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Sur la branche nv_it
Modifications qui seront validées : modifié : ha.py modifié : supertrend.py modifié : ../volatility/atr.py
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fff-git
@@ -7,23 +7,23 @@ 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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# indicator : Heikin Ashi
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# Validate Arguments
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open = verify_series(open)
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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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offset = get_offset(offset)
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#calculate ha_close
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ha_close = 0.25 * (open + high + low + close)
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# calculate ha_close
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ha_close = 0.25 * (open_ + high + low + close)
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# Initialization of the ha_open array
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ha_open = np.zeros(shape=(len(close)))
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# ha_open of the first element
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ha_open[0] = 0.5 * (open[0] + close[0])
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ha_open[0] = 0.5 * (open_[0] + close[0])
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#calculate ha_open. Based on previous ha_open & ha_close
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for i in range (1, len(close)):
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# calculate ha_open. Based on previous ha_open & ha_close
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for i in range(1, len(close)):
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ha_open[i] = 0.5 * (ha_open[i-1] + ha_close[i-1])
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# calculation of ha_high & ha_low
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@@ -51,7 +51,7 @@ def ha(open, high, low, close, offset=None, **kwargs):
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ha.__doc__ = \
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"""Heikin Ashi (HA)
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"""Heikin Ashi (HA)
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The Heikin-Ashi technique averages price data to create a Japanese candlestick chart that filters out market noise.
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Heikin-Ashi charts, developed by Munehisa Homma in the 1700s,
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@@ -84,7 +84,7 @@ HA=Heikin-Ashi
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With the first HA calculated, 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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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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@@ -4,57 +4,51 @@ from pandas import DataFrame
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from ..utils import get_offset, verify_series
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from ..volatility import atr
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def supertrend(high, low, close, period=None, multiplier=None, mamode=None, drift=None, offset=None, **kwargs):
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def supertrend(high, low, close, length=None, multiplier=None, mamode=None, drift=None, offset=None, **kwargs):
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# indicator : supertrend
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# Validate Arguments
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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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offset = get_offset(offset)
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period = int(period) if period and period > 0 else 10
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multiplier = float(multiplier) if multiplier and multiplier > 0 else 1.5
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length = int(length) if length and length > 0 else 10
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multiplier = float(multiplier) if multiplier and multiplier > 0 else 3
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min_periods = int(kwargs['min_periods']) if 'min_periods' in kwargs and kwargs[
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'min_periods'] is not None else period
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'min_periods'] is not None else length
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st_updown = np.zeros(shape=(len(close)))
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supertrend_dir = np.zeros(shape=(len(close)))
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strend = np.zeros(shape=(len(close)))
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# Bands initial calculation
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midrange = 0.5 * (high + low)
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distance = multiplier * atr(high, low, close, period, mamode, drift, offset, min_periods=min_periods)
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distance = multiplier * atr(high, low, close, length, mamode, drift, offset, min_periods=min_periods)
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lowerband = midrange - distance
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upperband = midrange + distance
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# final calculation loop
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for i in range(1, len(close)):
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if close[i] > upperband[i-1]:
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st_updown[i] = 1
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elif close[i] < lowerband[i-1]:
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st_updown[i] = -1
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if close[i] > upperband[i - 1]:
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supertrend_dir[i] = 1
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elif close[i] < lowerband[i - 1]:
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supertrend_dir[i] = -1
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else:
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st_updown[i] = st_updown[i-1]
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if st_updown[i] > 0 and lowerband[i] < lowerband[i-1]:
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lowerband[i] = lowerband[i-1]
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if st_updown[i] < 0 and upperband[i] > upperband[i-1]:
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upperband[i] = upperband[i-1]
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if st_updown[i] < 0 and st_updown[i-1] > 0:
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upperband = midrange + distance
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if st_updown[i] > 0 and st_updown[i-1] < 0:
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lowerband = midrange - distance
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if st_updown[i] < 0 :
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supertrend_dir[i] = supertrend_dir[i - 1]
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if supertrend_dir[i] > 0 and lowerband[i] < lowerband[i - 1]:
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lowerband[i] = lowerband[i - 1]
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if supertrend_dir[i] < 0 and upperband[i] > upperband[i - 1]:
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upperband[i] = upperband[i - 1]
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if supertrend_dir[i] < 0:
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strend[i] = upperband[i]
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else:
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strend[i] = lowerband[i]
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# Prepare DataFrame to return
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data = {f"supertrend_{period}_{multiplier}": strend, f"st_updown_{period}_{multiplier}": st_updown}
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data = {f"supertrend_{length}_{multiplier}": strend, f"supertrend_dir_{length}_{multiplier}": supertrend_dir}
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supertrend_df = DataFrame(data)
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supertrend_df.name = f"supertrend_{period}_{multiplier}"
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supertrend_df.name = f"supertrend_{length}_{multiplier}"
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supertrend_df.category = 'trend'
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# Apply offset if needed
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if offset != 0:
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supertrend_df = supertrend_df.shift(offset)
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@@ -66,36 +60,45 @@ def supertrend(high, low, close, period=None, multiplier=None, mamode=None, drif
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if 'fill_method' in kwargs:
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supertrend_df.fillna(method=kwargs['fill_method'], inplace=True)
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return supertrend_df
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supertrend.__doc__ = \
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"""Supertrend (supertrend)
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"""Supertrend (supertrend)
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Supertrend is a trend indicator. It was created by Olivier Seban
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Supertrend is a trend indicator. It is usually used to help identify trend direction, setting stop loss,
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identify support and resistance, and / or generate buy & sell signals.
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Calculation is in 2 steps : first a multiple of ATR is added and substracted to the middle of the high - low range.
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This gives the upperband and lowerband.
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The direction of the trend is then calculated : if close > previous upperband or < previous lowerband,
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then trend direction is changed, else it is the same as previous value.
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If trend direction is unchanged and down, upperband is set to minimum between current and previous value
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If trend direction is unchanged and up, lowerband is set to maximum between current and previous value.
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Sources:
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https://www.abcbourse.com/apprendre/11_le_supertrend.html
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(in french, but many other can be found using a search engine)
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The final band is then choosen according to the direction of the trend : upperband if trend is downward,
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lowerband if trend is upward.
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Returned values are : float for final band level, int (1 : upward trend, -1 : downward trend) for trend direction
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Calculation:
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Default Inputs:
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period = 10
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multiplier = 1.5
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length = 10
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multiplier = 3
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Args:
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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) : length for ATR calculation. Default : 10
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multiplier : coefficient for upper and lower band distance to midrange. Default : 3
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mamode: parameter used for ATR calculation. See ATR documentation. Default : None (= ema)
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drift : parameter used for ATR calculation. See ATR documentation. Default : None (= 1)
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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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min_periods (int, optional) : parameter used for ATR calculation. See ATR documentation. Default : length
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Returns:
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pd.DataFrame: supertrend, st_updown, slowk, slowd columns.
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"""
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pd.DataFrame: supertrend (float), supertrend_dir (int) columns.
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"""
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@@ -75,6 +75,7 @@ Args:
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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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min_periods (int, optional) : Minimum number of periods before calculating ATR. Default : length
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Returns:
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pd.Series: New feature generated.
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