mirror of
https://github.com/wassname/pandas-ta.git
synced 2026-06-27 16:10:07 +08:00
Add 2 TI : Heikin-Ashi and supertrend
This commit is contained in:
fff-git
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{
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"execution_count": 23,
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"source": [
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"import pandas_ta as ta"
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]
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{
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"cell_type": "code",
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"source": [
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"import pandas as pd"
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"source": [
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"df = pd.read_csv('/virt/admin/Fic_entree/Dwx-NDXH8.csv', sep=',', header=None)"
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]
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|
||||
" <td>5399.8</td>\n",
|
||||
" <td>5418.8</td>\n",
|
||||
" <td>7241</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>2017.04.20</td>\n",
|
||||
" <td>16:00</td>\n",
|
||||
" <td>5418.5</td>\n",
|
||||
" <td>5455.3</td>\n",
|
||||
" <td>5412.0</td>\n",
|
||||
" <td>5444.3</td>\n",
|
||||
" <td>21038</td>\n",
|
||||
" <td>82.258065</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>2017.04.21</td>\n",
|
||||
" <td>00:00</td>\n",
|
||||
" <td>5442.8</td>\n",
|
||||
" <td>5450.0</td>\n",
|
||||
" <td>5442.5</td>\n",
|
||||
" <td>5448.5</td>\n",
|
||||
" <td>2181</td>\n",
|
||||
" <td>87.747748</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>2017.04.21</td>\n",
|
||||
" <td>08:00</td>\n",
|
||||
" <td>5448.4</td>\n",
|
||||
" <td>5455.3</td>\n",
|
||||
" <td>5447.8</td>\n",
|
||||
" <td>5448.5</td>\n",
|
||||
" <td>7406</td>\n",
|
||||
" <td>84.295612</td>\n",
|
||||
" <td>84.767141</td>\n",
|
||||
" <td>84.767141</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>...</th>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2387</th>\n",
|
||||
" <td>2020.05.26</td>\n",
|
||||
" <td>00:00</td>\n",
|
||||
" <td>9532.4</td>\n",
|
||||
" <td>9597.8</td>\n",
|
||||
" <td>9509.8</td>\n",
|
||||
" <td>9587.6</td>\n",
|
||||
" <td>15120</td>\n",
|
||||
" <td>93.098782</td>\n",
|
||||
" <td>95.820450</td>\n",
|
||||
" <td>95.820450</td>\n",
|
||||
" <td>96.319133</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2388</th>\n",
|
||||
" <td>2020.05.26</td>\n",
|
||||
" <td>08:00</td>\n",
|
||||
" <td>9587.8</td>\n",
|
||||
" <td>9609.1</td>\n",
|
||||
" <td>9542.5</td>\n",
|
||||
" <td>9555.6</td>\n",
|
||||
" <td>31628</td>\n",
|
||||
" <td>46.122860</td>\n",
|
||||
" <td>78.475307</td>\n",
|
||||
" <td>78.475307</td>\n",
|
||||
" <td>90.117018</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2389</th>\n",
|
||||
" <td>2020.05.26</td>\n",
|
||||
" <td>16:00</td>\n",
|
||||
" <td>9556.1</td>\n",
|
||||
" <td>9574.8</td>\n",
|
||||
" <td>9378.1</td>\n",
|
||||
" <td>9417.3</td>\n",
|
||||
" <td>73867</td>\n",
|
||||
" <td>16.969697</td>\n",
|
||||
" <td>52.063780</td>\n",
|
||||
" <td>52.063780</td>\n",
|
||||
" <td>75.453179</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2390</th>\n",
|
||||
" <td>2020.05.27</td>\n",
|
||||
" <td>00:00</td>\n",
|
||||
" <td>9413.5</td>\n",
|
||||
" <td>9500.5</td>\n",
|
||||
" <td>9380.7</td>\n",
|
||||
" <td>9487.5</td>\n",
|
||||
" <td>20104</td>\n",
|
||||
" <td>47.359307</td>\n",
|
||||
" <td>36.817288</td>\n",
|
||||
" <td>36.817288</td>\n",
|
||||
