960
 961    metrics["Gain/Pain Ratio"] = _stats.gain_to_pain_ratio(df, rf)
 962    metrics["Gain/Pain (1M)"] = _stats.gain_to_pain_ratio(df, rf, "M")
 963    # if mode.lower() == 'full': 964    #     metrics['GPR (3M)'] = _stats.gain_to_pain_ratio(df, rf, "Q")
 965    #     metrics['GPR (6M)'] = _stats.gain_to_pain_ratio(df, rf, "2Q")
 966    #     metrics['GPR (1Y)'] = _stats.gain_to_pain_ratio(df, rf, "A")

 854    if mode.lower() == "full":
 855        # metrics['Prob. Sortino Ratio %'] = _stats.probabilistic_sortino_ratio(df, rf, win_year, False) * pct
 856        metrics["Smart Sortino"] = _stats.smart_sortino(df, rf, win_year, True)
 857        # metrics['Prob. Smart Sortino Ratio %'] = _stats.probabilistic_sortino_ratio(df, rf, win_year, False, True) * pct 858
 859    metrics["Sortino/√2"] = metrics["Sortino"] / _sqrt(2)
 860    if mode.lower() == "full":

 852
 853    metrics["Sortino"] = _stats.sortino(df, rf, win_year, True)
 854    if mode.lower() == "full":
 855        # metrics['Prob. Sortino Ratio %'] = _stats.probabilistic_sortino_ratio(df, rf, win_year, False) * pct 856        metrics["Smart Sortino"] = _stats.smart_sortino(df, rf, win_year, True)
 857        # metrics['Prob. Smart Sortino Ratio %'] = _stats.probabilistic_sortino_ratio(df, rf, win_year, False, True) * pct
 858

 848    )
 849    if mode.lower() == "full":
 850        metrics["Smart Sharpe"] = _stats.smart_sharpe(df, rf, win_year, True)
 851        # metrics['Prob. Smart Sharpe Ratio %'] = _stats.probabilistic_sharpe_ratio(df, rf, win_year, False, True) * pct 852
 853    metrics["Sortino"] = _stats.sortino(df, rf, win_year, True)
 854    if mode.lower() == "full":

 759    else:
 760        blank = [""]
 761
 762    # if isinstance(returns, _pd.DataFrame): 763    #     if len(returns.columns) > 1:
 764    #         raise ValueError("`returns` needs to be a Pandas Series or one column DataFrame. multi colums DataFrame was passed")
 765    #     returns = returns[returns.columns[0]]

1028            cmap=cmap,
1029            cbar_kws={"format": "%.0f%%"},
1030        )
1031    # _sns.set(font_scale=1)1032
1033    # align plot to match other
1034    if ylabel:

 978    fig.set_facecolor("white")
 979    ax.set_facecolor("white")
 980
 981    # _sns.set(font_scale=.9) 982    if active and benchmark is not None:
 983        ax.set_title(
 984            f"{returns_label} - Monthly Active Returns (%)\n",

 953    show=True,
 954    active=False,
 955):
 956    # colors, ls, alpha = _core._get_colors(grayscale) 957    cmap = "gray" if grayscale else "RdYlGn"
 958
 959    returns = _stats.monthly_returns(returns, eoy=eoy, compounded=compounded) * 100

 226    axes[2].axhline(0, color=colors[-1], linestyle="--", lw=1, zorder=2)
 227
 228    axes[2].set_yscale("symlog" if log_scale else "linear")
 229    # axes[2].legend(fontsize=12) 230
 231    retmax = _utils._round_to_closest(returns.max() * 100, 5)
 232    retmin = _utils._round_to_closest(returns.min() * 100, 5)

 208                )
 209
 210    axes[1].set_yscale("symlog" if log_scale else "linear")
 211    # axes[1].legend(fontsize=12) 212
 213    axes[2].set_ylabel(
 214        "Daily Return", fontname=fontname, fontweight="bold", fontsize=12

