# -*- 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
df.name = 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.
"""