How to use the finta.finta.TA function in finta

def FVE(cls, ohlc: DataFrame, period: int = 22, factor: int = 0.3) -> Series:
        """
        FVE is a money flow indicator, but it has two important innovations: first, the F VE takes into account both intra and
        interday price action, and second, minimal price changes are taken into account by introducing a price threshold.
        """

        hl2 = (ohlc["high"] + ohlc["low"]) / 2
        tp = TA.TP(ohlc)
        smav = ohlc["volume"].rolling(window=period).mean()
        mf = pd.Series((ohlc["close"] - hl2 + tp.diff()), name="mf")

        _mf = pd.concat([ohlc["close"], ohlc["volume"], mf], axis=1)

        def vol_shift(row):

            if row["mf"] > factor * row["close"] / 100:
                return row["volume"]
            elif row["mf"] < -factor * row["close"] / 100:
                return -row["volume"]
            else:
                return 0

        _mf["vol_shift"] = _mf.apply(vol_shift, axis=1)
        _sum = _mf["vol_shift"].rolling(window=period).sum()

def PERCENT_B(
        cls, ohlc: DataFrame, period: int = 20, MA: Series = None, column: str = "close"
    ) -> Series:
        """
        %b (pronounced 'percent b') is derived from the formula for Stochastics and shows where price is in relation to the bands.
        %b equals 1 at the upper band and 0 at the lower band.
        """

        BB = TA.BBANDS(ohlc, period, MA, column)
        percent_b = pd.Series(
            (ohlc["close"] - BB["BB_LOWER"]) / (BB["BB_UPPER"] - BB["BB_LOWER"]),
            name="%b",
        )

        return percent_b

name="EMA_slow",
        )

        MACD = pd.Series((EMA_fast - EMA_slow), name="MACD")
        STOK = ((MACD - MACD.rolling(window=period).min()) / (
                MACD.rolling(window=period).max() - MACD.rolling(window=period).min())) * 100
        STOD = STOK.rolling(window=period).mean()

        return pd.Series(
            100 * (MACD - (STOK * MACD)) / ((STOD * MACD) - (STOK * MACD)),
            name="{0} period STC.".format(period)
        )


if __name__ == "__main__":
    print([k for k in TA.__dict__.keys() if k[0] not in "_"])