diff --git a/alphalens/plotting.py b/alphalens/plotting.py
index 96cf7a04..a69d0054 100644
--- a/alphalens/plotting.py
+++ b/alphalens/plotting.py
@@ -35,6 +35,7 @@ def customize(func):
     """
     Decorator to set plotting context and axes style during function call.
     """
+
     @wraps(func)
     def call_w_context(*args, **kwargs):
         set_context = kwargs.pop('set_context', True)
@@ -45,6 +46,7 @@ def call_w_context(*args, **kwargs):
                 return func(*args, **kwargs)
         else:
             return func(*args, **kwargs)
+
     return call_w_context
 
 
@@ -133,7 +135,8 @@ def axes_style(style='darkgrid', rc=None):
 
 def plot_returns_table(alpha_beta,
                        mean_ret_quantile,
-                       mean_ret_spread_quantile):
+                       mean_ret_spread_quantile,
+                       return_table=False):
     returns_table = pd.DataFrame()
     returns_table = returns_table.append(alpha_beta)
     returns_table.loc["Mean Period Wise Return Top Quantile (bps)"] = \
@@ -145,9 +148,12 @@ def plot_returns_table(alpha_beta,
 
     print("Returns Analysis")
     utils.print_table(returns_table.apply(lambda x: x.round(3)))
+    if return_table:
+        return returns_table
 
 
-def plot_turnover_table(autocorrelation_data, quantile_turnover):
+def plot_turnover_table(autocorrelation_data, quantile_turnover,
+                        return_table=False):
     turnover_table = pd.DataFrame()
     for period in sorted(quantile_turnover.keys()):
         for quantile, p_data in quantile_turnover[period].iteritems():
@@ -161,9 +167,12 @@ def plot_turnover_table(autocorrelation_data, quantile_turnover):
     print("Turnover Analysis")
     utils.print_table(turnover_table.apply(lambda x: x.round(3)))
     utils.print_table(auto_corr.apply(lambda x: x.round(3)))
+    if return_table:
+        return [turnover_table, auto_corr]
 
 
-def plot_information_table(ic_data):
+def plot_information_table(ic_data,
+                           return_table=False):
     ic_summary_table = pd.DataFrame()
     ic_summary_table["IC Mean"] = ic_data.mean()
     ic_summary_table["IC Std."] = ic_data.std()
@@ -177,19 +186,25 @@ def plot_information_table(ic_data):
 
     print("Information Analysis")
     utils.print_table(ic_summary_table.apply(lambda x: x.round(3)).T)
+    if return_table:
+        return ic_summary_table
 
 
-def plot_quantile_statistics_table(factor_data):
+def plot_quantile_statistics_table(factor_data,
+                                   return_table=False):
     quantile_stats = factor_data.groupby('factor_quantile') \
         .agg(['min', 'max', 'mean', 'std', 'count'])['factor']
-    quantile_stats['count %'] = quantile_stats['count'] \
-        / quantile_stats['count'].sum() * 100.
+    quantile_stats['count %'] = \
+        quantile_stats['count'] / quantile_stats['count'].sum() * 100.
 
     print("Quantiles Statistics")
     utils.print_table(quantile_stats)
+    if return_table:
+        return quantile_stats
 
 
-def plot_ic_ts(ic, ax=None):
+def plot_ic_ts(ic, ax=None,
+               return_fig=False):
     """
     Plots Spearman Rank Information Coefficient and IC moving
     average for a given factor.
@@ -209,6 +224,7 @@ def plot_ic_ts(ic, ax=None):
     ic = ic.copy()
 
     num_plots = len(ic.columns)
+    f = None
     if ax is None:
         f, ax = plt.subplots(num_plots, 1, figsize=(18, num_plots * 7))
         ax = np.asarray([ax]).flatten()
@@ -241,11 +257,14 @@ def plot_ic_ts(ic, ax=None):
 
     for a in ax:
         a.set_ylim([ymin, ymax])
-
-    return ax
+    if return_fig:
+        return f, ax
+    else:
+        return ax
 
 
-def plot_ic_hist(ic, ax=None):
+def plot_ic_hist(ic, ax=None,
+                 return_fig=False):
     """
     Plots Spearman Rank Information Coefficient histogram for a given factor.
 
