mirror of
https://github.com/wassname/pandas-ta.git
synced 2026-08-02 12:50:22 +08:00
DOC candles and cycles category doc refactor
This commit is contained in:
Kevin Johnson
@@ -5,7 +5,45 @@ from pandas_ta.utils import real_body, verify_series
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def cdl_doji(open_, high, low, close, length=None, factor=None, scalar=None, asint=True, offset=None, **kwargs):
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"""Candle Type: Doji"""
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"""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.
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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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# Validate Arguments
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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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@@ -45,45 +83,3 @@ def cdl_doji(open_, high, low, close, length=None, factor=None, scalar=None, asi
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doji.category = "candles"
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return doji
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cdl_doji.__doc__ = \
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"""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.
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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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@@ -4,7 +4,40 @@ from pandas_ta.utils import verify_series
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def cdl_inside(open_, high, low, close, asbool=False, offset=None, **kwargs):
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"""Candle Type: Inside Bar"""
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"""Candle Type: Inside Bar
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An Inside Bar is a bar that is engulfed by the prior highs and lows of it's
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previous bar. In other words, the current bar is smaller than it's previous bar.
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Set asbool=True if you want to know if it is an Inside Bar. Note by default
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asbool=False so this returns a 0 if it is not an Inside Bar, 1 if it is an
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Inside Bar and close > open, and -1 if it is an Inside Bar but close < open.
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Sources:
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https://www.tradingview.com/script/IyIGN1WO-Inside-Bar/
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Calculation:
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Default Inputs:
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asbool=False
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inside = (high.diff() < 0) & (low.diff() > 0)
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if not asbool:
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inside *= candle_color(open_, close)
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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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asbool (bool): Returns the boolean result. Default: False
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offset (int): How many periods to offset the result. Default: 0
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Kwargs:
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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: New feature
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"""
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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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@@ -33,40 +66,3 @@ def cdl_inside(open_, high, low, close, asbool=False, offset=None, **kwargs):
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inside.category = "candles"
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return inside
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cdl_inside.__doc__ = \
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"""Candle Type: Inside Bar
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An Inside Bar is a bar that is engulfed by the prior highs and lows of it's
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previous bar. In other words, the current bar is smaller than it's previous bar.
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Set asbool=True if you want to know if it is an Inside Bar. Note by default
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asbool=False so this returns a 0 if it is not an Inside Bar, 1 if it is an
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Inside Bar and close > open, and -1 if it is an Inside Bar but close < open.
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Sources:
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https://www.tradingview.com/script/IyIGN1WO-Inside-Bar/
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Calculation:
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Default Inputs:
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asbool=False
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inside = (high.diff() < 0) & (low.diff() > 0)
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if not asbool:
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inside *= candle_color(open_, close)
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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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asbool (bool): Returns the boolean result. Default: False
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offset (int): How many periods to offset the result. Default: 0
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Kwargs:
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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: New feature
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"""
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@@ -24,7 +24,37 @@ ALL_PATTERNS = [
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def cdl_pattern(open_, high, low, close, name: Union[str, Sequence[str]]="all", scalar=None, offset=None, **kwargs) -> DataFrame:
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"""Candle Pattern"""
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"""TA Lib Candle Patterns
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A wrapper around all TA Lib's candle patterns.
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Examples:
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Get all candle patterns (This is the default behaviour)
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>>> df = df.ta.cdl_pattern(name="all")
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Get only one pattern
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>>> df = df.ta.cdl_pattern(name="doji")
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Get some patterns
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>>> df = df.ta.cdl_pattern(name=["doji", "inside"])
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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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name: (Union[str, Sequence[str]]): name of the patterns
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scalar (float): How much to magnify. Default: 100
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offset (int): How many periods to offset the result. Default: 0
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Kwargs:
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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.DataFrame: one column for each pattern.
