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rc9494
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SP500_Date_Offset

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finance

economics

time series

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SP500_Date_Offset / SP500_Date_Offset.py

rc9494

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	# -- coding: utf-8 --
	"""
	Created on Wed May 1 13:17:02 2024

	@author: RC
	"""





	# ================================ LIBRARIES ================================ #
	import pandas as pd
	import datasets
	from typing import List
	import logging

	import warnings
	from time import sleep
	from urllib.request import Request, urlopen

	# Special Libraries
	import importlib
	import subprocess
	specialLibraries = ['bs4', 'fredapi', 'yfinance']
	for lib in specialLibraries:
	try:
	importlib.import_module(lib)
	except ImportError:
	subprocess.check_call(['pip', 'install', lib])
	# end try
	# end for
	from bs4 import BeautifulSoup as soup
	import yfinance as yf
	from fredapi import Fred



	dictArgs = {'key_file_path' : 'fred_api_key.txt', # set local directory
	'fred_source_path' : 'fred.csv', # set location of data dictionary
	'security_sym' : '^GSPC', # set security symbol
	'security_name' : 'SP500', # set security name
	'export_path' : 'SP500_Date_Offset.csv' # set export destination
	}

	# =========================================================================== #





	# ================================== INFO =================================== #
	_CITATION = """\
	@online{BEA_GDP,
	author = {{U.S. Bureau of Economic Analysis}},
	title = {Real Gross Domestic Product [GDPC1]},
	year = {2024},
	url = {https://fred.stlouisfed.org/series/GDPC1},
	organization = {FRED, Federal Reserve Bank of St. Louis},
	urldate = {2024-03-13}
	}
	@online{Consumer_Sentiment,
	author = {{Surveys of Consumers, University of Michigan}},
	title = {University of Michigan: Consumer Sentiment © [UMCSENT]},
	year = {2024},
	url = {https://fred.stlouisfed.org/series/UMCSENT},
	organization = {FRED, Federal Reserve Bank of St. Louis},
	urldate = {2024-03-13}
	}
	@online{CPI_All_Items,
	author = {{U.S. Bureau of Labor Statistics}},
	title = {Consumer Price Index for All Urban Consumers: All Items in U.S. City Average [CPIAUCSL]},
	year = {2024},
	url = {https://fred.stlouisfed.org/series/CPIAUCSL},
	organization = {FRED, Federal Reserve Bank of St. Louis},
	urldate = {2024-03-13}
	}
	@online{CPI_All_Items_Less_Food_Energy,
	author = {{U.S. Bureau of Labor Statistics}},
	title = {Consumer Price Index for All Urban Consumers: All Items Less Food and Energy in U.S. City Average [CPILFESL]},
	year = {2024},
	url = {https://fred.stlouisfed.org/series/CPILFESL},
	organization = {FRED, Federal Reserve Bank of St. Louis},
	urldate = {2024-03-13}
	}
	@online{Fed_Funds_Rate,
	author = {{Board of Governors of the Federal Reserve System (US)}},
	title = {Federal Funds Effective Rate [DFF]},
	year = {2024},
	url = {https://fred.stlouisfed.org/series/DFF},
	organization = {FRED, Federal Reserve Bank of St. Louis},
	urldate = {2024-03-20}
	}
	@online{New_Housing_Units_Started,
	author = {{U.S. Census Bureau and U.S. Department of Housing and Urban Development}},
	title = {New Privately-Owned Housing Units Started: Total Units [HOUST]},
	year = {2024},
	url = {https://fred.stlouisfed.org/series/HOUST},
	organization = {FRED, Federal Reserve Bank of St. Louis},
	urldate = {2024-03-19}
	}
	@online{New_One_Family_Houses_Sold,
	author = {{U.S. Census Bureau and U.S. Department of Housing and Urban Development}},
	title = {New One Family Houses Sold: United States [HSN1F]},
	year = {2024},
	url = {https://fred.stlouisfed.org/series/HSN1F},
	organization = {FRED, Federal Reserve Bank of St. Louis},
	urldate = {2024-03-13}
	}
	@online{PCE_Chain_Price_Index,
	author = {{U.S. Bureau of Economic Analysis}},
	title = {Personal Consumption Expenditures: Chain-type Price Index [PCEPI]},
	year = {2024},
	url = {https://fred.stlouisfed.org/series/PCEPI},
	organization = {FRED, Federal Reserve Bank of St. Louis},
	urldate = {2024-03-13}
	}
	@online{PCE_Excluding_Food_Energy,
	author = {{U.S. Bureau of Economic Analysis}},
	title = {Personal Consumption Expenditures Excluding Food and Energy (Chain-Type Price Index) [PCEPILFE]},
	year = {2024},
	url = {https://fred.stlouisfed.org/series/PCEPILFE},
	organization = {FRED, Federal Reserve Bank of St. Louis},
	urldate = {2024-03-13}
	}
	@online{SP500,
	author = {{S&P Dow Jones Indices LLC}},
	title = {S\&P 500 [SP500]},
	year = {2024},
	url = {https://fred.stlouisfed.org/series/SP500},
	organization = {FRED, Federal Reserve Bank of St. Louis},
	urldate = {2024-03-20}
	}
	@online{Total_Construction_Spending,
	author = {{U.S. Census Bureau}},
	title = {Total Construction Spending: Total Construction in the United States [TTLCONS]},
	year = {2024},
	url = {https://fred.stlouisfed.org/series/TTLCONS},
	organization = {FRED, Federal Reserve Bank of St. Louis},
	urldate = {2024-03-13}
	}
	@online{Total_Nonfarm_Employees,
	author = {{U.S. Bureau of Labor Statistics}},
	title = {All Employees, Total Nonfarm [PAYEMS]},
	year = {2024},
	url = {https://fred.stlouisfed.org/series/PAYEMS},
	organization = {FRED, Federal Reserve Bank of St. Louis},
	urldate = {2024-03-13}
	}
	@online{Unemployment_Rate,
	author = {{U.S. Bureau of Labor Statistics}},
	title = {Unemployment Rate [UNRATE]},
	year = {2024},
	url = {https://fred.stlouisfed.org/series/UNRATE},
	organization = {FRED, Federal Reserve Bank of St. Louis},
	urldate = {2024-03-13}
	}
	"""

