def main():
log('Starting')
# Need to pre-pend 'D@' to show the provider.
ser = fetch('D@Eurostat/namq_10_gdp/Q.CP_MEUR.SCA.B1GQ.EL',
database='TEXT')
# Load the same series multiple times; see the log to see that it is only fetched at most once from
# the provider
log('Load loop')
for i in range(0, 10):
fetch('D@Eurostat/namq_10_gdp/Q.CP_MEUR.SCA.B1GQ.EL', database='TEXT')
quick_plot(ser)
"""
Plot the 10-year US Treasury/Euro area AAA govvie spread in 4 -- count'em, 4 -- lines of code.
(Couldn't find a daily bund series...)
"""
from econ_platform.start import fetch, quick_plot
ust10 = fetch('F@DGS10')
euro_AAA_10 = fetch('D@Eurostat/irt_h_euryld_d/D.PAR.Y10.EA')
quick_plot(100 * (ust10 - euro_AAA_10),
title='U.S. 10Y Spread Over Euro Govvie')
"""
Plot the 10-year US Treasury/Euro area AAA govvie spread in 4 -- count'em, 4 -- lines of code.
(Couldn't find a daily bund series...)
"""
from econ_platform.start import fetch, quick_plot
ust10 = fetch('F@DGS10')
euro_AAA_10 = fetch('D@Eurostat/irt_euryld_d/D.EA.PYC_RT.Y10.CGB_EA_AAA')
quick_plot(ust10 - euro_AAA_10,
title='U.S. 10Y Spread Over AAA-Rated Euro Govvie')
annualised = pow(factor, 12. / float(N_months))
annualised = 100. * (annualised - 1.)
group_df[group] = annualised
group_df.dropna(axis=0, inplace=True)
if return_type == 'MIN':
out = group_df.min(axis=1)
series_meta.series_name = f'Minimum {N_months}-annualised inflation for major groups in U.S.'
else:
out = group_df.max(axis=1)
series_meta.series_name = f'Maximum {N_months}-annualised inflation for major groups in U.S.'
series_meta.series_description = 'Calculated series'
return out, series_meta
# Push the handler into the UserProvider
user_provider = Providers.UserProvider
user_provider.FunctionMapper['US_INF_DISPERSION'] = inflation_fn
# End of code that should be in a library.
#--------------------------------------------------------------------
# Now we can fetch it.
# Note that since this calculated series uses the US CPI index level, the system
# automatically also creates the CPI series on the database when this is called.
# Call reset_update_time() to force an update of the series while the code is under development.
# Note that this does not affect the series fetched from the external provider.
reset_update_time('U@US_INF_DISPERSION(24, MAX)')
inf = fetch('U@US_INF_DISPERSION(24, MAX)')
quick_plot(inf)
"""
hello_world.py
Minimal econ_platform example.
Note: Will not run "out of the box"; need to install dependencies, mainly Pandas, DB.nomics, and matplotlib...
"""
# Step 0: Overhead; need to import the package, and initialise it.
# (Using econ_platform.start forces the initialisation step.)
from econ_platform.start import fetch, quick_plot
# Step 1: Fetch input data - Iceland HICP Inflation (Monthly)
level = fetch('D@Eurostat/prc_hicp_midx/M.I05.CP00.IS')
# Step 2: Highly complex quantitative stuff!
inflation_rate = 100. * level.pct_change(12)
# Step 3: Output.
quick_plot(inflation_rate, title='Iceland HICP Inflation')
:return:
"""
if not str(series_meta.ticker_query) in ('GREEK_DRACHMA_OVERNIGHT_AVG',
'GREEK_DRACHMA_OVERNIGHT_EOM'):
raise ValueError('Wrong series!')
full_ticker = tickers.TickerFull(series_meta.ticker_full)
# I will assume that the file is downloaded to the current directory
sheet = pandas.read_excel('drachma_overnight_rate.xls', header=2)
if str(series_meta.ticker_query) == 'GREEK_DRACHMA_OVERNIGHT_AVG':
ser = pandas.Series(sheet['monthly averages'])
ser.index = sheet['Unnamed: 0']
series_meta.series_name = 'Average pre-Euro Greek Drachma Bank Overnight Rate'
series_meta.series_description = 'Monthly average bank overnight rate from Bank of Greece'
# quick_plot(ser)
else:
print(sheet)
raise NotImplementedError('Bam!')
ser.name = str(full_ticker)
return ser
# Push the handler into the UserProvider
user_provider = Providers.UserProvider
user_provider.SeriesMapper['GREEK_DRACHMA_OVERNIGHT_AVG'] = greek_overnight
user_provider.SeriesMapper['GREEK_DRACHMA_OVERNIGHT_EOM'] = greek_overnight
# End of code that should be in a library.
#--------------------------------------------------------------------
# Now we can fetch it.
ser = fetch('U@GREEK_DRACHMA_OVERNIGHT_AVG')
quick_plot(ser)