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Downloader.py
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Downloader.py
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# (0) Load necessary libraries
import pandas as pd
import pandas_datareader.data as web
import os
# (1) Save stock_data to csv-files
def save_stock_data( source, tickers, start_date, end_date ):
# (0) prepare folder in which we save the data
if not os.path.exists('stockdata'):
os.makedirs('stockdata')
# (1) download data for the tickers and save it in this folder
print( 'Download started' )
print( 'Tickers of interest: ' + str( tickers ) + '\n' )
for ticker in tickers:
try:
data = web.DataReader( ticker, 'yahoo', start_date, end_date )
print( 'Download finished for ' + str( ticker ) )
except Exception as e:
print( e, 'error' )
# (2) prepare the data
# 2.1: We work with the opening values
# 2.1: We work with the opening values
data_selected = data['Open']
# 2.2 Index the data by all working days between start and end
# 2.2 Whenever the corresponding data is not present, we will in the missing value.
# 2.2 For this we use the latest available price
all_weekdays = pd.date_range(start=start_date, end=end_date, freq='B')
data_selected = data_selected.reindex(all_weekdays)
data_selected = data_selected.fillna(method='ffill')
# 2.3 Save selected data to csv-file
data_selected.to_csv('stockdata/'+ticker+'.csv')
# 2.4 Inform about status
print( 'Data saved to stockdata/' + str( ticker ) + '.csv \n' )
# (2) Get stock_data
def get_stock_data( source, tickers, start_date, end_date ):
# (1) download data of the tickers
print( 'Download started' )
print( 'Tickers of interest: ' + str( tickers ) )
try:
data = web.DataReader( tickers, 'yahoo', start_date, end_date )
print( 'Download finished \n' )
except Exception as e:
print( e, 'error' )
# (2) prepare the data
# 2.1: We work with the opening values
# 2.1: We work with the opening values
data_selected = data['Open']
# 2.2 Index the data by all working days between start and end
# 2.2 Whenever the corresponding data is not present, we will in the missing value.
# 2.2 For this we use the latest available price
all_weekdays = pd.date_range(start=start_date, end=end_date, freq='B')
data_selected = data_selected.reindex(all_weekdays)
data_selected = data_selected.fillna(method='ffill')
# 2.3 Extract data for each ticker
data_selected_split = []
for ticker in tickers:
data_selected_split.append( data_selected.loc[:, ticker] )
# now return the data
return data_selected_split