	# You can copy an official description
	_DESCRIPTION = """\
	The S&P 500 Date Offset project seeks to offer an alternative way of modeling
	financial trends from economic conditions.

	Due to the rigorous tabulation process, the gap between when economic data is
	reported and the time which it is meant to describe can be months. Moreover,
	when this data is released, it is usually backdated to correspond with the date
	of the first day of the time period it reflects. That said, if the data causes
	a correction in financial markets, that change will be reflected in the data
	for the day of the release (and not the back dated day!).

	That prompts the immediate question: would data offset to reflect investors'
	knowledge in the moment provide a better model for the markets than the
	traditionally structured data?

	In addition to the S&P 500 daily close price--which is used here to represent
	the stock market overall--variables were chosen from the list of Leading,
	Lagging and Coincident Indicators as maintained by the Conference Board.
	Those variables and their transformations are:
	(M/M = Month-over-month percent change,
	Q/Q = Quarter-over-quarter percent change,
	Y/Y = Year-over-year percent change
	)

	- Consumer Sentiment, University of Michigan
	Freq: Monthly
	Tran: M/M, Y/Y

	- Consumer Price Index
	- All Items
	- All Items less Food & Energy
	Freq: Monthly
	Tran: M/M, Y/Y

	- Federal Funds Rate
	Freq: Daily
	Tran: None

	- Gross Domestic Product
	Freq: Quarterly
	Tran: Q/Q, Y/Y

	- New Housing Units Started
	Freq: Monthly
	Tran: M/M, Y/Y

	- New One Family Houses Sold
	Freq: Monthly
	Tran: M/M, Y/Y

	- Personal Consumption Expenditure: Chain-type Price Index
	- All Items
	- All Items excluding Food & Energy
	Freq: Monthly
	Tran: M/M, Y/Y

