"""STAGE 1:
IMPORT LIBRARY"""
#-------------------------
import pandas as pd
import systemicRiskMeasures1 as srm
import matplotlib.pyplot as plt
#Import Systemic Risk Measures library
#----------------------------------------------------------------------------------------------------------------------------------
"""STAGE 2:
IMPORT DATA"""
#--------------------------
US_sectors= pd.load('USsectors')
US_sectors_returns= srm.logreturns(Returns=US_sectors)
FmmaFrench49_from1926=(pd.load('FF49_1926').resample('M'))[607+155:]
Recession_Values= pd.load('USARECM')
#-------------------------
#----------------------------------------------------------------------------------------------------------------------------------
"""STAGE 3:
IMPORT SYSTEMIC RISK MEASURES AND RUN SIGNALS"""
#-------------------------
Inputs=FmmaFrench49_from1926
#Input= US_sectors_returns.resample('M') #input monthly returns
"""Mahalanobis Distance"""
#Input
MD_input=Inputs #Change this value for data required
#Run
import systemicRiskMeasures1 as srm
import matplotlib.pyplot as plt
#Import Systemic Risk Measures library
#----------------------------------------------------------------------------------------------------------------------------------
"""STAGE 2:
IMPORT DATA"""
#--------------------------
Start='19950131'
Start_Recession_Values='19950201'
End='20140131' #20140630 latest St Louis Recession data date
Recession_Values= pd.load('USARECM')
Balanced_port= srm.logreturns(Returns=pd.load('Probit_portfolio')).resample('M',how='sum').loc[Start:End]
Recession_Values= Recession_Values[Start_Recession_Values:]
w=5
window=w*12
window_range= window
"""Returns"""
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
FmmaFrench47_from1926=(pd.load('FF49_1926').resample('M',how='sum')).loc[Start:End]
noa=47
length=len(FmmaFrench47_from1926)