 189        ddmin_ticks = ddmin / 3
 190    ddmin_ticks = int(_utils._round_to_closest(ddmin_ticks, 5))
 191
 192    # ddmin_ticks = int(_utils._round_to_closest(ddmin, 5)) 193    axes[1].set_ylabel("Drawdown", fontname=fontname, fontweight="bold", fontsize=12)
 194    axes[1].set_yticks(_np.arange(-ddmin, 0, step=ddmin_ticks))
 195    if isinstance(dd, _pd.Series):

 178    axes[0].axhline(0, color="silver", lw=1, zorder=0)
 179
 180    axes[0].set_yscale("symlog" if log_scale else "linear")
 181    # axes[0].legend(fontsize=12) 182
 183    dd = _stats.to_drawdown_series(returns) * 100
 184    ddmin = _utils._round_to_closest(abs(dd.min()), 5)

1003    colors = _FLATUI_COLORS
1004    if grayscale:
1005        colors = ["#f9f9f9", "#dddddd", "#bbbbbb", "#999999", "#808080"]
1006    # colors, ls, alpha = _get_colors(grayscale)1007
1008    port = _pd.DataFrame(returns.fillna(0))
1009    port.columns = ["Daily"]

 955        ax.yaxis.set_label_coords(-0.1, 0.5)
 956
 957    ax.yaxis.set_major_formatter(_FuncFormatter(format_pct_axis))
 958    # ax.yaxis.set_major_formatter(_plt.FuncFormatter( 959    #     lambda x, loc: "{:,}%".format(int(x*100))))
 960
 961    fig.autofmt_xdate()

 567    )
 568    ax.yaxis.set_label_coords(-0.1, 0.5)
 569
 570    # fig.autofmt_xdate() 571
 572    try:
 573        _plt.subplots_adjust(hspace=0, bottom=0, top=1)

 552            color="red",
 553        )
 554
 555    # _plt.setp(x.get_legend().get_texts(), fontsize=11) 556    ax.xaxis.set_major_formatter(
 557        _plt.FuncFormatter(lambda x, loc: "{:,}%".format(int(x * 100)))
 558    )

 528                .reset_index()
 529                .rename(columns={"level_1": "", 0: "Returns"})
 530            )
 531            # _sns.kdeplot(data=combined_returns, color='black', ax=ax) 532            x = _sns.histplot(
 533                data=combined_returns,
 534                x="Returns",

 418    show=True,
 419):
 420
 421    # colors = ['#348dc1', '#003366', 'red'] 422    # if grayscale:
 423    #     colors = ['silver', 'gray', 'black']
 424

 361
 362    if percent:
 363        ax.yaxis.set_major_formatter(_FuncFormatter(format_pct_axis))
 364        # ax.yaxis.set_major_formatter(_plt.FuncFormatter( 365        #     lambda x, loc: "{:,}%".format(int(x*100))))
 366
 367    ax.set_xlabel("")

 354    ax.axhline(0, ls="-", lw=1, color="gray", zorder=1)
 355    ax.axhline(0, ls="--", lw=1, color="white" if grayscale else "black", zorder=2)
 356
 357    # if isinstance(benchmark, _pd.Series) or hline is not None: 358    ax.legend(fontsize=11)
 359
 360    _plt.yscale("symlog" if log_scale else "linear")

 326    if isinstance(returns, _pd.Series):
 327        ax.plot(returns, lw=lw, label=returns.name, color=colors[1], alpha=alpha)
 328    elif isinstance(returns, _pd.DataFrame):
 329        # color_dict = {col: colors[i+1] for i, col in enumerate(returns.columns)} 330        for i, col in enumerate(returns.columns):
 331            ax.plot(returns[col], lw=lw, label=col, alpha=alpha, color=colors[i + 1])
 332

 196
 197    ax.axhline(0, ls="--", lw=1, color="#000000", zorder=2)
 198
 199    # if isinstance(benchmark, _pd.Series) or hline: 200    ax.legend(fontsize=11)
 201
 202    _plt.yscale("symlog" if log_scale else "linear")

 176        ax.set_xticklabels(df.index)
 177        years = sorted(list(set(df.index)))
 178
 179    # ax.fmt_xdata = _mdates.DateFormatter('%Y-%m-%d') 180    # years = sorted(list(set(df.index.year)))
 181    if len(years) > 10:
 182        mod = int(len(years) / 10)