Upload SP500_Date_Offset.py

SP500_Date_Offset.py

@@ -0,0 +1,812 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Wed May  1 13:17:02 2024
+
+@author: RC
+"""
+
+
+
+
+
+# ================================ LIBRARIES ================================ #
+import numpy as np
+import pandas as pd
+import yfinance as yf
+import datasets
+from typing import List
+import csv
+import json
+import logging
+
+import warnings
+from fredapi import Fred
+from time import sleep
+from urllib.request import Request, urlopen
+from bs4 import BeautifulSoup as soup
+
+
+
+
+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']
+'''
+
+
+
+
+