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econ_helper_functions_v6c.py
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econ_helper_functions_v6c.py
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import smtplib # for sending automatic email
from email.mime.text import MIMEText # for sending automatic email
from email.mime.multipart import MIMEMultipart # for sending automatic email
from email.mime.application import MIMEApplication # for sending automatic email
import pandas as pd
import pandas_datareader as pdr
import datetime
import os
import matplotlib
# Global variables:
cwd = os.getcwd()
today_str = datetime.datetime.today().strftime('%Y-%m-%d')
today_dtime = datetime.datetime.today()
now = datetime.datetime.now()
dtime_string = now.strftime("%Y-%m-%d-%H-%M-%S")
dir_to_send = cwd + str('\\_TO_SEND_\\') + dtime_string + str('\\')
os.mkdir(dir_to_send)
def send_mail_gmail(username, password, toaddrs_list,
msg_text, fromaddr=None, subject="Test mail",
attachment_path_list=None):
s = smtplib.SMTP('smtp.gmail.com:587')
s.starttls()
s.login(username, password)
msg = MIMEMultipart()
sender = fromaddr
recipients = toaddrs_list
msg['Subject'] = subject
if fromaddr is not None:
msg['From'] = sender
msg['To'] = ", ".join(recipients)
if attachment_path_list is not None:
os.chdir(attachment_path_list)
files = os.listdir()
for f in files: # add files to the message
try:
file_path = os.path.join(attachment_path_list, f)
attachment = MIMEApplication(open(file_path, "rb").read(), _subtype="txt")
attachment.add_header('Content-Disposition', 'attachment', filename=f)
msg.attach(attachment)
except:
print("could not attach file")
msg.attach(MIMEText(msg_text, 'html'))
s.sendmail(sender, recipients, msg.as_string())
def get_usa_inflation_objects():
df = []
plot_figure = []
most_recent_str = []
# Load the FRED series we care about:
start_date = datetime.datetime(1913, 1, 1) # 1913
end_date = today_dtime
df_fred = pdr.DataReader(['CPIAUCNS', 'CPIAUCSL', 'CPILFENS', 'CPILFESL'], \
'fred', start_date, end_date)
# Tickers:
# CPIAUCNS: all items, NSA
# CPIAUCSL: all items, SA
# CPILFENS: all items less food+energy, NSA
# CPILFESL: all items less food+energy, SA
# Outputs:
# (1a) df_yoy = year over year inflation
# (1b) df_mom = month over month inflation, not annualized
# (1c) df_mom_ann = month over month inflation, annualized by multiplying all "df_mom" by 12.0
# (2) df_ratios = the "what was 1970 dollars" ratio dataframe
df_yoy = (df_fred - df_fred.shift(12)) / df_fred
df_mom = (df_fred - df_fred.shift(1)) / df_fred
df_mom_ann = df_mom * 12
# Create plots to be included as email attachments:
# Semicolon suppresses output to console and greatly speeds this up; all I care about
# is saving as a ".png" to be emailed:
plotA = df_yoy.plot(y=['CPIAUCNS', 'CPILFENS'], color=['red', 'green']);
plotA.set_ylabel("Inflation YoY");
figA = plotA.get_figure();
figA_str = dir_to_send + str('CPI_1919_') + str(dtime_string) + str('.png')
figA.savefig(figA_str);
matplotlib.pyplot.close(figA);
plotB1 = df_yoy.iloc[-840:-1, :].plot(y=['CPIAUCNS', 'CPILFENS'], color=['red', 'green']);
plotB1.set_ylabel("Inflation YoY - Previous 70 Years");
figB1 = plotB1.get_figure();
figB1_str = dir_to_send + str('CPI_70Y_') + str(dtime_string) + str('.png')
figB1.savefig(figB1_str);
matplotlib.pyplot.close(figB1);
plotB2 = df_yoy.iloc[-120:-1, :].plot(y=['CPIAUCNS', 'CPILFENS'], color=['red', 'green']);
plotB2.set_ylabel("Inflation YoY - Previous 10 Years");
figB2 = plotB2.get_figure();
figB2_str = dir_to_send + str('CPI_10Y_') + str(dtime_string) + str('.png')
figB2.savefig(figB2_str);
matplotlib.pyplot.close(figB2);
return df_fred, plot_figure, most_recent_str, \
df_yoy, df_mom, df_mom_ann, \
dir_to_send
def get_uk_inflation_objects():
df = []
plot_figure = []
most_recent_str = []
# Load the FRED series we care about:
start_date = datetime.datetime(1952, 1, 1) # 1952
end_date = today_dtime
df_fred = pdr.DataReader(['GBRCPIALLMINMEI'], \
'fred', start_date, end_date)
# Outputs:
# (1) df_yoy = year over year inflation
# (2) df_ratios = the "what was 1970 dollars" ratio dataframe
return df, plot_figure, most_recent_str
def get_gdp_objects():
df = []
plot_figure = []
most_recent_str = []
# Load the FRED series we care about:
start_date = datetime.datetime(1952, 1, 1) # 1952
end_date = today_dtime
df_gdp_quarterly = pdr.DataReader(['GDP', 'CHNGDPNQDSMEI', \
'JPNNGDP', 'CPMNACNSAB1GQDE', 'UKNGDP'],
'fred', start_date, end_date)
# Actual Outputs:
# (1) USA GDP - BILLIONS OF USD (Q3 2021 RELEASED DEC 22, 2021)
# (2) CHINA GDP - YUAN (Not Billions or Millions) - (Q3 2021 RELEASED DEC 14, 2021)
# (3A) JAPAN GDP - BILLIONS OF YEN (Q3 2021 RELEASED DEC 7, 2021)
# (3B) GERMANY GDP - *MILLIONS* OF EUROS (Q3 2021 RELEASED DEC 7, 2021)
# (3C) UK GDP - *MILLIONS* OF POUNDS (Q3 2021 RELEASED DEC 22, 2021)
df_trillions = df_gdp_quarterly.copy()
