How to use the qstrader.statistics.performance.create_sharpe_ratio function in qstrader

dd_s, max_dd, dd_dur = perf.create_drawdowns(curve['CumReturns'])

        # Equity curve and returns
        stats['equity_curve'] = JSONStatistics._series_to_tuple_list(curve['Equity'])
        stats['returns'] = JSONStatistics._series_to_tuple_list(curve['Returns'])
        stats['cum_returns'] = JSONStatistics._series_to_tuple_list(curve['CumReturns'])

        # Drawdown statistics
        stats['drawdowns'] = JSONStatistics._series_to_tuple_list(dd_s)
        stats['max_drawdown'] = max_dd
        stats['max_drawdown_duration'] = dd_dur

        # Performance
        stats['cagr'] = perf.create_cagr(curve['CumReturns'], self.periods)
        stats['annualised_vol'] = curve['Returns'].std() * np.sqrt(self.periods)
        stats['sharpe'] = perf.create_sharpe_ratio(curve['Returns'], self.periods)
        stats['sortino'] = perf.create_sortino_ratio(curve['Returns'], self.periods)

        return stats

trd_yr = 0
        else:
            positions = stats['positions']
            trd_yr = positions.shape[0] / (
                (returns.index[-1] - returns.index[0]).days / 365.0
            )

        if ax is None:
            ax = plt.gca()

        y_axis_formatter = FuncFormatter(format_perc)
        ax.yaxis.set_major_formatter(FuncFormatter(y_axis_formatter))

        tot_ret = cum_returns[-1] - 1.0
        cagr = perf.create_cagr(cum_returns, self.periods)
        sharpe = perf.create_sharpe_ratio(returns, self.periods)
        sortino = perf.create_sortino_ratio(returns, self.periods)
        rsq = perf.rsquared(range(cum_returns.shape[0]), cum_returns)
        dd, dd_max, dd_dur = perf.create_drawdowns(cum_returns)

        ax.text(0.25, 8.9, 'Total Return', fontsize=8)
        ax.text(7.50, 8.9, '{:.0%}'.format(tot_ret), fontweight='bold', horizontalalignment='right', fontsize=8)

        ax.text(0.25, 7.9, 'CAGR', fontsize=8)
        ax.text(7.50, 7.9, '{:.2%}'.format(cagr), fontweight='bold', horizontalalignment='right', fontsize=8)

        ax.text(0.25, 6.9, 'Sharpe Ratio', fontsize=8)
        ax.text(7.50, 6.9, '{:.2f}'.format(sharpe), fontweight='bold', horizontalalignment='right', fontsize=8)

        ax.text(0.25, 5.9, 'Sortino Ratio', fontsize=8)
        ax.text(7.50, 5.9, '{:.2f}'.format(sortino), fontweight='bold', horizontalalignment='right', fontsize=8)

ax.text(0.25, 2.9, 'Max Daily Drawdown', fontsize=8)
        ax.text(7.50, 2.9, '{:.2%}'.format(dd_max), color='red', fontweight='bold', horizontalalignment='right', fontsize=8)

        ax.text(0.25, 1.9, 'Max Drawdown Duration', fontsize=8)
        ax.text(7.50, 1.9, '{:.0f}'.format(dd_dur), fontweight='bold', horizontalalignment='right', fontsize=8)

        ax.text(0.25, 0.9, 'Trades per Year', fontsize=8)
        ax.text(7.50, 0.9, '{:.1f}'.format(trd_yr), fontweight='bold', horizontalalignment='right', fontsize=8)
        ax.set_title('Curve', fontweight='bold')

        if self.benchmark is not None:
            returns_b = stats['returns_b']
            equity_b = stats['cum_returns_b']
            tot_ret_b = equity_b[-1] - 1.0
            cagr_b = perf.create_cagr(equity_b)
            sharpe_b = perf.create_sharpe_ratio(returns_b)
            sortino_b = perf.create_sortino_ratio(returns_b)
            rsq_b = perf.rsquared(range(equity_b.shape[0]), equity_b)
            dd_b, dd_max_b, dd_dur_b = perf.create_drawdowns(equity_b)

