def get_latest_bar(self) -> List[dict]:
bar_size = self._bar_size
bar_type = self._bar_type
# Define data range
end_date = datetime.today()
start_date = end_date - timedelta(minutes=15)
start = str(milliseconds_since_epoch(dt_object=start_date))
end = str(milliseconds_since_epoch(dt_object=end_date))
latest_prices = []
for symbol in self.portfolio.positions:
historical_price_response = self.session.get_price_history(
symbol=symbol,
period_type='day',
start_date=start,
end_date=end,
frequency_type=bar_type,
frequency=bar_size,
extended_hours=True)
if 'error' in historical_price_response:
time_true.sleep(2)
historical_price_response = self.session.get_price_history(
symbol=symbol,
period_type='day',
start_date=start,
end_date=end,
frequency_type=bar_type,
frequency=bar_size,
extended_hours=True)
for candle in historical_price_response['candles'][-1:]:
new_price_mini_dict = {}
new_price_mini_dict['symbol'] = symbol
new_price_mini_dict['open'] = candle['open']
new_price_mini_dict['close'] = candle['close']
new_price_mini_dict['high'] = candle['high']
new_price_mini_dict['low'] = candle['low']
new_price_mini_dict['volume'] = candle['low']
new_price_mini_dict['datetime'] = candle['datetime']
latest_prices.append(new_price_mini_dict)
return latest_prices
def grab_historical_prices(
self,
start: datetime,
end: datetime,
bar_size: int = 1,
bar_type: str = 'minute',
symbols: Optional[List[str]] = None) -> List[dict]:
self.bar_size = bar_size
self.bar_type = bar_type
start = str(milliseconds_since_epoch(dt_object=start))
end = str(milliseconds_since_epoch(dt_object=end))
new_prices = []
if not symbols:
symbols = self.portfolio.positions
for symbol in symbols:
historical_price_response = self.session.get_price_history(
symbol=symbol,
period_type='day',
start_date=start,
end_date=end,
frequency_type=bar_type,
frequency=bar_size,
extended_hours=True)
self.historical_prices[symbol] = {}
self.historical_prices[symbol][
'candles'] = historical_price_response['candles']
for candle in historical_price_response['candles']:
new_price_mini_dict = {}
new_price_mini_dict['symbol'] = symbol
new_price_mini_dict['open'] = candle['open']
new_price_mini_dict['close'] = candle['close']
new_price_mini_dict['high'] = candle['high']
new_price_mini_dict['low'] = candle['low']
new_price_mini_dict['volume'] = candle['low']
new_price_mini_dict['datetime'] = candle['datetime']
new_prices.append(new_price_mini_dict)
self.historical_prices['aggregated'] = new_prices
return self.historical_prices
def get_latest_bar(self) -> List[dict]:
"""Returns the latest bar for each symbol in the portfolio.
Returns:
---
{List[dict]} -- A simplified quote list.
Usage:
----
>>> trading_robot = PyRobot(
client_id=CLIENT_ID,
redirect_uri=REDIRECT_URI,
credentials_path=CREDENTIALS_PATH
)
>>> latest_bars = trading_robot.get_latest_bar()
>>> latest_bars
"""
# Grab the info from the last quest.
bar_size = self._bar_size
bar_type = self._bar_type
# Define the start and end date.
start_date = datetime.today()
end_date = start_date - timedelta(minutes=bar_size * 15)
start = str(milliseconds_since_epoch(dt_object=start_date))
end = str(milliseconds_since_epoch(dt_object=end_date))
latest_prices = []
# Loop through each symbol.
for symbol in self.portfolio.positions:
# Grab the request.
historical_prices_response = self.session.get_price_history(
symbol=symbol,
period_type='day',
start_date=start,
end_date=end,
frequency_type=bar_type,
frequency=bar_size,
extended_hours=True)
if 'error' in historical_prices_response:
time_true.sleep(2)
# Grab the request.
historical_prices_response = self.session.get_price_history(
symbol=symbol,
period_type='day',
start_date=start,
end_date=end,
frequency_type=bar_type,
frequency=bar_size,
extended_hours=True)
# parse the candles.
for candle in historical_prices_response['candles'][-1:]:
new_price_mini_dict = {}
new_price_mini_dict['symbol'] = symbol
new_price_mini_dict['open'] = candle['open']
new_price_mini_dict['close'] = candle['close']
new_price_mini_dict['high'] = candle['high']
new_price_mini_dict['low'] = candle['low']
new_price_mini_dict['volume'] = candle['volume']
new_price_mini_dict['datetime'] = candle['datetime']
latest_prices.append(new_price_mini_dict)
return latest_prices
def grab_historical_prices(
self,
start: datetime,
end: datetime,
bar_size: int = 1,
bar_type: str = 'minute',
symbols: Optional[List[str]] = None) -> List[dict]:
"""Grabs the historical prices for all the postions in a portfolio.