	- Total Construction Spending
	Freq: Monthly
	Tran: M/M, Y/Y

	- Total Nonfarm Employment
	Freq: Monthly
	Tran: M/M, Y/Y

	- Unemployment Rate
	Freq: Monthly
	Tran: M/M, Y/Y

	"""

	# Homepage
	_HOMEPAGE = "https://github.com/RileyTheEcon/SP500_Date_Offset"

	# License is a mix of Public Domain and Creative Commons
	# Sourcing the data so that it is all Public Domain is a longer term goal for
	# this project
	_LICENSE = ""

	# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
	# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
	_URL = "https://huggingface.co/datasets/rc9494/SP500_Date_Offset/dataset/"
	_URLS = {
	"dev": _URL + "blob/main/SP500_Date_Offset.csv"
	}
	# =========================================================================== #





	# ================================ FUNCTIONS ================================ #
	# I originally developed the below function for a personal project and built
	# on it for this assignment: originally took data series names and ID codes as
	# List of Tuples, expanded functionality to take table instead and create the
	# list of tuples internally
	def get_fred_data (fred_key, dfFred,
	col_names = {'Name':'Name', 'SeriesID':'SeriesID'},
	try_limit=5, courtesy_sleep = 0.5
	) :
	'''
	Parameters
	----------
	fred_key : STR
	Valid FRED API as str
	dfFred : DataFrame-like
	DataFrame-like with an array of desired variable names, and FRED
	series ID codes
	col_names : DICT, optional
	Dictionary matching column names of dfFred column names with the column
	names assumed by the function.
	try_limit : INT, optional
	Function will attempt to access the data associated with a given series
	ID this many times before issuing a warning and continuing.
	The default is 5.
	courtesy_sleep: FLT, optional
	Wait between making new server requests to avoid flooding the server,
	or if the server is erroring. The default is 0.5 seconds.
	Returns : dfData
	-------
	DATAFRAME
	Returns a dataframe of data requested from FRED server. Each data
	series is in its own column, joined on datetime index, and sorted
	chronologically
	'''

	dfFred = pd.DataFrame(dfFred) # convert to DF object for version control
	dfData = pd.DataFrame() # create place in memory
	fred = Fred(fred_key) # convert to API key object



	# Version control df names
	col_names = {value:key for key, value in col_names.items()}
	dfFred.rename(columns=col_names, inplace=True)



	# Remove gaps & warn duplicates
	dfFred = dfFred.dropna()

	item_dupe = []
	for name in dfFred.columns :
	item_dupe = dfFred[dfFred.duplicated(name)][name].tolist()
	if len(item_dupe)>0 :
	warnings.warn(f"Duplicated entries found in '{name}': {item_dupe}")
	# end if
	# end for
	dfFred = dfFred[~dfFred['Name'].duplicated(keep='first')]



	# Download data -- using item-wise iter to be nice to hosting server
	for indx, row in dfFred.iterrows() :
	bContinue = 0
	intErrorCount = 0

	while (bContinue==0)&(intErrorCount<try_limit) :
	try : # Attempt dl through API
	data = pd.DataFrame(fred.get_series(row['SeriesID'])
	).rename(columns={0:row['Name']})
	data.index.name = 'date'
	except : # Extract data from raw txt page if API fails for any reason
	try:
	htmlPage = dlURL('https://fred.stlouisfed.org/data/'+
	row['SeriesID']+'.txt')

	listRows = htmlPage.text.split('\n')
	listRows = listRows[listRows.index([x for x in listRows
	if 'DATE' in x][0])+1:]
	listRows = [[pd.to_datetime(x[:x.index(' ')]),
	float(isolate_better(x,' ','\r',b_end=1))
	]
	for x in listRows if x!=''
	]

	data = pd.DataFrame(listRows,columns=['index',row['Name']]
	).set_index('index')
	data.index.name = 'date'
	except :
	intErrorCount+=1
	sleep(1)
	else : bContinue = 1
	# endtry
	else : bContinue = 1
	# endtry
	# endwhile

