Source code for pandas_ta.candles.cdl_doji

# -*- 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 = 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. """