async def evaluate(self, cryptocurrency, symbol, time_frame, candle_data,
candle):
try:
if len(candle_data) >= self.period * 2:
stochrsi_value = tulipy.stochrsi(
data_util.drop_nan(candle_data), self.period)[-1]
if stochrsi_value * self.TULIPY_INDICATOR_MULTIPLICATOR >= self.evaluator_config[
self.HIGH_LEVEL]:
self.eval_note = 1
elif stochrsi_value * self.TULIPY_INDICATOR_MULTIPLICATOR <= self.evaluator_config[
self.LOW_LEVEL]:
self.eval_note = -1
else:
self.eval_note = stochrsi_value - 0.5
except tulipy.lib.InvalidOptionError as e:
self.logger.debug(f"Error when computing StochRSI: {e}")
self.logger.exception(e, False)
self.eval_note = commons_constants.START_PENDING_EVAL_NOTE
await self.evaluation_completed(
cryptocurrency,
symbol,
time_frame,
eval_time=evaluators_util.get_eval_time(full_candle=candle,
time_frame=time_frame))
async def eval_impl(self):
stochrsi_value = tulipy.stochrsi(self.data[PriceIndexes.IND_PRICE_CLOSE.value],
self.evaluator_config[self.STOCHRSI_PERIOD])[-1]
if stochrsi_value * self.TULIPY_INDICATOR_MULTIPLICATOR >= self.evaluator_config[self.HIGH_LEVEL]:
self.eval_note = 1
elif stochrsi_value * self.TULIPY_INDICATOR_MULTIPLICATOR <= self.evaluator_config[self.LOW_LEVEL]:
self.eval_note = -1
else:
self.eval_note = stochrsi_value - 0.5
async def eval_impl(self):
try:
stochrsi_value = tulipy.stochrsi(
self.data[PriceIndexes.IND_PRICE_CLOSE.value],
self.evaluator_config[self.STOCHRSI_PERIOD])[-1]
if stochrsi_value * self.TULIPY_INDICATOR_MULTIPLICATOR >= self.evaluator_config[
self.HIGH_LEVEL]:
self.eval_note = 1
elif stochrsi_value * self.TULIPY_INDICATOR_MULTIPLICATOR <= self.evaluator_config[
self.LOW_LEVEL]:
self.eval_note = -1
else:
self.eval_note = stochrsi_value - 0.5
except InvalidOptionError as e:
self.logger.debug(f"Error when computing stochrsi: {e}")
self.logger.exception(e)
self.eval_note = START_PENDING_EVAL_NOTE
def inds(self):
Indicators = {}
for i in range(len(self.time)):
#i/o?
''' 2 = High, 3 = Low, 4 = Close, 5 = Volume
collects the needed market data into one list to push to the indicators'''
close = self.time[i][4].values.copy(order='C')
high = self.time[i][2].values.copy(order='C')
low = self.time[i][3].values.copy(order='C')
volume = self.time[i][5].values.copy(order='C')
# !!!This needs to be changed. Each volume of the base time must be indexed up to the slice
__time = self.time[i][6]
# these are the indicators currently being used, and recored
Indicators[i] = {
'stochrsi': ti.stochrsi(close, 5),
'rsi': ti.rsi(close, 5),
# indicators that need to be doublechecked
'mfi': ti.mfi(high, low, close, volume, 5),
'sar': ti.psar(high, low, .2, 2),
'cci': ti.cci(high, low, close, 5),
'ema': ti.ema(close, 5)
}
# this one is good
Indicators[i]['stoch_k'], Indicators[i]['stoch_d'] = ti.stoch(
high, low, close, 5, 3, 3)
# check on this, to see if it functions properly
Indicators[i]['bbands_lower'], Indicators[i]['bbands_middle'],
Indicators[i]['bbands_upper'] = ti.bbands(close, 5, 2)
Indicators[i]['macd'], Indicators[i]['macd_signal'],
Indicators[i]['macd_histogram'] = ti.macd(close, 12, 26, 9)
Indicators[i]['time'] = __time
Indicators[i]['close'] = close
''' below changes the length of each array to match the longest one, for a pandas df
np.nan for the tail of the shorter ones'''
Indicators[i] = to_pd(Indicator=Indicators[i]).dropna(how='all')
return Indicators
def add_stochrsi(df, period):
df[f'stochrsi_{period}'] = pad_left(ti.stochrsi(
np.array(df.close), period), len(np.array(df.close)))