functions.py

import urllib2
import pandas as pd

#TODO: Fix revenue Financial Firms (GS, WFC, etc.)



def financials_download(ticker,report,frequency):
    if frequency == "A" or frequency == "a":
        frequency = "12"
    elif frequency == "Q" or frequency == "q":
        frequency = "3"
    url = 'http://financials.morningstar.com/ajax/ReportProcess4CSV.html?&t='+ticker+'&region=usa&culture=en-US&cur=USD&reportType='+report+'&period='+frequency+'&dataType=R&order=desc&columnYear=5&rounding=3&view=raw&r=640081&denominatorView=raw&number=3'
    df = pd.read_csv(url, skiprows=1, index_col=0)
    #df.to_csv('test.csv')
    return df

def ratios_download(ticker):
    url = 'http://financials.morningstar.com/ajax/exportKR2CSV.html?&callback=?&t='+ticker+'&region=usa&culture=en-US&cur=USD&order=desc'
    df = pd.read_csv(url, skiprows=2, index_col=0)
    #df.to_csv('test.csv')
    return df

def y_stats(symbol, stat):
    url = 'http://finance.yahoo.com/d/quotes.csv?s=%s&f=%s' % (symbol, stat)
    return urllib2.urlopen(url).read().strip().strip('"')

def cash_and_equivalents(ticker,frequency,time):
    df = financials_download(ticker,'bs',frequency)
    return df.loc['Cash and cash equivalents',time]

def short_term_investments(ticker,frequency,time):
    df = financials_download(ticker,'bs',frequency)
    return df.loc['Short-term investments',time]

def total_cash(ticker,frequency,time):
    df = financials_download(ticker,'bs',frequency)
    return df.loc['Total cash',time]

def receivables(ticker,frequency,time):
    df = financials_download(ticker,'bs',frequency)
    return df.loc['Receivables',time]

def inventories(ticker,frequency,time):
    df = financials_download(ticker,'bs',frequency)
    return df.loc['Inventories',time]

def prepaid_expenses(ticker,frequency,time):
    df = financials_download(ticker,'bs',frequency)
    return df.loc['Prepaid expenses',time]

def other_current_assets(ticker,frequency,time):
    df = financials_download(ticker,'bs',frequency)
    return df.loc['Other current assets',time]

def gross_ppe(ticker,frequency,time):
    df = financials_download(ticker,'bs',frequency)
    return df.loc['Gross property, plant and equipment',time]

def accumulated_depreciation(ticker,frequency,time):
    df = financials_download(ticker,'bs',frequency)
    return df.loc['Accumulated Depreciation',time]

def net_ppe(ticker,frequency,time):
    df = financials_download(ticker,'bs',frequency)
    return df.loc['Net property, plant and equipment',time]

def equity_and_other_investments(ticker,frequency,time):
    df = financials_download(ticker,'bs',frequency)
    return df.loc['Equity and other investments',time]

def goodwill(ticker,frequency,time):
    df = financials_download(ticker,'bs',frequency)
    return df.loc['Goodwill',time]

def intangible_assets(ticker,frequency,time):
    df = financials_download(ticker,'bs',frequency)
    return df.loc['Intangible assets',time]

def other_longterm_assets(ticker,frequency,time):
    df = financials_download(ticker,'bs',frequency)
    return df.loc['Other long-term assets',time]

def total_noncurrent_assets(ticker,frequency,time):
    df = financials_download(ticker,'bs',frequency)
    return df.loc['Total non-current assets',time]

def total_assets(ticker,frequency,time):
    df = financials_download(ticker,'bs',frequency)
    return df.loc['Total assets',time]

def accounts_payable(ticker,frequency,time):
    df = financials_download(ticker,'bs',frequency)
    return df.loc['Accounts payable',time]

def shortterm_debt(ticker,frequency,time):
    df = financials_download(ticker,'bs',frequency)
    return df.loc['Short-term debt',time]

def accrued_liabilities(ticker,frequency,time):
    df = financials_download(ticker,'bs',frequency)
    return df.loc['Accrued liabilities',time]

def other_current_liabilities(ticker,frequency,time):
    df = financials_download(ticker,'bs',frequency)
    return df.loc['Other current liabilities',time]

def total_current_liabilities(ticker,frequency,time):
    df = financials_download(ticker,'bs',frequency)
    return df.loc['Total current liabilities',time]

