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paper_pictures_30Portfolios_comparison_fincancialcrisis.py
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paper_pictures_30Portfolios_comparison_fincancialcrisis.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Feb 3 13:34:20 2018
@author: jeremiasknoblauch
Description: Use 30 Portfolios to zoom in to financial crisis and look at MAP
segmentation
"""
import pickle
import numpy as np
from Evaluation_tool import EvaluationTool
from matplotlib import pyplot as plt
import csv
import datetime
import matplotlib
import datetime
#only needed if you want to generate demo data yourself
from cp_probability_model import CpModel
from detector import Detector
import synthetic_simulations_prepare_data
import synthetic_simulations_prepare_models
#ensure that we have type 1 fonts (for ICML publishing guiedlines)
matplotlib.rcParams['pdf.fonttype'] = 42
matplotlib.rcParams['ps.fonttype'] = 42
baseline_working_directory = ("/Users//jeremiasknoblauch//Documents////OxWaSP//BOCPDMS//Code//" +
"SpatialBOCD//Paper//30PortfolioData")
date_file = "portfolio_dates.csv"
results_file= "results_30PortfolioData_comparison.txt"
"""Read in the dates & results"""
myl = []
count = 0
with open(baseline_working_directory + "//" + date_file) as csvfile:
reader = csv.reader(csvfile)
for row in reader:
#DEBUG: Unclear if this is needed
if count > 0:
myl += row
count += 1
if count % 2500 == 0:
print(count)
dates = []
for e in myl:
dates.append(int(e))
result_path = baseline_working_directory
EvT = EvaluationTool()
EvT.build_EvaluationTool_via_results(result_path + "//" + results_file)
"""Using the dates, select your range and indices: select
03/07/1975 -- 31/12/2008, i.e. find indices that correspond"""
start_test = dates.index(19740703)
start_algo = dates.index(19750703)
stop = dates.index(20081231)
"""time period for which we want RLD"""
start_comparison = dates.index(20070801)#dates.index(19980102)
stop_comparison = stop#stop
all_dates = []
for d in dates[start_comparison:stop-2]:
s = str(d)
all_dates.append(datetime.date(year=int(s[0:4]), month=int(s[4:6]), day=int(s[6:8])))
"""indices of that time period"""
start_range = start_comparison - start_test
stop_range = stop_comparison - start_test
time_range = np.linspace(start_range, stop_range,
stop_range - start_range, dtype = int)
"""Create list of event times and labels"""
label_list, event_time_list= [],[]
afghanistan = False
crash2002 = True
iraq = True
london = True
number_labels = True
arrow_color_list = []
color_us = "orange"
color_they = "black"
#if comparison:
# label_list.append("Asia Crisis")
# event_time_list.append((1998 + (8)/12)) #date: start of last quarter 98
# arrow_color_list.append(color_they)
# label_list.append("DotCom bubble bursts")
# event_time_list.append((2000 + (3/12))) #date: April, when inflation reports came in
# arrow_color_list.append(color_they)
# label_list.append("9/11")
# event_time_list.append((2001 + (8 + 9/31)/12)) #date: airplane day
# arrow_color_list.append(color_they)
# if afghanistan:
# label_list.append("Afghanistan war")
# event_time_list.append(2001 + (9 + 7/31)/12) #Operation Enduring Freedom begins
# arrow_color_list.append(color_us)
# if crash2002:
# label_list.append("2002 Crash")
# event_time_list.append(2002 + (6 + 23/31)/12) #Crash of 2002, 23/7 lowest Dow Jones value
# arrow_color_list.append(color_us)
# if iraq:
# label_list.append("Iraq war")
# event_time_list.append(2003 + (2+20/31)/12) #Date of invasion
# arrow_color_list.append(color_us)
#
# label_list.append("Bush cuts")
# event_time_list.append((2003 + ((4+23/31)/12))) #date the regulation was passed
# arrow_color_list.append(color_they)
# if london:
# label_list.append("London Underground attacks, Hurricanes Katrina & Irma")
# event_time_list.append(2005 + (6 + 7/31)/12) #date bombing 'terror premium'
# arrow_color_list.append(color_us)
# label_list.append("US Election")
# event_time_list.append(2005 + (10 + 8/31)/12) #election day
# arrow_color_list.append(color_they)
# label_list.append("Northern Rock bank run")
# event_time_list.append(2007 + (8 + 14/31)/12) #day of run
# arrow_color_list.append(color_they)
# label_list.append("Lehman collapse")
# event_time_list.append(2008 + (8 + 15/31)/12) #day of bankruptcy
# arrow_color_list.append(color_they)
#taken from https://www.theguardian.com/business/2012/aug/07/credit-crunch-boom-bust-timeline
