def performInitialSetup(DBFilePath=None, forceDBCreation=False, logFilePath=None, recordLog=False, quietShell=False):
# Establish Global Variables
global db_cursor
global db_connection
# Initialize Log
if quietShell and not recordLog:
recordLevel=log.INFO
else:
recordLevel=None
EM_util.initializeLog(recordLog=recordLog, logFilePath=logFilePath, recordLevel=recordLevel)
log.info('Log Initialized.')
# Connect to FRED
log.info('Connecting to FRED.')
fred.key(EM_util.FREDAPIKey)
# Create Database
log.info('Connecting to Database: \n%s', DBFilePath)
if DBFilePath is None:
DBFilePath = EM_util.defaultDB
if not isfile(DBFilePath):
log.info('Database not found. Creating new database...')
EM_DBMake.doOneTimeDBCreation(force=forceDBCreation, DBFilePath=DBFilePath)
# Store Database Connection
db_connection = sq.connect(DBFilePath)
db_cursor = db_connection.cursor()
log.info('Database opened successfully')
def fred_series_mult_pd(symbol_list, api_key):
"""
Use fred api to retrieve time series data.
Args:
symbol_list: list of strings representing fred series symbols
api_key: developer API key from https://fred.stlouisfed.org/
Returns:
merged pandas dataframe with date ('dt') and numeric value (<symbol>) columns
Dependencies:
pandas, fred, functools.reduce
"""
# Use API key
fred.key(api_key)
# Define inner functions
def inner_join_pandas_list(pandas_list, join_on):
return reduce(lambda a, b: pd.merge(a, b, on=join_on), pandas_list)
# Query each series in symbol_list, append to df_list
df_list = []
for symbol in symbol_list:
# Import series, convert to dataframe, drop query timestamps, rename columns, format dates
series_df = pd.DataFrame(fred.observations(symbol)['observations']).\
drop(['realtime_start','realtime_end'], axis = 1).\
rename(columns = {'date' : 'dt', 'value' : symbol.lower()})
series_df['dt'] = [x.date() for x in pd.to_datetime(series_df['dt'])]
df_list.append(series_df)
# Merge list of dataframes
return inner_join_pandas_list(df_list, 'dt')
def get_series_notes(series_term: str) -> pd.DataFrame:
"""Get Series notes. [Source: FRED]
Parameters
----------
series_term : str
Search for this series term
Returns
----------
pd.DataFrame
DataFrame of matched series
"""
fred.key(cfg.API_FRED_KEY)
d_series = fred.search(series_term)
df_fred = pd.DataFrame()
if "error_message" in d_series:
if "api_key" in d_series["error_message"]:
console.print("[red]Invalid API Key[/red]\n")
else:
console.print(d_series["error_message"])
else:
if "seriess" in d_series:
if d_series["seriess"]:
df_fred = pd.DataFrame(d_series["seriess"])
df_fred["notes"] = df_fred["notes"].fillna(
"No description provided.")
else:
console.print("No matches found. \n")
else:
console.print("No matches found. \n")
return df_fred
def get_series_ids(series_term: str, num: int) -> Tuple[List[str], List[str]]:
"""Get Series IDs. [Source: FRED]
Parameters
----------
series_term : str
Search for this series term
num : int
Maximum number of series IDs to output
Returns
----------
List[str]
List of series IDs
List[str]
List of series Titles
"""
fred.key(cfg.API_FRED_KEY)
d_series = fred.search(series_term)
if "seriess" not in d_series:
return [], []
if not d_series["seriess"]:
return [], []
df_series = pd.DataFrame(d_series["seriess"])
df_series = df_series.sort_values(by=["popularity"],
ascending=False).head(num)
return df_series["id"].values, df_series["title"].values
def __init__(self, obsStart, obsEnd, *args):
self.tags = []
self.obsStart = obsStart
self.obsEnd = obsEnd
fred.key('4cfe54d7bef3609a5f3eae6e5d87f790')
for i in range(0, len(args)):
self.tags.append(args[i])
def get_hour_value(self):
fred.key(settings.FRED_KEY)
last_observation = fred.observations(
settings.FRED_SERIES)['observations'][-1]
h = last_observation['value']
try:
return float(h)
except:
return settings.DEFAULT_HOUR_VALUE_IN_USD
def get_hour_value(self):
fred.key(settings.FRED_KEY)
last_observation = fred.observations(
settings.FRED_SERIES)['observations'][-1]
h = last_observation['value']
try:
return float(h)
except:
return settings.DEFAULT_HOUR_VALUE_IN_USD
def test_fred_series_vintage_dates(self):
fred.key('123abc')
fred.vintage('AAA', sort='desc')
expected = 'https://api.stlouisfed.org/fred/series/vintagedates'
params = {
'api_key': '123abc',
'series_id': 'AAA',
'sort_order': 'desc',
'file_type': 'json'}
self.get.assert_called_with(expected, params=params)
