dates alone do not establish market impact or provide performance evidence.

Tags

BacktestingEvent-drivenUS markets
Full text
# interesting_periods.py


```py
#
# Copyright 2016 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['Lehman'] = (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('20080401'))
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'))

# Market regimes
PERIODS['Low Volatility Bull Market'] = (pd.Timestamp('20050101'),
                                         pd.Timestamp('20070801'))

PERIODS['GFC Crash'] = (pd.Timestamp('20070801'),
                        pd.Timestamp('20090401'))

PERIODS['Recovery'] = (pd.Timestamp('20090401'),
                       pd.Timestamp('20130101'))

PERIODS['New Normal'] = (pd.Timestamp('20130101'),
                         pd.Timestamp('today'))

```

Shown in full with attribution under the source's licence. Licence: Apache-2.0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.