In [1]:

    

%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as dts
import pandas as pd
from fredpy import series, window_equalize, quickplot
import datetime,dateutil,urllib,runProcs
import requests


    

In [2]:

    

url = "https://www.philadelphiafed.org/-/media/research-and-data/real-time-center/survey-of-professional-forecasters/historical-data/inflation.xls?la=en"
r = requests.get(url,verify=False)
with open("inflationForecasts.xls", "wb") as code:
    code.write(r.content)
    
url = "https://www.philadelphiafed.org/-/media/research-and-data/real-time-center/survey-of-professional-forecasters/historical-data/medianlevel.xls?la=en"
r = requests.get(url,verify=False)
with open("medianLevel.xls", "wb") as code:
    code.write(r.content)


    

    




/Users/bcjenkin/anaconda/lib/python3.4/site-packages/requests/packages/urllib3/connectionpool.py:791: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.org/en/latest/security.html
  InsecureRequestWarning)
/Users/bcjenkin/anaconda/lib/python3.4/site-packages/requests/packages/urllib3/connectionpool.py:791: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.org/en/latest/security.html
  InsecureRequestWarning)

In [3]:

    

inflationForecasts = pd.read_excel('inflationForecasts.xls')
inflationForecasts=inflationForecasts.interpolate()
# inflationForecasts.iloc[0:5]


    

In [4]:

    

deflatorForecasts = pd.read_excel('medianLevel.xls',sheetname = 'PGDP')
deflatorForecasts=deflatorForecasts.interpolate()
deflatorForecasts = deflatorForecasts.iloc[5:]
# deflatorForecasts.iloc[0:5]


    

In [5]:

    

cpiForecasts = pd.read_excel('medianLevel.xls',sheetname = 'CPI')
cpiForecasts = cpiForecasts.iloc[5:]
cpiForecasts=cpiForecasts.interpolate()
# cpiForecasts.iloc[0:5]


    

In [6]:

    

# Create some fredpy instances
defl_forecast_1q = series('GDPDEF')
defl_forecast_2q = series('GDPDEF')
defl_forecast_1y = series('GDPDEF')

# Initialize forecast lists
forecast_1q = []
forecast_2q = []
forecast_1y = []

# Associate forecasts with dates. The date should coincide with the start of the period for which the forecast applies.
dates = []
for i,ind in enumerate(deflatorForecasts.index):
    year =int(deflatorForecasts.iloc[i]['YEAR'])
    quart=int(deflatorForecasts.iloc[i]['QUARTER'])
    if quart == 1:
        month = '01'
    elif quart == 2:
        month = '04'
    elif quart == 3:
        month = '07'
    else:
        month = '10'
        year=year
    date = month+'-01-'+str(year)
    dates.append(date)
    
    forecast_1q.append(400*(deflatorForecasts.iloc[i]['PGDP3']/deflatorForecasts.iloc[i]['PGDP2']-1))
    forecast_2q.append(200*(deflatorForecasts.iloc[i]['PGDP4']/deflatorForecasts.iloc[i]['PGDP2']-1))
    forecast_1y.append(100*(deflatorForecasts.iloc[i]['PGDP6']/deflatorForecasts.iloc[i]['PGDP2']-1))
    
dateNumbers = [dateutil.parser.parse(s) for s in dates]

# Update the FRED instances
defl_forecast_1q.data = np.array(forecast_1q)
defl_forecast_1q.dates = dates
defl_forecast_1q.datenumbers = dateNumbers

defl_forecast_2q.data = np.array(forecast_2q)
defl_forecast_2q.dates = dates
defl_forecast_2q.datenumbers = dateNumbers

defl_forecast_1y.data = np.array(forecast_1y)
defl_forecast_1y.dates = dates
defl_forecast_1y.datenumbers = dateNumbers

deflator_frame = pd.DataFrame({'deflator inflation - 3mo forecast':defl_forecast_1q.data,
                          'deflator inflation - 6mo forecast':defl_forecast_2q.data,
                          'deflator inflation - 1yr forecast':defl_forecast_1y.data,})

deflator_frame = deflator_frame.set_index(pd.DatetimeIndex(defl_forecast_1q.datenumbers))


    

In [7]:

    

# Create some fredpy instances

cpi_forecast_1q = series('GDPDEF')
cpi_forecast_2q = series('GDPDEF')
cpi_forecast_1y = series('GDPDEF')