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"""
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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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@@ -34,9 +64,7 @@ def cdl_pattern(open_, high, low, close, name: Union[str, Sequence[str]]="all",
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scalar = float(scalar) if scalar else 100
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# Patterns that implemented in pandas-ta
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pta_patterns = {
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"doji": cdl_doji, "inside": cdl_inside,
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}
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pta_patterns = {"doji": cdl_doji, "inside": cdl_inside}
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if name == "all":
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name = ALL_PATTERNS
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@@ -84,44 +112,4 @@ def cdl_pattern(open_, high, low, close, name: Union[str, Sequence[str]]="all",
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df.category = "candles"
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return df
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cdl_pattern.__doc__ = \
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"""Candle Pattern
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A wrapper around all candle patterns.
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Examples:
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Get all candle patterns (This is the default behaviour)
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>>> df = df.ta.cdl_pattern(name="all")
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Or
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>>> df.ta.cdl("all", append=True) # = df.ta.cdl_pattern("all", append=True)
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Get only one pattern
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>>> df = df.ta.cdl_pattern(name="doji")
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Or
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>>> df.ta.cdl("doji", append=True)
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Get some patterns
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>>> df = df.ta.cdl_pattern(name=["doji", "inside"])
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Or
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>>> df.ta.cdl(["doji", "inside"], append=True)
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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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name: (Union[str, Sequence[str]]): name of the patterns
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scalar (float): How much to magnify. Default: 100
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offset (int): How many periods to offset the result. Default: 0
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Kwargs:
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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.DataFrame: one column for each pattern.
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"""
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cdl = cdl_pattern
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cdl = cdl_pattern # Alias
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+33
-37
@@ -5,7 +5,39 @@ from pandas_ta.utils import get_offset, verify_series
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def cdl_z(open_, high, low, close, length=None, full=None, ddof=None, offset=None, **kwargs):
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"""Candle Type: Z Score"""
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"""Candle Type: Z
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Normalizes OHLC Candles with a rolling Z Score.
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Source: Kevin Johnson
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Calculation:
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Default values:
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length=30, full=False, ddof=1
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Z = ZSCORE
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open = Z( open, length, ddof)
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high = Z( high, length, ddof)
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low = Z( low, length, ddof)
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close = Z(close, length, ddof)
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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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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.
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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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# Validate Arguments
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length = int(length) if length and length > 0 else 30
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ddof = int(ddof) if ddof and ddof >= 0 and ddof < length else 1
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@@ -54,39 +86,3 @@ def cdl_z(open_, high, low, close, length=None, full=None, ddof=None, offset=Non
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df.category = "candles"
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return df
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cdl_z.__doc__ = \
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"""Candle Type: Z
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Normalizes OHLC Candles with a rolling Z Score.
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Source: Kevin Johnson
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Calculation:
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Default values:
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length=30, full=False, ddof=1
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Z = ZSCORE
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open = Z( open, length, ddof)
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high = Z( high, length, ddof)
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low = Z( low, length, ddof)
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close = Z(close, length, ddof)
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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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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.
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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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+47
-51
@@ -4,7 +4,53 @@ from pandas_ta.utils import get_offset, verify_series
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def ha(open_, high, low, close, offset=None, **kwargs):
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"""Candle Type: Heikin Ashi"""
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"""Heikin Ashi Candles (HA)
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The Heikin-Ashi technique averages price data to create a Japanese
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candlestick chart that filters out market noise. Heikin-Ashi charts,
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developed by Munehisa Homma in the 1700s, share some characteristics
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with standard candlestick charts but differ based on the values used
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to create each candle. Instead of using the open, high, low, and close
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like standard candlestick charts, the Heikin-Ashi technique uses a
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modified formula based on two-period averages. This gives the chart a
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smoother appearance, making it easier to spots trends and reversals,
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but also obscures gaps and some price data.
