def get_dataframe(self,
tickers,
startDate=None,
endDate=None,
metric_name=None,
frequency='daily',
afterHours='false'):
""" Return a pandas.DataFrame of historical prices for one or more ticker symbols.
By default, return latest EOD Composite Price for a list of stock tickers.
On average, each feed contains 3 data sources.
Supported tickers + Available Day Ranges are here:
https://apimedia.tiingo.com/docs/tiingo/daily/supported_tickers.zip
or from the TiingoClient.list_tickers() method.
Args:
tickers (string/list): One or more unique identifiers for a stock ticker.
startDate (string): Start of ticker range in YYYY-MM-DD format.
endDate (string): End of ticker range in YYYY-MM-DD format.
metric_name (string): Optional parameter specifying metric to be returned for each
ticker. In the event of a single ticker, this is optional and if not specified
all of the available data will be returned. In the event of a list of tickers,
this parameter is required.
frequency (string): Resample frequency (defaults to daily).
afterHours (string): If set to true, includes pre and post market data if available.
"""
valid_columns = {
'open', 'high', 'low', 'close', 'volume', 'adjOpen', 'adjHigh',
'adjLow', 'adjClose', 'adjVolume', 'divCash', 'splitFactor'
}
if metric_name is not None and metric_name not in valid_columns:
raise APIColumnNameError('Valid data items are: ' +
str(valid_columns))
if metric_name is None and isinstance(tickers, list):
raise MissingRequiredArgumentError(
"""When tickers is provided as a list, metric_name is a required argument.
Please provide a metric_name, or call this method with one ticker at a time."""
)
params = {
'format': 'json',
'resampleFreq': frequency,
'afterHours': afterHours
}
if startDate:
params['startDate'] = startDate
if endDate:
params['endDate'] = endDate
if pandas_is_installed:
if type(tickers) is str:
stock = tickers
url = self._get_url(stock, frequency)
response = self._request('GET', url, params=params)
df = pd.DataFrame(response.json())
if metric_name is not None:
prices = df[metric_name]
prices.index = df['date']
else:
prices = df
prices.index = df['date']
del (prices['date'])
else:
prices = pd.DataFrame()
for stock in tickers:
url = self._get_url(stock, frequency)
response = self._request('GET', url, params=params)
df = pd.DataFrame(response.json())
df.index = df['date']
df.rename(index=str,
columns={metric_name: stock},
inplace=True)
prices = pd.concat([prices, df[stock]], axis=1)
prices.index = pd.to_datetime(prices.index)
return prices
else:
error_message = (
"Pandas is not installed, but .get_ticker_price() was "
"called with fmt=pandas. In order to install tiingo with "
"pandas, reinstall with pandas as an optional dependency. \n"
"Install tiingo with pandas dependency: \'pip install tiingo[pandas]\'\n"
"Alternatively, just install pandas: pip install pandas.")
raise InstallPandasException(error_message)
def get_dataframe(
self,
tickers,
startDate=None,
endDate=None,
metric_name=None,
frequency="daily",
fmt="json",
):
"""Return a pandas.DataFrame of historical prices for one or more ticker symbols.
By default, return latest EOD Composite Price for a list of stock tickers.
On average, each feed contains 3 data sources.
Supported tickers + Available Day Ranges are here:
https://apimedia.tiingo.com/docs/tiingo/daily/supported_tickers.zip
or from the TiingoClient.list_tickers() method.
Args:
tickers (string/list): One or more unique identifiers for a stock ticker.
startDate (string): Start of ticker range in YYYY-MM-DD format.
endDate (string): End of ticker range in YYYY-MM-DD format.
metric_name (string): Optional parameter specifying metric to be returned for each
ticker. In the event of a single ticker, this is optional and if not specified
all of the available data will be returned. In the event of a list of tickers,
this parameter is required.
frequency (string): Resample frequency (defaults to daily).
fmt (string): 'csv' or 'json'
"""
valid_columns = {
"open",
"high",
"low",
"close",
"volume",
"adjOpen",
"adjHigh",
"adjLow",
"adjClose",
"adjVolume",
"divCash",
"splitFactor",
}
if metric_name is not None and metric_name not in valid_columns:
raise APIColumnNameError("Valid data items are: " +
str(valid_columns))
if metric_name is None and isinstance(tickers, list):
raise MissingRequiredArgumentError(
"""When tickers is provided as a list, metric_name is a required argument.
Please provide a metric_name, or call this method with one ticker at a time."""
)
params = {"format": fmt, "resampleFreq": frequency}
if startDate:
params["startDate"] = startDate
if endDate:
params["endDate"] = endDate
if pandas_is_installed:
if type(tickers) is str:
prices = self._request_pandas(ticker=tickers,
params=params,
metric_name=metric_name)
else:
prices = pd.DataFrame()
for stock in tickers:
ticker_series = self._request_pandas(
ticker=stock, params=params, metric_name=metric_name)
ticker_series = ticker_series.rename(stock)
prices = pd.concat([prices, ticker_series],
axis=1,
sort=True)
return prices
else:
error_message = (
"Pandas is not installed, but .get_ticker_price() was "
"called with fmt=pandas. In order to install tiingo with "
"pandas, reinstall with pandas as an optional dependency. \n"
"Install tiingo with pandas dependency: 'pip install tiingo[pandas]'\n"
"Alternatively, just install pandas: pip install pandas.")
raise InstallPandasException(error_message)