            ax.text(9.75, 8.9, '{:.0%}'.format(tot_ret_b), fontweight='bold', horizontalalignment='right', fontsize=8)
            ax.text(9.75, 7.9, '{:.2%}'.format(cagr_b), fontweight='bold', horizontalalignment='right', fontsize=8)
            ax.text(9.75, 6.9, '{:.2f}'.format(sharpe_b), fontweight='bold', horizontalalignment='right', fontsize=8)
            ax.text(9.75, 5.9, '{:.2f}'.format(sortino_b), fontweight='bold', horizontalalignment='right', fontsize=8)
            ax.text(9.75, 4.9, '{:.2%}'.format(returns_b.std() * np.sqrt(252)), fontweight='bold', horizontalalignment='right', fontsize=8)
            ax.text(9.75, 3.9, '{:.2f}'.format(rsq_b), fontweight='bold', horizontalalignment='right', fontsize=8)
            ax.text(9.75, 2.9, '{:.2%}'.format(dd_max_b), color='red', fontweight='bold', horizontalalignment='right', fontsize=8)
            ax.text(9.75, 1.9, '{:.0f}'.format(dd_dur_b), fontweight='bold', horizontalalignment='right', fontsize=8)

            ax.set_title('Curve vs. Benchmark', fontweight='bold')

        ax.grid(False)

# Rolling Annualised Sharpe
        rolling = returns_s.rolling(window=self.periods)
        rolling_sharpe_s = np.sqrt(self.periods) * (
            rolling.mean() / rolling.std()
        )

        # Cummulative Returns
        cum_returns_s = np.exp(np.log(1 + returns_s).cumsum())

        # Drawdown, max drawdown, max drawdown duration
        dd_s, max_dd, dd_dur = perf.create_drawdowns(cum_returns_s)

        statistics = {}

        # Equity statistics
        statistics["sharpe"] = perf.create_sharpe_ratio(
            returns_s, self.periods
        )
        statistics["drawdowns"] = dd_s
        # TODO: need to have max_drawdown so it can be printed at end of test
        statistics["max_drawdown"] = max_dd
        statistics["max_drawdown_pct"] = max_dd
        statistics["max_drawdown_duration"] = dd_dur
        statistics["equity"] = equity_s
        statistics["returns"] = returns_s
        statistics["rolling_sharpe"] = rolling_sharpe_s
        statistics["cum_returns"] = cum_returns_s

        positions = self._get_positions()
        if positions is not None:
            statistics["positions"] = positions

positions = self._get_positions()
        if positions is not None:
            statistics["positions"] = positions

        # Benchmark statistics if benchmark ticker specified
        if self.benchmark is not None:
            equity_b = pd.Series(self.equity_benchmark).sort_index()
            returns_b = equity_b.pct_change().fillna(0.0)
            rolling_b = returns_b.rolling(window=self.periods)
            rolling_sharpe_b = np.sqrt(self.periods) * (
                rolling_b.mean() / rolling_b.std()
            )
            cum_returns_b = np.exp(np.log(1 + returns_b).cumsum())
            dd_b, max_dd_b, dd_dur_b = perf.create_drawdowns(cum_returns_b)
            statistics["sharpe_b"] = perf.create_sharpe_ratio(returns_b)
            statistics["drawdowns_b"] = dd_b
            statistics["max_drawdown_pct_b"] = max_dd_b
            statistics["max_drawdown_duration_b"] = dd_dur_b
            statistics["equity_b"] = equity_b
            statistics["returns_b"] = returns_b
            statistics["rolling_sharpe_b"] = rolling_sharpe_b
            statistics["cum_returns_b"] = cum_returns_b

        return statistics