def test1():
fred.key('f412b494756f8cbd24c8310e01d14630')
pp = pprint.PrettyPrinter(indent=2)
##########data for real PCE vs average hourly earnings ##########
##get_fred returns a list of dictionaries
pce_list = get_fred("PCEC96")
pp.pprint(pce_list)
def test_fred_specific_release(self):
fred.key('my_key')
fred.release('123')
expected = 'http://api.stlouisfed.org/fred/release'
params = {
'api_key': 'my_key',
'release_id': '123',
'file_type': 'json'
}
self.get.assert_called_with(expected, params=params)
def test_fred_category_children(self):
fred.key('abc123')
fred.children()
expected = 'http://api.stlouisfed.org/fred/category/children'
params = {
'api_key': 'abc123',
'category_id': None,
'file_type': 'json'
}
self.get.assert_called_with(expected, params=params)
def test_fred_releases_dates_with_start_and_end_keywords(self):
fred.key('github')
fred.dates(start='2012-01-01', end='2012-03-16')
expected = 'http://api.stlouisfed.org/fred/releases/dates'
params = {
'api_key': 'github',
'realtime_start': '2012-01-01',
'realtime_end': '2012-03-16'
}
self.get.assert_called_with(expected, params=params)
def test_fred_series_search(self):
fred.key('123')
fred.search('money stock')
expected = 'http://api.stlouisfed.org/fred/series/search'
params = {
'api_key': '123',
'search_text': 'money stock',
'file_type': 'json'
}
self.get.assert_called_with(expected, params=params)
def test_fred_category_series_function(self):
fred.key('my_fred_key')
fred.category_series(123)
expected = 'http://api.stlouisfed.org/fred/category/series'
params = {
'api_key': 'my_fred_key',
'category_id': 123,
'file_type': 'json'
}
self.get.assert_called_with(expected, params=params)
def thirdAPI(): #main loop
fred.key('3b7e7d31bcc6d28556c82c290eb3572e')
obs, more = {}, True
if raw_input('Feeling lucky? (Y/N): ').upper() == 'Y':
obs, more = lucky(), False
while (more):
obs.update(getObs(searchTitle()))
if raw_input('Search again(Y/N) ').upper() == 'N':
break
if obs != {}: printCSV(obs)
else: print 'No data recorded -> good bye :)'
def set_hour_rate(self, h=None):
if h:
self.hour = h
print("Hour value of work (self.hour) was set to %s usd from FRED API. Retrieving currency rates.." % self.hour)
self.set_currenc_rates()
print("Done.")
else:
fred.key('0c9a5ec8dd8c63ab8cbec6514a8f5b37')
last_observation = fred.observations(
settings.FRED_SERIES)['observations'][-1]
h = last_observation['value']
try:
self.hour = float(h)
print("Hour value of work (self.hour) was set to %s usd from FRED API." % self.hour)
except:
self.hour = 25.
print("Failed to retrieve rates from FRED API. Assuming 1h = 25usd.")
def set_hour_rate(self, h=None):
if h:
self.hour = h
print "Hour value of work (self.hour) was set to %s usd from FRED API. Retrieving currency rates.." % self.hour
self.set_currenc_rates()
print "Done."
else:
fred.key('0c9a5ec8dd8c63ab8cbec6514a8f5b37')
last_observation = fred.observations(
settings.FRED_SERIES)['observations'][-1]
h = last_observation['value']
try:
self.hour = float(h)
print "Hour value of work (self.hour) was set to %s usd from FRED API." % self.hour
except:
self.hour = 25.
print "Failed to retrieve rates from FRED API. Assuming 1h = 25usd."
def get_series_notes(series_term: str) -> pd.DataFrame:
"""Get Series notes. [Source: FRED]
Parameters
----------
series_term : str
Search for this series term
Returns
----------
pd.DataFrame
DataFrame of matched series
"""
fred.key(cfg.API_FRED_KEY)
d_series = fred.search(series_term)
if "seriess" not in d_series:
return pd.DataFrame()
if not d_series["seriess"]:
return pd.DataFrame()
df_fred = pd.DataFrame(d_series["seriess"])
df_fred["notes"] = df_fred["notes"].fillna("No description provided.")
return df_fred
def set_hour_rate(self, h=None):
if h:
self.hour = h
print("Hour value of work (self.hour) was set to %s usd from FRED API. Retrieving currency rates.." % self.hour)
self.set_currenc_rates()
print("Done.")
else:
if not settings.FRED_KEY:
print("Set settings FRED_KEY. Get one at https://fred.stlouisfed.org")
fred.key(settings.FRED_KEY)
last_observation = fred.observations(settings.FRED_SERIES)['observations'][-1]
h = last_observation['value']
try:
self.hour = float(h)
print("Hour value of work (self.hour) was set to %s usd from FRED API." % self.hour)
except:
self.hour = 28.18
print("Failed to retrieve rates from FRED API. Assuming 1h = 28.18 usd.")