" <td>55.785458</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2391</th>\n",
|
||||
" <td>2020.05.27</td>\n",
|
||||
" <td>08:00</td>\n",
|
||||
" <td>9487.2</td>\n",
|
||||
" <td>9513.0</td>\n",
|
||||
" <td>9391.7</td>\n",
|
||||
" <td>9395.5</td>\n",
|
||||
" <td>33445</td>\n",
|
||||
" <td>8.845958</td>\n",
|
||||
" <td>24.391654</td>\n",
|
||||
" <td>24.391654</td>\n",
|
||||
" <td>37.757574</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"<p>2392 rows × 11 columns</p>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" date time open high low close ticks STOCHFk_14 \\\n",
|
||||
"0 2017.04.20 00:00 5395.3 5455.3 5393.3 5401.5 34502 NaN \n",
|
||||
"1 2017.04.20 08:00 5401.8 5421.5 5399.8 5418.8 7241 NaN \n",
|
||||
"2 2017.04.20 16:00 5418.5 5455.3 5412.0 5444.3 21038 82.258065 \n",
|
||||
"3 2017.04.21 00:00 5442.8 5450.0 5442.5 5448.5 2181 87.747748 \n",
|
||||
"4 2017.04.21 08:00 5448.4 5455.3 5447.8 5448.5 7406 84.295612 \n",
|
||||
"... ... ... ... ... ... ... ... ... \n",
|
||||
"2387 2020.05.26 00:00 9532.4 9597.8 9509.8 9587.6 15120 93.098782 \n",
|
||||
"2388 2020.05.26 08:00 9587.8 9609.1 9542.5 9555.6 31628 46.122860 \n",
|
||||
"2389 2020.05.26 16:00 9556.1 9574.8 9378.1 9417.3 73867 16.969697 \n",
|
||||
"2390 2020.05.27 00:00 9413.5 9500.5 9380.7 9487.5 20104 47.359307 \n",
|
||||
"2391 2020.05.27 08:00 9487.2 9513.0 9391.7 9395.5 33445 8.845958 \n",
|
||||
"\n",
|
||||
" STOCHFd_3 STOCHk_3 STOCHd_3 \n",
|
||||
"0 NaN NaN NaN \n",
|
||||
"1 NaN NaN NaN \n",
|
||||
"2 NaN NaN NaN \n",
|
||||
"3 NaN NaN NaN \n",
|
||||
"4 84.767141 84.767141 NaN \n",
|
||||
"... ... ... ... \n",
|
||||
"2387 95.820450 95.820450 96.319133 \n",
|
||||
"2388 78.475307 78.475307 90.117018 \n",
|
||||
"2389 52.063780 52.063780 75.453179 \n",
|
||||
"2390 36.817288 36.817288 55.785458 \n",
|
||||
"2391 24.391654 24.391654 37.757574 \n",
|
||||
"\n",
|
||||
"[2392 rows x 11 columns]"
|
||||
]
|
||||
},
|
||||
"execution_count": 20,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 24,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"ename": "AttributeError",
|
||||
"evalue": "module 'pandas_ta' has no attribute 'ha'",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
|
||||
"\u001b[0;32m<ipython-input-24-c6639b8e7a6b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhelp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mta\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mha\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
|
||||
"\u001b[0;31mAttributeError\u001b[0m: module 'pandas_ta' has no attribute 'ha'"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"help(ta.ha)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.3"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
@@ -881,6 +881,17 @@ class AnalysisIndicators(BasePandasObject):
|
||||
self._append(result, **kwargs)
|
||||
return result
|
||||
|
||||
def ha(self, open=None, high=None, low=None, close=None, offset=None, **kwargs):
|
||||
open = self._get_column(open, 'open')
|
||||
high = self._get_column(high, 'high')
|
||||
low = self._get_column(low, 'low')
|
||||
close = self._get_column(close, 'close')
|
||||
|
||||
result = ha(open=open, high=high, low=low, close=close, offset=offset, **kwargs)
|
||||
self._add_prefix_suffix(result, **kwargs)
|
||||
self._append(result, **kwargs)
|
||||
return result
|
||||
|
||||
def increasing(self, close=None, length=None, asint=True, offset=None, **kwargs):
|
||||
close = self._get_column(close, 'close')
|
||||
|
||||
@@ -939,6 +950,18 @@ class AnalysisIndicators(BasePandasObject):
|
||||
self._append(result, **kwargs)
|
||||
return result
|
||||
|
||||
