How to use the quantstats.stats function in QuantStats

def run(data, runs=1000):
    weights = []
    sharpes = np.zeros(runs)
    returns = np.zeros(sharpes.shape)
    drawdowns = np.zeros(sharpes.shape)
    volatility = np.zeros(sharpes.shape)

    for i in range(runs):
        w = create_random_weights(len(data.columns))
        r = (data * w).sum(axis=1)

        weights.append(w)
        returns[i] = r.add(1).prod()
        sharpes[i] = stats.sharpe(r)
        drawdowns[i] = stats.max_drawdown(r)
        volatility[i] = stats.volatility(r)

    return Weights({
        'data': data,
        'weights': weights,
        'sharpes': sharpes,
        'returns': returns,
        'drawdowns': drawdowns,
        'volatility': volatility
    })

metrics['Best Month %'] = _stats.best(df, aggregate='M') * pct
        metrics['Worst Month %'] = _stats.worst(df, aggregate='M') * pct
        metrics['Best Year %'] = _stats.best(df, aggregate='A') * pct
        metrics['Worst Year %'] = _stats.worst(df, aggregate='A') * pct

    # dd
    metrics['~~~~'] = blank
    for ix, row in dd.iterrows():
        metrics[ix] = row
    metrics['Recovery Factor'] = _stats.recovery_factor(df)
    metrics['Ulcer Index'] = _stats.ulcer_index(df, rf)

    # win rate
    if mode.lower() == 'full':
        metrics['~~~~~'] = blank
        metrics['Avg. Up Month %'] = _stats.avg_win(df, aggregate='M') * pct
        metrics['Avg. Down Month %'] = _stats.avg_loss(df, aggregate='M') * pct
        metrics['Win Days %%'] = _stats.win_rate(df) * pct
        metrics['Win Month %%'] = _stats.win_rate(df, aggregate='M') * pct
        metrics['Win Quarter %%'] = _stats.win_rate(df, aggregate='Q') * pct
        metrics['Win Year %%'] = _stats.win_rate(df, aggregate='A') * pct

        if "benchmark" in df:
            metrics['~~~~~~~'] = blank
            greeks = _stats.greeks(df['returns'], df['benchmark'])
            metrics['Beta'] = [str(round(greeks['beta'], 2)), '-']
            metrics['Alpha'] = [str(round(greeks['alpha'], 2)), '-']

    # prepare for display
    for col in metrics.columns:
        try:
            metrics[col] = metrics[col].astype(float).round(2)

df, aggregate='M') * pct
        metrics['Expected Yearly %%'] = _stats.expected_return(
            df, aggregate='A') * pct
        metrics['Kelly Criterion %'] = _stats.kelly_criterion(df) * pct
        metrics['Risk of Ruin %'] = _stats.risk_of_ruin(df)

        metrics['Daily Value-at-Risk %'] = -abs(_stats.var(df) * pct)
        metrics['Expected Shortfall (cVaR) %'] = -abs(_stats.cvar(df) * pct)

    metrics['~~~~~~'] = blank

    metrics['Payoff Ratio'] = _stats.payoff_ratio(df)
    metrics['Profit Factor'] = _stats.profit_factor(df)
    metrics['Common Sense Ratio'] = _stats.common_sense_ratio(df)
    metrics['CPC Index'] = _stats.cpc_index(df)
    metrics['Tail Ratio'] = _stats.tail_ratio(df)
    metrics['Outlier Win Ratio'] = _stats.outlier_win_ratio(df)
    metrics['Outlier Loss Ratio'] = _stats.outlier_loss_ratio(df)

