async def evaluate(self, cryptocurrency, symbol, time_frame, candle, high,
low, close):
hl2 = EvaluatorUtil.CandlesUtil.HL2(high, low)[-1]
atr = tulipy.atr(high, low, close, self.length)[-1]
previous_value = self.get_previous_value(symbol, time_frame)
upper_band = hl2 + self.factor * atr
lower_band = hl2 - self.factor * atr
prev_upper_band = previous_value.get(self.PREV_UPPER_BAND, 0)
prev_lower_band = previous_value.get(self.PREV_LOWER_BAND, 0)
lower_band = lower_band if (
lower_band > prev_lower_band
or close[-2] < prev_lower_band) else prev_lower_band
upper_band = upper_band if (
upper_band < prev_upper_band
or close[-2] > prev_upper_band) else prev_upper_band
prev_super_trend = previous_value.get(self.PREV_SUPERTREND, 0)
if previous_value.get(self.PREV_ATR, None) is None:
self.eval_note = -1
elif prev_super_trend == prev_upper_band:
self.eval_note = 1 if close[-1] > upper_band else -1
else:
self.eval_note = -1 if close[-1] < lower_band else 1
previous_value[self.PREV_ATR] = atr
previous_value[self.PREV_UPPER_BAND] = upper_band
previous_value[self.PREV_LOWER_BAND] = lower_band
previous_value[
self.
PREV_SUPERTREND] = lower_band if self.eval_note == 1 else upper_band
return
def _ATR(self, data, timeframe=None, symbol=None):
timeframe = '1h'
ohlcv_high = self.ohlcv[:, 1].copy(order='C')
ohlcv_low = self.ohlcv[:, 2].copy(order='C')
ohlcv_close = self.ohlcv[:, 3].copy(order='C')
__atr = ti.atr(ohlcv_high, ohlcv_low, ohlcv_close, 14)
self.ATR = __atr[-1]
def test_atr(data):
arr_high = data["high"].to_numpy().astype(float)
arr_low = data["low"].to_numpy().astype(float)
arr_close = data["close"].to_numpy().astype(float)
ti_atr = np.round(np.append([np.nan for i in range(13)],
ti.atr(arr_high, arr_low, arr_close,
period=14)),
decimals=3)
atr = indicator.Indicate(data)\
.average_true_range()['atr']\
.astype(float)
assert np.allclose(ti_atr, atr, atol=1e-03, equal_nan=True)
def calculate(self, candles):
candles_len = len(candles)
if candles_len < self.period + 1:
return float(0)
high_array = np.array(
[float(x['ohlc'].high) for x in candles[-(self.period + 1):]])
low_array = np.array(
[float(x['ohlc'].low) for x in candles[-(self.period + 1):]])
close_array = np.array(
[float(x['ohlc'].close) for x in candles[-(self.period + 1):]])
#calculate
cur_atr = ti.atr(high_array,
low_array,
close_array,
period=self.period)
return float(cur_atr[-1])
# # positions = api.list_positions()
# api.submit_order{
# symbol = 'IWM',
# side='sell',
# qty=57,
# time_in_force = 'day',
# type = 'trailing_stop',
# trail_price = '0.20'
# }
# api.submit_order{
# symbol = 'DIA',
# side='sell',
# qty=5,
# time_in_force = 'day',
# type = 'trailing_stop',
# trail_percent = '0.70'
# }
daily_bars = api.polygon.historic_agg_v2('NIO',1,'day',_from='2021-01-20', to='2021-01-21').df
print(daily_bars)
atr = tulipy.atr(daily_bars.high.values, daily_bars.low.values, daily_bars. close.values, 14)
print(atr)
# trail_price='0.20'
# )
#
# api.submit_order(
# symbol='DIA',
# side='sell',
# qty=5,
# time_in_force='day',
# type='trailing_stop',
# trail_percent='0.70'
# )
start_bar = "2020-10-01T09:30:00-05:00"
end_bar = "2020-11-13T10:00:00-05:00"
# print(start_bar)
# print(type(start_bar))
#
# current_date = date.today().isoformat()
# NY = 'America/New_York'
# end = pd.Timestamp(f"{current_date} 16:00", tz=NY).isoformat()
# print(end)
# print(type(end))
# get data
daily_bars = api.get_barset('NIO', 'day', start=start_bar, end=end_bar).df
atr = tulipy.atr(daily_bars['NIO']['high'].values,
daily_bars['NIO']['low'].values,
daily_bars['NIO']['close'].values, 14)
print(atr)
def add_atr(df, period=5):
l = len(df.close.values)
df[f'atr_{period}'] = pad_left(ti.atr(
np.array(df.high),
np.array(df.low),
np.array(df.close), period), l)
def compute(self, data_dict):
high = data_dict.get('high')
low = data_dict.get('low')
close = data_dict.get('close')
return ti.atr(high, low, close, self.per)