Example #1
0
def add_volatility_ta(df: pd.DataFrame,
                      high: str,
                      low: str,
                      close: str,
                      fillna: bool = False,
                      colprefix: str = "") -> pd.DataFrame:
    """Add volatility 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

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

    # Average True Range
    df[f'{colprefix}volatility_atr'] = AverageTrueRange(
        close=df[close], high=df[high], low=df[low], n=10,
        fillna=fillna).average_true_range()

    # Bollinger Bands
    indicator_bb = BollingerBands(close=df[close], n=20, ndev=2, fillna=fillna)
    df[f'{colprefix}volatility_bbm'] = indicator_bb.bollinger_mavg()
    df[f'{colprefix}volatility_bbh'] = indicator_bb.bollinger_hband()
    df[f'{colprefix}volatility_bbl'] = indicator_bb.bollinger_lband()
    df[f'{colprefix}volatility_bbw'] = indicator_bb.bollinger_wband()
    df[f'{colprefix}volatility_bbhi'] = indicator_bb.bollinger_hband_indicator(
    )
    df[f'{colprefix}volatility_bbli'] = indicator_bb.bollinger_lband_indicator(
    )

    # Keltner Channel
    indicator_kc = KeltnerChannel(close=df[close],
                                  high=df[high],
                                  low=df[low],
                                  n=10,
                                  fillna=fillna)
    df[f'{colprefix}volatility_kcc'] = indicator_kc.keltner_channel_central()
    df[f'{colprefix}volatility_kch'] = indicator_kc.keltner_channel_hband()
    df[f'{colprefix}volatility_kcl'] = indicator_kc.keltner_channel_lband()
    df[f'{colprefix}volatility_kchi'] = indicator_kc.keltner_channel_hband_indicator(
    )
    df[f'{colprefix}volatility_kcli'] = indicator_kc.keltner_channel_lband_indicator(
    )

    # Donchian Channel
    indicator_dc = DonchianChannel(close=df[close], n=20, fillna=fillna)
    df[f'{colprefix}volatility_dcl'] = indicator_dc.donchian_channel_lband()
    df[f'{colprefix}volatility_dch'] = indicator_dc.donchian_channel_hband()
    df[f'{colprefix}volatility_dchi'] = indicator_dc.donchian_channel_hband_indicator(
    )
    df[f'{colprefix}volatility_dcli'] = indicator_dc.donchian_channel_lband_indicator(
    )

    return df
Example #2
0
    def _run(self):
        # Bollinger Bands
        bb_indicator = BollingerBands(close=self._close,
                                      n=self._chan_period,
                                      ndev=self._bol_band_std_dev,
                                      fillna=self._fillna)
        self._bb_hband = bb_indicator.bollinger_hband()
        self._bb_lband = bb_indicator.bollinger_lband()

        # Keltner Channel
        kb_indicator = KeltnerChannel(high=self._high,
                                      low=self._low,
                                      close=self._close,
                                      n=self._chan_period,
                                      n_atr=self._kelt_std_dev,
                                      fillna=self._fillna,
                                      ov=False)
        self._kb_hband = kb_indicator.keltner_channel_hband()
        self._kb_lband = kb_indicator.keltner_channel_lband()

        # Momentum Oscillator
        smo_indicator = SMOIndicator(high=self._high,
                                     low=self._low,
                                     close=self._close,
                                     n=self._mom_period,
                                     fillna=self._fillna)
        self._oscillator = smo_indicator.smo()

        # Bar and Signal Colors
        self._squeeze = bb_indicator.bollinger_wband(
        ) - kb_indicator.keltner_channel_wband()
        self._squeeze = self._squeeze.ge(0).astype(int)
Example #3
0
def add_volatility_ta(
    df: pd.DataFrame,
    high: str,
    low: str,
    close: str,
    fillna: bool = False,
    colprefix: str = "",
    vectorized: bool = False,
) -> pd.DataFrame:
    """Add volatility 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.
    """