|
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Sources:
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https://www.investopedia.com/terms/h/heikinashi.asp
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Calculation:
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HA_OPEN[0] = (open[0] + close[0]) / 2
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HA_CLOSE = (open[0] + high[0] + low[0] + close[0]) / 4
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for i > 1 in df.index:
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HA_OPEN = (HA_OPEN[i−1] + HA_CLOSE[i−1]) / 2
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HA_HIGH = MAX(HA_OPEN, HA_HIGH, HA_CLOSE)
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HA_LOW = MIN(HA_OPEN, HA_LOW, HA_CLOSE)
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How to Calculate Heikin-Ashi
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Use one period to create the first Heikin-Ashi (HA) candle, using
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the formulas. For example use the high, low, open, and close to
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create the first HA close price. Use the open and close to create
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the first HA open. The high of the period will be the first HA high,
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and the low will be the first HA low. With the first HA calculated,
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it is now possible to continue computing the HA candles per the formulas.
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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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Kwargs:
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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.DataFrame: ha_open, ha_high,ha_low, ha_close columns.
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"""
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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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@@ -42,53 +88,3 @@ def ha(open_, high, low, close, offset=None, **kwargs):
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df.category = "candles"
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return df
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ha.__doc__ = \
|
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"""Heikin Ashi Candles (HA)
|
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|
||||
The Heikin-Ashi technique averages price data to create a Japanese
|
||||
candlestick chart that filters out market noise. Heikin-Ashi charts,
|
||||
developed by Munehisa Homma in the 1700s, share some characteristics
|
||||
with standard candlestick charts but differ based on the values used
|
||||
to create each candle. Instead of using the open, high, low, and close
|
||||
like standard candlestick charts, the Heikin-Ashi technique uses a
|
||||
modified formula based on two-period averages. This gives the chart a
|
||||
smoother appearance, making it easier to spots trends and reversals,
|
||||
but also obscures gaps and some price data.
|
||||
|
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Sources:
|
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https://www.investopedia.com/terms/h/heikinashi.asp
|
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|
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Calculation:
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HA_OPEN[0] = (open[0] + close[0]) / 2
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HA_CLOSE = (open[0] + high[0] + low[0] + close[0]) / 4
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for i > 1 in df.index:
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HA_OPEN = (HA_OPEN[i−1] + HA_CLOSE[i−1]) / 2
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HA_HIGH = MAX(HA_OPEN, HA_HIGH, HA_CLOSE)
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HA_LOW = MIN(HA_OPEN, HA_LOW, HA_CLOSE)
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|
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How to Calculate Heikin-Ashi
|
||||
|
||||
Use one period to create the first Heikin-Ashi (HA) candle, using
|
||||
the formulas. For example use the high, low, open, and close to
|
||||
create the first HA close price. Use the open and close to create
|
||||
the first HA open. The high of the period will be the first HA high,
|
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and the low will be the first HA low. With the first HA calculated,
|
||||
it is now possible to continue computing the HA candles per the formulas.
|
||||
|
||||
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
|
||||
|
||||
Kwargs:
|
||||
fillna (value, optional): pd.DataFrame.fillna(value)
|
||||
fill_method (value, optional): Type of fill method
|
||||
|
||||
Returns:
|
||||
pd.DataFrame: ha_open, ha_high,ha_low, ha_close columns.
|
||||
"""
|
||||
|
||||
+41
-45
@@ -13,7 +13,47 @@ from pandas_ta.utils import get_offset, verify_series
|
||||
|
||||
|
||||
def ebsw(close, length=None, bars=None, offset=None, initial_version=False, **kwargs):
|
||||
"""Indicator: Even Better SineWave (EBSW)"""
|
||||
"""Even Better SineWave (EBSW)
|
||||
|
||||
This indicator measures market cycles and uses a low pass filter to remove noise.
|
||||
Its output is bound signal between -1 and 1 and the maximum length of a detected
|
||||
trend is limited by its length input.
|
||||
|
||||
Written by rengel8 for Pandas TA based on a publication at 'prorealcode.com' and
|
||||
a book by J.F.Ehlers. According to the suggestion by Squigglez2* and major differences between
|
||||
the initial version's output close to the implementation from Ehler's, the default version is now
|
||||
more closely related to the code from pro-realcode.
