def _create_financial_report_model(model_name):
field_names = [to_pythonic_name(name) for name in get_numerical_column_names(model_name)]
return type(
model_name,
(EventModel,),
{name: pw.DoubleField(null=True) for name in field_names}
)
def csv_rows_to_records(symbol, csv_rows):
""" Returns a generator of database records that come from every row in <csv_rows>,
with <symbol> added and the keys of each record being the pythonic version of column names.
symbol: a string,
csv_row: an iterator of csv rows (tuples), first row must be headers.
"""
keys = [to_pythonic_name(verbose_name) for verbose_name in next(csv_rows)] # headers to keys
for row in csv_rows:
record = dict(zip(keys, row))
record["symbol_obj"] = symbol
yield record
def load_fundamental_data(data_type, symbol, columns=None):
""" Returns a DataFrame object containing <data_type> fundamental data of <symbol> with <columns>.
data_type: name of *_updated_at fields in Symbol model without _updated_at
symbol: e.g. 'C6L.SI'
columns: a sequence of column names as defined in fa.database.*_numerical_columns modules.
Default: None - include all.
"""
if columns is None:
model_name = data_type.replace("_", "")
columns = get_numerical_column_names(model_name)
columns = ["Date"] + list(columns)
field_names = [to_pythonic_name(c) for c in columns]
records = list(get_fundamentals(data_type, symbol, field_names))
return pd.DataFrame.from_records(records, columns=columns, index="Date")
def test_to_pythonic_name(self):
name = fa_util.to_pythonic_name("Cash & ST Investments / Total Assets")
self.assertEqual(name, "cash_st_investments_total_assets")