    # returns
    metrics['~~'] = blank
    comp_func = _stats.comp if compounded else _np.sum

    today = df.index[-1]  # _dt.today()
    metrics['MTD %'] = comp_func(
        df[df.index >= _dt(today.year, today.month, 1)]) * pct

    d = today - _td(3*365/12)
    metrics['3M %'] = comp_func(
        df[df.index >= _dt(d.year, d.month, d.day)]) * pct

    d = today - _td(6*365/12)

else:
            metrics['Volatility (ann.) %'] = [ret_vol]

        metrics['Calmar'] = _stats.calmar(df)
        metrics['Skew'] = _stats.skew(df)
        metrics['Kurtosis'] = _stats.kurtosis(df)

        metrics['~~~~~~~~~~'] = blank

        metrics['Expected Daily %%'] = _stats.expected_return(df) * pct
        metrics['Expected Monthly %%'] = _stats.expected_return(
            df, aggregate='M') * pct
        metrics['Expected Yearly %%'] = _stats.expected_return(
            df, aggregate='A') * pct
        metrics['Kelly Criterion %'] = _stats.kelly_criterion(df) * pct
        metrics['Risk of Ruin %'] = _stats.risk_of_ruin(df)

        metrics['Daily Value-at-Risk %'] = -abs(_stats.var(df) * pct)
        metrics['Expected Shortfall (cVaR) %'] = -abs(_stats.cvar(df) * pct)

    metrics['~~~~~~'] = blank

    metrics['Payoff Ratio'] = _stats.payoff_ratio(df)
    metrics['Profit Factor'] = _stats.profit_factor(df)
    metrics['Common Sense Ratio'] = _stats.common_sense_ratio(df)
    metrics['CPC Index'] = _stats.cpc_index(df)
    metrics['Tail Ratio'] = _stats.tail_ratio(df)
    metrics['Outlier Win Ratio'] = _stats.outlier_win_ratio(df)
    metrics['Outlier Loss Ratio'] = _stats.outlier_loss_ratio(df)

    # returns
    metrics['~~'] = blank

metrics['Sortino'] = _stats.sortino(df, rf)
    metrics['Max Drawdown %'] = blank
    metrics['Longest DD Days'] = blank

    if mode.lower() == 'full':
        ret_vol = _stats.volatility(df['returns']) * pct
        if "benchmark" in df:
            bench_vol = _stats.volatility(df['benchmark']) * pct
            metrics['Volatility (ann.) %'] = [ret_vol, bench_vol]
            metrics['R^2'] = _stats.r_squared(df['returns'], df['benchmark'])
        else:
            metrics['Volatility (ann.) %'] = [ret_vol]

        metrics['Calmar'] = _stats.calmar(df)
        metrics['Skew'] = _stats.skew(df)
        metrics['Kurtosis'] = _stats.kurtosis(df)

        metrics['~~~~~~~~~~'] = blank

        metrics['Expected Daily %%'] = _stats.expected_return(df) * pct
        metrics['Expected Monthly %%'] = _stats.expected_return(
            df, aggregate='M') * pct
        metrics['Expected Yearly %%'] = _stats.expected_return(
            df, aggregate='A') * pct
        metrics['Kelly Criterion %'] = _stats.kelly_criterion(df) * pct
        metrics['Risk of Ruin %'] = _stats.risk_of_ruin(df)

        metrics['Daily Value-at-Risk %'] = -abs(_stats.var(df) * pct)
        metrics['Expected Shortfall (cVaR) %'] = -abs(_stats.cvar(df) * pct)

    metrics['~~~~~~'] = blank

_po.avg_win = stats.avg_win
    _po.avg_loss = stats.avg_loss
    _po.volatility = stats.volatility
    _po.implied_volatility = stats.implied_volatility
    _po.sharpe = stats.sharpe
    _po.sortino = stats.sortino
    _po.cagr = stats.cagr
    _po.rar = stats.rar
    _po.skew = stats.skew
    _po.kurtosis = stats.kurtosis
    _po.calmar = stats.calmar
    _po.ulcer_index = stats.ulcer_index
    _po.ulcer_performance_index = stats.ulcer_performance_index
    _po.upi = stats.upi
    _po.risk_of_ruin = stats.risk_of_ruin
    _po.ror = stats.ror
    _po.value_at_risk = stats.value_at_risk
    _po.var = stats.var
    _po.conditional_value_at_risk = stats.conditional_value_at_risk
    _po.cvar = stats.cvar
    _po.expected_shortfall = stats.expected_shortfall
    _po.tail_ratio = stats.tail_ratio
    _po.payoff_ratio = stats.payoff_ratio
    _po.win_loss_ratio = stats.win_loss_ratio
    _po.profit_ratio = stats.profit_ratio
    _po.profit_factor = stats.profit_factor
    _po.gain_to_pain_ratio = stats.gain_to_pain_ratio
    _po.cpc_index = stats.cpc_index
    _po.common_sense_ratio = stats.common_sense_ratio
    _po.outlier_win_ratio = stats.outlier_win_ratio
    _po.outlier_loss_ratio = stats.outlier_loss_ratio
    _po.recovery_factor = stats.recovery_factor