# Initialize forecast lists
forecast_1q = []
forecast_2q = []
forecast_1y = []

# Associate forecasts with dates. The date should coincide with the start of the period for which the forecast applies.
dates = []
for i,ind in enumerate(cpiForecasts.index):
    year =int(cpiForecasts.iloc[i]['YEAR'])
    quart=int(cpiForecasts.iloc[i]['QUARTER'])
    if quart == 1:
        month = '01'
    elif quart == 2:
        month = '04'
    elif quart == 3:
        month = '07'
    else:
        month = '10'
        year=year
    date = month+'-01-'+str(year)
    dates.append(date)
    
    forecast_1q.append(cpiForecasts.iloc[i]['CPI3'])
    forecast_2q.append(100*(((1 + cpiForecasts.iloc[i]['CPI3']/100)*(1 + cpiForecasts.iloc[i]['CPI4']/100))**(1/2)-1))
    forecast_1y.append(100*(((1 + cpiForecasts.iloc[i]['CPI3']/100)*(1 + cpiForecasts.iloc[i]['CPI4']/100)*(1 + cpiForecasts.iloc[i]['CPI5']/100)*(1 + cpiForecasts.iloc[i]['CPI6']/100))**(1/4)-1))
    
dateNumbers = [dateutil.parser.parse(s) for s in dates]

# Update the FRED instances
cpi_forecast_1q.data = np.array(forecast_1q)
cpi_forecast_1q.dates = dates
cpi_forecast_1q.datenumbers = dateNumbers

cpi_forecast_2q.data = np.array(forecast_2q)
cpi_forecast_2q.dates = dates
cpi_forecast_2q.datenumbers = dateNumbers

cpi_forecast_1y.data = np.array(forecast_1y)
cpi_forecast_1y.dates = dates
cpi_forecast_1y.datenumbers = dateNumbers


cpi_frame = pd.DataFrame({'cpi inflation - 3mo forecast':cpi_forecast_1q.data,
                          'cpi inflation - 6mo forecast':cpi_forecast_2q.data,
                          'cpi inflation - 1yr forecast':cpi_forecast_1y.data,})

cpi_frame = cpi_frame.set_index(pd.DatetimeIndex(cpi_forecast_1q.datenumbers))


    

In [8]:

    

fr = pd.concat([cpi_frame,deflator_frame],axis=1)
# fr.plot(legend=False)


    

In [9]:

    

cpi_forecast_10y = series('GDPDEF')

cpiForecast_10y = []


# 3.3 Associate forecasts with dates. The date should coincide with the start of the period for which the forecast applies.
dates = []
for i,ind in enumerate(inflationForecasts.index):
    year =int(inflationForecasts.iloc[i]['YEAR'])
    quart=int(inflationForecasts.iloc[i]['QUARTER'])
    if quart == 1:
        month = '01'
    elif quart == 2:
        month = '04'
    elif quart == 3:
        month = '07'
    else:
        month = '10'
        year=year
    date = month+'-01-'+str(year)
    dates.append(date)
    
    cpiForecast_10y.append(inflationForecasts.iloc[i]['INFCPI10YR'])
    
dateNumbers = [dateutil.parser.parse(s) for s in dates]

# # 3.4 Create the FRED instances
cpi_forecast_10y.data = np.array(cpiForecast_10y)
cpi_forecast_10y.dates = dates
cpi_forecast_10y.datenumbers = dateNumbers

long_run_frame = pd.DataFrame({'cpi inflation - 10yr forecast':cpi_forecast_10y.data})

long_run_frame = long_run_frame.set_index(pd.DatetimeIndex(cpi_forecast_10y.datenumbers))

long_run_frame.index[-2]


    

    
Out[9]:





Timestamp('2015-10-01 00:00:00')

In [10]:

    

fr = pd.concat([cpi_frame,deflator_frame,long_run_frame],axis=1)
# fr.plot(legend=False)


    

In [11]:

    

inflationForecasts_frame = pd.concat([long_run_frame,cpi_frame,deflator_frame],axis=1)

fig = plt.figure(figsize = (12,8))
for c in inflationForecasts_frame.columns:
    plt.plot_date(inflationForecasts_frame.index,inflationForecasts_frame[c],'-',lw=4,alpha = 0.6)
    
plt.legend()
plt.grid()


    

In [12]:

    

interest3mo = series('TB3MS')
interest6mo = series('TB6MS')
interest1yr = series('GS1')
interest10yr= series('GS10')

interest3mo.monthtoquarter()
interest6mo.monthtoquarter()
interest1yr.monthtoquarter()
interest10yr.monthtoquarter()

window_equalize([interest3mo,interest6mo,interest1yr,interest10yr])

interest_frame = pd.DataFrame({'interest 3mo':interest3mo.data,
                             'interest 6mo':interest6mo.data,
                             'interest 1yr':interest1yr.data,
                             'interest 10yr':interest10yr.data})
interest_frame = interest_frame.set_index(pd.DatetimeIndex(interest3mo.datenumbers))


    

In [13]:

    

fr = pd.concat([inflationForecasts_frame,interest_frame],axis=1)
# fr.plot(legend=False)


    

In [14]:

    

defl_3mo = series('GDPDEF')
defl_6mo = series('GDPDEF')
defl_1yr = series('GDPDEF')

defl_3mo.pc(method='forward',annualized=True)

data = []
for i in range(len(defl_6mo.data)-2):
    data.append(200*(defl_6mo.data[i+2]/defl_6mo.data[i]-1))
    
defl_6mo.data = np.array(data)  
defl_6mo.dates = defl_6mo.dates[:-2]
defl_6mo.datenumbers = defl_6mo.datenumbers[:-2]


data = []
for i in range(len(defl_1yr.data)-4):
    data.append(100*(defl_1yr.data[i+4]/defl_1yr.data[i]-1))
    
defl_1yr.data = np.array(data)  
defl_1yr.dates = defl_1yr.dates[:-4]
defl_1yr.datenumbers = defl_1yr.datenumbers[:-4]

defl_3mo_frame = pd.DataFrame({'deflator inflation - 3mo actual':defl_3mo.data})
defl_3mo_frame = defl_3mo_frame.set_index(pd.DatetimeIndex(defl_3mo.datenumbers))

defl_6mo_frame = pd.DataFrame({'deflator inflation - 6mo actual':defl_6mo.data})
defl_6mo_frame = defl_6mo_frame.set_index(pd.DatetimeIndex(defl_6mo.datenumbers))

defl_1yr_frame = pd.DataFrame({'deflator inflation - 1yr actual':defl_1yr.data})
defl_1yr_frame = defl_1yr_frame.set_index(pd.DatetimeIndex(defl_1yr.datenumbers))

# defl_1yr_frame.index[-2]


    

In [15]:

    

fr = pd.concat([fr,defl_1yr_frame],axis=1)
# fr.plot(legend=False)


    

In [16]:

    

cpi_3mo = series('CPIAUCSL')
cpi_6mo = series('CPIAUCSL')
cpi_1yr = series('CPIAUCSL')
cpi_10yr = series('CPIAUCSL')

cpi_3mo.monthtoquarter()
cpi_6mo.monthtoquarter()
cpi_1yr.monthtoquarter()
cpi_10yr.monthtoquarter()


cpi_3mo.pc(method='forward',annualized=True)

data = []
for i in range(len(cpi_6mo.data)-2):
    data.append(200*(cpi_6mo.data[i+2]/cpi_6mo.data[i]-1))
    
cpi_6mo.data = np.array(data)  
cpi_6mo.dates = cpi_6mo.dates[:-2]
cpi_6mo.datenumbers = cpi_6mo.datenumbers[:-2]


data = []
for i in range(len(cpi_1yr.data)-4):
    data.append(100*(cpi_1yr.data[i+4]/cpi_1yr.data[i]-1))
    
cpi_1yr.data = np.array(data)  
cpi_1yr.dates = cpi_1yr.dates[:-4]
cpi_1yr.datenumbers = cpi_1yr.datenumbers[:-4]

data = []
for i in range(len(cpi_10yr.data)-40):
    data.append(400*((cpi_10yr.data[i+40]/cpi_10yr.data[i])**(1/40)-1))
    
cpi_10yr.data = np.array(data)  
cpi_10yr.dates = cpi_10yr.dates[:-40]
cpi_10yr.datenumbers = cpi_10yr.datenumbers[:-40]

cpi_3mo_frame = pd.DataFrame({'cpi inflation - 3mo actual':cpi_3mo.data})
cpi_3mo_frame = cpi_3mo_frame.set_index(pd.DatetimeIndex(cpi_3mo.datenumbers))

cpi_6mo_frame = pd.DataFrame({'cpi inflation - 6mo actual':cpi_6mo.data})
cpi_6mo_frame = cpi_6mo_frame.set_index(pd.DatetimeIndex(cpi_6mo.datenumbers))

cpi_1yr_frame = pd.DataFrame({'cpi inflation - 1yr actual':cpi_1yr.data})
cpi_1yr_frame = cpi_1yr_frame.set_index(pd.DatetimeIndex(cpi_1yr.datenumbers))

cpi_10yr_frame = pd.DataFrame({'cpi inflation - 10yr actual':cpi_10yr.data})
cpi_10yr_frame = cpi_10yr_frame.set_index(pd.DatetimeIndex(cpi_10yr.datenumbers))


