def test_BuildsIncomeStatementAddress(self):
scraper = WSJdownloader(Storage(""))
for ticker in tickers:
scraper_address = scraper.get_address(ticker, "income")
expected_address = annual_income_statement.replace(
"[ticker]", ticker)
self.assertEqual(scraper_address, expected_address)
def storeValuationSummaryBrief(tickers = None):
store = Storage()
if tickers is None:
xls = XLSio(store)
xls.loadWorkbook("StockSummary")
xls.table = xls.table[xls.table["P/E Ratio (TTM)"].notnull()]
tickers = xls.getTickers()
summary = {}
count = 1.0
errors = {}
print("Assessing " + str(len(tickers)) + " companies")
for ticker in tickers:
try:
reporter = Reporter(ticker)
summary[ticker] = reporter.oneLineValuation()
except MissingStatementEntryError as E:
errors[ticker] = E.message
except InsufficientDataError as E:
errors[ticker] = E.message
except Exception as E:
errors[ticker] = E.message
if count % max(len(tickers) / 4, 1) == 0:
pct_complete = round(100.0 * count / len(tickers))
print(str(pct_complete) + "% complete")
count += 1
index = summary.items()[0][1].index
summary = pandas.DataFrame(summary, index = index).T
summary.to_excel(store.excel("ValuationSummary"))
print(str(len(errors)) + " Failed")
print(str(len(summary)) + " Succeeded")
return errors
def setUp(self):
self.existing_tickers = ["1PG", "ONT", "1ST", "TGP", "TIX"]
self.existing_headings = [
"Company name", "ASX code", "GICS industry group"
]
self.xls = XLSio(Storage("..\\testData\\"))
self.xls.loadWorkbook("ASXListedCompanies")
def setUp(self):
self.filename = "test_file"
store = Storage("..\\testData\\")
self.filepath = store.excel("test_file")
self.xls = XLSio(store)
self.xls.table = Mock()
self.xls.table.to_excel = Mock()
def setUp(self):
self.ticker = "GNG"
self.type = "balance"
data_dir = "..\\testData\\"
store = Storage(data_dir)
self.assets = pandas.read_pickle(data_dir + "GNG\\GNGassets.pkl")
self.liabilities = pandas.read_pickle(data_dir +
"GNG\\GNGliabilities.pkl")
self.scraper = WSJscraper(store)
def retrieveOverviewData(storage_dir, headings = None):
store = Storage(storage_dir)
xls = XLSio(store)
scraper = WSJscraper(store)
xls.loadWorkbook("ASXListedCompanies")
tickers = xls.getTickers()
new_data = {}
for ticker in tickers:
scraper.load_overview(ticker)
try:
new_data[ticker] = scraper.keyStockData()
except Exception:
print("Problem with: " + ticker)
xls.updateTable(new_data)
xls.saveAs("StockSummary")
def saveAnalysisToExcel(ticker):
results = Reporter(ticker)
store = Storage()
writer = pandas.ExcelWriter(store.excel(ticker + "analysis"))
results.summaryTable().to_excel(writer, "Summary")
results.financials.income.income_sheet.to_excel(writer, "Income")
assets = results.financials.balance.asset_sheet
liabilities = results.financials.balance.liabilities
assets.to_excel(writer, "Balance")
liabilities.to_excel(writer, "Balance", startrow = len(assets) + 1)
operating = results.financials.cashflow.operating
investing = results.financials.cashflow.investing
financing = results.financials.cashflow.financing
operating.to_excel(writer, "Cashflow")
investing.to_excel(writer, "Cashflow", startrow = len(operating) + 1)
financing.to_excel(writer, "Cashflow", startrow = len(operating) + len(investing) + 2)
writer.save()
def setUp(self):
self.ticker = "SRV"
self.type = "overview"
self.data_dir = "..\\testData\\"
self.store = Storage(self.data_dir)
self.expected = {
"P/E Ratio (TTM)": 21.68,
"EPS (TTM)": 0.34,
"Market Cap": 698.87,
"Shares Outstanding": 98.43,
"Public Float": 43.03,
"Yield": 3.01,
"Latest Dividend": 0.11,
"Ex-Dividend Date": "09/07/15"
}
self.scraper = WSJscraper(self.store)
self.scraper.overview = BeautifulSoup(
open(self.store.html(self.ticker, self.type)), "lxml")
def setUp(self):
self.root_location = "..\\testData\\"
self.store = Storage(self.root_location)
def buildFinancialAnalyst(ticker, storage_dir = "D:\\Investing\\Data"):
resource = StockFinancialsResource(Storage(storage_dir))
income = IncomeStatement(resource.getFinancials(ticker, "income"))
balance = BalanceSheet(resource.getFinancials(ticker, "assets"), resource.getFinancials(ticker, "liabilities"))
cashflow = CashflowStatement(resource.getFinancials(ticker, "operating"), resource.getFinancials(ticker, "investing"), resource.getFinancials(ticker, "financing"))
return FinanceAnalyst(income, balance, cashflow)
def buildPriceAnalyser(ticker, storage_dir = "D:\\Investing\\Data"):
store = Storage(storage_dir)
prices = pandas.read_pickle(store.yahoo(ticker))
return PriceAnalyser(prices)
def setUp(self):
self.ticker = "SRV"
self.type = "overview"
self.data_dir = "..\\testData\\"
self.store = Storage(self.data_dir)
self.scraper = WSJscraper(self.store)
def test_BuildsCashFlowAddress(self):
scraper = WSJdownloader(Storage(""))
for ticker in tickers:
scraper_address = scraper.get_address(ticker, "cashflow")
expected_address = annual_cash_flow.replace("[ticker]", ticker)
self.assertEqual(scraper_address, expected_address)
def test_BuildsBalanceSheetAddress(self):
scraper = WSJdownloader(Storage(""))
for ticker in tickers:
scraper_address = scraper.get_address(ticker, "balance")
expected_address = annual_balance_sheet.replace("[ticker]", ticker)
self.assertEqual(scraper_address, expected_address)
def test_BuildsFinancialsOverviewAddress(self):
scraper = WSJdownloader(Storage(""))
for ticker in tickers:
scraper_address = scraper.get_address(ticker, "financials")
expected_address = financials_overview.replace("[ticker]", ticker)
self.assertEqual(scraper_address, expected_address)