#, http://www.cityam.com/269810/global-financial-crisis-10-years-timeline-global-events
# http://www.telegraph.co.uk/finance/financialcrisis/8592990/Timeline-of-world-financial-crisis.html
# https://lauder.wharton.upenn.edu/wp-content/uploads/2015/06/Chronology_Economic_Financial_Crisis.pdf
# label_list.append("UBS reports 3.4bn$ losses")
# event_time_list.append(2007 + (9)/12) #date of announcement
# arrow_color_list.append("blue")
# label_list.append("UBS announces 4.3bn$ losses")
# event_time_list.append(2007 + (9)/12) #date of trend reversal
# arrow_color_list.append("blue")
"""helper function getting closest date in all_dates"""
def nearest(pivot):
return min(all_dates, key=lambda x: abs(x - pivot))
label_list.append("BNP Paribas freezes funds")
event_time_list.append(nearest(datetime.date(2007,8,9))) #day of freeze
arrow_color_list.append(color_us)
label_list.append("Fed cuts lending rate by 0.5% points")
event_time_list.append(nearest(datetime.date(2007,8,17))) #day of freeze
arrow_color_list.append(color_us)
label_list.append("IKB announces 1bn$ losses related to subprime market")
event_time_list.append(nearest(datetime.date(2007,9,3))) #day of freeze
arrow_color_list.append(color_us)
label_list.append("Northern Rock bank run")
event_time_list.append(nearest(datetime.date(2007,9,14))) #day of run
arrow_color_list.append(color_they)
label_list.append("Fed cuts interest rate by 0.5% points, BoE injects 10bn£ into market")
event_time_list.append(nearest(datetime.date(2007,9,19))) #day of run
arrow_color_list.append(color_us)
#label_list.append("UBS announces 3.4bn$, Citigroup 3.1bn$ loss related to subprime mortgage markets")
#event_time_list.append(nearest(datetime.date(2007, 10, 1))) #date of CEO resigning
#arrow_color_list.append(color_us)
#label_list.append("Meryll Lynch unveils 7.9bn$ exposure")
#event_time_list.append(nearest(datetime.date(2007, 10, 30)))#date of CEO resigning
#arrow_color_list.append(color_us)
#label_list.append("S&P downgrades bond insurers")
#event_time_list.append(nearest(datetime.date(2007, 12, 19))) #date of CEO resigning
#arrow_color_list.append(color_us)
#label_list.append("Credit crunch hits US and EU")
#event_time_list.append(nearest(datetime.date(2008,1, 9)))
#arrow_color_list.append(color_us)
label_list.append("Bush announces plans to help >1 million homeowners facing foreclosure")
event_time_list.append(nearest(datetime.date(2007,12,6)))
arrow_color_list.append(color_us)
label_list.append("Fed, ECB, BoE offer loans to banks")
event_time_list.append(nearest(datetime.date(2007,12,13)))
arrow_color_list.append(color_us)
label_list.append("Fed slashes federal funds rate to 3.5%")
event_time_list.append(nearest(datetime.date(2008, 1, 22)))
arrow_color_list.append(color_us)
#label_list.append("Bond insurer MBIA announces 2.3bn$ loss in the last 3 months")
#event_time_list.append(nearest(datetime.date(2008, 1, 31)))
#arrow_color_list.append(color_us)
label_list.append("G7 estimates subprime mortgage related losses at 400bn$ worldwide")
event_time_list.append(nearest(datetime.date(2008, 2, 10))) #day of privatization
arrow_color_list.append(color_us)
#label_list.append("Northern Rock nationalized")
#event_time_list.append(nearest(datetime.date(2008, 2, 17))) #day of privatization
#arrow_color_list.append(color_us)
#label_list.append("Biggest Fed intervention yet makes 200bn$ available in liquidity")
#event_time_list.append(nearest(datetime.date(2008,3,7))) #day of privatization
#arrow_color_list.append(color_us)
label_list.append("JP Morgan buys Bear Stearns, Fed intervention makes 200bn$ available in liquidity")
event_time_list.append(nearest(datetime.date(2008,3,16))) #day of purchase
arrow_color_list.append(color_us)
label_list.append("IMF estimates worldwide losses at >1 trillion $")
