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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def omega_ratio(returns, risk_free=0.0, required_return=0.0, annualization=APPROX_BDAYS_PER_YEAR):
 """Determines the Omega ratio of a strategy. Parameters returns : pd.Series or np.ndarray Daily returns of the strategy, noncumulative. - See full explanation in :func:`~empyrical.stats.cum_returns`. risk_free : int, float Constant risk-free return throughout the period required_return : float, optional Minimum acceptance return of the investor. Threshold over which to consider positive vs negative returns. It will be converted to a value appropriate for the period of the returns. E.g. An annual minimum acceptable return of 100 will translate to a minimum acceptable return of 0.018. annualization : int, optional Factor used to convert the required_return into a daily value. Enter 1 if no time period conversion is necessary. Returns ------- omega_ratio : float Note ----- See https://en.wikipedia.org/wiki/Omega_ratio for more details. """


    if len(returns) < 2:
        return np.nan

    if annualization == 1:
        return_threshold = required_return
    elif required_return <= -1:
        return np.nan
    else:
        return_threshold = (1 + required_return) ** \
            (1. / annualization) - 1

    returns_less_thresh = returns - risk_free - return_threshold

    numer = sum(returns_less_thresh[returns_less_thresh > 0.0])
    denom = -1.0 * sum(returns_less_thresh[returns_less_thresh < 0.0])

    if denom > 0.0:
        return numer / denom
    else:
        return np.nan