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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 stability_of_timeseries(returns):
 """Determines R-squared of a linear fit to the cumulative log returns. Computes an ordinary least squares linear fit, and returns R-squared. Parameters returns : pd.Series or np.ndarray Daily returns of the strategy, noncumulative. - See full explanation in :func:`~empyrical.stats.cum_returns`. Returns ------- float R-squared. """

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

    returns = np.asanyarray(returns)
    returns = returns[~np.isnan(returns)]

    cum_log_returns = np.log1p(returns).cumsum()
    rhat = stats.linregress(np.arange(len(cum_log_returns)),
                            cum_log_returns)[2]

    return rhat ** 2