def __init__(self, metric: str, num_prev_bars: int,
expected_imbalance_window: int, exp_num_ticks_init: int,
exp_num_ticks_constraints: List, batch_size: int,
analyse_thresholds: bool):
"""
Constructor
:param metric: (str) type of imbalance bar to create. Example: "dollar_imbalance"
:param num_prev_bars: (int) Window size for E[T]s (number of previous bars to use for expected number of ticks estimation)
:param expected_imbalance_window: (int) EMA window used to estimate expected imbalance
:param exp_num_ticks_init: (int) Initial number of expected ticks
:param exp_num_ticks_constraints (list) Minimum and maximum possible number of expected ticks. Used to control bars sampling convergence
:param batch_size: (int) Number of rows to read in from the csv, per batch
:param analyse_thresholds: (bool) flag to return thresholds values (theta, exp_num_ticks, exp_imbalance) in a
form of Pandas DataFrame
"""
BaseImbalanceBars.__init__(self, metric, batch_size,
expected_imbalance_window,
exp_num_ticks_init, analyse_thresholds)
# EMA Imbalance specific hyper parameters
self.num_prev_bars = num_prev_bars
if exp_num_ticks_constraints is None:
self.min_exp_num_ticks = 0
self.max_exp_num_ticks = np.inf
else:
self.min_exp_num_ticks = exp_num_ticks_constraints[0]
self.max_exp_num_ticks = exp_num_ticks_constraints[1]
def __init__(self, metric: str, expected_imbalance_window: int,
exp_num_ticks_init: int, batch_size: int,
analyse_thresholds: bool):
"""
Constructor
:param metric: (str) type of imbalance bar to create. Example: "dollar_imbalance"
:param expected_imbalance_window: (int) EMA window used to estimate expected imbalance
:param exp_num_ticks_init: (int) Initial number of expected ticks
:param batch_size: (int) Number of rows to read in from the csv, per batch
:param analyse_thresholds: (bool) Flag to save and return thresholds used to sample imbalance bars
"""
BaseImbalanceBars.__init__(self, metric, batch_size,
expected_imbalance_window,
exp_num_ticks_init, analyse_thresholds)
def __init__(self, file_path_or_df: Tuple[str, pd.DataFrame], metric: str,
expected_imbalance_window: int, exp_num_ticks_init: int,
batch_size: int, analyse_thresholds: bool):
"""
Constructor
:param file_path_or_df: (str or pd.DataFrame) Path to the csv file or Pandas Data Frame containing raw tick data in the format[date_time, price, volume]
:param metric: (str) type of imbalance bar to create. Example: "dollar_imbalance"
:param expected_imbalance_window: (int) EMA window used to estimate expected imbalance
:param exp_num_ticks_init: (int) Initial number of expected ticks
:param batch_size: (int) Number of rows to read in from the csv, per batch
:param analyse_thresholds: (bool) Flag to save and return thresholds used to sample imbalance bars
"""
BaseImbalanceBars.__init__(self, file_path_or_df, metric, batch_size,
expected_imbalance_window,
exp_num_ticks_init, analyse_thresholds)