def get_series_ids(series_term: str, num: int) -> Tuple[List[str], List[str]]:
"""Get Series IDs. [Source: FRED]
Parameters
----------
series_term : str
Search for this series term
num : int
Maximum number of series IDs to output
Returns
----------
List[str]
List of series IDs
List[str]
List of series Titles
"""
fred.key(cfg.API_FRED_KEY)
d_series = fred.search(series_term)
# Cover invalid api and empty search terms
if "error_message" in d_series:
if "api_key" in d_series["error_message"]:
console.print("[red]Invalid API Key[/red]\n")
else:
console.print(d_series["error_message"])
return [], []
if "seriess" not in d_series:
return [], []
if not d_series["seriess"]:
return [], []
df_series = pd.DataFrame(d_series["seriess"])
df_series = df_series.sort_values(by=["popularity"],
ascending=False).head(num)
return df_series["id"].values, df_series["title"].values
def get_series_notes(series_term: str, num: int) -> str:
"""Get Series notes. [Source: FRED]
Parameters
----------
series_term : str
Search for this series term
num : int
Maximum number of series notes to display
Returns
----------
notes : str
Series notes output
"""
fred.key(cfg.API_FRED_KEY)
d_series = fred.search(series_term)
if "seriess" not in d_series:
return "No Series found using this term!\n"
df_fred = pd.DataFrame(d_series["seriess"])
if df_fred.empty:
return "No Series found using this term!\n"
df_fred = df_fred.sort_values(by=["popularity"], ascending=False).head(num)
notes = ""
for _, series in df_fred.iterrows():
if series["notes"]:
notes += series["id"] + "\n"
notes += "-" * len(series["id"]) + "\n"
notes += series["notes"] + "\n\n"
if not notes:
return "Series notes not found!\n"
return notes
def test_fred_releases_dates(self):
fred.key('123')
fred.dates()
expected = 'https://api.stlouisfed.org/fred/releases/dates'
params = {'api_key': '123', 'file_type': 'json'}
self.get.assert_called_with(expected, params=params)
import fred
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from datetime import date,datetime
FRED_API_KEY="055ba538c874e5974ee22d786f27fdda"
FRED_SERIES=["INDPRO", "PAYEMS", "HOUST", "RRSFS", "NAPM"]
FRED_START=date(1990,1,1)
#set to false to output graphs to a file, uses current working directory. All numerical analysis is printed to the console
DISPLAY=False
fred.key(FRED_API_KEY)
def get_fred_series(series):
def filter(o):
return {'date': datetime.strptime(o['date'],'%Y-%m-%d').date(),
series: o['value']}
return pd.DataFrame(map(filter,fred.observations(series)['observations']),dtype='float64').set_index('date').dropna()
fred_data=get_fred_series(FRED_SERIES[0]) # Build an initial DataFrame
for s in FRED_SERIES[1:]:
fred_data=fred_data.join(get_fred_series(s))
def test_fred_specific_release(self):
fred.key('my_key')
fred.release('123')
expected = 'https://api.stlouisfed.org/fred/release'
params = {'api_key': 'my_key', 'release_id': '123', 'file_type': 'json'}
self.get.assert_called_with(expected, params=params)
def test_fred_series_observations(self):
fred.key('ohai')
fred.observations("AAA")
expected = 'http://api.stlouisfed.org/fred/series/observations'
params = {'api_key': 'ohai', 'series_id': 'AAA', 'file_type': 'json'}
self.get.assert_called_with(expected, params=params)
def test_fred_series_updates(self):
fred.key('ALL THE FRED API!')
fred.updates()
expected = 'http://api.stlouisfed.org/fred/series/updates'
params = {'api_key': 'ALL THE FRED API!', 'file_type': 'json'}
self.get.assert_called_with(expected, params=params)
def __init__(self):
'''
Constructor
'''
fred.key('e732114c863993dcb376dac62f9e5473')
def test_fred_releases(self):
fred.key('123')
fred.releases()
expected = 'http://api.stlouisfed.org/fred/releases'
params = {'api_key': '123'}
self.get.assert_called_with(expected, params=params)
def main_routine():
fred.key('f412b494756f8cbd24c8310e01d14630')
##########data for real PCE vs average hourly earnings ##########
##get_fred returns a list of dictionaries
pce_list = get_fred("PCEC96")
avghr_list = get_fred("AHETPI")
pcedef_list = get_fred("PCEPI")
##get the index for the last date in the series
##pce_list[-1] is the final dictionary in the series
end_pce = pce_list[-1]
##retrieve the date value of the dictionary
enddate_pce = end_pce['date']
start_date_pce = "1999-01-01"
pce_dates = make_index(start_date_pce,enddate_pce)
##find the position of the 1995-1-1 entry (entries prior to 1995-1-1 are zero)
start_index = (i for i in range(len(pce_list)) if (pce_list[i])['date'] == start_date_pce).next()
##create a dictionary of PCE date:values
##Real PCE values start as of 1995-1-1, as per start_index
pce_d = make_dict(pce_list[start_index:])
##calculate the trailing 3 month averages, start from index 2 (need 3 values to make a 3 month average)
pce_3mo_d = dict((date,make_avg(date,pce_d)) for date in pce_dates[2:])
##calculate the yoy change
pce_yoy_d = make_yoy_dict(pce_dates[14:],pce_3mo_d)
########## avg hourly earnings #########
##process avg hourly earnings
start_avghr, end_avghr = avghr_list[0],avghr_list[-1]
start_date_avghr = start_avghr['date']
enddate_avghr = end_avghr['date']
avghr_dates = make_index(start_date_avghr,enddate_avghr)
##the pce deflator series is used in the govt rate vs. pce deflator. I'm more certain that the
##fedfunds rate will already be available
fedfund_list = get_fred("FEDFUNDS")
discrt_list = get_fred("MDISCRT")
pcedef_govrt_end = (fedfund_list[-1])['date']
##these are the dates to be used for the plotting
##start from the max of the 14th entry of the pce deflator series
##or the discrt start. We need at least 14 data points to