Overview:
----
Any of the historical price data returned will include extended hours
price data by default.
Arguments:
----
start {datetime} -- Defines the start date for the historical prices.
end {datetime} -- Defines the end date for the historical prices.
Keyword Arguments:
----
bar_size {int} -- Defines the size of each bar. (default: {1})
bar_type {str} -- Defines the bar type, can be one of the following:
`['minute', 'week', 'month', 'year']` (default: {'minute'})
symbols {List[str]} -- A list of ticker symbols to pull. (default: None)
Returns:
----
{List[Dict]} -- The historical price candles.
Usage:
----
>>> trading_robot = PyRobot(
client_id=CLIENT_ID,
redirect_uri=REDIRECT_URI,
credentials_path=CREDENTIALS_PATH
)
>>> start_date = datetime.today()
>>> end_date = start_date - timedelta(days=30)
>>> historical_prices = trading_robot.grab_historical_prices(
start=end_date,
end=start_date,
bar_size=1,
bar_type='minute'
)
"""
self._bar_size = bar_size
self._bar_type = bar_type
start = str(milliseconds_since_epoch(dt_object=start))
end = str(milliseconds_since_epoch(dt_object=end))
new_prices = []
if not symbols:
symbols = self.portfolio.positions
for symbol in symbols:
historical_prices_response = self.session.get_price_history(
symbol=symbol,
period_type='day',
start_date=start,
end_date=end,
frequency_type=bar_type,
frequency=bar_size,
extended_hours=True)
self.historical_prices[symbol] = {}
self.historical_prices[symbol][
'candles'] = historical_prices_response['candles']
for candle in historical_prices_response['candles']:
new_price_mini_dict = {}
new_price_mini_dict['symbol'] = symbol
new_price_mini_dict['open'] = candle['open']
new_price_mini_dict['close'] = candle['close']
new_price_mini_dict['high'] = candle['high']
new_price_mini_dict['low'] = candle['low']
new_price_mini_dict['volume'] = candle['volume']
new_price_mini_dict['datetime'] = candle['datetime']
new_prices.append(new_price_mini_dict)
self.historical_prices['aggregated'] = new_prices
return self.historical_prices
def grab_historical_prices(
self,
start: datetime,
end: datetime,
bar_size: int = 1,
bar_type: str = 'minute',
symbols: List[str] = None) -> Union[List[Dict], pd.DataFrame]:
"""Grabs the historical prices for all the postions in a portfolio.
Overview:
----
Any of the historical price data returned will include extended hours
price data by default.
Arguments:
----
start {datetime} -- Defines the start date for the historical prices.
end {datetime} -- Defines the end date for the historical prices.
Keyword Arguments:
----
bar_size {int} -- Defines the size of each bar. (default: {1})
bar_type {str} -- Defines the bar type, can be one of the following:
`['minute', 'week', 'month', 'year']` (default: {'minute'})
symbols {List[str]} -- A list of ticker symbols to pull. (default: None)
Returns:
----
{List[Dict]} -- The historical price candles.
Usage:
----
"""
start = str(milliseconds_since_epoch(dt_object=start))
end = str(milliseconds_since_epoch(dt_object=end))
new_prices = []
if not symbols:
symbols = self.portfolio.positions
for symbol in symbols:
historical_prices_response = self.session.get_price_history(
symbol=symbol,
period_type='day',
start_date=start,
end_date=end,
frequency_type=bar_type,
frequency=bar_size,
extended_hours=True)
self.historical_prices[symbol] = {}
self.historical_prices[symbol][
'candles'] = historical_prices_response['candles']
for candle in historical_prices_response['candles']:
new_price_mini_dict = {}
new_price_mini_dict['symbol'] = symbol
new_price_mini_dict['open'] = candle['open']
new_price_mini_dict['close'] = candle['close']
new_price_mini_dict['high'] = candle['high']
new_price_mini_dict['low'] = candle['low']
new_price_mini_dict['volume'] = candle['volume']
new_price_mini_dict['datetime'] = candle['datetime']
new_prices.append(new_price_mini_dict)
self.historical_prices['aggregated'] = new_prices
return self.historical_prices