Example #1
0
class TestPSARIndicator(unittest.TestCase):
    """
    https://school.stockcharts.com/doku.php?id=technical_indicators:parabolic_sar
    """

    _filename = 'ta/tests/data/cs-psar.csv'

    def setUp(self):
        self._df = pd.read_csv(self._filename, sep=',')
        self._indicator = PSARIndicator(high=self._df['High'],
                                        low=self._df['Low'],
                                        close=self._df['Close'],
                                        fillna=False)

    def tearDown(self):
        del (self._df)

    def test_psar_up(self):
        target = 'psar_up'
        result = self._indicator.psar_up()
        pd.testing.assert_series_equal(self._df[target].tail(),
                                       result.tail(),
                                       check_names=False)

    def test_psar_down(self):
        target = 'psar_down'
        result = self._indicator.psar_down()
        pd.testing.assert_series_equal(self._df[target].tail(),
                                       result.tail(),
                                       check_names=False)

    def test_psar_up_indicator(self):
        target = 'psar_up_ind'
        result = self._indicator.psar_up_indicator()
        pd.testing.assert_series_equal(self._df[target].tail(),
                                       result.tail(),
                                       check_names=False)

    def test_psar_down_indicator(self):
        target = 'psar_down_ind'
        result = self._indicator.psar_down_indicator()
        pd.testing.assert_series_equal(self._df[target].tail(),
                                       result.tail(),
                                       check_names=False)
Example #2
0
def add_trend_ta(
    df: pd.DataFrame,
    high: str,
    low: str,
    close: str,
    fillna: bool = False,
    colprefix: str = "",
    vectorized: bool = False,
) -> pd.DataFrame:
    """Add trend technical analysis features to dataframe.

    Args:
        df (pandas.core.frame.DataFrame): Dataframe base.
        high (str): Name of 'high' column.
        low (str): Name of 'low' column.
        close (str): Name of 'close' column.
        fillna(bool): if True, fill nan values.
        colprefix(str): Prefix column names inserted
        vectorized(bool): if True, use only vectorized functions indicators

    Returns:
        pandas.core.frame.DataFrame: Dataframe with new features.
    """

    # MACD
    indicator_macd = MACD(close=df[close],
                          window_slow=26,
                          window_fast=12,
                          window_sign=9,
                          fillna=fillna)
    df[f"{colprefix}trend_macd"] = indicator_macd.macd()
    df[f"{colprefix}trend_macd_signal"] = indicator_macd.macd_signal()
    df[f"{colprefix}trend_macd_diff"] = indicator_macd.macd_diff()

    # SMAs
    df[f"{colprefix}trend_sma_fast"] = SMAIndicator(
        close=df[close], window=12, fillna=fillna).sma_indicator()
    df[f"{colprefix}trend_sma_slow"] = SMAIndicator(
        close=df[close], window=26, fillna=fillna).sma_indicator()

    # EMAs
    df[f"{colprefix}trend_ema_fast"] = EMAIndicator(
        close=df[close], window=12, fillna=fillna).ema_indicator()
    df[f"{colprefix}trend_ema_slow"] = EMAIndicator(
        close=df[close], window=26, fillna=fillna).ema_indicator()

    # Vortex Indicator
    indicator_vortex = VortexIndicator(high=df[high],
                                       low=df[low],
                                       close=df[close],
                                       window=14,
                                       fillna=fillna)
    df[f"{colprefix}trend_vortex_ind_pos"] = indicator_vortex.vortex_indicator_pos(
    )
    df[f"{colprefix}trend_vortex_ind_neg"] = indicator_vortex.vortex_indicator_neg(
    )
    df[f"{colprefix}trend_vortex_ind_diff"] = indicator_vortex.vortex_indicator_diff(
    )

    # TRIX Indicator
    df[f"{colprefix}trend_trix"] = TRIXIndicator(close=df[close],
                                                 window=15,
                                                 fillna=fillna).trix()

    # Mass Index
    df[f"{colprefix}trend_mass_index"] = MassIndex(high=df[high],
                                                   low=df[low],
                                                   window_fast=9,
                                                   window_slow=25,
                                                   fillna=fillna).mass_index()

