Python talib.CCI Examples

def CCI(self, period: int, bars: list):
        """
        Return CCI (Commodity Chanel Index) for n bars close price.
​
        CCI = (Typical Price − MA) / 0.015 * Mean Deviation

        where:
            Typical Price = ∑P((H + L + C) / 3))
            P = number of bars (period)
            MA = Moving Average = (∑P Typical Price) / P
            Mean Deviation=(∑P | Typical Price - MA |) / P
        """

        self.check_bars_type(bars)

        cci = ta.CCI(
            bars['high'], bars['low'], bars['close'], timeperiod=period)

        return cci 

def cci(candles: np.ndarray, period=14, sequential=False) -> Union[float, np.ndarray]:
    """
    CCI - Commodity Channel Index

    :param candles: np.ndarray
    :param period: int - default=14
    :param sequential: bool - default=False

    :return: float | np.ndarray
    """
    if not sequential and len(candles) > 240:
        candles = candles[-240:]

    res = talib.CCI(candles[:, 3], candles[:, 4], candles[:, 2], timeperiod=period)

    if sequential:
        return res
    else:
        return None if np.isnan(res[-1]) else res[-1] 

def CCI(frame, n=14, high_col='high', low_col='low', close_col='close'):
    return _frame_to_series(frame, [high_col, low_col, close_col], talib.CCI, n) 

def TA_CCI(high:np.ndarray, 
           low:np.ndarray, 
           close:np.ndarray, 
           timeperiod:int=14) -> np.ndarray:
    """
    名称：平均趋向指数的趋向指数
    简介：使用CCI指标，指标判断CCI趋势。
    CCI - Commodity Channel Index
    """
    real = talib.CCI(high, 
                     low, 
                     close, 
                     timeperiod=timeperiod)
    delta = np.r_[np.nan, np.diff(real)]
    return np.c_[real, delta] 

def CCI(DataFrame, N=14):
    res = talib.CCI(DataFrame.high.values, DataFrame.low.values, DataFrame.close.values, N)
    return pd.DataFrame({'CCI': res}, index=DataFrame.index) 

def cci(self, sym, frequency, period=14):
        if not self.kbars_ready(sym, frequency):
            return []

        highs = self.high(sym, frequency)
        lows = self.low(sym, frequency)
        closes = self.close(sym, frequency)

        cci = ta.CCI(highs, lows, closes, timeperiod=period)

        return cci 

def add_CCI(self, timeperiod=14,
            type='line', color='secondary', **kwargs):
    """Channel Commodity Index."""

    if not (self.has_high and self.has_low and self.has_close):
        raise Exception()

    utils.kwargs_check(kwargs, VALID_TA_KWARGS)
    if 'kind' in kwargs:
        type = kwargs['kind']

    name = 'CCI({})'.format(str(timeperiod))
    self.sec[name] = dict(type=type, color=color)
    self.ind[name] = talib.CCI(self.df[self.hi].values,
                               self.df[self.lo].values,
                               self.df[self.cl].values,
                               timeperiod) 

def cci(self, n, array=False):
        """
        Commodity Channel Index (CCI).
        """
        result = talib.CCI(self.high, self.low, self.close, n)
        if array:
            return result
        return result[-1] 

def cci(self, n, array=False):
        """
        Commodity Channel Index (CCI).
        """
        result = talib.CCI(self.high, self.low, self.close, n)
        if array:
            return result
        return result[-1] 

def cci(self, n, array=False):
        """CCI指标"""
        result = talib.CCI(self.high, self.low, self.close, n)
        if array:
            return result
        return result[-1]

    # ---------------------------------------------------------------------- 

def test_cci(self):
        result = pandas_ta.cci(self.high, self.low, self.close)
        self.assertIsInstance(result, Series)
        self.assertEqual(result.name, 'CCI_14_0.015')

        try:
            expected = tal.CCI(self.high, self.low, self.close)
            pdt.assert_series_equal(result, expected, check_names=False)
        except AssertionError as ae:
            try:
                corr = pandas_ta.utils.df_error_analysis(result, expected, col=CORRELATION)
                self.assertGreater(corr, CORRELATION_THRESHOLD)
            except Exception as ex:
                error_analysis(result, CORRELATION, ex) 

def CCI(data, **kwargs):
    _check_talib_presence()
    _, phigh, plow, pclose, _ = _extract_ohlc(data)
    return talib.CCI(phigh, plow, pclose, **kwargs) 

def __recountCci(self):
        """CCI计算
        顺势指标又叫CCI指标，CCI指标是美国股市技术分析 家唐纳德·蓝伯特(Donald Lambert)于20世纪80年代提出的，专门测量股价、外汇或者贵金属交易
        是否已超出常态分布范围。属于超买超卖类指标中较特殊的一种。波动于正无穷大和负无穷大之间。但是，又不需要以0为中轴线，这一点也和波动于正无穷大
        和负无穷大的指标不同。
        它最早是用于期货市场的判断，后运用于股票市场的研判，并被广泛使用。与大多数单一利用股票的收盘价、开盘价、最高价或最低价而发明出的各种技术分析
        指标不同，CCI指标是根据统计学原理，引进价格与固定期间的股价平均区间的偏离程度的概念，强调股价平均绝对偏差在股市技术分析中的重要性，是一种比
        较独特的技术指标。
        它与其他超买超卖型指标又有自己比较独特之处。象KDJ、W%R等大多数超买超卖型指标都有“0-100”上下界限，因此，它们对待一般常态行情的研判比较适用
        ，而对于那些短期内暴涨暴跌的股票的价格走势时，就可能会发生指标钝化的现象。而CCI指标却是波动于正无穷大到负无穷大之间，因此不会出现指标钝化现
        象，这样就有利于投资者更好地研判行情，特别是那些短期内暴涨暴跌的非常态行情。
        http://baike.baidu.com/view/53690.htm?fromtitle=CCI%E6%8C%87%E6%A0%87&fromid=4316895&type=syn


        """
        if self.inputCciLen <= 0:
            return

        # 1、lineBar满足长度才执行计算
        if len(self.lineBar) < self.inputCciLen+2:
            self.debugCtaLog(u'数据未充分,当前Bar数据数量：{0}，计算CCI需要：{1}'.
                             format(len(self.lineBar), self.inputCciLen + 2))
            return

        # 计算第1根RSI曲线

        # 3、inputCc1Len(包含当前周期）
        if self.mode == self.TICK_MODE:
            listClose = [x.close for x in self.lineBar[-self.inputCciLen - 2:-1]]
            listHigh = [x.high for x in self.lineBar[-self.inputCciLen - 2:-1]]
            listLow  = [x.low for x in self.lineBar[-self.inputCciLen - 2:-1]]
            idx = 2
        else:
            listClose = [x.close for x in self.lineBar[-self.inputCciLen-1:]]
            listHigh = [x.high for x in self.lineBar[-self.inputCciLen - 1:]]
            listLow = [x.low for x in self.lineBar[-self.inputCciLen - 1:]]
            idx = 1

        barCci = ta.CCI(high=np.array(listHigh, dtype=float), low=np.array(listLow, dtype=float),
                        close=np.array(listClose, dtype=float), timeperiod=self.inputCciLen)[-1]

        barCci = round(float(barCci), 3)

        l = len(self.lineCci)
        if l > self.inputCciLen*8:
            del self.lineCci[0]
        self.lineCci.append(barCci)