df_trillions.iloc[:, 0] = df_trillions.iloc[:, 0] / 1000
df_trillions.iloc[:, 1] = df_trillions.iloc[:, 1] / 1000000000000
df_trillions.iloc[:, 2] = df_trillions.iloc[:, 2] / 1000
df_trillions.iloc[:, 3] = df_trillions.iloc[:, 3] / 1000000
df_trillions.iloc[:, 4] = df_trillions.iloc[:, 4] / 1000000
# df_gdp_trillions = [df_gdp_quarterly.loc[:, 'GDP'] / 1000 , \
# df_gdp_quarterly.loc[:, 'CHNGDPNQDSMEI'] / 1000000000000, \
# df_gdp_quarterly.loc[:, 'JPNNGDP'] / 1000 , \
# df_gdp_quarterly.loc[:, 'CPMNACNSAB1GQDE'] / 1000000,
# df_gdp_quarterly.loc[:, 'UKNGDP'] / 1000000 ]
# Desired Outputs:
# (1) USA GDP
# (2) CHINA GDP
# (3) JAPAN, GERMANY, UK, INDIA(?)
# (4A) WORLD GDP
# (4B) DEVELOPED EX-US GDP
# (4C) EMERGING EX-CHINA GDP
# Equity Market Cap ???
df_fred = pdr.DataReader(['NCBEILQ027S', 'FBCELLQ027S'], \
'fred', start_date, end_date)
df_usa_mkt_cap_to_gdp = []
# Standardize units:
us_equities_billions = (df_fred.loc[:, 'NCBEILQ027S'] + df_fred.loc[:, 'FBCELLQ027S']) / 1000
return df_gdp_quarterly, plot_figure, most_recent_str, \
df_usa_mkt_cap_to_gdp, df_trillions
def get_usa_unemployment_objects():
df = []
plot_figure = []
most_recent_str = []
# Load the FRED series we care about:
start_date = datetime.datetime(1952, 1, 1) # 1952
end_date = today_dtime
df_fred = pdr.DataReader(['UNRATE', 'U6RATE'], \
'fred', start_date, end_date)
plotB1 = df_fred.plot(y=['UNRATE', 'U6RATE'], color=['red', 'green']);
plotB1.set_ylabel("USA Unemployment");
figB1 = plotB1.get_figure();
figB1_str = dir_to_send + str('USA_Unemployment') + str(dtime_string) + str('.png')
figB1.savefig(figB1_str);
matplotlib.pyplot.close(figB1);
plotB2 = df_fred.iloc[-240:-1, :].plot(y=['UNRATE', 'U6RATE'], color=['red', 'green']);
plotB2.set_ylabel("USA Unemployment - Previous 20 Years");
figB2 = plotB2.get_figure();
figB2_str = dir_to_send + str('USA_Unemployment_Prev20') + str(dtime_string) + str('.png')
figB2.savefig(figB2_str);
matplotlib.pyplot.close(figB2);
return df_fred, plot_figure, most_recent_str
def get_equity_supply_objects():
df = []
plot_figure = []
most_recent_str = []
# Load the FRED series we care about:
start_date = datetime.datetime(1952, 1, 1) # 1952
end_date = today_dtime
df_fred = pdr.DataReader(['NCBEILQ027S', 'FBCELLQ027S', \
'TCMILBSNNCB', 'WCMITCMFODNS', \
'SLGTCMDODNS', 'TCMILBSHNO', 'FGTCMDODNS'], \
'fred', start_date, end_date)