	# If both approaches above fail - warn user
	if intErrorCount>=try_limit :
	warnings.warn('\nFailure in accessing data from:\n'+
	f'Name: {row["Name"]}\n'+
	f'ID: {row["SeriesID"]}\n'
	)

	# If the above ran successfully - append along date index
	else :
	if len(dfData)==0 : dfData = data
	else : dfData = dfData.join(data,how='outer',
	)
	# endif

	sleep(courtesy_sleep) # Let's do our best to be polite to the hosting server
	# endfor

	return dfData.sort_index()
	####
	def get_historic_data (SeriesID, api_key,
	series_name = 'value',
	stale_data = 500
	) :

	# Get data
	fred = Fred(api_key)
	df = fred.get_series_all_releases(SeriesID)

	# Calc gap between reported date and actual date; drop stale data
	df['diff'] = df['realtime_start'] - df['date']
	df = df[df['diff'] <= pd.Timedelta(str(stale_data)+' days')
	].copy()

	# Get most recent data by actual date
	# Some reports contain original data and revisions, so we grab the most
	# current data from each reporting date
	max_order_indices = (df.sort_values('date')
	.groupby('realtime_start')['date']
	.idxmax()
	)
	df = df.loc[max_order_indices].copy()

	# Drop unneeded columns; set index
	for col in ['date', 'diff'] : del df[col]

	dict_rename = {'realtime_start' : 'date'}
	if series_name!='value' : dict_rename['value'] = series_name

	df.rename(columns = dict_rename,
	inplace = True
	)
	df.set_index('date', inplace = True)

	return df
	####
	def dlURL (url , parser = "html.parser" ) :
	req = Request(url,headers={'User-Agent':'Mozilla/5.0'})
	urlClient = urlopen(req)
	pageRough = urlClient.read()
	urlClient.close()
	pageSoup = soup(pageRough,parser)

	return pageSoup
	#### / ####
	# "isolate_better" and its helper function "reverse" are functions I originally
	# wrote for a personal project while still teaching myself Python basics.
	# Is it a crude and inefficient way to do something that there are probably
	# native functions/methods for? Probably, but it works with the other
	# pre-existing code I have.
	def reverse (stri) :
	x = ""
	for i in stri :
	x = i + x
	return x
	####
	def isolate_better (stri , start , end, b_end = 0) :
	strShort = ''
	posStart = 0
	posEnd = 0

	if b_end==1 :
	posEnd = stri.find(end)
	strShort = stri[:posEnd]
	strShort = reverse(strShort)
	start = reverse(start)
	posStart = posEnd - strShort.find(start)
	#
	else :
	posStart = stri.find(start)+len(start)
	strShort = stri[posStart:]
	posEnd = posStart + strShort.find(end)
	#
	return stri[posStart:posEnd]
	####
	def check_data (dfFred, fred_key) :
	# Check to make sure sufficient data is available
	df = pd.DataFrame() # create space in memory

	for i,r in dfFred[~dfFred['Freq'].isin(['Daily', 'Weekly'])].iterrows() :
	# Download data
	df = get_historic_data(r['SeriesID'],
	fred_key,
	r['Name']
	)

	# Report series statistics
	print(r['Name'],'\n',
	'First Obs.: ', df.first_valid_index(), '\n',
	'Count Obs.: ', len(df), '\n',
	'\n'
	)
	# end for i,r
	#### / ####
	def main(key_file_path, # File path for FRED API key, txt
	fred_source_path, # File path for variable names & FRED series ID, csv
	security_sym, # Ticker symbol for security of interest (S&P 500)
	security_name, # Name of security of interest
	export_path # File path to save data
	) :