def supertrend(self, high=None, low=None, close=None, period=None, multiplier=None, mamode=None, drift=None,
|
||||
offset=None, **kwargs):
|
||||
high = self._get_column(high, 'high')
|
||||
low = self._get_column(low, 'low')
|
||||
close = self._get_column(close, 'close')
|
||||
|
||||
result = supertrend(high=high, low=low, close=close, period=period, multiplier=multiplier, mamode=mamode, drift=drift, offset=offset, **kwargs)
|
||||
self._add_prefix_suffix(result, **kwargs)
|
||||
self._append(result, **kwargs)
|
||||
return result
|
||||
|
||||
|
||||
def vortex(self, high=None, low=None, close=None, drift=None, offset=None, **kwargs):
|
||||
high = self._get_column(high, 'high')
|
||||
low = self._get_column(low, 'low')
|
||||
|
||||
@@ -6,10 +6,12 @@ from .chop import chop
|
||||
from .cksp import cksp
|
||||
from .decreasing import decreasing
|
||||
from .dpo import dpo
|
||||
from .ha import ha
|
||||
from .increasing import increasing
|
||||
from .linear_decay import linear_decay
|
||||
from .long_run import long_run
|
||||
from .psar import psar
|
||||
from .qstick import qstick
|
||||
from .short_run import short_run
|
||||
from .supertrend import supertrend
|
||||
from .vortex import vortex
|
||||
@@ -1,11 +1,11 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
import numpy as np
|
||||
from pandas import DataFrame
|
||||
from ..utils import get_offset, verify_series
|
||||
from pandas_ta.utils import get_offset, verify_series
|
||||
|
||||
|
||||
def ha(open, high, low, close, offset=None, **kwargs):
|
||||
# indicator : Heiken Ashi
|
||||
# indicator : Heikin Ashi
|
||||
# Validate Arguments
|
||||
open = verify_series(open)
|
||||
high = verify_series(high)
|
||||
@@ -13,32 +13,28 @@ def ha(open, high, low, close, offset=None, **kwargs):
|
||||
close = verify_series(close)
|
||||
offset = get_offset(offset)
|
||||
|
||||
# Initialization of the ha_open serie
|
||||
#calculate ha_close
|
||||
ha_close = 0.25 * (open + high + low + close)
|
||||
|
||||
# Initialization of the ha_open array
|
||||
ha_open = np.zeros(shape=(len(close)))
|
||||
|
||||
# ha_open of the first element
|
||||
ha_open[0] = 0.5 * (open[0] + close[0])
|
||||
|
||||
# shift open and close series by one to calculate ha_open other elements
|
||||
open_shifted = np.empty_like(open)
|
||||
open_shifted[:1] = np.nan
|
||||
open_shifted[1:] = open[:-1]
|
||||
close_shifted = np.empty_like(close)
|
||||
close_shifted[:1] = np.nan
|
||||
close_shifted[1:] = close[:-1]
|
||||
# Calculation of ha_open except first element
|
||||
ha_open[1:] = 0.5 * (open_shifted[1:] + close_shifted[1:])
|
||||
#calculate ha_open. Based on previous ha_open & ha_close
|
||||
for i in range (1, len(close)):
|
||||
ha_open[i] = 0.5 * (ha_open[i-1] + ha_close[i-1])
|
||||
|
||||
# calculation of ha_close, ha_high, ha_low
|
||||
ha_close = 0.25 * (open + high + low + close)
|
||||
# calculation of ha_high & ha_low
|
||||
ha_high = np.maximum.reduce([high, ha_open, ha_close])
|
||||
ha_low = np.minimum.reduce([low, ha_open, ha_close])
|
||||
|
||||
# Prepare DataFrame to return
|
||||
data = {'ha_open': ha_open, 'ha_high': ha_high, 'ha_low': ha_low, 'ha_close': ha_close}
|
||||
hadf = DataFrame(data)
|
||||
hadf.name = "Heiken-Ashi"
|
||||
hadf.category = 'overlap'
|
||||
hadf.name = "Heikin-Ashi"
|
||||
hadf.category = 'trend'
|
||||
|
||||
# Apply offset if needed
|
||||
if offset != 0:
|
||||
@@ -55,7 +51,7 @@ def ha(open, high, low, close, offset=None, **kwargs):
|
||||
|
||||
|
||||
ha.__doc__ = \
|
||||
"""Heiken Ashi (HA)
|
||||
"""Heikin Ashi (HA)
|
||||
|
||||
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,
|
||||
@@ -69,7 +65,7 @@ Sources:
|
||||
