def cci(self, name='cci', period=20):
if len(self.df_ohlc) > period:
self._add_column_to_ohlc(
name,
ti.cci(self.df_ohlc.pricehigh.values,
self.df_ohlc.pricelow.values,
self.df_ohlc.priceclose.values, period))
return True
else:
return False
def cci(self, name='cci', period=20):
try:
self._add_column_to_ohlc(
name,
ti.cci(self.df_ohlc.pricehigh.values,
self.df_ohlc.pricelow.values,
self.df_ohlc.priceclose.values, period))
except Exception as err:
print("cci error>>>", err.__repr__())
return False
return True
def calculate(self, candles):
candles_len = len(candles)
if candles_len < self.period:
return 0
close_array = np.array(
[float(x['ohlc'].close) for x in candles[-self.period:]])
high_array = np.array(
[float(x['ohlc'].high) for x in candles[-self.period:]])
low_array = np.array(
[float(x['ohlc'].low) for x in candles[-self.period:]])
#calculate
cci = ti.cci(high_array, low_array, close_array, period=self.period)
return cci[-1]
async def evaluate(self, ctx):
self.eval_pending()
if len(ctx.close) >= self.period:
try:
cci = tulipy.cci(ctx.high, ctx.low, ctx.close, self.period)[-1]
except tulipy.lib.InvalidOptionError as e:
self.logger.debug(f"Error when computing Commodity Channel Index: {e}")
self.logger.exception(e, False)
else:
if cci <= self.long:
self.eval_long()
elif cci >= self.short:
self.eval_short()
await self.evaluation_completed(ctx.cryptocurrency, ctx.symbol, ctx.time_frame, eval_time=ctx.eval_time())
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_cci(df, period=5):
l = len(df.close.values)
df[f'cci_{period}'] = pad_left(ti.cci(
np.array(df.high),
np.array(df.low),
np.array(df.close), period), l)