	# Seek API key; Prompt user if not found; access from repo if not given
	bDownload = False # Bool: Dl from repo or generate fresh?
	# true = download pre-generated data from repo ; false = gen new

	try :
	# try to get key from file
	with open(key_file_path, 'r') as file :
	fred_key = file.read()
	# endwith

	except FileNotFoundError :
	print('FRED api key not found!\n'+
	'Please enter api key or hit enter to download static dataset from repo:'
	)
	fred_key = input()

	if len(fred_key)==0 : bDownload = True
	else :
	pass # test validity of api key
	# end if len

	except Exception as oops : print(f"Something odd happened: {oops}")
	#





	# Import list of variables if it exists ; else download from repo
	if not bDownload : # skip chunk if we're dl'ing from repo
	try :
	# import list of variable to pull
	dfFred = pd.read_csv(fred_source_path)

	except FileNotFoundError :
	print('Could not find list of variables to generate: '+
	fred_source_path+'\n'+
	'Switching to download static dataset from repo instead!\n'
	)
	bDownload = True

	# end try/except
	# end if bDownload

	#





	# If above checks fail, then download from existing repo
	if bDownload :
	dfData = pd.read_csv('https://raw.githubusercontent.com/RileyTheEcon/'+
	'SP500_Date_Offset/main/SP500_Offset.csv',
	index_col='Date'
	)

	# If all above checks pass, generate fresh data from FRED api
	else :

	# Download YFinance data
	dfFinance = yf.download(security_sym)['Adj Close']
	dfFinance.rename(security_name, inplace=True)
	#





	# Iter thru data series; handle as specified
	dfEcon = pd.DataFrame() # make place in memory

	for i,r in dfFred.iterrows() :
	if not pd.notnull(r['SeriesID']) : # skip if info missing
	continue
	# end if

	# Create space in memory
	df = pd.DataFrame()

	# Import data
	if r['Freq'] in ['Daily', 'Weekly'] :
	# Dl data for daily/ weekly freq
	df = get_fred_data(fred_key,
	pd.DataFrame(r).T[['Name','SeriesID']]
	)

	else :
	# Dl data for daily/ weekly freq
	df = get_historic_data(r['SeriesID'],
	fred_key
	)
	df.rename(columns = {'value': r['Name']},
	inplace = True
	)

	# Indicate report date
	df[r['Name']+'_release'] = 1

	# end if import

	# Attach to full dataframe
	dfEcon = dfEcon.join(df, how='outer')

	# end for iterrows
	#





	# Combine & fill numeric vars & export
	# Ffill numeric vars & fillna(0) indicators
	# left append to stock data
	dfData = (pd.DataFrame(dfFinance)
	.join(dfEcon[[x for x in dfEcon.columns
	if len(dfEcon[x].unique())>3]
	].ffill(),
	how='left'
	)
	.join(dfEcon[[x for x in dfEcon.columns
	if len(dfEcon[x].unique())<=3]
	].fillna(0),
	how='left'
	)
	)

	# Export
	if len(export_path)>0 :
	dfData.to_csv(export_path)
	# end if
	#

	# end if bDownload

	return dfData
	#

	####
	class SP500_Date_Offset(datasets.GeneratorBasedBuilder):
	""" . """

	_URLS = _URLS
	VERSION = datasets.Version("1.1.0")