##to generate the 3 month average and the YoY calculation
pcedef_start = datetime.datetime.strptime((pcedef_list[14])['date'],"%Y-%m-%d")
discrt_start = datetime.datetime.strptime((discrt_list[0])['date'],"%Y-%m-%d")
pcedef_govrt_start = max(pcedef_start,discrt_start).strftime("%Y-%m-%d")
##generate the dates for the pce deflator series
pcedef_govrt_dates = make_index(pcedef_govrt_start,pcedef_govrt_end)
##create a dictionary for avg hr date:value
avghr_d = make_dict(avghr_list)
##create a dictionary for pce deflator date:value
pcedef_d = make_dict(pcedef_list)
##if current month for pce deflator hasn't be reported, calculate an estimated value
if pcedef_govrt_end not in pcedef_d or avghr_dates[-1] not in pcedef_d:
prior1 = (pcedef_list[-1])['date']
prior2 = add_months(datetime.datetime.strptime((pcedef_list[-1])['date'],"%Y-%m-%d"),-1).strftime("%Y-%m-%d")
nextMonth = add_months(datetime.datetime.strptime((pcedef_list[-1])['date'],"%Y-%m-%d"),1).strftime("%Y-%m-%d")
pcedef_d[nextMonth] = 2*pcedef_d[prior1] - pcedef_d[prior2]
##create the deflated average hourly earnings
def_avghr_d = dict((key,100*avghr_d[key]/pcedef_d[key]) for key in avghr_d)
##create the trailing 3 months
avghr_3mo_d = dict((date,make_avg(date,def_avghr_d)) for date in avghr_dates[2:])
##calculate avg hourly earnings yoy change
avghr_yoy_d = make_yoy_dict(avghr_dates[14:],avghr_3mo_d)
##########data for real PCE vs dicount rate/fed funds rates ##########
##get the federal funds rate data
##switch from discount rate to federal funds rate as of 1-1-2000
switch_date = datetime.datetime(2000,01,01)
govrt_d = dict((each['date'],float(each['value'])) for each in fedfund_list if datetime.datetime.strptime(each['date'],"%Y-%m-%d")>= switch_date )
for obs in discrt_list:
if datetime.datetime.strptime(obs['date'],"%Y-%m-%d") < switch_date:
govrt_d[obs['date']] = float(obs['value'])
##create an index of dates. Starts from index 12 since the calculation looks back 12 months
govrt_yoy_dates = make_index((discrt_list[0])['date'],(fedfund_list[-1])['date'])[12:]
##make dictionary of the YoY change
govrt_yoy_d = dict((date,govrt_d[date] - govrt_d[add_months(datetime.datetime.strptime(date,"%Y-%m-%d"),-12).strftime("%Y-%m-%d")]) for date in govrt_yoy_dates)
##########data for the effect of inflation on real average hourly wages #########
##calculate the pce deflator trailing 3 month average
if datetime.datetime.strptime(pcedef_govrt_dates[-1],"%Y-%m-%d") > datetime.datetime.strptime((pcedef_list[-1])['date'],"%Y-%m-%d"):
suffix = [pcedef_govrt_dates[-1]]
elif datetime.datetime.strptime(avghr_dates[-1],"%Y-%m-%d") > datetime.datetime.strptime((pcedef_list[-1])['date'],"%Y-%m-%d"):
suffix = [avghr_dates[-1]]
else:
suffix = []
pcedef_dates = [obs['date'] for obs in pcedef_list] + suffix
pcedef_3mo_d = dict((date,make_avg(date,pcedef_d)) for date in pcedef_dates[2:])
##calculate the pce deflator YoY
pcedef_yoy_d = make_yoy_dict(pcedef_dates[14:],pcedef_3mo_d)
##create the trailing 3 months (nominal)
navghr_3mo_d = dict((date,make_avg(date,avghr_d)) for date in avghr_dates[2:])
##calculate avg hourly earnings (nominal) yoy change
navghr_yoy_d = make_yoy_dict(avghr_dates[14:],navghr_3mo_d)
###########data for discount rate/fed funds rate vs pce deflator ##########
###########certain date calculations on line 401 ##########
###########all calculations done above as other series use same data ###########
##########data for the unemployment rate ##########
unrate_list = get_fred("UNRATE")
##make dictionary from unemployment rate observations
unrate_d = make_dict(unrate_list)
##########data for the employment ##########
emply_list = get_fred("CE16OV")
##get employment dates
emply_dates = [obs['date'] for obs in emply_list]
##make dictionary from unemployment rate observations
emply_d = make_dict(emply_list)
##calculate the 3 mo average
emply_3mo_d = dict((date,make_avg(date,emply_d)) for date in emply_dates[2:])
##calculate the YoY value
emply_yoy_d = make_yoy_dict(emply_dates[14:], emply_3mo_d)
##########data for domestic debt and 10 year treasury #########
dmdebt_list = get_fred("TCMDODNS")
trsy_10yr_list = get_fred("GS10")
dmdebt_start = '1952-01-01'
start_index = (dmdebt_list.index(obs) for obs in dmdebt_list if obs['date'] == dmdebt_start).next()
dmdebt_dates = make_index(dmdebt_start,(dmdebt_list[-1])['date'],3)
##make dictionary
dmdebt_d = dict((obs['date'],float(obs['value'])) for obs in dmdebt_list[start_index:])
##calculate YoY change
dmdebt_yoy_d = dict((date,100*(dmdebt_d[date]/ \
dmdebt_d[add_months(datetime.datetime.strptime(date,"%Y-%m-%d"),-12).strftime("%Y-%m-%d")]-1)) for date in dmdebt_dates[4:])
##make dictionary
trsy_10yr_d = dict((obs['date'],float(obs['value'])) for obs in trsy_10yr_list)
########## date for domestic debt and prime rate##########
prime_list = get_fred("MPRIME")
##make dictionary
prime_d = dict((obs['date'],float(obs['value'])) for obs in prime_list)
write_pce_avghr(pce_yoy_d,avghr_yoy_d,pce_dates)
write_pce_govrt(pce_yoy_d,govrt_yoy_d,pce_dates)
write_avghr(avghr_yoy_d,navghr_yoy_d,pcedef_yoy_d,avghr_dates)
write_dscrt(pcedef_yoy_d,govrt_d,pcedef_govrt_dates)
write_unemployment_rate(pce_yoy_d,unrate_d,pce_dates)
write_employment(pce_yoy_d,emply_yoy_d,pce_dates)
write_domestic_debt(dmdebt_yoy_d,trsy_10yr_d,dmdebt_dates)
write_prime(dmdebt_yoy_d,prime_d,dmdebt_dates)
def test_fred_series(self):
fred.key('abc')
fred.series()
expected = 'http://api.stlouisfed.org/fred/series'
params = {'api_key': 'abc'}
self.get.assert_called_with(expected, params=params)
def test_fred_series_updates(self):
fred.key('ALL THE FRED API!')
