MAINT Remove unnecessary if statement.

Author: twiecki

pyfolio/tears.py

@@ -486,80 +486,79 @@ def create_bayesian_tear_sheet_oos(returns, live_start_date,
                                                start=returns.index[0],
                                                end=returns.index[-1])
 
-    if live_start_date is not None:
-        live_start_date = utils.get_utc_timestamp(live_start_date)
-        df_train = returns.loc[returns.index < live_start_date]
-        df_test = returns.loc[returns.index >= live_start_date]
-
-        # Run T model with missing data
-        trace_t = bayesian.run_model('t', df_train, returns_test=df_test,
-                                     samples=2000)
-
-        # Compute BEST model
-        trace_best = bayesian.run_model('best', df_train,
-                                        returns_test=df_test,
-                                        samples=2000)
-
-        # Plot results
-
-        fig = plt.figure(figsize=(14, 10 * 2))
-        gs = gridspec.GridSpec(9, 2, wspace=0.3, hspace=0.3)
-
-        axs = []
-        row = 0
-
-        # Plot Bayesian cone
-        ax_cone = plt.subplot(gs[row, :])
-        bayesian.plot_bayes_cone(df_train, df_test,
-                                 trace=trace_t,
-                                 ax=ax_cone)
-
-        # Plot BEST results
-        row += 1
-        axs.append(plt.subplot(gs[row, 0]))
-        axs.append(plt.subplot(gs[row, 1]))
-        row += 1
-        axs.append(plt.subplot(gs[row, 0]))
-        axs.append(plt.subplot(gs[row, 1]))
-        row += 1
-        axs.append(plt.subplot(gs[row, 0]))
-        axs.append(plt.subplot(gs[row, 1]))
-        row += 1
-        # Effect size across two
-        axs.append(plt.subplot(gs[row, :]))
-
-        bayesian.plot_best(trace=trace_best, axs=axs)
-
-        # Compute Bayesian predictions
-        row += 1
-        ax_ret_pred_day = plt.subplot(gs[row, 0])
-        ax_ret_pred_week = plt.subplot(gs[row, 1])
-        day_pred = trace_t['returns_missing'][:, 0]
-        p5 = scipy.stats.scoreatpercentile(day_pred, 5)
-        sns.distplot(day_pred,
-                     ax=ax_ret_pred_day
-                     )
-        ax_ret_pred_day.axvline(p5, linestyle='--', linewidth=3.)
-        ax_ret_pred_day.set_xlabel('Predicted returns 1 day')
-        ax_ret_pred_day.set_ylabel('Frequency')
-        ax_ret_pred_day.text(0.4, 0.9, 'Bayesian VaR = %.2f' % p5,
-                             verticalalignment='bottom',
-                             horizontalalignment='right',
-                             transform=ax_ret_pred_day.transAxes)
-        # Plot Bayesian VaRs
-        week_pred = (
-            np.cumprod(trace_t['returns_missing'][:, :5] + 1, 1) - 1)[:, -1]
-        p5 = scipy.stats.scoreatpercentile(week_pred, 5)
-        sns.distplot(week_pred,
-                     ax=ax_ret_pred_week
-                     )
-        ax_ret_pred_week.axvline(p5, linestyle='--', linewidth=3.)
-        ax_ret_pred_week.set_xlabel('Predicted cum returns 5 days')
-        ax_ret_pred_week.set_ylabel('Frequency')
-        ax_ret_pred_week.text(0.4, 0.9, 'Bayesian VaR = %.2f' % p5,
-                              verticalalignment='bottom',
-                              horizontalalignment='right',
-                              transform=ax_ret_pred_week.transAxes)
+    live_start_date = utils.get_utc_timestamp(live_start_date)
+    df_train = returns.loc[returns.index < live_start_date]
+    df_test = returns.loc[returns.index >= live_start_date]
+
+    # Run T model with missing data
+    trace_t = bayesian.run_model('t', df_train, returns_test=df_test,
+                                 samples=2000)
+
+    # Compute BEST model
+    trace_best = bayesian.run_model('best', df_train,
+                                    returns_test=df_test,
+                                    samples=2000)
+
+    # Plot results
+
+    fig = plt.figure(figsize=(14, 10 * 2))
+    gs = gridspec.GridSpec(9, 2, wspace=0.3, hspace=0.3)
+
+    axs = []
+    row = 0
+
+    # Plot Bayesian cone
+    ax_cone = plt.subplot(gs[row, :])
+    bayesian.plot_bayes_cone(df_train, df_test,
+                             trace=trace_t,
+                             ax=ax_cone)
+
+    # Plot BEST results
+    row += 1
+    axs.append(plt.subplot(gs[row, 0]))
+    axs.append(plt.subplot(gs[row, 1]))
+    row += 1
+    axs.append(plt.subplot(gs[row, 0]))
+    axs.append(plt.subplot(gs[row, 1]))
+    row += 1
+    axs.append(plt.subplot(gs[row, 0]))
+    axs.append(plt.subplot(gs[row, 1]))
+    row += 1
+    # Effect size across two
+    axs.append(plt.subplot(gs[row, :]))
+
+    bayesian.plot_best(trace=trace_best, axs=axs)
+
+    # Compute Bayesian predictions
+    row += 1
+    ax_ret_pred_day = plt.subplot(gs[row, 0])
+    ax_ret_pred_week = plt.subplot(gs[row, 1])
+    day_pred = trace_t['returns_missing'][:, 0]
+    p5 = scipy.stats.scoreatpercentile(day_pred, 5)
+    sns.distplot(day_pred,
+                 ax=ax_ret_pred_day
+                 )
+    ax_ret_pred_day.axvline(p5, linestyle='--', linewidth=3.)
+    ax_ret_pred_day.set_xlabel('Predicted returns 1 day')
+    ax_ret_pred_day.set_ylabel('Frequency')
+    ax_ret_pred_day.text(0.4, 0.9, 'Bayesian VaR = %.2f' % p5,
+                         verticalalignment='bottom',
+                         horizontalalignment='right',
+                         transform=ax_ret_pred_day.transAxes)
+    # Plot Bayesian VaRs
+    week_pred = (
+        np.cumprod(trace_t['returns_missing'][:, :5] + 1, 1) - 1)[:, -1]
+    p5 = scipy.stats.scoreatpercentile(week_pred, 5)
+    sns.distplot(week_pred,
+                 ax=ax_ret_pred_week
+                 )
+    ax_ret_pred_week.axvline(p5, linestyle='--', linewidth=3.)
+    ax_ret_pred_week.set_xlabel('Predicted cum returns 5 days')
+    ax_ret_pred_week.set_ylabel('Frequency')
+    ax_ret_pred_week.text(0.4, 0.9, 'Bayesian VaR = %.2f' % p5,
+                          verticalalignment='bottom',
+                          horizontalalignment='right',
+                          transform=ax_ret_pred_week.transAxes)
 
 
     # Run alpha beta model