def compute_exposures(positions, factor_loadings):
    """
    Compute daily risk factor exposures.

    Parameters
    ----------
    positions: pd.Series
        A series of holdings as percentages indexed by date and ticker.
        - Examples:
            dt          ticker
            2017-01-01  AAPL      0.417582
                        TLT       0.010989
                        XOM       0.571429
            2017-01-02  AAPL      0.202381
                        TLT       0.535714
                        XOM       0.261905

    factor_loadings : pd.DataFrame
        Factor loadings for all days in the date range, with date and ticker as
        index, and factors as columns.
        - Example:
                               momentum  reversal
            dt         ticker
            2017-01-01 AAPL   -1.592914  0.852830
                       TLT     0.184864  0.895534
                       XOM     0.993160  1.149353
            2017-01-02 AAPL   -0.140009 -0.524952
                       TLT    -1.066978  0.185435
                       XOM    -1.798401  0.761549

    Returns
    -------
    risk_exposures_portfolio : pd.DataFrame
        df indexed by datetime, with factors as columns
        - Example:
                        momentum  reversal
            dt
            2017-01-01 -0.238655  0.077123
            2017-01-02  0.821872  1.520515
    """
    risk_exposures = factor_loadings.multiply(positions, axis="rows")
    return risk_exposures.groupby(level="dt").sum()