fred.updates()
expected = 'https://api.stlouisfed.org/fred/series/updates'
params = {'api_key': 'ALL THE FRED API!', 'file_type': 'json'}
self.get.assert_called_with(expected, params=params)
def test_fred_source(self):
fred.key('123')
fred.source(25)
expected = 'http://api.stlouisfed.org/fred/source'
params = {'api_key': '123', 'source_id': 25, 'file_type': 'json'}
self.get.assert_called_with(expected, params=params)
def test_fred_series_search(self):
fred.key('123')
fred.search('money stock')
expected = 'https://api.stlouisfed.org/fred/series/search'
params = {'api_key': '123', 'search_text': 'money stock', 'file_type': 'json'}
self.get.assert_called_with(expected, params=params)
def test_fred_series_observations(self):
fred.key('ohai')
fred.observations("AAA")
expected = 'https://api.stlouisfed.org/fred/series/observations'
params = {'api_key': 'ohai', 'series_id': 'AAA', 'file_type': 'json'}
self.get.assert_called_with(expected, params=params)
def test_fred_series_release(self):
fred.key('abc')
fred.series(releases=True)
expected = 'https://api.stlouisfed.org/fred/series/release'
params = {'api_key': 'abc', 'file_type': 'json'}
self.get.assert_called_with(expected, params=params)
import os
import math
from datetime import datetime, timedelta
import fred
fred_api_key = os.environ.get('FRED_API_KEY')
fred.key(fred_api_key)
days_in_a_year = 365.25
def get_series_data(series, years=5):
today = datetime.today().strftime('%Y-%m-%d')
five_years_ago = (
datetime.today() -
timedelta(days=years * days_in_a_year)).strftime('%Y-%m-%d')
api_call_result = fred.observations(series,
observation_start=five_years_ago,
observation_end=today)
series_data = _unpack_series_data(series, api_call_result)
return series_data
def get_series_metadata(series):
api_call_result = fred.series(series)
metadata = api_call_result['seriess'][0]
metadata_dict = {
'Table Info': ['Title:', 'Units:', 'Frequency:'],
"""
import fred
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from datetime import date, datetime
FRED_API_KEY="055ba538c874e5974ee22d786f27fdda"
FRED_SERIES=["INDPRO", "PAYEMS", "HOUST", "RRSFS", "NAPM"]
FRED_START=date(1990,1,1)
#set to false to output graphs to a file, uses current working directory.
# All numerical analysis is printed to the console
DISPLAY=False
fred.key(FRED_API_KEY)
def get_fred_series(series):
def filter(o):
return {'date': datetime.strptime(o['date'],'%Y-%m-%d').date(),
series: o['value']}
return pd.DataFrame(map(filter,fred.observations(series)['observations']),
dtype='float64').set_index('date').dropna()
fred_data=get_fred_series(FRED_SERIES[0]) # Build an initial DataFrame
for s in FRED_SERIES[1:]:
fred_data=fred_data.join(get_fred_series(s))
fred_data=np.log(fred_data).diff(12)[FRED_START:].dropna()
def refresh(self, bulk=False, skip_to=None, fn=None, no_download=False, ids=None, force=False, **kwargs):
"""
Reads the associated API and saves data to tables.