    # Bollinger Bands
    indicator_bb = BollingerBands(close=df[close],
                                  window=20,
                                  window_dev=2,
                                  fillna=fillna)
    df[f"{colprefix}volatility_bbm"] = indicator_bb.bollinger_mavg()
    df[f"{colprefix}volatility_bbh"] = indicator_bb.bollinger_hband()
    df[f"{colprefix}volatility_bbl"] = indicator_bb.bollinger_lband()
    df[f"{colprefix}volatility_bbw"] = indicator_bb.bollinger_wband()
    df[f"{colprefix}volatility_bbp"] = indicator_bb.bollinger_pband()
    df[f"{colprefix}volatility_bbhi"] = indicator_bb.bollinger_hband_indicator(
    )
    df[f"{colprefix}volatility_bbli"] = indicator_bb.bollinger_lband_indicator(
    )

    # Keltner Channel
    indicator_kc = KeltnerChannel(close=df[close],
                                  high=df[high],
                                  low=df[low],
                                  window=10,
                                  fillna=fillna)
    df[f"{colprefix}volatility_kcc"] = indicator_kc.keltner_channel_mband()
    df[f"{colprefix}volatility_kch"] = indicator_kc.keltner_channel_hband()
    df[f"{colprefix}volatility_kcl"] = indicator_kc.keltner_channel_lband()
    df[f"{colprefix}volatility_kcw"] = indicator_kc.keltner_channel_wband()
    df[f"{colprefix}volatility_kcp"] = indicator_kc.keltner_channel_pband()
    df[f"{colprefix}volatility_kchi"] = indicator_kc.keltner_channel_hband_indicator(
    )
    df[f"{colprefix}volatility_kcli"] = indicator_kc.keltner_channel_lband_indicator(
    )

    # Donchian Channel
    indicator_dc = DonchianChannel(high=df[high],
                                   low=df[low],
                                   close=df[close],
                                   window=20,
                                   offset=0,
                                   fillna=fillna)
    df[f"{colprefix}volatility_dcl"] = indicator_dc.donchian_channel_lband()
    df[f"{colprefix}volatility_dch"] = indicator_dc.donchian_channel_hband()
    df[f"{colprefix}volatility_dcm"] = indicator_dc.donchian_channel_mband()
    df[f"{colprefix}volatility_dcw"] = indicator_dc.donchian_channel_wband()
    df[f"{colprefix}volatility_dcp"] = indicator_dc.donchian_channel_pband()

    if not vectorized:
        # Average True Range
        df[f"{colprefix}volatility_atr"] = AverageTrueRange(
            close=df[close],
            high=df[high],
            low=df[low],
            window=10,
            fillna=fillna).average_true_range()

        # Ulcer Index
        df[f"{colprefix}volatility_ui"] = UlcerIndex(
            close=df[close], window=14, fillna=fillna).ulcer_index()

    return df
Example #4
0
    def applyIndicator(self, full_company_price):
        self.data = full_company_price

        high = self.data['high']
        low = self.data['low']
        close = self.data['close']
        volume = self.data['volume']

