def _convert_to_numpy_array(arr, name):
if type(arr) is np.ndarray:
return arr
try:
# create numpy array from list
arr = np.array(arr, dtype=float)
if jh.is_live():
# in livetrade mode, we'll need them rounded
price = arr[0][1]
prices = jh.round_price_for_live_mode(price, arr[:, 1])
qtys = jh.round_qty_for_live_mode(price, arr[:, 0])
arr[:, 0] = qtys
arr[:, 1] = prices
return arr
except ValueError:
raise exceptions.InvalidShape(
'The format of {} is invalid. \n'
'It must be (qty, price) or [(qty, price), (qty, price)] for multiple points; but {} was given'.format(
name, arr
)
)
def _convert_to_numpy_array(self, arr, name) -> np.ndarray:
if type(arr) is np.ndarray:
return arr
try:
# create numpy array from list
arr = np.array(arr, dtype=float)
if jh.is_live():
# in livetrade mode, we'll need them rounded
current_exchange = selectors.get_exchange(self.exchange)
# skip rounding if the exchange doesn't have values for 'precisions'
if 'precisions' not in current_exchange.vars:
return arr
price_precision = current_exchange.vars['precisions'][self.symbol]['price_precision']
qty_precision = current_exchange.vars['precisions'][self.symbol]['qty_precision']
prices = jh.round_price_for_live_mode(arr[:, 1], price_precision)
qtys = jh.round_qty_for_live_mode(arr[:, 0], qty_precision)
arr[:, 0] = qtys
arr[:, 1] = prices
return arr
except ValueError:
raise exceptions.InvalidShape(
f'The format of {name} is invalid. \n'
f'It must be (qty, price) or [(qty, price), (qty, price)] for multiple points; but {arr} was given'
)