event_time_list.append(nearest(datetime.date(2008,4,8))) #day of purchase
arrow_color_list.append(color_us)
#label_list.append("RBS largest rights issue in UK history")
#event_time_list.append(nearest(datetime.date(2008, 4, 22))) #day of purchase
#arrow_color_list.append(color_us)
#label_list.append("UBS rights issue to recover 37bn$")
#event_time_list.append(nearest(datetime.date(2008,5,22))) #day of purchase
#arrow_color_list.append(color_us)
#label_list.append("FBI arrests 406 mortgage fraudsters")
#event_time_list.append(nearest(datetime.date(2008, 6, 19))) #day of purchase
#arrow_color_list.append(color_us)
#label_list.append("First Fannie Mae & Freddie Mac support")
#event_time_list.append(nearest(datetime.date(2008, 7, 14))) #day of bailout
#arrow_color_list.append(color_us)
label_list.append("HBOS' rights issue fails")
event_time_list.append(nearest(datetime.date(2008, 7, 21))) #day of bailout
arrow_color_list.append(color_us)
#label_list.append("HSBC announces substantial fall in profits")
#event_time_list.append(nearest(datetime.date(2008, 8, 4))) #day of bailout
#arrow_color_list.append(color_us)
label_list.append("ECB efforts for liquidity pumping 200bn€ into the market")
event_time_list.append(nearest(datetime.date(2008,8,9))) #day of bailout
arrow_color_list.append(color_us)
label_list.append("Fannie Mae & Freddie Mac bailout")
event_time_list.append(nearest(datetime.date(2008,9,7))) #day of bailout
arrow_color_list.append(color_us)
label_list.append("Lehman collapse")
event_time_list.append(nearest(datetime.date(2008,9,15))) #day of bankruptcy
arrow_color_list.append(color_they)
# label_list.append("Lloyds saves HBOS")
# event_time_list.append(2008 + (8 + 17/31)/12) #day of purchase
# arrow_color_list.append(color_us)
# label_list.append("TARP programme")
# event_time_list.append(2008 + (9 + 3/31)/12) #day of ratification
# arrow_color_list.append("blue")
# label_list.append("FED, ECB, BoE cut interest rates")
# event_time_list.append(2008 + (9 + 8/26)/12) #day of cut
# arrow_color_list.append(color_us)
# label_list.append("UK bails out Lloyds, RBS, HBOS")
# event_time_list.append(2008 + (9 + 13/26)/12) #day of bail
# arrow_color_list.append(color_us)
#label_list.append("Ireland officially enters recession")
#event_time_list.append(nearest(datetime.date(2008,9,25))) #day of ratification
#arrow_color_list.append(color_us)
label_list.append("Russia announces 500bn roubles to fight the crisis, BoE injects 10bn£ into market")
event_time_list.append(nearest(datetime.date(2008,9,19))) #day of ratification
arrow_color_list.append(color_us)
label_list.append("BENELUX bail out Fortis with 11.8bn$")
event_time_list.append(nearest(datetime.date(2008,9,28))) #day of ratification
arrow_color_list.append(color_us)
label_list.append("UK announces 500bn£ bank rescue package")
event_time_list.append(nearest(datetime.date(2008,10,8))) #day of bail
arrow_color_list.append(color_us)
#label_list.append("UK bails out Lloyds, RBS, HBOS; NL bails out ING")
#event_time_list.append(nearest(datetime.date(2008,10,19))) #day of bail
#arrow_color_list.append(color_us)
label_list.append("IMF loan over 16.4bn$ to Ukraine, BoE and ECB cut interest rates")
event_time_list.append(nearest(datetime.date(2008, 11, 6))) #day of bail
arrow_color_list.append(color_us)
label_list.append("G20 pledge for fiscal stimuli")
event_time_list.append(nearest(datetime.date(2008, 11, 17))) #
arrow_color_list.append(color_us)
#label_list.append("Fed starts Quantitative Easing")
#event_time_list.append(nearest(datetime.date(2008, 11, 25))) #
#arrow_color_list.append(color_us)
# label_list.append("Ireland enters recession")
# event_time_list.append(2008 + (11 + 11/26)/12) #
# arrow_color_list.append(color_us)
#label_list.append("Citigroup bailout for total of 351bn$")
#event_time_list.append(nearest(datetime.date(2008, 11, 23))) #
#arrow_color_list.append(color_us)
#label_list.append("BoE cuts interest to lowest rate since '51," +
