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https://github.com/wassname/pandas-ta.git
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Merge branch 'nv_it' into development
Modifications qui seront validées : nouveau fichier : ../../__init__.py modifié : ../core.py modifié : ../momentum/stoch.py modifié : __init__.py nouveau fichier : ha.py nouveau fichier : supertrend.py modifié : ../volatility/atr.py
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fff-git
+23
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@@ -910,7 +910,17 @@ class AnalysisIndicators(BasePandasObject):
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result = dpo(close=close, length=length, centered=centered, offset=offset, **kwargs)
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return result
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@finalize
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def ha(self, open=None, high=None, low=None, close=None, offset=None, **kwargs):
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open = self._get_column(open, 'open')
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high = self._get_column(high, 'high')
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low = self._get_column(low, 'low')
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close = self._get_column(close, 'close')
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result = ha(open=open, high=high, low=low, close=close, offset=offset, **kwargs)
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self._add_prefix_suffix(result, **kwargs)
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self._append(result, **kwargs)
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return result
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def increasing(self, close=None, length=None, asint=True, offset=None, **kwargs):
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close = self._get_column(close, 'close')
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@@ -966,6 +976,18 @@ class AnalysisIndicators(BasePandasObject):
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self._append(result, **kwargs)
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return result
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def supertrend(self, high=None, low=None, close=None, period=None, multiplier=None, mamode=None, drift=None,
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offset=None, **kwargs):
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high = self._get_column(high, 'high')
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low = self._get_column(low, 'low')
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close = self._get_column(close, 'close')
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result = supertrend(high=high, low=low, close=close, period=period, multiplier=multiplier, mamode=mamode, drift=drift, offset=offset, **kwargs)
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self._add_prefix_suffix(result, **kwargs)
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self._append(result, **kwargs)
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return result
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@finalize
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def vortex(self, high=None, low=None, close=None, drift=None, offset=None, **kwargs):
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high = self._get_column(high, 'high')
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@@ -48,6 +48,7 @@ def stoch(high, low, close, fast_k=None, slow_k=None, slow_d=None, offset=None,
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fastd.name = f"STOCHFd_{slow_d}"
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slowk.name = f"STOCHk_{slow_k}"
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slowd.name = f"STOCHd_{slow_d}"
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fastk.category = fastd.category = slowk.category = slowd.category = 'momentum'
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# Prepare DataFrame to return
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@@ -98,4 +99,4 @@ Kwargs:
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Returns:
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pd.DataFrame: fastk, fastd, slowk, slowd columns.
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"""
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"""
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@@ -6,10 +6,12 @@ from .chop import chop
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from .cksp import cksp
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from .decreasing import decreasing
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from .dpo import dpo
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from .ha import ha
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from .increasing import increasing
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from .linear_decay import linear_decay
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from .long_run import long_run
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from .psar import psar
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from .qstick import qstick
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from .short_run import short_run
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from .supertrend import supertrend
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from .vortex import vortex
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@@ -0,0 +1,99 @@
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# -*- coding: utf-8 -*-
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import numpy as np
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from pandas import DataFrame
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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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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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# 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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# 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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ha_high = np.maximum.reduce([high, ha_open, ha_close])
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ha_low = np.minimum.reduce([low, ha_open, ha_close])
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# Prepare DataFrame to return
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data = {'ha_open': ha_open, 'ha_high': ha_high, 'ha_low': ha_low, 'ha_close': ha_close}
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hadf = DataFrame(data)
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hadf.name = "Heikin-Ashi"
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hadf.category = 'trend'
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# Apply offset if needed
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if offset != 0:
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hadf = hadf.shift(offset)
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# Handle fills
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if 'fillna' in kwargs:
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hadf.fillna(kwargs['fillna'], inplace=True)
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if 'fill_method' in kwargs:
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hadf.fillna(method=kwargs['fill_method'], inplace=True)
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return hadf
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ha.__doc__ = \
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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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share some characteristics with standard candlestick charts but differ based on the values used to create each candle.
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Instead of using the open, high, low, and close like standard candlestick charts,
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the Heikin-Ashi technique uses a modified formula based on two-period averages.
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This gives the chart a 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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The Formula for the Heikin-Ashi technique is:
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Heikin-Ashi Close=(Open0+High0+Low0+Close0)/4
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Heikin-Ashi Open=(HA Open−1+HA Close−1)/2
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Heikin-Ashi High=Max (High0,HA Open0,HA Close0)
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Heikin-Ashi Low=Min (Low0,HA Open0,HA Close0)
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where:Open0 etc.=Values from the current period
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Open−1 etc.=Values from the prior period
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HA=Heikin-Ashi
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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 the formulas.
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For example use the high, low, open, and close to create the first HA close price.
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Use the open and close to create the first HA open.
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The high of the period will be the first HA high, and the low will be the first HA low.
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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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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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@@ -0,0 +1,104 @@
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# -*- coding: utf-8 -*-
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import numpy as np
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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, 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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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 length
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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, 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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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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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_{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_{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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# Handle fills
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if 'fillna' in kwargs:
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supertrend_df.fillna(kwargs['fillna'], inplace=True)
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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 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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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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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 (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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