	def _info(self):
	raise ValueError('woops!')
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"Date": datasets.Value("datetime"),
	"SP500": datasets.Value("float"),
	"Fed-Rate": datasets.Value("float"),
	"Yield-10Y": datasets.Value("float"),
	"Yield-1M": datasets.Value("float"),
	"Yield-1Y": datasets.Value("float"),
	"Yield-20Y": datasets.Value("float"),
	"Yield-2Y": datasets.Value("float"),
	"Yield-30Y": datasets.Value("float"),
	"Yield-3M": datasets.Value("float"),
	"Yield-3Y": datasets.Value("float"),
	"Yield-5Y": datasets.Value("float"),
	"Yield-6M": datasets.Value("float"),
	"Yield-7Y": datasets.Value("float"),
	"Bus-Apps": datasets.Value("float"),
	"Loans-CI": datasets.Value("float"),
	"Loans-Cons": datasets.Value("float"),
	"Loans-RE": datasets.Value("float"),
	"Unemp-Claims": datasets.Value("float"),
	"Con-Sentim": datasets.Value("float"),
	"Con-Sentim_release": datasets.Value("bool"),
	"Con-Spends": datasets.Value("float"),
	"Con-Spends_release": datasets.Value("bool"),
	"CPI": datasets.Value("float"),
	"CPI_release": datasets.Value("bool"),
	"CPI-Core": datasets.Value("float"),
	"CPI-Core_release": datasets.Value("bool"),
	"CPI-Services": datasets.Value("float"),
	"CPI-Services_release": datasets.Value("bool"),
	"Home-Sales": datasets.Value("float"),
	"Home-Sales_release": datasets.Value("bool"),
	"Home-Starts": datasets.Value("float"),
	"Home-Starts_release": datasets.Value("bool"),
	"Income-Trans": datasets.Value("float"),
	"Income-Trans_release": datasets.Value("bool"),
	"Indust-Prod": datasets.Value("float"),
	"Indust-Prod_release": datasets.Value("bool"),
	"Inventory-Sales": datasets.Value("float"),
	"Inventory-Sales_release": datasets.Value("bool"),
	"Manu-Hours": datasets.Value("float"),
	"Manu-Hours_release": datasets.Value("bool"),
	"MT-Sales": datasets.Value("float"),
	"MT-Sales_release": datasets.Value("bool"),
	"NO-Capital": datasets.Value("float"),
	"NO-Capital_release": datasets.Value("bool"),
	"NO-Consumer": datasets.Value("float"),
	"NO-Consumer_release": datasets.Value("bool"),
	"NO-Durables": datasets.Value("float"),
	"NO-Durables_release": datasets.Value("bool"),
	"NO-Unfilled": datasets.Value("float"),
	"NO-Unfilled_release": datasets.Value("bool"),
	"PCE": datasets.Value("float"),
	"PCE_release": datasets.Value("bool"),
	"PCE-Core": datasets.Value("float"),
	"PCE-Core_release": datasets.Value("bool"),
	"PPI-Architect": datasets.Value("float"),
	"PPI-Architect_release": datasets.Value("bool"),
	"Total-Emp": datasets.Value("float"),
	"Total-Emp_release": datasets.Value("bool"),
	"Unemploy": datasets.Value("float"),
	"Unemploy_release": datasets.Value("bool"),
	"Unemp-Weeks": datasets.Value("float"),
	"Unemp-Weeks_release": datasets.Value("bool"),
	"Delinq-CreditC": datasets.Value("float"),
	"Delinq-CreditC_release": datasets.Value("bool"),
	"GDP": datasets.Value("float"),
	"GDP_release": datasets.Value("bool"),
	}
	),
	# No default supervised_keys (as we have to pass both question
	# and context as input).
	supervised_keys=None,
	homepage="https://github.com/RileyTheEcon/SP500_Date_Offset",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
	urls_to_download = self._URLS
	downloaded_files = dl_manager.download_and_extract(urls_to_download)

	return [
	datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]})
	]

	def _generate_examples(self, filepath):
	"""This function returns the examples in the raw (text) form."""
	logging.info("generating examples from = %s", filepath)


	dictArgs = {'key_file_path' : 'fred_api_key.txt', # set local directory
	'fred_source_path' : 'fred.csv', # set location of data dictionary
	'security_sym' : '^GSPC', # set security symbol
	'security_name' : 'SP500', # set security name
	'export_path' : 'SP500_Date_Offset.csv' # set export destination
	}

	dfData = main(**dictArgs)