@@ -267,7 +286,7 @@ def plot_ic_hist(ic, ax=None):
     num_plots = len(ic.columns)
 
     v_spaces = ((num_plots - 1) // 3) + 1
-
+    f = None
     if ax is None:
         f, ax = plt.subplots(v_spaces, 3, figsize=(18, v_spaces * 6))
         ax = ax.flatten()
@@ -286,10 +305,14 @@ def plot_ic_hist(ic, ax=None):
     if num_plots < len(ax):
         ax[-1].set_visible(False)
 
-    return ax
+    if return_fig:
+        return f, ax
+    else:
+        return ax
 
 
-def plot_ic_qq(ic, theoretical_dist=stats.norm, ax=None):
+def plot_ic_qq(ic, theoretical_dist=stats.norm, ax=None,
+               return_fig=False):
     """
     Plots Spearman Rank Information Coefficient "Q-Q" plot relative to
     a theoretical distribution.
@@ -315,7 +338,7 @@ def plot_ic_qq(ic, theoretical_dist=stats.norm, ax=None):
     num_plots = len(ic.columns)
 
     v_spaces = ((num_plots - 1) // 3) + 1
-
+    f = None
     if ax is None:
         f, ax = plt.subplots(v_spaces, 3, figsize=(18, v_spaces * 6))
         ax = ax.flatten()
@@ -331,17 +354,21 @@ def plot_ic_qq(ic, theoretical_dist=stats.norm, ax=None):
         sm.qqplot(ic.replace(np.nan, 0.).values, theoretical_dist, fit=True,
                   line='45', ax=a)
         a.set(title="{} Period IC {} Dist. Q-Q".format(
-              period_num, dist_name),
-              ylabel='Observed Quantile',
-              xlabel='{} Distribution Quantile'.format(dist_name))
+            period_num, dist_name),
+            ylabel='Observed Quantile',
+            xlabel='{} Distribution Quantile'.format(dist_name))
 
-    return ax
+    if return_fig:
+        return f, ax
+    else:
+        return ax
 
 
 def plot_quantile_returns_bar(mean_ret_by_q,
                               by_group=False,
                               ylim_percentiles=None,
-                              ax=None):
+                              ax=None,
+                              return_fig=False):
     """
     Plots mean period wise returns for factor quantiles.
 
@@ -361,7 +388,9 @@ def plot_quantile_returns_bar(mean_ret_by_q,
     ax : matplotlib.Axes
         The axes that were plotted on.
     """
-
+    if ax is not None and return_fig:
+        print("Error argument combination: ax should be None if return_fig")
+        return
     mean_ret_by_q = mean_ret_by_q.copy()
 
     if ylim_percentiles is not None:
@@ -372,11 +401,10 @@ def plot_quantile_returns_bar(mean_ret_by_q,
     else:
         ymin = None
         ymax = None
-
+    f = None
     if by_group:
         num_group = len(
             mean_ret_by_q.index.get_level_values('group').unique())
-
         if ax is None:
             v_spaces = ((num_group - 1) // 2) + 1
             f, ax = plt.subplots(v_spaces, 2, sharex=False,
@@ -385,8 +413,8 @@ def plot_quantile_returns_bar(mean_ret_by_q,
 
         for a, (sc, cor) in zip(ax, mean_ret_by_q.groupby(level='group')):
             (cor.xs(sc, level='group')
-                .multiply(DECIMAL_TO_BPS)
-                .plot(kind='bar', title=sc, ax=a))
+             .multiply(DECIMAL_TO_BPS)
+             .plot(kind='bar', title=sc, ax=a))
 
             a.set(xlabel='', ylabel='Mean Return (bps)',
                   ylim=(ymin, ymax))
@@ -394,24 +422,31 @@ def plot_quantile_returns_bar(mean_ret_by_q,
         if num_group < len(ax):
             ax[-1].set_visible(False)
 
-        return ax
+        if return_fig:
+            return f, ax
+        else:
+            return ax
 
     else:
         if ax is None:
             f, ax = plt.subplots(1, 1, figsize=(18, 6))
 
         (mean_ret_by_q.multiply(DECIMAL_TO_BPS)
-            .plot(kind='bar',
-                  title="Mean Period Wise Return By Factor Quantile", ax=ax))
+         .plot(kind='bar',
+               title="Mean Period Wise Return By Factor Quantile", ax=ax))
         ax.set(xlabel='', ylabel='Mean Return (bps)',
                ylim=(ymin, ymax))
 
-        return ax
+        if return_fig:
+            return f, ax
+        else:
+            return ax
 
 
 def plot_quantile_returns_violin(return_by_q,
                                  ylim_percentiles=None,
-                                 ax=None):
+                                 ax=None,
+                                 return_fig=False):
     """
     Plots a violin box plot of period wise returns for factor quantiles.
 