https://www.investopedia.com/terms/h/heikinashi.asp
|
||||
|
||||
Calculation:
|
||||
The Formula for the Heikin-Ashi Technique Is:
|
||||
The Formula for the Heikin-Ashi technique is:
|
||||
|
||||
Heikin-Ashi Close=(Open0+High0+Low0+Close0)/4
|
||||
Heikin-Ashi Open=(HA Open−1+HA Close−1)/2
|
||||
@@ -0,0 +1,101 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
import numpy as np
|
||||
from pandas import DataFrame
|
||||
from ..utils import get_offset, verify_series
|
||||
from ..volatility import atr
|
||||
|
||||
def supertrend(high, low, close, period=None, multiplier=None, mamode=None, drift=None, offset=None, **kwargs):
|
||||
# indicator : supertrend
|
||||
# Validate Arguments
|
||||
high = verify_series(high)
|
||||
low = verify_series(low)
|
||||
close = verify_series(close)
|
||||
offset = get_offset(offset)
|
||||
period = int(period) if period and period > 0 else 10
|
||||
multiplier = float(multiplier) if multiplier and multiplier > 0 else 1.5
|
||||
min_periods = int(kwargs['min_periods']) if 'min_periods' in kwargs and kwargs[
|
||||
'min_periods'] is not None else period
|
||||
|
||||
st_updown = np.zeros(shape=(len(close)))
|
||||
strend = np.zeros(shape=(len(close)))
|
||||
|
||||
# Bands initial calculation
|
||||
midrange = 0.5 * (high + low)
|
||||
distance = multiplier * atr(high, low, close, period, mamode, drift, offset, min_periods=min_periods)
|
||||
lowerband = midrange - distance
|
||||
upperband = midrange + distance
|
||||
|
||||
# final calculation loop
|
||||
for i in range(1, len(close)):
|
||||
if close[i] > upperband[i-1]:
|
||||
st_updown[i] = 1
|
||||
elif close[i] < lowerband[i-1]:
|
||||
st_updown[i] = -1
|
||||
else:
|
||||
st_updown[i] = st_updown[i-1]
|
||||
if st_updown[i] > 0 and lowerband[i] < lowerband[i-1]:
|
||||
lowerband[i] = lowerband[i-1]
|
||||
if st_updown[i] < 0 and upperband[i] > upperband[i-1]:
|
||||
upperband[i] = upperband[i-1]
|
||||
if st_updown[i] < 0 and st_updown[i-1] > 0:
|
||||
upperband = midrange + distance
|
||||
if st_updown[i] > 0 and st_updown[i-1] < 0:
|
||||
lowerband = midrange - distance
|
||||
if st_updown[i] < 0 :
|
||||
strend[i] = upperband[i]
|
||||
else:
|
||||
strend[i] = lowerband[i]
|
||||
|
||||
|
||||
# Prepare DataFrame to return
|
||||
data = {f"supertrend_{period}_{multiplier}": strend, f"st_updown_{period}_{multiplier}": st_updown}
|
||||
supertrend_df = DataFrame(data)
|
||||
supertrend_df.name = f"supertrend_{period}_{multiplier}"
|
||||
supertrend_df.category = 'trend'
|
||||
|
||||
|
||||
|
||||
# Apply offset if needed
|
||||
if offset != 0:
|
||||
supertrend_df = supertrend_df.shift(offset)
|
||||
|
||||
# Handle fills
|
||||
if 'fillna' in kwargs:
|
||||
supertrend_df.fillna(kwargs['fillna'], inplace=True)
|
||||
|
||||
if 'fill_method' in kwargs:
|
||||
supertrend_df.fillna(method=kwargs['fill_method'], inplace=True)
|
||||
|
||||
|
||||
|
||||
return supertrend_df
|
||||
|
||||
supertrend.__doc__ = \
|
||||
"""Supertrend (supertrend)
|
||||
|
||||
Supertrend is a trend indicator. It was created by Olivier Seban
|
||||
|
||||
Sources:
|
||||
https://www.abcbourse.com/apprendre/11_le_supertrend.html
|
||||
(in french, but many other can be found using a search engine)
|
||||
|
||||
Calculation:
|
||||
Default Inputs:
|
||||
period = 10
|
||||
multiplier = 1.5
|
||||
|
||||
|
||||
Args:
|
||||
high (pd.Series): Series of 'high's
|
||||
low (pd.Series): Series of 'low's
|
||||
close (pd.Series): Series of 'close's
|
||||
|
||||
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.DataFrame: supertrend, st_updown, slowk, slowd columns.
|
||||
"""
|
||||
Reference in New Issue
Block a user