|
||||
|
||||
Remark:
|
||||
The default version is now more cycle oriented and tends to be less whipsaw-prune. Thus the older version
|
||||
might offer earlier signals at medium and stronger reversals.
|
||||
A test against the version at TradingView showed very close results with the advantage to be one bar/candle
|
||||
faster, than the corresponding reference value. This might be pre-roll related and was not further investigated.
|
||||
* https://github.com/twopirllc/pandas-ta/issues/350
|
||||
|
||||
|
||||
Sources:
|
||||
- https://www.prorealcode.com/prorealtime-indicators/even-better-sinewave/
|
||||
- J.F.Ehlers 'Cycle Analytics for Traders', 2014
|
||||
|
||||
Calculation:
|
||||
refer to 'sources' or implementation
|
||||
|
||||
Args:
|
||||
close (pd.Series): Series of 'close's
|
||||
length (int): It's max cycle/trend period. Values between 40-48 work like
|
||||
expected with minimum value: 39. Default: 40.
|
||||
bars (int): Period of low pass filtering. Default: 10
|
||||
drift (int): The difference period. Default: 1
|
||||
offset (int): How many periods to offset the result. Default: 0
|
||||
|
||||
Kwargs:
|
||||
fillna (value, optional): pd.DataFrame.fillna(value)
|
||||
fill_method (value, optional): Type of fill method
|
||||
|
||||
Returns:
|
||||
pd.Series: New feature generated.
|
||||
"""
|
||||
# Validate arguments
|
||||
length = int(length) if isinstance(length, int) and length > 10 else 40
|
||||
bars = int(bars) if isinstance(bars, int) and bars > 0 else 10
|
||||
@@ -113,47 +153,3 @@ def ebsw(close, length=None, bars=None, offset=None, initial_version=False, **kw
|
||||
ebsw.category = "cycles"
|
||||
|
||||
return ebsw
|
||||
|
||||
|
||||
ebsw.__doc__ = \
|
||||
"""Even Better SineWave (EBSW)
|
||||
|
||||
This indicator measures market cycles and uses a low pass filter to remove noise.
|
||||
Its output is bound signal between -1 and 1 and the maximum length of a detected
|
||||
trend is limited by its length input.
|
||||
|
||||
Written by rengel8 for Pandas TA based on a publication at 'prorealcode.com' and
|
||||
a book by J.F.Ehlers. According to the suggestion by Squigglez2* and major differences between
|
||||
the initial version's output close to the implementation from Ehler's, the default version is now
|
||||
more closely related to the code from pro-realcode.
|
||||
|
||||
Remark:
|
||||
The default version is now more cycle oriented and tends to be less whipsaw-prune. Thus the older version
|
||||
might offer earlier signals at medium and stronger reversals.
|
||||
A test against the version at TradingView showed very close results with the advantage to be one bar/candle
|
||||
faster, than the corresponding reference value. This might be pre-roll related and was not further investigated.
|
||||
* https://github.com/twopirllc/pandas-ta/issues/350
|
||||
|
||||
|
||||
Sources:
|
||||
- https://www.prorealcode.com/prorealtime-indicators/even-better-sinewave/
|
||||
- J.F.Ehlers 'Cycle Analytics for Traders', 2014
|
||||
|
||||
Calculation:
|
||||
refer to 'sources' or implementation
|
||||
|
||||
Args:
|
||||
close (pd.Series): Series of 'close's
|
||||
length (int): It's max cycle/trend period. Values between 40-48 work like
|
||||
expected with minimum value: 39. Default: 40.
|
||||
bars (int): Period of low pass filtering. Default: 10
|
||||
drift (int): The difference period. Default: 1
|
||||
offset (int): How many periods to offset the result. Default: 0
|
||||
|
||||
Kwargs:
|
||||
fillna (value, optional): pd.DataFrame.fillna(value)
|
||||
fill_method (value, optional): Type of fill method
|
||||
|
||||
Returns:
|
||||
pd.Series: New feature generated.