# Standardize units:
us_equities_billions = (df_fred.loc[:, 'NCBEILQ027S'] + df_fred.loc[:, 'FBCELLQ027S']) / 1000
us_debt_billions = df_fred.loc[:, 'TCMILBSNNCB'] + \
df_fred.loc[:, 'WCMITCMFODNS'] + \
df_fred.loc[:, 'SLGTCMDODNS'] + \
df_fred.loc[:, 'TCMILBSHNO'] + \
df_fred.loc[:, 'FGTCMDODNS']
us_equity_allocation = us_equities_billions / (us_equities_billions + us_debt_billions)
# Save DFs as CSV:
now = datetime.datetime.now()
dtime_string = now.strftime("%Y-%m-%d-%H-%M-%S")
csv_dir = cwd + str('\\CSVs\\df_fred_') + dtime_string + str('.csv')
df_fred.to_csv(csv_dir)
# df = df_fred
df = us_equity_allocation
df_eq_sup = us_equity_allocation
plot_figure = []
most_recent_str = str(df_fred.index[-1])
plotC1 = df_eq_sup.plot(y=['Equity Allocation'], color=['red']);
plotC1.set_ylabel("Equity Alloc");
figC1 = plotC1.get_figure();
figC1_str = dir_to_send + str('Equity_Alloc_1955_') + str(dtime_string) + str('.png')
figC1.savefig(figC1_str);
matplotlib.pyplot.close(figC1);
plotC2 = df_eq_sup.iloc[-40:-1].plot(y=['Equity Allocation'], color=['red']);
plotC2.set_ylabel("Equity Alloc - Previous 10 Years");
figC2 = plotC2.get_figure();
figC2_str = dir_to_send + str('Equity_Alloc_10Y_') + str(dtime_string) + str('.png')
figC2.savefig(figC2_str);
matplotlib.pyplot.close(figC2);
return df, plot_figure, most_recent_str, \
us_equities_billions, us_debt_billions
def get_monetary_aggregates_objects():
df = []
plot_figure = []
most_recent_str = []
# Load the FRED series we care about:
start_date = datetime.datetime(1952, 1, 1) # 1952
end_date = today_dtime
df_fred = pdr.DataReader(['GBRCPIALLMINMEI'], \
'fred', start_date, end_date)
# Outputs:
# (1) df_yoy = year over year inflation
# (2) df_ratios = the "what was 1970 dollars" ratio dataframe
return df, plot_figure, most_recent_str
class EconObject:
"""A simple object for keeping track of FRED data"""
#
# above docstring can be called via "__doc__"
#
# Has these properties, possibly NULL:
# (1) Most recent data point (String)
# (2) Dataframe
# (3) Plot/figure
# (4) HTML
#
# Any methods?
# (1) Populate most recent date
# (2) retrieve data - will be specific for particular
# class - CPI we'll get from FRED; ETFs we'll get from
# Yahoo, VIX term structure we'll get from elsewhere.
# (3) Create plot
#
#####################################
def __init__(self, df, plot_figure, most_recent_str):
# self.df = pd.Dataframe()
self.df = df
self.plot_figure = plot_figure
self.most_recent_str = most_recent_str