Python talib.AD屬性代碼示例

# 需要導入模塊: import talib [as 別名]
# 或者: from talib import AD [as 別名]
def ad(candles: np.ndarray, sequential=False) -> Union[float, np.ndarray]:
    """
    AD - Chaikin A/D Line

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

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

    res = talib.AD(candles[:, 3], candles[:, 4], candles[:, 2], candles[:, 5])

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

# 需要導入模塊: import talib [as 別名]
# 或者: from talib import AD [as 別名]
def AD(DataFrame):
    res = talib.AD(DataFrame.high.values, DataFrame.low.values,
                   DataFrame.close.values, DataFrame.volume.values)
    return pd.DataFrame({'AD': res}, index=DataFrame.index) 

# 需要導入模塊: import talib [as 別名]
# 或者: from talib import AD [as 別名]
def ad(self, sym, frequency):
        if not self.kbars_ready(sym, frequency):
            return []

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

        v = ta.AD(highs, lows, closes, volumes)

        return v 

# 需要導入模塊: import talib [as 別名]
# 或者: from talib import AD [as 別名]
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 

# 需要導入模塊: import talib [as 別名]
# 或者: from talib import AD [as 別名]
def AD(frame, high_col='high', low_col='low', close_col='close', vol_col='Volume'):
    """Chaikin A/D Line"""
    return _frame_to_series(frame, [high_col, low_col, close_col, vol_col], talib.AD) 

# 需要導入模塊: import talib [as 別名]
# 或者: from talib import AD [as 別名]
def test_ad(self):
        result = pandas_ta.ad(self.high, self.low, self.close, self.volume_)
        self.assertIsInstance(result, Series)
        self.assertEqual(result.name, 'AD')

        try:
            expected = tal.AD(self.high, self.low, self.close, self.volume_)
            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) 

# 需要導入模塊: import talib [as 別名]
# 或者: from talib import AD [as 別名]
def AD(data, **kwargs):
    _check_talib_presence()
    popen, phigh, plow, pclose, pvolume = _extract_ohlc(data)
    return talib.AD(popen, phigh, plow, pclose, pvolume, **kwargs)