def get_history(
self, tickers: Union[CcyTicker, Sequence[CcyTicker]], fields: Union[None, str, Sequence[str]] = None,
start_date: datetime = None, end_date: datetime = None, **kwargs) \
-> Union[QFSeries, QFDataFrame, QFDataArray]:
tickers, got_single_ticker = convert_to_list(tickers, CcyTicker)
got_single_date = start_date is not None and (start_date == end_date)
if fields is not None:
fields, got_single_field = convert_to_list(fields,
(PriceField, str))
else:
got_single_field = False # all existing fields will be present in the result
tickers_data_dict = {}
with Session() as session:
for ticker in tickers:
single_ticker_data = self._get_single_ticker(
ticker, fields, start_date, end_date, session)
tickers_data_dict[ticker] = single_ticker_data
if fields is None:
fields = get_fields_from_tickers_data_dict(tickers_data_dict)
result_data_array = tickers_dict_to_data_array(tickers_data_dict,
tickers, fields)
result = normalize_data_array(result_data_array, tickers, fields,
got_single_date, got_single_ticker,
got_single_field)
return result
def _get_history(
self, convert_to_prices_types: bool, tickers: Union[QuandlTicker, Sequence[QuandlTicker]],
fields: Union[None, str, Sequence[str], PriceField, Sequence[PriceField]] = None,
start_date: datetime = None, end_date: datetime = None) -> \
Union[QFSeries, QFDataFrame, QFDataArray]:
"""
NOTE: Only use one Quandl Database at the time.
Do not mix multiple databases in one query - this is the natural limitation coming from the fact that column
names (fields) are different across databases.
"""
tickers, got_single_ticker = convert_to_list(tickers, QuandlTicker)
got_single_date = start_date is not None and (start_date == end_date)
if fields is not None:
fields, got_single_field = convert_to_list(fields,
(PriceField, str))
else:
got_single_field = False # all existing fields will be present in the result
result_dict = {}
for db_name, ticker_group in groupby(tickers,
lambda t: t.database_name):
ticker_group = list(ticker_group)
partial_result_dict = self._get_result_for_single_database(
convert_to_prices_types, ticker_group, fields, start_date,
end_date)
result_dict.update(partial_result_dict)
if fields is None:
fields = get_fields_from_tickers_data_dict(result_dict)
result_data_array = tickers_dict_to_data_array(result_dict, tickers,
fields)
normalized_result = normalize_data_array(
result_data_array,
tickers,
fields,
got_single_date,
got_single_ticker,
got_single_field,
use_prices_types=convert_to_prices_types)
return normalized_result