def longterm_debt(ticker,frequency,time):
    df = financials_download(ticker,'bs',frequency)
    return df.loc['Long-term debt',time]

def deferred_taxes_liabilities(ticker,frequency,time):
    df = financials_download(ticker,'bs',frequency)
    return df.loc['Deferred taxes liabilities',time]

def pensions_postretirement_benefits(ticker,frequency,time):
    df = financials_download(ticker,'bs',frequency)
    return df.loc['Pensions and other postretirement benefits',time]

def minority_interest(ticker,frequency,time):
    df = financials_download(ticker,'bs',frequency)
    return df.loc['Minority interest',time]

def other_longterm_liabilities(ticker,frequency,time):
    df = financials_download(ticker,'bs',frequency)
    return df.loc['Minority interest',time]

def total_noncurrent_liabilities(ticker,frequency,time):
    df = financials_download(ticker,'bs',frequency)
    return df.loc['Total non-current liabilities',time]

def total_liabilities(ticker,frequency,time):
    df = financials_download(ticker,'bs',frequency)
    return df.loc['Total liabilities',time]

def common_stock(ticker,frequency,time):
    df = financials_download(ticker,'bs',frequency)
    return df.loc['Common stock',time]

def additional_paidin_capital(ticker,frequency,time):
    df = financials_download(ticker,'bs',frequency)
    return df.loc['Additional_paidin_capital',time]

def retained_earnings(ticker,frequency,time):
    df = financials_download(ticker,'bs',frequency)
    return df.loc['Retained earnings',time]

def treasury_stock(ticker,frequency,time):
    df = financials_download(ticker,'bs',frequency)
    return df.loc['Treasury stock',time]

def accumulated_other_comprehensive_income(ticker,frequency,time):
    df = financials_download(ticker,'bs',frequency)
    return df.loc['Accumulated other comprehensive income',time]

def total_stockholder_equity(ticker,frequency,time):
    df = financials_download(ticker,'bs',frequency)
    return df.loc["Total Stockholders' equity",time]

def total_liabilities_and_stockholders_equity(ticker,frequency,time):
    df = financials_download(ticker,'bs',frequency)
    return df.loc["Total liabilities and stockholders' equity",time]

def revenue_5yr(ticker,frequency,time):
    df = financials_download(ticker,'is',frequency)
    return df.loc["Revenue",time]

def cost_of_revenue(ticker,frequency,time):
    df = financials_download(ticker,'is',frequency)
    return df.loc["Cost of revenue",time]

def gross_profit(ticker,frequency,time):
    df = financials_download(ticker,'is',frequency)
    return df.loc["Gross profit",time]

def sales_administrative_expense(ticker,frequency,time):
    df = financials_download(ticker,'is',frequency)
    return df.loc["Sales, General and administrative",time]

def depreciation_amort_expense(ticker,frequency,time):
    df = financials_download(ticker,'is',frequency)
    return df.loc["Depreciation and amortization",time]

def interest_expense(ticker,frequency,time):
    df = financials_download(ticker,'is',frequency)
    return df.loc["Interest expense",time]

def other_operating_expenses(ticker,frequency,time):
    df = financials_download(ticker,'is',frequency)
    return df.loc["Other operating expenses",time]

def total_costs_and_expenses(ticker,frequency,time):
    df = financials_download(ticker,'is',frequency)
    return df.loc["Total costs and expenses",time]

def income_tax_expense(ticker,frequency,time):
    df = financials_download(ticker,'is',frequency)
    return df.loc["Provision for income taxes",time]

def net_income_contin_ops(ticker,frequency,time):
    df = financials_download(ticker,'is',frequency)
    return df.loc["Net income from continuing operations",time]

def net_income(ticker,frequency,time):
    df = financials_download(ticker,'is',frequency)
    return df.loc["Net income",time]

def preferred_dividend(ticker,frequency,time):
    df = financials_download(ticker,'is',frequency)
    return df.loc["Preferred dividend",time]

def net_income_to_common_shareholders(ticker,frequency,time):
    df = financials_download(ticker,'is',frequency)
    return df.loc["Net income available to common shareholders",time]

def EPS_basic(ticker,frequency,time):
    df = financials_download(ticker,'is',frequency)
    df = df[0:len(df)-3]
    return df.loc["Basic",time]

