Python talib.ADXR Examples

def technical_indicators_df(self, daily_data):
        """
        Assemble a dataframe of technical indicator series for a single stock
        """
        o = daily_data['Open'].values
        c = daily_data['Close'].values
        h = daily_data['High'].values
        l = daily_data['Low'].values
        v = daily_data['Volume'].astype(float).values
        # define the technical analysis matrix

        # Most data series are normalized by their series' mean
        ta = pd.DataFrame()
        ta['MA5'] = tb.MA(c, timeperiod=5) / tb.MA(c, timeperiod=5).mean()
        ta['MA10'] = tb.MA(c, timeperiod=10) / tb.MA(c, timeperiod=10).mean()
        ta['MA20'] = tb.MA(c, timeperiod=20) / tb.MA(c, timeperiod=20).mean()
        ta['MA60'] = tb.MA(c, timeperiod=60) / tb.MA(c, timeperiod=60).mean()
        ta['MA120'] = tb.MA(c, timeperiod=120) / tb.MA(c, timeperiod=120).mean()
        ta['MA5'] = tb.MA(v, timeperiod=5) / tb.MA(v, timeperiod=5).mean()
        ta['MA10'] = tb.MA(v, timeperiod=10) / tb.MA(v, timeperiod=10).mean()
        ta['MA20'] = tb.MA(v, timeperiod=20) / tb.MA(v, timeperiod=20).mean()
        ta['ADX'] = tb.ADX(h, l, c, timeperiod=14) / tb.ADX(h, l, c, timeperiod=14).mean()
        ta['ADXR'] = tb.ADXR(h, l, c, timeperiod=14) / tb.ADXR(h, l, c, timeperiod=14).mean()
        ta['MACD'] = tb.MACD(c, fastperiod=12, slowperiod=26, signalperiod=9)[0] / \
                     tb.MACD(c, fastperiod=12, slowperiod=26, signalperiod=9)[0].mean()
        ta['RSI'] = tb.RSI(c, timeperiod=14) / tb.RSI(c, timeperiod=14).mean()
        ta['BBANDS_U'] = tb.BBANDS(c, timeperiod=5, nbdevup=2, nbdevdn=2, matype=0)[0] / \
                         tb.BBANDS(c, timeperiod=5, nbdevup=2, nbdevdn=2, matype=0)[0].mean()
        ta['BBANDS_M'] = tb.BBANDS(c, timeperiod=5, nbdevup=2, nbdevdn=2, matype=0)[1] / \
                         tb.BBANDS(c, timeperiod=5, nbdevup=2, nbdevdn=2, matype=0)[1].mean()
        ta['BBANDS_L'] = tb.BBANDS(c, timeperiod=5, nbdevup=2, nbdevdn=2, matype=0)[2] / \
                         tb.BBANDS(c, timeperiod=5, nbdevup=2, nbdevdn=2, matype=0)[2].mean()
        ta['AD'] = tb.AD(h, l, c, v) / tb.AD(h, l, c, v).mean()
        ta['ATR'] = tb.ATR(h, l, c, timeperiod=14) / tb.ATR(h, l, c, timeperiod=14).mean()
        ta['HT_DC'] = tb.HT_DCPERIOD(c) / tb.HT_DCPERIOD(c).mean()
        ta["High/Open"] = h / o
        ta["Low/Open"] = l / o
        ta["Close/Open"] = c / o

        self.ta = ta 

def add_ADXR(self, timeperiod=14,
             type='line', color='secondary', **kwargs):
    """Average Directional Movement Index Rating."""

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

def adxr(candles: np.ndarray, period=14, sequential=False) -> Union[float, np.ndarray]:
    """
    ADXR - Average Directional Movement Index Rating

    :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.ADXR(candles[:, 3], candles[:, 4], candles[:, 2], timeperiod=period)

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

def before_trading(context):
    prices = history_bars(context.s1, context.window, '1d', fields=['high', 'low', 'close', 'open'])
    highP = prices['high']
    lowP = prices['low']
    closeP = prices['close']
    openP = prices['open']

    context.ADX = ta.ADXR(highP, lowP, closeP, timeperiod=14)
    context.Pdi = ta.PLUS_DI(highP, lowP, closeP, timeperiod=14)
    context.Ndi = ta.MINUS_DI(highP, lowP, closeP, timeperiod=14)

    context.MA_tw = ta.MA(closeP, timeperiod=20)[-5:]
    context.MA_fi = ta.MA(closeP, timeperiod=50)[-5:]
    context.MA_fork = context.MA_tw > context.MA_fi

    context.SAR = ta.SAR(highP, lowP, acceleration=context.acceleration, maximum=0.2)

    # context.JQ_selOpen = (context.ADX[-1]>=20) #& (context.ADX[-2]>=20) & (context.ADX[-1]<=30) & (context.ADX[-2]<=30)
    context.JW_selOpen = (context.Pdi[-1] <= context.Ndi[-1]) & (context.Pdi[-2] >= context.Ndi[-2])
    context.JE_selOpen = (context.MA_fork[-1]) & (context.MA_fork[-2]) & (not context.MA_fork[-3])
    context.JR_selOpen = (context.SAR[-1] >= 0.95 * openP[-1]) & (context.SAR[-2] <= 1.05 * closeP[-2])
    context.J_selOpen = context.JQ_selOpen & context.JW_selOpen & context.JE_selOpen & context.JR_selOpen

    # context.JQ_buyOpen = context.JQ_selOpen
    context.JW_buyOpen = (context.Pdi[-1] >= context.Ndi[-1]) & (context.Pdi[-2] <= context.Ndi[-2])
    context.JE_buyOpen = (not context.MA_fork[-1]) & (not context.MA_fork[-2]) & (not context.MA_fork[-3])
    context.JR_buyOpen = (context.SAR[-2] >= 0.95 * openP[-2]) & (context.SAR[-1] <= 1.05 * closeP[-1])
    context.J_buyOpen = context.JQ_buyOpen & context.JW_buyOpen & context.JE_buyOpen & context.JR_buyOpen


# 你选择的期货数据更新将会触发此段逻辑，例如日线或分钟线更新 

def TA_ADXR(high, low, close, timeperiod=14) -> np.ndarray:
    """
    名称：平均趋向指数的趋向指数
    简介：使用ADXR指标，指标判断ADX趋势。
    ADXR - Average Directional Movement Index Rating
    """
    real = talib.ADXR(high, low, close, timeperiod=timeperiod)
    return np.c_[real] 

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

def adxr(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)

        return ta.ADXR(highs, lows, closes, timeperiod=period) 

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

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