mirror of
https://github.com/wassname/pandas-ta.git
synced 2026-06-27 16:10:07 +08:00
James Sawyer
97 lines
2.6 KiB
Python
97 lines
2.6 KiB
Python
# -*- coding: utf-8 -*-
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from pandas_ta.overlap import sma
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from pandas_ta.utils import get_offset, high_low_range, is_percent
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from pandas_ta.utils import non_zero_range, real_body, verify_series
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def cdl_doji(
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open_,
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high,
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low,
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close,
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length=None,
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factor=None,
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scalar=None,
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asint=True,
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offset=None,
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**kwargs,
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):
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"""Candle Type: Doji"""
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# Validate Arguments
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open_ = verify_series(open_)
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high = verify_series(high)
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low = verify_series(low)
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close = verify_series(close)
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length = int(length) if length and length > 0 else 10
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factor = float(factor) if is_percent(factor) else 10
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scalar = float(scalar) if scalar else 100
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offset = get_offset(offset)
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naive = kwargs.pop("naive", False)
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# Calculate Result
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body = real_body(open_, close).abs()
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hl_range = high_low_range(high, low).abs()
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hl_range_avg = sma(hl_range, length)
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doji = body < 0.01 * factor * hl_range_avg
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if naive:
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doji.iloc[:length] = body < 0.01 * factor * hl_range
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if asint:
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doji = scalar * doji.astype(int)
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# Offset
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if offset != 0:
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doji = doji.shift(offset)
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# Handle fills
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if "fillna" in kwargs:
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doji.fillna(kwargs["fillna"], inplace=True)
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if "fill_method" in kwargs:
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doji.fillna(method=kwargs["fill_method"], inplace=True)
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# Name and Categorize it
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doji.name = f"CDL_DOJI_{length}_{0.01 * factor}"
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doji.category = "candles"
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return doji
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cdl_doji.__doc__ = """Candle Type: Doji
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A candle body is Doji, when it's shorter than 10% of the
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average of the 10 previous candles' high-low range.
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Sources:
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TA-Lib: 96.56% Correlation
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Calculation:
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Default values:
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length=10, percent=10 (0.1), scalar=100
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ABS = Absolute Value
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SMA = Simple Moving Average
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BODY = ABS(close - open)
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HL_RANGE = ABS(high - low)
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DOJI = scalar IF BODY < 0.01 * percent * SMA(HL_RANGE, length) ELSE 0
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Args:
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open_ (pd.Series): Series of 'open's
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high (pd.Series): Series of 'high's
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low (pd.Series): Series of 'low's
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close (pd.Series): Series of 'close's
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length (int): The period. Default: 10
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factor (float): Doji value. Default: 100
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scalar (float): How much to magnify. Default: 100
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asint (bool): Keep results numerical instead of boolean. Default: True
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Kwargs:
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naive (bool, optional): If True, prefills potential Doji less than
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the length if less than a percentage of it's high-low range. Default: False
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fillna (value, optional): pd.DataFrame.fillna(value)
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fill_method (value, optional): Type of fill method
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Returns:
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pd.Series: CDL_DOJI column.
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"""
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