    

In [17]:

    

fr = pd.concat([fr,cpi_3mo_frame,cpi_6mo_frame,cpi_1yr_frame,cpi_10yr_frame],axis=1)
# fr.plot(legend=False)


    

In [18]:

    

actual_rates_frame = pd.concat([interest_frame,defl_3mo_frame,defl_6mo_frame,defl_1yr_frame,cpi_3mo_frame,
                                cpi_6mo_frame,cpi_1yr_frame,cpi_10yr_frame],axis = 1)


    

In [19]:

    

full_data_frame = pd.concat([actual_rates_frame,inflationForecasts_frame],axis=1)


    

In [20]:

    

full_data_frame.columns
full_data_frame[[
        'interest 3mo', 'interest 6mo', 'interest 1yr', 'interest 10yr',
       'deflator inflation - 3mo actual', 'deflator inflation - 6mo actual',
       'deflator inflation - 1yr actual', 'cpi inflation - 3mo actual',
       'cpi inflation - 6mo actual', 'cpi inflation - 1yr actual','cpi inflation - 10yr actual',
       'cpi inflation - 1yr forecast', 'cpi inflation - 3mo forecast',
       'cpi inflation - 6mo forecast', 'deflator inflation - 1yr forecast','cpi inflation - 10yr forecast',
       'deflator inflation - 3mo forecast','deflator inflation - 6mo forecast']].to_csv('inflationForecastData.csv')


    

In [21]:

    

fig = plt.figure(figsize = (12,8))
for c in full_data_frame.columns:
    plt.plot_date(full_data_frame.index,full_data_frame[c],'-',lw=4,alpha = 0.6)
    
plt.grid()


    

In [22]:

    

full_data_frame[['interest 1yr','deflator inflation - 1yr forecast','deflator inflation - 1yr actual']].dropna().plot()
# full_data_frame[].dropna().plot()


    

    
Out[22]:





<matplotlib.axes._subplots.AxesSubplot at 0x1174c29b0>






    

In [23]:

    

data_frame = full_data_frame[['deflator inflation - 1yr forecast','deflator inflation - 1yr actual','interest 1yr'   ]]
data_frame = data_frame.dropna(subset = ['deflator inflation - 1yr forecast','interest 1yr'])
# data_frame = data_frame.resample('A', how='mean',convention='start',kind='timestamp')

r = data_frame['interest 1yr'] - full_data_frame['deflator inflation - 1yr forecast']
pie = data_frame['deflator inflation - 1yr forecast']
pi = data_frame['deflator inflation - 1yr actual']
i = data_frame['interest 1yr']


    

In [24]:

    

# r.dropna().resample('A', how='mean').plot(lw=3,alpha =.65)
# i.dropna().resample('A', how='mean').plot(lw=3,alpha =.65)
pie.plot(lw=3,alpha =.65)
pi.plot(lw=3,alpha =.65)
plt.grid()


    

In [25]:

    

data_frame


    

    
Out[25]:






  

    

      

      
deflator inflation - 1yr forecast
      
deflator inflation - 1yr actual
      
interest 1yr
    
  
  

    

      
1970-01-01
      
3.409091
      
5.155929
      
7.553333
    
    

      
1970-04-01
      
2.985075
      
5.076232
      
7.453333
    
    

      
1970-07-01
      
3.703704
      
5.279720
      
6.936667
    
    

      
1970-10-01
      
3.541438
      
4.770943
      
5.646667
    
    

      
1971-01-01
      
3.530259
      
4.792658
      
4.050000
    
    

      
1971-04-01
      
3.416370
      
4.063575
      
4.993333
    
    

      
1971-07-01
      
3.135889
      
3.985387
      
5.750000
    
    

      
1971-10-01
      
3.419400
      
4.438406
      
4.726667
    
    

      
1972-01-01
      
3.539209
      
4.042329
      
4.406667
    
    