"""
if skip_to:
skip_to = int(skip_to)
tmp_debug = settings.DEBUG
settings.DEBUG = False
django.db.transaction.enter_transaction_management()
django.db.transaction.managed(True)
try:
if bulk:
local_fn = self.download_bulk_data(fn=fn, no_download=no_download)
dsfile, _ = DataSourceFile.objects.get_or_create(name=local_fn)
if dsfile.complete:
return
# Process CSV.
print 'Reading file...'
sys.stdout.flush()
source = zipfile.ZipFile(local_fn, 'r')
if dsfile.total_lines_complete:
total = dsfile.total_lines
if not skip_to:
skip_to = dsfile.total_lines_complete
else:
total = len(source.open(s.BULK_INDEX_FN, 'r').readlines())
DataSourceFile.objects.filter(id=dsfile.id).update(
complete=False,
total_lines=total,
total_lines_complete=0,
percent=0,
)
django.db.transaction.commit()
line_iter = iter(source.open(s.BULK_INDEX_FN, 'r'))
offset = 0
while 1:
try:
line = line_iter.next()
offset += 1
#print 'line:',line.strip()
if line.lower().startswith('series '):
line_iter.next()
offset += 1
break
except StopIteration:
break
total -= offset
i = 0
just_skipped = False
data = csv.DictReader(line_iter, delimiter=';')
for row in data:
i += 1
if skip_to and i < skip_to:
if not just_skipped:
print
print '\rSkipping from %s to %s...' % (i, skip_to),
sys.stdout.flush()
just_skipped = True
continue
elif just_skipped:
just_skipped = False
print
DataSourceFile.objects.filter(id=dsfile.id).update(
downloaded=True,
complete=False,
total_lines=total,
total_lines_complete=i,
percent=i/float(total)*100,
)
if not i % 10:
django.db.transaction.commit()
row = dict(
(
(k or '').strip().lower().replace(' ', '_'),
(v or '').strip()
)
for k,v in row.iteritems()
)
if not row.get('file'):
continue
print '\rLoading %s %.02f%% (%i of %i)...' % (row.get('file'), i/float(total)*100, i, total),
sys.stdout.flush()
row['id'] = row['file'].split('\\')[-1].split('.')[0]
section_fn = row['file'] # FRED2_csv_2/data/4/4BIGEURORECP.csv
del row['file']
if row['last_updated']:
row['last_updated'] = dateutil.parser.parse(row['last_updated'])
row['last_updated'] = date(row['last_updated'].year, row['last_updated'].month, row['last_updated'].day)
#print row
series, _ = Series.objects.get_or_create(id=row['id'], defaults=row)
series.last_updated = row['last_updated']
series_min_date = series.min_date
series_max_date = series.max_date
prior_series_dates = set(series.data.all().values_list('date', flat=True))
if series.max_date and series.last_updated > (series.max_date - timedelta(days=s.LAST_UPDATE_DAYS)):
continue
elif not section_fn.endswith('.csv'):
continue
section_fn = 'FRED2_csv_2/data/' + section_fn.replace('\\', '/')
#print 'section_fn:',section_fn
lines = source.open(section_fn, 'r').readlines()
#last_data = None
last_data_date = None
last_data_value = None
total2 = len(source.open(section_fn, 'r').readlines())
i2 = 0
if s.EXPAND_DATA_TO_DAYS:
print
series_data_pending = []
for row in csv.DictReader(source.open(section_fn, 'r')):
i2 += 1
if s.EXPAND_DATA_TO_DAYS:
print '\r\tLine %.02f%% (%i of %i)' % (i2/float(total2)*100, i2, total2),
sys.stdout.flush()
row['date'] = dateutil.parser.parse(row['DATE'])
row['date'] = date(row['date'].year, row['date'].month, row['date'].day)
# series_min_date = min(series_min_date or row['date'], row['date'])
# series_max_date = max(series_max_date or row['date'], row['date'])
del row['DATE']
try:
row['value'] = float(row['VALUE'])
except ValueError:
print
print 'Invalid value: "%s"' % (row['VALUE'],)
sys.stdout.flush()
continue
del row['VALUE']
#print row
if s.EXPAND_DATA_TO_DAYS and last_data_date:
intermediate_days = (row['date'] - last_data_date).days
#print 'Expanding data to %i intermediate days...' % (intermediate_days,)
#sys.stdout.flush()
#Data.objects.bulk_create([
series_data_pending.extend([
Data(series=series, date=last_data_date+timedelta(days=_days), value=last_data_value)
for _days in xrange(1, intermediate_days)
if (last_data_date+timedelta(days=_days)) not in prior_series_dates
])
#data, _ = Data.objects.get_or_create(series=series, date=row['date'], defaults=row)
if row['date'] not in prior_series_dates:
data = Data(series=series, date=row['date'], value=row['value'])
series_data_pending.append(data)
#data.save()
last_data_date = row['date']
last_data_value = row['value']
if series_data_pending:
Data.objects.bulk_create(series_data_pending)
# print '\r\tLine %.02f%% (%i of %i)' % (100, i2, total2),
# print
series.last_refreshed = date.today()
series.save()
# Cleanup.
django.db.transaction.commit()
Series.objects.update()
Data.objects.update()
gc.collect()
DataSourceFile.objects.filter(id=dsfile.id).update(
complete=True,
downloaded=True,
total_lines=total,
total_lines_complete=total,
percent=100,
)
else:
#TODO:use API to download data for each series_id individually
#e.g. http://api.stlouisfed.org/fred/series/observations?series_id=DEXUSEU&api_key=<api_key>
#TODO:check for revised values using output_type?
