def __init__(self,
historical=None,
balance_sheet=None,
income_statement=None,
cash_flow=None,
financial_report_preparation_lag=np.timedelta64(30, 'D')
):
""" Returns an object that is capable of calculating various metrics of the stock.
historical, balance_sheet, income_statement, cash_flow: respective DataFrame objects, default: None
financial_report_preparation_lag (np.timedelta64 object):
how long does it take to release the financial report after the end of fiscal year?
default: 30 days.
"""
self.historical = historical
self.balance_sheet = balance_sheet
self.income_statement = income_statement
self.cash_flow = cash_flow
self.financial_report_preparation_lag = financial_report_preparation_lag
if historical is not None:
# historical data with missing dates filled in, with values from future
self.historical_fillforward = historical.resample('1D', fill_method="ffill")
# historical data with missing dates filled in, with values from past
self.historical_fillbackward = historical.resample('1D', fill_method="bfill")
# financial reports with the lag in information release factored in
if balance_sheet is not None:
self.balance_sheet_lagged = translate_index(balance_sheet, self.financial_report_preparation_lag)
if income_statement is not None:
self.income_statement_lagged = translate_index(income_statement, self.financial_report_preparation_lag)
if cash_flow is not None:
self.cash_flow_lagged = translate_index(cash_flow, self.financial_report_preparation_lag)
def test_translate_index(self):
result = calculator.translate_index(pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]}), 3)
self.assertFrameEqual(result, pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]}, index=[3, 4, 5]))