def history(
self,
period="ytd",
interval="1d",
start=None,
end=None,
adj_timezone=True,
adj_ohlc=False,
):
"""
Historical pricing data
Pulls historical pricing data for a given symbol(s)
Parameters
----------
period: str, default ytd, optional
Length of time
interval: str, default 1d, optional
Time between data points
start: str or datetime.datetime, default None, optional
Specify a starting point to pull data from. Can be expressed as a
string with the format YYYY-MM-DD or as a datetime object
end: str of datetime.datetime, default None, optional
Specify a ending point to pull data from. Can be expressed as a
string with the format YYYY-MM-DD or as a datetime object.
adj_timezone: bool, default True, optional
Specify whether or not to apply the GMT offset to the timestamp
received from the API. If True, the datetimeindex will be adjusted
to the specified ticker's timezone.
adj_ohlc: bool, default False, optional
Calculates an adjusted open, high, low and close prices according
to split and dividend information
Returns
-------
pandas.DataFrame
historical pricing data
"""
config = self._CONFIG["chart"]
intervals = config["query"]["interval"]["options"]
if start or period is None or period.lower() == "max":
start = _convert_to_timestamp(start)
end = _convert_to_timestamp(end, start=False)
params = {"period1": start, "period2": end}
else:
params = {"range": period.lower()}
if interval not in intervals:
raise ValueError(
"Interval values must be one of {}".format(", ".join(intervals))
)
params["interval"] = interval.lower()
if params["interval"] == "1m" and period == "1mo":
df = self._history_1m(adj_timezone, adj_ohlc)
else:
data = self._get_data("chart", params)
df = self._historical_data_to_dataframe(data, params, adj_timezone)
if adj_ohlc and "adjclose" in df:
df = self._adjust_ohlc(df)
return df
def history(
self,
period='ytd',
interval='1d',
start=None,
end=None,
adj_timezone=True):
"""
Historical pricing data
Pulls historical pricing data for a given symbol(s)
Parameters
----------
period: str, default ytd, optional
Length of time
interval: str, default 1d, optional
Time between data points
start: str or datetime.datetime, default None, optional
Specify a starting point to pull data from. Can be expressed as a
string with the format YYYY-MM-DD or as a datetime object
end: str of datetime.datetime, default None, optional
Specify a ending point to pull data from. Can be expressed as a
string with the format YYYY-MM-DD or as a datetime object.
adj_timezone: bool, default True, optional
Specify whether or not to apply the GMT offset to the timestamp
received from the API. If True, the datetimeindex will be adjusted
to the specified ticker's timezone.
Returns
-------
pandas.DataFrame
historical pricing data
"""
config = self._CONFIG['chart']
periods = config['query']['range']['options']
intervals = config['query']['interval']['options']
if start or period is None or period.lower() == 'max':
start = _convert_to_timestamp(start)
end = _convert_to_timestamp(end, start=False)
params = {'period1': start, 'period2': end}
else:
period = period.lower()
if period not in periods:
raise ValueError("Period values must be one of {}".format(
', '.join(periods)))
params = {'range': period}
if interval not in intervals:
raise ValueError("Interval values must be one of {}".format(
', '.join(intervals)))
params['interval'] = interval.lower()
data = self._get_data('chart', params)
df = self._historical_data_to_dataframe(data, params, adj_timezone)
return df
def news(self, count=25, start=None):
"""News articles related to given symbol(s)
Obtain news articles related to a given symbol(s). Data includes
the title of the article, summary, url, author_name, publisher
Parameters
----------
count: int
Desired number of news items to return
start: str or datetime
Date to begin retrieving news items. If date is a str, utilize
the following format: YYYY-MM-DD.
Notes
-----
It's recommended to use only one symbol for this property as the data
returned does not distinguish between what symbol the news stories
belong to
Returns
-------
dict
"""
if start:
start = _convert_to_timestamp(start)
return self._get_data('news', {
'count': count,
'start': start
}, **{'list_result': True})
def _history_1m(self, adj_timezone=True, adj_ohlc=False):
params = {"interval": "1m"}
today = datetime.today()
dates = [
_convert_to_timestamp((today - timedelta(7 * x)).date())
for x in range(5)
]
dataframes = []
for i in range(len(dates) - 1):
params["period1"] = dates[i + 1]
params["period2"] = dates[i]
data = self._get_data("chart", params)
dataframes.append(
self._historical_data_to_dataframe(data, params, adj_timezone))
df = pd.concat(dataframes, sort=True)
df.sort_values(by=["symbol", "date"], inplace=True)
df.fillna(value=0, inplace=True)
return df