def EPS_diluted(ticker,frequency,time):
    df = financials_download(ticker,'is',frequency)
    df = df[0:len(df)-3]
    return df.loc["Diluted",time]

def EBITDA(ticker,frequency,time):
    df = financials_download(ticker,'is',frequency)
    return df.loc["EBITDA",time]

def net_cash_from_ops(ticker,frequency,time):
    df = financials_download(ticker,'cf',frequency)
    return df.loc["Net cash provided by operating activities",time]

def investments_in_ppe(ticker,frequency,time):
    df = financials_download(ticker,'cf',frequency)
    return df.loc["Investments in property, plant, and equipment",time]

def net_cash_used_for_investing_activities(ticker,frequency,time):
    df = financials_download(ticker,'cf',frequency)
    return df.loc["Net cash used for investing activities",time]

def debt_issued(ticker,frequency,time):
    df = financials_download(ticker,'cf',frequency)
    return df.loc["Debt issued",time]

def debt_repayment(ticker,frequency,time):
    df = financials_download(ticker,'cf',frequency)
    return df.loc["Debt repayment",time]

def net_cash_from_financing_activities(ticker,frequency,time):
    df = financials_download(ticker,'cf',frequency)
    return df.loc["Net cash provided by (used for) financing activities",time]

def net_change_in_cash(ticker,frequency,time):
    df = financials_download(ticker,'cf',frequency)
    return df.loc["Net change in cash",time]

def cash_at_beginning_period(ticker,frequency,time):
    df = financials_download(ticker,'cf',frequency)
    return df.loc["Cash at beginning of period",time]

def cash_at_end_period(ticker,frequency,time):
    df = financials_download(ticker,'cf',frequency)
    return df.loc["Cash at end of period",time]

def operating_cash_flow_5yr(ticker,frequency,time):
    df = financials_download(ticker,'cf',frequency)
    return df.loc["Operating cash flow",time]

def capital_expenditure_5yr(ticker,frequency,time):
    df = financials_download(ticker,'cf',frequency)
    return df.loc["Capital expenditure",time]

def free_cash_flow_5yr(ticker,frequency,time):
    df = financials_download(ticker,'cf',frequency)
    return df.loc["Free cash flow",time]

def gross_margin(ticker,time):
    df = ratios_download(ticker)
    return df.loc["Gross Margin %",time]

def operating_margin(ticker,time):
    df = ratios_download(ticker)
    return df.loc["Operating Margin %",time]

def EPS(ticker,time):
    df = ratios_download(ticker)
    return df.loc["Earnings Per Share USD",time]

def DPS(ticker,time):
    df = ratios_download(ticker)
    return df.loc["Dividends USD",time]

def payout_ratio(ticker,time):
    df = ratios_download(ticker)
    return df.loc["Payout Ratio %",time]

def BVPS(ticker,time):
    df = ratios_download(ticker)
    return df.loc["Book Value Per Share USD",time]

def operating_cash_flow(ticker,time):
    df = ratios_download(ticker)
    return df.loc["Operating Cash Flow USD Mil",time]

def revenue(ticker,time):
    df = ratios_download(ticker)
    return df.loc["Revenue USD Mil",time]

def operating_income(ticker,time):
    df = ratios_download(ticker)
    return df.loc["Operating Income USD Mil",time]

def capital_expenditure(ticker,time):
    df = ratios_download(ticker)
    return df.loc["Cap Spending USD Mil",time]

def free_cash_flow(ticker,time):
    df = ratios_download(ticker)
    return df.loc["Free Cash Flow USD Mil",time]

def FCFPS(ticker,time):
    df = ratios_download(ticker)
    return df.loc["Free Cash Flow Per Share USD",time]

def working_capital(ticker,time):
    df = ratios_download(ticker)
    return df.loc["Working Capital USD Mil",time]

def tax_rate(ticker,time):
    df = ratios_download(ticker)
    return df.loc["Tax Rate %",time]

def net_margin(ticker,time):
    df = ratios_download(ticker)
    return df.loc["Net Margin %",time]

def asset_turnover(ticker,time):
    df = ratios_download(ticker)
    return df.loc["Asset Turnover (Average)",time]

def ROA(ticker,time):
    df = ratios_download(ticker)
    return df.loc["Return on Assets %",time]

def financial_leverage(ticker,time):
    df = ratios_download(ticker)
    return df.loc["Financial Leverage (Average)",time]

