Pull currency and stock data

stock.py

#! /usr/bin/env python
"""
Author: Bastin Robin
"""

import os
import logging
from urllib import urlencode
import datetime
import pandas as pd

CURRENCIES = 'AUD BRL CAD CHF CNY EUR GBP HKD ILS INR ISK JPY \
KRW MXN MYR NZD PHP PKR RUB SAR SEK SGD TWD XAG XAU XPT'.split(' ')
STOCKS = '^FTSE ^GSPC ^IXIC'.split(' ')

def get_start_date(end_date):
    '''
    get the start date from data.csv
    '''
    if os.path.exists('data.csv'):
        data = pd.read_csv('data.csv')
        data['Date'] = pd.to_datetime(data["Date"])
        return data['Date'].max().date()
    else:
        return end_date - datetime.timedelta(days=90)

def get_currency(frames, start_date, end_date):
    '''
    get the currency updated details
    '''
    base = 'http://www.oanda.com/currency/historical-rates/download?'
    for currency in CURRENCIES:
        logging.info(currency)
        currency_url = base + urlencode({
            'quote_currency': currency,
            'end_date': end_date.strftime('%Y-%m-%d'),
            'start_date': start_date.strftime('%Y-%m-%d'),
            'period': 'daily',
            'display': 'absolute',
            'rate': '0',
            'data_range': 'c',
            'price': 'mid',
            'view': 'graph',
            'base_currency_0': 'USD',
            'download': '.csv'
        })
        data = pd.read_csv(currency_url,
            skiprows=5, skipfooter=4, names=
            ['Date', 'Rate', 'x1', 'x2', 'x3', 'x4'])
        data['Name'] = currency
        frames.append(data[['Date', 'Rate', 'Name']])

def get_stock(frames, start_date, end_date):
    '''
    get the stock details
    '''
    for stock in STOCKS:
        logging.info(stock)
        stock_url = 'http://ichart.finance.yahoo.com/table.csv?' + \
            urlencode({
                's': stock,
                'a': start_date.month - 1,
                'b': start_date.day,
                'c': start_date.year,
                'd': end_date.month - 1,
                'e': end_date.day,
                'f': end_date.year,
                'g': 'd',
                'ignore': '.csv',
                })
        data = pd.read_csv(stock_url)[['Date', 'Close']].rename(columns={\
            'Close': 'Rate'})
        data['Name'] = stock
        frames.append(data[['Date', 'Rate', 'Name']])

if __name__ == '__main__':
    logging.basicConfig(level=logging.INFO)

    END_DATE = datetime.date.today()
    START_DATE = get_start_date(END_DATE)
    DATAFRAME = []
    get_currency(DATAFRAME, START_DATE, END_DATE)
    get_stock(DATAFRAME, START_DATE, END_DATE)
    pd.concat(DATAFRAME).to_csv('data.csv', index=False)