	for i,r in dfData.iteritems() :
	# Features currently used are "context", "question", and "answers".
	# Others are extracted here for the ease of future expansions.
	yield i, {
	'Date': i,
	"SP500": r["SP500"],
	"Fed-Rate": r["Fed-Rate"],
	"Yield-10Y": r["Yield-10Y"],
	"Yield-1M": r["Yield-1M"],
	"Yield-1Y": r["Yield-1Y"],
	"Yield-20Y": r["Yield-20Y"],
	"Yield-2Y": r["Yield-2Y"],
	"Yield-30Y": r["Yield-30Y"],
	"Yield-3M": r["Yield-3M"],
	"Yield-3Y": r["Yield-3Y"],
	"Yield-5Y": r["Yield-5Y"],
	"Yield-6M": r["Yield-6M"],
	"Yield-7Y": r["Yield-7Y"],
	"Bus-Apps": r["Bus-Apps"],
	"Loans-CI": r["Loans-CI"],
	"Loans-Cons": r["Loans-Cons"],
	"Loans-RE": r["Loans-RE"],
	"Unemp-Claims": r["Unemp-Claims"],
	"Con-Sentim": r["Con-Sentim"],
	"Con-Sentim_release": r["Con-Sentim_release"],
	"Con-Spends": r["Con-Spends"],
	"Con-Spends_release": r["Con-Spends_release"],
	"CPI": r["CPI"],
	"CPI_release": r["CPI_release"],
	"CPI-Core": r["CPI-Core"],
	"CPI-Core_release": r["CPI-Core_release"],
	"CPI-Services": r["CPI-Services"],
	"CPI-Services_release": r["CPI-Services_release"],
	"Home-Sales": r["Home-Sales"],
	"Home-Sales_release": r["Home-Sales_release"],
	"Home-Starts": r["Home-Starts"],
	"Home-Starts_release": r["Home-Starts_release"],
	"Income-Trans": r["Income-Trans"],
	"Income-Trans_release": r["Income-Trans_release"],
	"Indust-Prod": r["Indust-Prod"],
	"Indust-Prod_release": r["Indust-Prod_release"],
	"Inventory-Sales": r["Inventory-Sales"],
	"Inventory-Sales_release": r["Inventory-Sales_release"],
	"Manu-Hours": r["Manu-Hours"],
	"Manu-Hours_release": r["Manu-Hours_release"],
	"MT-Sales": r["MT-Sales"],
	"MT-Sales_release": r["MT-Sales_release"],
	"NO-Capital": r["NO-Capital"],
	"NO-Capital_release": r["NO-Capital_release"],
	"NO-Consumer": r["NO-Consumer"],
	"NO-Consumer_release": r["NO-Consumer_release"],
	"NO-Durables": r["NO-Durables"],
	"NO-Durables_release": r["NO-Durables_release"],
	"NO-Unfilled": r["NO-Unfilled"],
	"NO-Unfilled_release": r["NO-Unfilled_release"],
	"PCE": r["PCE"],
	"PCE_release": r["PCE_release"],
	"PCE-Core": r["PCE-Core"],
	"PCE-Core_release": r["PCE-Core_release"],
	"PPI-Architect": r["PPI-Architect"],
	"PPI-Architect_release": r["PPI-Architect_release"],
	"Total-Emp": r["Total-Emp"],
	"Total-Emp_release": r["Total-Emp_release"],
	"Unemploy": r["Unemploy"],
	"Unemploy_release": r["Unemploy_release"],
	"Unemp-Weeks": r["Unemp-Weeks"],
	"Unemp-Weeks_release": r["Unemp-Weeks_release"],
	"Delinq-CreditC": r["Delinq-CreditC"],
	"Delinq-CreditC_release": r["Delinq-CreditC_release"],
	"GDP": r["GDP"],
	"GDP_release": r["GDP_release"],
	}
	# end for
	# end def
	# end class
	# =========================================================================== #





	# =================================== MAIN ================================== #
	if __name__ == "__main__" :
	print(__doc__)
	main(**dictArgs)
	# endif
	# =========================================================================== #



	''' DEBUG
	key_file_path = dictArgs['key_file_path']
	fred_source_path = dictArgs['fred_source_path']
	security_sym = dictArgs['security_sym']
	security_name = dictArgs['security_name']
	export_path = dictArgs['export_path']
	'''