      
1972-04-01
      
3.385773
      
4.984888
      
4.843333
    
    

      
1972-07-01
      
3.612815
      
5.992494
      
5.153333
    
    

      
1972-10-01
      
3.699932
      
6.745250
      
5.436667
    
    

      
1973-01-01
      
3.760913
      
7.525038
      
6.310000
    
    

      
1973-04-01
      
3.772336
      
8.429295
      
7.016667
    
    

      
1973-07-01
      
4.099936
      
9.484350
      
8.506667
    
    

      
1973-10-01
      
5.365079
      
10.510766
      
7.413333
    
    

      
1974-01-01
      
5.652174
      
10.905335
      
7.353333
    
    

      
1974-04-01
      
5.612553
      
9.951147
      
8.690000
    
    

      
1974-07-01
      
7.023460
      
8.755634
      
9.010000
    
    

      
1974-10-01
      
7.681406
      
7.405675
      
7.673333
    
    

      
1975-01-01
      
7.241189
      
6.117447
      
6.306667
    
    

      
1975-04-01
      
5.591748
      
5.615120
      
6.526667
    
    

      
1975-07-01
      
5.945367
      
5.105656
      
7.520000
    
    

      
1975-10-01
      
6.208719
      
5.214848
      
6.680000
    
    

      
1976-01-01
      
6.183206
      
5.807902
      
5.976667
    
    

      
1976-04-01
      
5.900151
      
6.212846
      
6.280000
    
    

      
1976-07-01
      
6.054978
      
6.151809
      
6.013333
    
    

      
1976-10-01
      
5.728975
      
6.568091
      
5.226667
    
    

      
1977-01-01
      
5.643994
      
6.443727
      
5.420000
    
    

      
1977-04-01
      
6.267806
      
6.891306
      
5.693333
    
    

      
...
      
...
      
...
      
...
    
    

      
2008-10-01
      
1.941156
      
0.354656
      
0.993333
    
    

      
2009-01-01
      
1.252960
      
0.459715
      
0.566667
    
    

      
2009-04-01
      
1.469472
      
1.074128
      
0.520000
    
    

      
2009-07-01
      
1.457615
      
1.557979
      
0.446667
    
    

      
2009-10-01
      
1.481566
      
1.776997
      
0.350000
    
    

      
2010-01-01
      
1.531630
      
1.867253
      
0.366667
    
    

      
2010-04-01
      
1.655804
      
2.156129
      
0.380000
    
    

      
2010-07-01
      
1.502599
      
2.306047
      
0.270000
    
    

      
2010-10-01
      
1.522063
      
1.930377
      
0.256667
    
    

      
2011-01-01
      
1.503271
      
2.018574
      
0.273333
    
    

      
2011-04-01
      
1.772938
      
1.743177
      
0.206667
    
    

      
2011-07-01
      
1.887376
      
1.676818
      
0.133333
    
    

      
2011-10-01
      
1.930209
      
1.941934
      
0.113333
    
    

      
2012-01-01
      
1.748252
      
1.815902
      
0.156667
    
    

      
2012-04-01
      
1.937535
      
1.600869
      
0.186667
    
    

      
2012-07-01
      
1.858800
      
1.535429
      
0.183333
    
    

      
2012-10-01
      
1.993264
      
1.561335
      
0.173333
    
    

      
2013-01-01
      
1.926356
      
1.547531
      
0.153333
    
    

      
2013-04-01
      
1.868340
      
1.859824
      
0.126667
    
    

      
2013-07-01
      
1.813135
      
1.788515
      
0.123333
    
    

      
2013-10-01
      
1.806657
      
1.373746
      
0.123333
    
    

      
2014-01-01
      
1.839171
      
1.009175
      
0.123333
    
    

      
2014-04-01
      
1.875095
      
0.983371
      
0.103333
    
    

      
2014-07-01
      
1.966019
      
0.903305
      
0.110000
    
    

      
2014-10-01
      
1.865361
      
1.117662
      
0.146667
    
    

      
2015-01-01
      
1.770680
      
NaN
      
0.223333
    
    

      
2015-04-01
      
1.814405
      
NaN
      
0.250000
    
    

      
2015-07-01
      
1.765613
      
NaN
      
0.350000
    
    

      
2015-10-01
      
1.852575
      
NaN
      
0.463333
    
    

      
2016-01-01
      
1.808494
      
NaN
      
0.576667
    
  

185 rows × 3 columns