metrics['Worst Month %'] = _stats.worst(df, aggregate='M') * pct
        metrics['Best Year %'] = _stats.best(df, aggregate='A') * pct
        metrics['Worst Year %'] = _stats.worst(df, aggregate='A') * pct

    # dd
    metrics['~~~~'] = blank
    for ix, row in dd.iterrows():
        metrics[ix] = row
    metrics['Recovery Factor'] = _stats.recovery_factor(df)
    metrics['Ulcer Index'] = _stats.ulcer_index(df, rf)

    # win rate
    if mode.lower() == 'full':
        metrics['~~~~~'] = blank
        metrics['Avg. Up Month %'] = _stats.avg_win(df, aggregate='M') * pct
        metrics['Avg. Down Month %'] = _stats.avg_loss(df, aggregate='M') * pct
        metrics['Win Days %%'] = _stats.win_rate(df) * pct
        metrics['Win Month %%'] = _stats.win_rate(df, aggregate='M') * pct
        metrics['Win Quarter %%'] = _stats.win_rate(df, aggregate='Q') * pct
        metrics['Win Year %%'] = _stats.win_rate(df, aggregate='A') * pct

        if "benchmark" in df:
            metrics['~~~~~~~'] = blank
            greeks = _stats.greeks(df['returns'], df['benchmark'])
            metrics['Beta'] = [str(round(greeks['beta'], 2)), '-']
            metrics['Alpha'] = [str(round(greeks['alpha'], 2)), '-']

    # prepare for display
    for col in metrics.columns:
        try:
            metrics[col] = metrics[col].astype(float).round(2)
            if display or "internal" in kwargs:

metrics['~~~~~~~~~~'] = blank

        metrics['Expected Daily %%'] = _stats.expected_return(df) * pct
        metrics['Expected Monthly %%'] = _stats.expected_return(
            df, aggregate='M') * pct
        metrics['Expected Yearly %%'] = _stats.expected_return(
            df, aggregate='A') * pct
        metrics['Kelly Criterion %'] = _stats.kelly_criterion(df) * pct
        metrics['Risk of Ruin %'] = _stats.risk_of_ruin(df)

        metrics['Daily Value-at-Risk %'] = -abs(_stats.var(df) * pct)
        metrics['Expected Shortfall (cVaR) %'] = -abs(_stats.cvar(df) * pct)

    metrics['~~~~~~'] = blank

    metrics['Payoff Ratio'] = _stats.payoff_ratio(df)
    metrics['Profit Factor'] = _stats.profit_factor(df)
    metrics['Common Sense Ratio'] = _stats.common_sense_ratio(df)
    metrics['CPC Index'] = _stats.cpc_index(df)
    metrics['Tail Ratio'] = _stats.tail_ratio(df)
    metrics['Outlier Win Ratio'] = _stats.outlier_win_ratio(df)
    metrics['Outlier Loss Ratio'] = _stats.outlier_loss_ratio(df)

    # returns
    metrics['~~'] = blank
    comp_func = _stats.comp if compounded else _np.sum

    today = df.index[-1]  # _dt.today()
    metrics['MTD %'] = comp_func(
        df[df.index >= _dt(today.year, today.month, 1)]) * pct

    d = today - _td(3*365/12)