#http://api.stlouisfed.org/docs/fred/series_observations.html#output_type
if force:
if ids:
q = Series.objects.all()
else:
q = Series.objects.get_loadable()
else:
q = Series.objects.get_stale(days=30)
if ids:
q = q.filter(id__in=ids)
fred.key(s.API_KEY)
i = 0
total = q.count()
print '%i series found.' % (total,)
for series in q.iterator():
i += 1
print '\rImporting %i of %i' % (i, total),
sys.stdout.flush()
observation_start = None
if series.max_date:
observation_start = series.max_date - timedelta(days=7)
try:
series_info = fred.series(series.id)['seriess'][0]
except KeyError:
print>>sys.stderr, 'Series %s is missing seriess: %s' % (series.id, fred.series(series.id),)
continue
except Exception as e:
print>>sys.stderr, 'Error on %s: %s' % (series.id, e,)
continue
#print 'series_info:',series_info
last_updated = series_info['last_updated'].strip()
series.last_updated = dateutil.parser.parse(last_updated) if last_updated else None
series.popularity = series_info['popularity']
series.save()
try:
series_data = fred.observations(
series.id,
observation_start=observation_start)
except ValueError as e:
print>>sys.stderr, e
continue
for data in series_data['observations']:
#print series, data['date'], data['value']
try:
value = float(data['value'])
except (ValueError, TypeError) as e:
print>>sys.stderr, e
continue
dt = date(*map(int, data['date'].split('-')))
data, created = Data.objects.get_or_create(
series=series,
date=dt,
defaults=dict(value=value))
if not created:
data.value = value
data.save()
series = Series.objects.get(id=series.id)
if series.last_updated:
most_recent_past_date = series.data.filter(date__lte=date.today()).aggregate(Max('date'))['date__max']
threshold = series.last_updated - timedelta(days=series.days)
# print
# print 'most_recent_past_date:',most_recent_past_date
# print 'last_updated:',series.last_updated
# print 'threshold:',threshold
if most_recent_past_date:
if series.frequency == c.QUARTERLY and most_recent_past_date.day == 1:
#TODO: Is this a safe assumption? Might not matter for series without future data.
series.date_is_start = True
elif most_recent_past_date >= threshold:
series.date_is_start = False
else:
series.date_is_start = True
series.last_refreshed = date.today()
series.save()
if force:
series.data.all().update(start_date_inclusive=None, end_date_inclusive=None)
missing_dates = series.data.filter(Q(start_date_inclusive__isnull=True)|Q(end_date_inclusive__isnull=True))
print 'Updating %i date ranges.' % (missing_dates.count(),)
for _ in missing_dates.iterator():
_.set_date_range()
_.save()
django.db.transaction.commit()
print
finally:
#print "Committing..."
settings.DEBUG = tmp_debug
django.db.transaction.commit()
django.db.transaction.leave_transaction_management()
def test_fred_sources_accidentally_passed_source_id(self):
fred.key('123')
fred.sources(123)
expected = 'http://api.stlouisfed.org/fred/source'
params = {'api_key': '123', 'source_id': 123}
self.get.assert_called_with(expected, params=params)
def test_fred_releases_with_id_calls_release(self):
fred.key('abc')
fred.releases('123')
expected = 'http://api.stlouisfed.org/fred/release'
params = {'api_key': 'abc', 'release_id': '123'}
self.get.assert_called_with(expected, params=params)
def test_fred_category_children(self):
fred.key('abc123')
fred.children()
expected = 'https://api.stlouisfed.org/fred/category/children'
params = {'api_key': 'abc123', 'category_id': None, 'file_type': 'json'}
self.get.assert_called_with(expected, params=params)
def test_fred_series_release(self):
fred.key('abc')
fred.series(releases=True)
expected = 'http://api.stlouisfed.org/fred/series/release'
params = {'api_key': 'abc', 'file_type': 'json'}
self.get.assert_called_with(expected, params=params)
def test_fred_category_series_function(self):
fred.key('my_fred_key')
fred.category_series(123)
expected = 'https://api.stlouisfed.org/fred/category/series'
params = {'api_key': 'my_fred_key', 'category_id': 123, 'file_type': 'json'}
self.get.assert_called_with(expected, params=params)
def test_fred_category_series_function(self):
fred.key('my_fred_key')
fred.category_series()
expected = 'http://api.stlouisfed.org/fred/category/series'
params = {'api_key': 'my_fred_key'}
self.get.assert_called_with(expected, params=params)
def test_fred_releases_with_id_calls_release(self):
fred.key('abc')
fred.releases('123')
expected = 'https://api.stlouisfed.org/fred/release'
params = {'api_key': 'abc', 'release_id': '123', 'file_type': 'json'}
self.get.assert_called_with(expected, params=params)
def test_fred_sources(self):
fred.key('moar fred')
fred.sources()
expected = 'http://api.stlouisfed.org/fred/sources'
params = {'api_key': 'moar fred', 'file_type': 'json'}
self.get.assert_called_with(expected, params=params)
def download_ids(api_key:str, sleep_time:int=60, rate_limit:int=100) -> None:
"""
Downloading all time series in the provided JSON file.
