pandas-ta
pandas-ta copied to clipboard
Published
20 hours ago •
twopirllc
twopirllc
Half trend by everget
https://www.tradingview.com/script/U1SJ8ubc-HalfTrend/
please implement half trend by everget
from finta import TA import numpy as np import pandas as pd import pandas_ta as ta
#%% def half_trend(data, amplitude=2, channel_deviation=2): ''' HalfTrend indicator, originally by Alex Orekhov (everget) on TradingView.
Parameters:
data (dataframe): OHLC price data
amplitude (int): lookback window
channel_deviation (int): ATR channel deviation factor
(translated to Python 15/11/21)
'''
# Initialisation
atr2 = data.ta.atr(100) / 2
dev = channel_deviation * atr2
high_price = data.High.rolling(amplitude).max().fillna(0)
low_price = data.Low.rolling(amplitude).min().fillna(0)
highma = TA.SMA(data, period=amplitude, column='High')
lowma = TA.SMA(data, period=amplitude, column='Low')
trend = np.zeros(len(data))
next_trend = np.zeros(len(data))
max_low_price = np.zeros(len(data))
max_low_price[0] = data.Low[0]
min_high_price = np.zeros(len(data))
min_high_price[0] = data.High[0]
for i in range(1, len(data)):
if next_trend[i - 1] == 1:
max_low_price[i] = max(low_price[i - 1], max_low_price[i - 1])
if highma[i] < max_low_price[i] and data.Close[i] < data.Low[i - 1]:
trend[i] = 1
next_trend[i] = 0
min_high_price[i] = high_price[i]
else:
# assign previous values again
trend[i] = trend[i - 1]
next_trend[i] = next_trend[i - 1]
min_high_price[i] = min_high_price[i - 1]
else:
min_high_price[i] = min(high_price[i - 1], min_high_price[i - 1])
if lowma[i] > min_high_price[i] and data.Close[i] > data.High[i - 1]:
trend[i] = 0
next_trend[i] = 1
max_low_price[i] = low_price[i]
else:
# assign previous values again
trend[i] = trend[i - 1]
next_trend[i] = next_trend[i - 1]
max_low_price[i] = max_low_price[i - 1]
up = np.zeros(len(data))
up[0] = max_low_price[0]
down = np.zeros(len(data))
down[0] = min_high_price[0]
atr_high = np.zeros(len(data))
atr_low = np.zeros(len(data))
arrow_up = np.zeros(len(data))
arrow_down = np.zeros(len(data))
for i in range(1, len(data)):
if trend[i] == 0:
if trend[i - 1] != 0:
up[i] = down[i - 1]
arrow_up[i] = up[i] - atr2[i]
else:
up[i] = max(max_low_price[i - 1], up[i - 1])
atr_high[i] = up[i] + dev[i]
atr_low[i] = up[i] - dev[i]
else:
if trend[i - 1] != 1:
down[i] = up[i - 1]
arrow_down[i] = down[i] + atr2[i]
else:
down[i] = min(min_high_price[i - 1], down[i - 1])
atr_high[i] = down[i] + dev[i]
atr_low[i] = down[i] - dev[i]
halftrend = np.where(trend == 0, up, down)
buy = np.where((trend == 0) & (np.roll(trend, 1) == 1), 1, 0)
sell = np.where((trend == 1) & (np.roll(trend, 1) == 0), 1, 0)
# Construct DataFrame
htdf = pd.DataFrame(data={'halftrend': halftrend,
'atrHigh': atr_high,
'atrLow': atr_low,
'arrowUp': arrow_up,
'arrowDown': arrow_down,
'buy': buy,
'sell': sell},
index=data.index)
return htdf
from finta import TA import numpy as np import pandas as pd import pandas_ta as ta
#%% def half_trend(data, amplitude=2, channel_deviation=2): ''' HalfTrend indicator, originally by Alex Orekhov (everget) on TradingView.
Parameters: data (dataframe): OHLC price data amplitude (int): lookback window channel_deviation (int): ATR channel deviation factor (translated to Python 15/11/21) ''' # Initialisation atr2 = data.ta.atr(100) / 2 dev = channel_deviation * atr2 high_price = data.High.rolling(amplitude).max().fillna(0) low_price = data.Low.rolling(amplitude).min().fillna(0) highma = TA.SMA(data, period=amplitude, column='High') lowma = TA.SMA(data, period=amplitude, column='Low') trend = np.zeros(len(data)) next_trend = np.zeros(len(data)) max_low_price = np.zeros(len(data)) max_low_price[0] = data.Low[0] min_high_price = np.zeros(len(data)) min_high_price[0] = data.High[0] for i in range(1, len(data)): if next_trend[i - 1] == 1: max_low_price[i] = max(low_price[i - 1], max_low_price[i - 1]) if highma[i] < max_low_price[i] and data.Close[i] < data.Low[i - 1]: trend[i] = 1 next_trend[i] = 0 min_high_price[i] = high_price[i] else: # assign previous values again trend[i] = trend[i - 1] next_trend[i] = next_trend[i - 1] min_high_price[i] = min_high_price[i - 1] else: min_high_price[i] = min(high_price[i - 1], min_high_price[i - 1]) if lowma[i] > min_high_price[i] and data.Close[i] > data.High[i - 1]: trend[i] = 0 next_trend[i] = 1 max_low_price[i] = low_price[i] else: # assign previous values again trend[i] = trend[i - 1] next_trend[i] = next_trend[i - 1] max_low_price[i] = max_low_price[i - 1] up = np.zeros(len(data)) up[0] = max_low_price[0] down = np.zeros(len(data)) down[0] = min_high_price[0] atr_high = np.zeros(len(data)) atr_low = np.zeros(len(data)) arrow_up = np.zeros(len(data)) arrow_down = np.zeros(len(data)) for i in range(1, len(data)): if trend[i] == 0: if trend[i - 1] != 0: up[i] = down[i - 1] arrow_up[i] = up[i] - atr2[i] else: up[i] = max(max_low_price[i - 1], up[i - 1]) atr_high[i] = up[i] + dev[i] atr_low[i] = up[i] - dev[i] else: if trend[i - 1] != 1: down[i] = up[i - 1] arrow_down[i] = down[i] + atr2[i] else: down[i] = min(min_high_price[i - 1], down[i - 1]) atr_high[i] = down[i] + dev[i] atr_low[i] = down[i] - dev[i] halftrend = np.where(trend == 0, up, down) buy = np.where((trend == 0) & (np.roll(trend, 1) == 1), 1, 0) sell = np.where((trend == 1) & (np.roll(trend, 1) == 0), 1, 0) # Construct DataFrame htdf = pd.DataFrame(data={'halftrend': halftrend, 'atrHigh': atr_high, 'atrLow': atr_low, 'arrowUp': arrow_up, 'arrowDown': arrow_down, 'buy': buy, 'sell': sell}, index=data.index) return htdf
This is awesome, Thanks for this! One problem though, when I change the amplitude to 1 it starts throwing bad parameters error. Any fix for that?
