# -*- coding: utf-8 -*-
from pandas_ta.overlap import sma
from pandas_ta.utils import get_offset, high_low_range, is_percent
from pandas_ta.utils import real_body, verify_series
[docs]def cdl_doji(open_, high, low, close, length=None, factor=None, scalar=None, asint=True, offset=None, **kwargs):
"""Candle Type: Doji"""
# Validate Arguments
length = int(length) if length and length > 0 else 10
factor = float(factor) if is_percent(factor) else 10
scalar = float(scalar) if scalar else 100
open_ = verify_series(open_, length)
high = verify_series(high, length)
low = verify_series(low, length)
close = verify_series(close, length)
offset = get_offset(offset)
naive = kwargs.pop("naive", False)
if open_ is None or high is None or low is None or close is None: return
# Calculate Result
body = real_body(open_, close).abs()
hl_range = high_low_range(high, low).abs()
hl_range_avg = sma(hl_range, length)
doji = body < 0.01 * factor * hl_range_avg
if naive:
doji.iloc[:length] = body < 0.01 * factor * hl_range
if asint:
doji = scalar * doji.astype(int)
# Offset
if offset != 0:
doji = doji.shift(offset)
# Handle fills
if "fillna" in kwargs:
doji.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
doji.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
doji.name = f"CDL_DOJI_{length}_{0.01 * factor}"
doji.category = "candles"
return doji
cdl_doji.__doc__ = \
"""Candle Type: Doji
A candle body is Doji, when it's shorter than 10% of the
average of the 10 previous candles' high-low range.
Sources:
TA-Lib: 96.56% Correlation
Calculation:
Default values:
length=10, percent=10 (0.1), scalar=100
ABS = Absolute Value
SMA = Simple Moving Average
BODY = ABS(close - open)
HL_RANGE = ABS(high - low)
DOJI = scalar IF BODY < 0.01 * percent * SMA(HL_RANGE, length) ELSE 0
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
factor (float): Doji value. Default: 100
scalar (float): How much to magnify. Default: 100
asint (bool): Keep results numerical instead of boolean. Default: True
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.
"""