def ROE(ticker,time):
    df = ratios_download(ticker)
    return df.loc["Return on Equity %",time]

def ROIC(ticker,time):
    df = ratios_download(ticker)
    return df.loc["Return on Invested Capital %",time]

def interest_coverage(ticker,time):
    df = ratios_download(ticker)
    return df.loc["Interest Coverage",time]

def revenue_yoy_growth(ticker,time):
    df = ratios_download(ticker)
    df = df[41:45]
    return df.loc["Year over Year",time+1]

def revenue_3yr_avg_growth(ticker,time):
    df = ratios_download(ticker)
    df = df[41:45]
    return df.loc["3-Year Average",time+1]

def revenue_5yr_avg_growth(ticker,time):
    df = ratios_download(ticker)
    df = df[41:45]
    return df.loc["5-Year Average",time+1]

def revenue_10yr_avg_growth(ticker,time):
    df = ratios_download(ticker)
    df = df[41:45]
    return df.loc["10-Year Average",time+1]

def operating_income_yoy_growth(ticker,time):
    df = ratios_download(ticker)
    df = df[46:50]
    return df.loc["Year over Year",time+1]

def operating_income_3yr_avg_growth(ticker,time):
    df = ratios_download(ticker)
    df = df[46:50]
    return df.loc["3-Year Average",time+1]

def operating_income_5yr_avg_growth(ticker,time):
    df = ratios_download(ticker)
    df = df[46:50]
    return df.loc["5-Year Average",time+1]

def operating_income_10yr_avg_growth(ticker,time):
    df = ratios_download(ticker)
    df = df[46:50]
    return df.loc["10-Year Average",time+1]

def net_income_yoy_growth(ticker,time):
    df = ratios_download(ticker)
    df = df[51:55]
    return df.loc["Year over Year",time+1]

def net_income_3yr_avg_growth(ticker,time):
    df = ratios_download(ticker)
    df = df[51:55]
    return df.loc["3-Year Average",time+1]

def net_income_5yr_avg_growth(ticker,time):
    df = ratios_download(ticker)
    df = df[51:55]
    return df.loc["5-Year Average",time+1]

def net_income_10yr_avg_growth(ticker,time):
    df = ratios_download(ticker)
    df = df[51:55]
    return df.loc["10-Year Average",time+1]

def EPS_yoy_growth(ticker,time):
    df = ratios_download(ticker)
    df = df[56:60]
    return df.loc["Year over Year",time+1]

def EPS_3yr_avg_growth(ticker,time):
    df = ratios_download(ticker)
    df = df[56:60]
    return df.loc["3-Year Average",time+1]

def EPS_5yr_avg_growth(ticker,time):
    df = ratios_download(ticker)
    df = df[56:60]
    return df.loc["5-Year Average",time+1]

def EPS_10yr_avg_growth(ticker,time):
    df = ratios_download(ticker)
    df = df[56:60]
    return df.loc["10-Year Average",time+1]

def current_ratio(ticker,time):
    df = ratios_download(ticker)
    return df.loc["Current Ratio",time]

def debt_to_equity(ticker,time):
    df = ratios_download(ticker)
    return df.loc["Debt/Equity",time]

def quick_ratio(ticker,time):
    df = ratios_download(ticker)
    return df.loc["Quick Ratio",time]

def stock_exchange(symbol):
    return y_stats(symbol, 'x')

def price_current(symbol):
    return y_stats(symbol, 'l1')

def market_cap(symbol):
    return y_stats(symbol, 'j1')

def book_value(symbol):
    return y_stats(symbol, 'b4')

def dividend_yield(symbol):
    return y_stats(symbol, 'y')

def high_52_week(symbol):
    return y_stats(symbol, 'k')

def low_52_week(symbol):
    return y_stats(symbol, 'j')

def moving_average_50(symbol):
    return y_stats(symbol, 'm3')

def moving_average_200(symbol):
    return y_stats(symbol, 'm4')

def PE_ratio(symbol):
    return y_stats(symbol, 'r')

def forward_PE(symbol):
    return y_stats(symbol, 'r')

def PEG_ratio(symbol):
    return y_stats(symbol, 'r5')

def price_to_sales(symbol):
    return y_stats(symbol, 'p5')

def price_to_book(symbol):
    return y_stats(symbol, 'p6')

def short_ratio(symbol):
    return y_stats(symbol, 's7')