        EMA12 = EMAIndicator(close, 12, fillna=False)
        EMA30 = EMAIndicator(close, 20, fillna=False)
        EMA60 = EMAIndicator(close, 60, fillna=False)
        MACD1226 = MACD(close, 26, 12, 9, fillna=False)
        MACD2452 = MACD(close, 52, 24, 18, fillna=False)
        ROC12 = ROCIndicator(close, 12, fillna=False)
        ROC30 = ROCIndicator(close, 30, fillna=False)
        ROC60 = ROCIndicator(close, 60, fillna=False)
        RSI14 = RSIIndicator(close, 14, fillna=False)
        RSI28 = RSIIndicator(close, 28, fillna=False)
        RSI60 = RSIIndicator(close, 60, fillna=False)
        AROON25 = AroonIndicator(close, 25, fillna=False)
        AROON50 = AroonIndicator(close, 50, fillna=False)
        AROON80 = AroonIndicator(close, 80, fillna=False)
        MFI14 = MFIIndicator(high, low, close, volume, 14, fillna=False)
        MFI28 = MFIIndicator(high, low, close, volume, 28, fillna=False)
        MFI80 = MFIIndicator(high, low, close, volume, 80, fillna=False)
        CCI20 = CCIIndicator(high, low, close, 20, 0.015, fillna=False)
        CCI40 = CCIIndicator(high, low, close, 40, 0.015, fillna=False)
        CCI100 = CCIIndicator(high, low, close, 100, 0.015, fillna=False)
        WILLR14 = WilliamsRIndicator(high, low, close, 14, fillna=False)
        WILLR28 = WilliamsRIndicator(high, low, close, 28, fillna=False)
        WILLR60 = WilliamsRIndicator(high, low, close, 60, fillna=False)
        BBANDS20 = BollingerBands(close, 20, 2, fillna=False)
        KC20 = KeltnerChannel(high, low, close, 20, 10, fillna=False)
        STOCH14 = StochasticOscillator(high, low, close, 14, 3, fillna=False)
        STOCH28 = StochasticOscillator(high, low, close, 28, 6, fillna=False)
        STOCH60 = StochasticOscillator(high, low, close, 60, 12, fillna=False)
        CMI20 = ChaikinMoneyFlowIndicator(high,
                                          low,
                                          close,
                                          volume,
                                          20,
                                          fillna=False)
        CMI40 = ChaikinMoneyFlowIndicator(high,
                                          low,
                                          close,
                                          volume,
                                          40,
                                          fillna=False)
        CMI100 = ChaikinMoneyFlowIndicator(high,
                                           low,
                                           close,
                                           volume,
                                           100,
                                           fillna=False)

        self.data['ema12'] = (close - EMA12.ema_indicator()) / close
        self.data['ema30'] = (close - EMA30.ema_indicator()) / close
        self.data['ema60'] = (close - EMA60.ema_indicator()) / close
        self.data['macd1226'] = MACD1226.macd() - MACD1226.macd_signal()
        self.data['macd2452'] = MACD2452.macd() - MACD2452.macd_signal()
        self.data['roc12'] = ROC12.roc()
        self.data['roc30'] = ROC30.roc()
        self.data['roc60'] = ROC60.roc()
        self.data['rsi14'] = RSI14.rsi()
        self.data['rsi28'] = RSI28.rsi()
        self.data['rsi60'] = RSI60.rsi()
        self.data['aroon25'] = AROON25.aroon_indicator()
        self.data['aroon50'] = AROON50.aroon_indicator()
        self.data['aroon80'] = AROON80.aroon_indicator()
        self.data['mfi14'] = MFI14.money_flow_index()
        self.data['mfi28'] = MFI28.money_flow_index()
        self.data['mfi80'] = MFI80.money_flow_index()
        self.data['cci20'] = CCI20.cci()
        self.data['cci40'] = CCI40.cci()
        self.data['cci100'] = CCI100.cci()
        self.data['willr14'] = WILLR14.wr()
        self.data['willr28'] = WILLR28.wr()
        self.data['willr60'] = WILLR60.wr()
        self.data['bband20up'] = (BBANDS20.bollinger_hband() - close) / close
        self.data['bband20down'] = (close - BBANDS20.bollinger_lband()) / close
        self.data['stoch14'] = STOCH14.stoch()
        self.data['stoch28'] = STOCH28.stoch()
        self.data['stoch60'] = STOCH60.stoch()
        self.data['cmi20'] = CMI20.chaikin_money_flow()
        self.data['cmi40'] = CMI40.chaikin_money_flow()
        self.data['cmi100'] = CMI100.chaikin_money_flow()
        self.data['kc20up'] = (KC20.keltner_channel_hband() - close) / close
        self.data['kc20down'] = (close - KC20.keltner_channel_lband()) / close
        return self.data