    # DPO Indicator
    df[f"{colprefix}trend_dpo"] = DPOIndicator(close=df[close],
                                               window=20,
                                               fillna=fillna).dpo()

    # KST Indicator
    indicator_kst = KSTIndicator(
        close=df[close],
        roc1=10,
        roc2=15,
        roc3=20,
        roc4=30,
        window1=10,
        window2=10,
        window3=10,
        window4=15,
        nsig=9,
        fillna=fillna,
    )
    df[f"{colprefix}trend_kst"] = indicator_kst.kst()
    df[f"{colprefix}trend_kst_sig"] = indicator_kst.kst_sig()
    df[f"{colprefix}trend_kst_diff"] = indicator_kst.kst_diff()

    # Ichimoku Indicator
    indicator_ichi = IchimokuIndicator(
        high=df[high],
        low=df[low],
        window1=9,
        window2=26,
        window3=52,
        visual=False,
        fillna=fillna,
    )
    df[f"{colprefix}trend_ichimoku_conv"] = indicator_ichi.ichimoku_conversion_line(
    )
    df[f"{colprefix}trend_ichimoku_base"] = indicator_ichi.ichimoku_base_line()
    df[f"{colprefix}trend_ichimoku_a"] = indicator_ichi.ichimoku_a()
    df[f"{colprefix}trend_ichimoku_b"] = indicator_ichi.ichimoku_b()

    # Schaff Trend Cycle (STC)
    df[f"{colprefix}trend_stc"] = STCIndicator(
        close=df[close],
        window_slow=50,
        window_fast=23,
        cycle=10,
        smooth1=3,
        smooth2=3,
        fillna=fillna,
    ).stc()

    if not vectorized:
        # Average Directional Movement Index (ADX)
        indicator_adx = ADXIndicator(high=df[high],
                                     low=df[low],
                                     close=df[close],
                                     window=14,
                                     fillna=fillna)
        df[f"{colprefix}trend_adx"] = indicator_adx.adx()
        df[f"{colprefix}trend_adx_pos"] = indicator_adx.adx_pos()
        df[f"{colprefix}trend_adx_neg"] = indicator_adx.adx_neg()

        # CCI Indicator
        df[f"{colprefix}trend_cci"] = CCIIndicator(
            high=df[high],
            low=df[low],
            close=df[close],
            window=20,
            constant=0.015,
            fillna=fillna,
        ).cci()

        # Ichimoku Visual Indicator
        indicator_ichi_visual = IchimokuIndicator(
            high=df[high],
            low=df[low],
            window1=9,
            window2=26,
            window3=52,
            visual=True,
            fillna=fillna,
        )
        df[f"{colprefix}trend_visual_ichimoku_a"] = indicator_ichi_visual.ichimoku_a(
        )
        df[f"{colprefix}trend_visual_ichimoku_b"] = indicator_ichi_visual.ichimoku_b(
        )

        # Aroon Indicator
        indicator_aroon = AroonIndicator(close=df[close],
                                         window=25,
                                         fillna=fillna)
        df[f"{colprefix}trend_aroon_up"] = indicator_aroon.aroon_up()
        df[f"{colprefix}trend_aroon_down"] = indicator_aroon.aroon_down()
        df[f"{colprefix}trend_aroon_ind"] = indicator_aroon.aroon_indicator()

        # PSAR Indicator
        indicator_psar = PSARIndicator(
            high=df[high],
            low=df[low],
            close=df[close],
            step=0.02,
            max_step=0.20,
            fillna=fillna,
        )
        # df[f'{colprefix}trend_psar'] = indicator.psar()
        df[f"{colprefix}trend_psar_up"] = indicator_psar.psar_up()
        df[f"{colprefix}trend_psar_down"] = indicator_psar.psar_down()
        df[f"{colprefix}trend_psar_up_indicator"] = indicator_psar.psar_up_indicator(
        )
        df[f"{colprefix}trend_psar_down_indicator"] = indicator_psar.psar_down_indicator(
        )

    return df