Exemplo n.º 1
0
 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
Exemplo n.º 2
0
 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
Exemplo n.º 3
0
    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]
Exemplo n.º 4
0
    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())
Exemplo n.º 5
0
    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
Exemplo n.º 6
0
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)