|
||||
"""
|
||||
|
||||
+31
-35
@@ -10,7 +10,37 @@ from pandas_ta.utils import get_offset, verify_series
|
||||
|
||||
|
||||
def reflex(close, length=None, smooth=None, alpha=None, offset=None, **kwargs):
|
||||
"""Indicator: Reflex"""
|
||||
"""Reflex (reflex)
|
||||
|
||||
John F. Ehlers introduced two indicators within the article
|
||||
"Reflex: A New Zero-Lag Indicator” in February 2020, TASC magazine. One of which
|
||||
is the Reflex, a lag reduced cycle indicator. Both indicators (Reflex/Trendflex)
|
||||
are oscillators and complement each other with the focus for cycle and trend.
|
||||
|
||||
Written for Pandas TA by rengel8 (2021-08-11) based on the implementation on
|
||||
ProRealCode (see Sources). Beyond the mentioned source, this implementation has
|
||||
a separate control parameter for the internal applied SuperSmoother.
|
||||
|
||||
Sources:
|
||||
https://www.prorealcode.com/prorealtime-indicators/reflex-and-trendflex-indicators-john-f-ehlers/
|
||||
|
||||
Calculation:
|
||||
Refer to provided source or the code above.
|
||||
|
||||
Args:
|
||||
close (pd.Series): Series of 'close's
|
||||
length (int): It's period. Default: 20
|
||||
smooth (int): Period of internal SuperSmoother. Default: 20
|
||||
alpha (float: Alpha weight of Difference Sums. Default: 0.04
|
||||
offset (int): How many periods to offset the result. Default: 0
|
||||
|
||||
Kwargs:
|
||||
fillna (value, optional): pd.DataFrame.fillna(value)
|
||||
fill_method (value, optional): Type of fill method
|
||||
|
||||
Returns:
|
||||
pd.Series: New feature generated.
|
||||
"""
|
||||
# Validate arguments
|
||||
close = verify_series(close, length)
|
||||
length = int(length) if isinstance(length, int) and length > 0 else 20
|
||||
@@ -71,37 +101,3 @@ def reflex(close, length=None, smooth=None, alpha=None, offset=None, **kwargs):
|
||||
result.category = "cycles"
|
||||
|
||||
return result
|
||||
|
||||
|
||||
reflex.__doc__ = \
|
||||
"""Reflex (reflex)
|
||||
|
||||
John F. Ehlers introduced two indicators within the article
|
||||
"Reflex: A New Zero-Lag Indicator” in February 2020, TASC magazine. One of which
|
||||
is the Reflex, a lag reduced cycle indicator. Both indicators (Reflex/Trendflex)
|
||||
are oscillators and complement each other with the focus for cycle and trend.
|
||||
|
||||
Written for Pandas TA by rengel8 (2021-08-11) based on the implementation on
|
||||
ProRealCode (see Sources). Beyond the mentioned source, this implementation has
|
||||
a separate control parameter for the internal applied SuperSmoother.
|
||||
|
||||
Sources:
|
||||
https://www.prorealcode.com/prorealtime-indicators/reflex-and-trendflex-indicators-john-f-ehlers/
|
||||
|
||||
Calculation:
|
||||
Refer to provided source or the code above.
|
||||
|
||||
Args:
|
||||
close (pd.Series): Series of 'close's
|
||||
length (int): It's period. Default: 20
|
||||
smooth (int): Period of internal SuperSmoother. Default: 20
|
||||
alpha (float: Alpha weight of Difference Sums. Default: 0.04
|
||||
offset (int): How many periods to offset the result. Default: 0
|
||||
|
||||
Kwargs:
|
||||
fillna (value, optional): pd.DataFrame.fillna(value)
|
||||
fill_method (value, optional): Type of fill method
|
||||
|
||||
Returns:
|
||||
pd.Series: New feature generated.
|
||||
"""
|
||||
|
||||
Reference in New Issue
Block a user