"""
fred.key(api_key)
#num_requests = 0
tot_downloaded = 0
num_files_written = 0
list_json = []
request_time_stamps = []
#curr_dir = f"dir{tot_downloaded // cfg.source.files_per_folder :04d}/"
dirs = glob.glob(cfg.source.path.FRED.meta + "/*")
for d in dirs:
file_names = glob.glob(d + "/*")
for fname in file_names:
with open(fname, "r") as fp:
ids_meta = json.load(fp)
fp.close()
for id_meta in ids_meta:
try:
observations = fred.observations(id_meta["id"])
request_time_stamps.append(time.time())
#num_requests += 1
ts = {
"id" : id_meta["id"],
"source" : id_meta["source"],
"node_id" : id_meta["node_id"],
"category_name" : id_meta["category_name"],
"parent_id" : id_meta["parent_id"],
"frequency" : id_meta["frequency"],
"observations" : [{"date" : obs["date"], "value" : obs["value"]} for obs in observations["observations"]]
}
tot_downloaded += 1
list_json.append(ts)
if len(list_json) > cfg.source.samples_per_json:
filename = f"raw_{num_files_written:>06}.json"
if num_files_written % cfg.source.files_per_folder == 0:
curr_dir = f"dir{num_files_written // cfg.source.files_per_folder :04d}/"
os.makedirs(os.path.join(cfg.source.path.FRED.raw, curr_dir), exist_ok=True)
with open(os.path.join(*[cfg.source.path.FRED.raw, curr_dir, filename]), "w") as fp:
json.dump(list_json, fp, sort_keys=True, indent=4, separators=(",", ": "))
fp.close()
with open(os.path.join(cfg.source.path.FRED.meta, "ids_downloaded.txt"), "a") as fp:
for j in list_json:
fp.write(j["id"])
fp.write("\n")
fp.close()
num_files_written += 1
list_json = []
if tot_downloaded % 10000 == 0:
logger.info(f"Downloaded {tot_downloaded} time series.")
except Exception as e:
logger.info(f"Failed to download id {id_meta['id']} from fname {fname}.")
logger.warning(e)
if len(request_time_stamps) > rate_limit:
first = request_time_stamps.pop(0)
if time.time() - first < sleep_time:
#logger.info(f"Sleeping for {request_time_stamps[0]-first}.")
time.sleep(request_time_stamps[0]-first)
logger.info(f"Written files in directory {d} and currently have {tot_downloaded:>6} time series saved")
filename = f"raw_{num_files_written:>06}.json"
if num_files_written % cfg.source.files_per_folder == 0:
curr_dir = f"dir{num_files_written // cfg.source.files_per_folder :04d}/"
os.makedirs(os.path.join(cfg.source.path.FRED.raw, curr_dir), exist_ok=True)
with open(os.path.join(*[cfg.source.path.FRED.raw, curr_dir, filename]), "w") as fp:
json.dump(list_json, fp, sort_keys=True, indent=4, separators=(",", ": "))
fp.close()
def test_fred_category_series(self):
fred.key('123abc')
fred.category(series=True)
expected = 'http://api.stlouisfed.org/fred/category/series'
params = {'api_key': '123abc', 'file_type': 'json'}
self.get.assert_called_with(expected, params=params)
def test_fred_sources(self):
fred.key('moar fred')
fred.sources()
expected = 'https://api.stlouisfed.org/fred/sources'
params = {'api_key': 'moar fred', 'file_type': 'json'}
self.get.assert_called_with(expected, params=params)
def test_fred_sources_accidentally_passed_source_id(self):
fred.key('123')
fred.sources(123)
expected = 'https://api.stlouisfed.org/fred/source'
params = {'api_key': '123', 'source_id': 123, 'file_type': 'json'}
self.get.assert_called_with(expected, params=params)
def test_fred_source(self):
fred.key('123')
fred.source(25)
expected = 'https://api.stlouisfed.org/fred/source'
params = {'api_key': '123', 'source_id': 25, 'file_type': 'json'}
self.get.assert_called_with(expected, params=params)
def test_fred_releases_dates(self):
fred.key('123')
fred.dates()
expected = 'http://api.stlouisfed.org/fred/releases/dates'
params = {'api_key': '123', 'file_type': 'json'}
self.get.assert_called_with(expected, params=params)
def test_fred_category_series(self):
fred.key('123abc')
fred.category(series=True)
expected = 'https://api.stlouisfed.org/fred/category/series'
params = {'api_key': '123abc', 'file_type': 'json'}
self.get.assert_called_with(expected, params=params)
second_series = d.keys()[1]
ret = pd.DataFrame.merge(d[first_series], d[second_series], on = "month", how = inner_or_outer)
# merge remaining series
for s in d.keys()[2:]:
ret = pd.DataFrame.merge(ret, d[s], on = "month", how = inner_or_outer)
return ret
def stripPunct(s):
return ''.join(c for c in s if c not in set(string.punctuation))
if __name__ == "__main__":
# FRED api key
api_key = "your-key-here"
fred.key(api_key)
# get series observed on a monthly or more-than-monthly (not quarterly, annually, bi-annually) basis
series = []
with open("../POSSIBLE_SERIES.txt", "r+") as f:
for line in f:
series.append(line.strip())
# inner join yields nice, full dataset
merged = seriesTable(series, "inner")
# write full data frame to file
# first, clear file
open('../series_monthly_inner.txt', 'w').close()
merged.to_csv(r'../series_monthly_inner.txt', header = True, index = None, sep = '\t', mode = 'a')
def test_fred_series_observations(self):
fred.key('ohai')
fred.observations()
expected = 'http://api.stlouisfed.org/fred/series/observations'
params = {'api_key': 'ohai'}
self.get.assert_called_with(expected, params=params)
