Source code for pandas_ta.candles.cdl_z

# -*- coding: utf-8 -*-
from pandas import DataFrame
from pandas_ta.statistics import zscore
from pandas_ta.utils import get_offset, verify_series

[docs]def cdl_z(open_, high, low, close, length=None, full=None, ddof=None, offset=None, **kwargs): """Candle Type: Z Score""" # Validate Arguments length = int(length) if length and length > 0 else 30 ddof = int(ddof) if ddof and ddof >= 0 and ddof < length else 1 open_ = verify_series(open_, length) high = verify_series(high, length) low = verify_series(low, length) close = verify_series(close, length) offset = get_offset(offset) full = bool(full) if full is not None and full else False if open_ is None or high is None or low is None or close is None: return # Calculate Result if full: length = close.size z_open = zscore(open_, length=length, ddof=ddof) z_high = zscore(high, length=length, ddof=ddof) z_low = zscore(low, length=length, ddof=ddof) z_close = zscore(close, length=length, ddof=ddof) _full = "a" if full else "" _props = _full if full else f"_{length}_{ddof}" df = DataFrame({ f"open_Z{_props}": z_open, f"high_Z{_props}": z_high, f"low_Z{_props}": z_low, f"close_Z{_props}": z_close, }) if full: df.fillna(method="backfill", axis=0, inplace=True) # Offset if offset != 0: df = df.shift(offset) # Handle fills if "fillna" in kwargs: df.fillna(kwargs["fillna"], inplace=True) if "fill_method" in kwargs: df.fillna(method=kwargs["fill_method"], inplace=True) # Name and Categorize it = f"CDL_Z{_props}" df.category = "candles" return df
cdl_z.__doc__ = \ """Candle Type: Z Normalizes OHLC Candles with a rolling Z Score. Source: Kevin Johnson Calculation: Default values: length=30, full=False, ddof=1 Z = ZSCORE open = Z( open, length, ddof) high = Z( high, length, ddof) low = Z( low, length, ddof) close = Z(close, length, ddof) Args: open_ (pd.Series): Series of 'open's high (pd.Series): Series of 'high's low (pd.Series): Series of 'low's close (pd.Series): Series of 'close's length (int): The period. Default: 10 Kwargs: naive (bool, optional): If True, prefills potential Doji less than the length if less than a percentage of it's high-low range. Default: False fillna (value, optional): pd.DataFrame.fillna(value) fill_method (value, optional): Type of fill method Returns: pd.Series: CDL_DOJI column. """