# "ECB lowers interest rates, France announces 26bn EUR stimulus package" +
# "Germany & Sweden announce stimulus package over 38bn and 8bn EUR")
#event_time_list.append(nearest(datetime.date(2008, 12, 4))) #
#arrow_color_list.append(color_us)
#label_list.append("Germany & Sweden announce stimulus package over 38bn EUR")
#event_time_list.append(nearest(datetime.date(2008, 12, 6))) #
#arrow_color_list.append(color_us)
label_list.append("Madoff's 50bn$ Ponzi scheme revealed, 14bn$ car rescue " +
"package in US does not pass senate, South Korean central" +
" bank lower interest to record low")
event_time_list.append(nearest(datetime.date(2008, 12, 11)))
arrow_color_list.append(color_us)
label_list.append("Fed cuts interest rate to 0.25%, " +
"Japanese central bank cuts interest rate to 0.3%")
event_time_list.append(nearest(datetime.date(2008, 12, 16)))
arrow_color_list.append(color_us)
# label_list.append("Japanese central bank cuts interest rate to 0.3%")
# event_time_list.append(2008 + (11 + 19/26)/12) #
# arrow_color_list.append(color_us)
#Idea: Maybe research the 2008/2009 period and match MAP CPs with individual events
if number_labels:
label_list = []
for i in range(0, len(event_time_list)):
label_list.append(str(i+1))
#arrow_color_list = ["black"]*len(event_time_list)
"""get MAP CPs in range"""
segmentation = np.array(EvT.results[EvT.names.index("MAP CPs")][-2])
segmentation_selection = segmentation[np.logical_and(
segmentation[:,0] >= start_range, segmentation[:,0] <= stop_range),:]
models = np.union1d([e[1] for e in segmentation_selection],
[e[1] for e in segmentation_selection])
"""Obtain the plot for RLD"""
#start, stop = (2007 + 7/12), 2009
#start, stop = datetime.date(2007, 8, 1), datetime.date(2008, 12, 31)
custom_colors = ["blue"] * 5 #["green", "darkviolet", "orange", "purple", "turquoise"]
#paper: figsize = (20,5) poster: figsize=(12,3)
fig, ax = plt.subplots(1, figsize = (20,5))
#ax = ax_[0]
_, myfig=EvT.plot_run_length_distr(buffer=0, show_MAP_CPs = True, mark_median = False,
mark_max = True, upper_limit = 115, enforce_upper_limit=True, print_colorbar = True,
colorbar_location= 'top',log_format = True, aspect_ratio = 'auto',
C1=700,C2=1,
time_range = np.linspace(start_comparison - start_test,
stop_comparison - start_test - 2,
stop_comparison-start_comparison-2, dtype=int),
#start=start, stop = stop,
all_dates = all_dates,
event_time_list=event_time_list,
label_list=label_list, space_to_colorbar = 0.9,
custom_colors = custom_colors, #["blue"]*len(event_time_list),
custom_linestyles = ["solid"]*len(event_time_list),
custom_linewidth = 3,
arrow_colors= arrow_color_list,
number_fontsize = 18,
arrow_length = 13, #paper: arrow-length = 13 poster arrow-length = 27
arrow_thickness = 4.0,
arrows_setleft_indices = [3,13,14,15, 20, 21],
#paper: arrows_setleft_indices = [3,13,14,15, 20, 21],
#poster: arrows_setleft_indices = [3,13,14,15,16,17,18, 20, 21],
#poster: arrows_setleft_by = [datetime.timedelta(days = 3),
# datetime.timedelta(days=15),datetime.timedelta(days=8),
# -datetime.timedelta(days = 1),
# -datetime.timedelta(days=6),
# -datetime.timedelta(days=9),
# datetime.timedelta(days = 3),
# datetime.timedelta(days=9), datetime.timedelta(days=1)],
#paper:
arrows_setleft_by = [datetime.timedelta(days = 3),
datetime.timedelta(days=7),datetime.timedelta(days=4),
-datetime.timedelta(days = 1),
datetime.timedelta(days=9), datetime.timedelta(days=1)],
zero_distance = datetime.timedelta(days=0),
xlab_fontsize =14,
ylab_fontsize = 14,
xticks_fontsize = 12,
yticks_fontsize = 12,
ax = ax, figure = fig,
xlab = "Year-Month")
fig.savefig(baseline_working_directory + "//30portfolio_picture_financial_crisis.pdf",
format = "pdf", dpi = 800)
#start_day = datetime.date(2007, 8, 1)
#stad = matplotlib.dates.date2num( start_day )
#stop_day = datetime.date(2008, 12, 31)
#stod = matplotlib.dates.date2num( stop_day )
#for mod, ind in zip(models, range(0, len(models))):
# EvT.plot_model_posterior(indices = [mod], time_range = np.linspace(
# start_range, stop_range-2, stop_range-start_range-2, dtype=int),
# log_format = True, show_MAP_CPs = False,
# custom_colors = [custom_colors[ind]],
# start_axis = stad, stop_axis = stod)