@@ -430,7 +465,9 @@ def plot_quantile_returns_violin(return_by_q,
     ax : matplotlib.Axes
         The axes that were plotted on.
     """
-
+    if ax is not None and return_fig:
+        print("Error argument combination: ax should be None if return_fig")
+        return
     return_by_q = return_by_q.copy()
 
     if ylim_percentiles is not None:
@@ -441,7 +478,7 @@ def plot_quantile_returns_violin(return_by_q,
     else:
         ymin = None
         ymax = None
-
+    f = None
     if ax is None:
         f, ax = plt.subplots(1, 1, figsize=(18, 6))
 
@@ -466,13 +503,17 @@ def plot_quantile_returns_violin(return_by_q,
 
     ax.axhline(0.0, linestyle='-', color='black', lw=0.7, alpha=0.6)
 
-    return ax
+    if return_fig:
+        return f, ax
+    else:
+        return ax
 
 
 def plot_mean_quantile_returns_spread_time_series(mean_returns_spread,
                                                   std_err=None,
                                                   bandwidth=1,
-                                                  ax=None):
+                                                  ax=None,
+                                                  return_fig=False):
     """
     Plots mean period wise returns for factor quantiles.
 
@@ -493,7 +534,10 @@ def plot_mean_quantile_returns_spread_time_series(mean_returns_spread,
     ax : matplotlib.Axes
         The axes that were plotted on.
     """
-
+    if ax is not None and return_fig:
+        print("Error argument combination: ax should be None if return_fig")
+        return
+    f = None
     if isinstance(mean_returns_spread, pd.DataFrame):
         if ax is None:
             ax = [None for a in mean_returns_spread.columns]
@@ -552,10 +596,14 @@ def plot_mean_quantile_returns_spread_time_series(mean_returns_spread,
            ylim=(-ylim, ylim))
     ax.axhline(0.0, linestyle='-', color='black', lw=1, alpha=0.8)
 
-    return ax
+    if return_fig:
+        return f, ax
+    else:
+        return ax
 
 
-def plot_ic_by_group(ic_group, ax=None):
+def plot_ic_by_group(ic_group, ax=None,
+                     return_fig=False):
     """
     Plots Spearman Rank Information Coefficient for a given factor over
     provided forward returns. Separates by group.
@@ -572,6 +620,10 @@ def plot_ic_by_group(ic_group, ax=None):
     ax : matplotlib.Axes
         The axes that were plotted on.
     """
+    if ax is not None and return_fig:
+        print("Error argument combination: ax should be None if return_fig")
+        return
+    f = None
     if ax is None:
         f, ax = plt.subplots(1, 1, figsize=(18, 6))
     ic_group.plot(kind='bar', ax=ax)
@@ -579,12 +631,16 @@ def plot_ic_by_group(ic_group, ax=None):
     ax.set(title="Information Coefficient By Group", xlabel="")
     ax.set_xticklabels(ic_group.index, rotation=45)
 
-    return ax
+    if return_fig:
+        return f, ax
+    else:
+        return ax
 
 
 def plot_factor_rank_auto_correlation(factor_autocorrelation,
                                       period=1,
-                                      ax=None):
+                                      ax=None,
+                                      return_fig=False):
     """
     Plots factor rank autocorrelation over time.
     See factor_rank_autocorrelation for more details.
@@ -604,6 +660,10 @@ def plot_factor_rank_auto_correlation(factor_autocorrelation,
     ax : matplotlib.Axes
         The axes that were plotted on.
     """
+    if ax is not None and return_fig:
+        print("Error argument combination: ax should be None if return_fig")
+        return
+    f = None
     if ax is None:
         f, ax = plt.subplots(1, 1, figsize=(18, 6))
 
@@ -617,10 +677,14 @@ def plot_factor_rank_auto_correlation(factor_autocorrelation,
             transform=ax.transAxes,
             verticalalignment='top')
 
-    return ax
+    if return_fig:
+        return f, ax
+    else:
+        return ax
 
 
-def plot_top_bottom_quantile_turnover(quantile_turnover, period=1, ax=None):
+def plot_top_bottom_quantile_turnover(quantile_turnover, period=1, ax=None,
+                                      return_fig=False):
     """
     Plots period wise top and bottom quantile factor turnover.
 
@@ -638,6 +702,10 @@ def plot_top_bottom_quantile_turnover(quantile_turnover, period=1, ax=None):
     ax : matplotlib.Axes
         The axes that were plotted on.
     """
+    if ax is not None and return_fig:
+        print("Error argument combination: ax should be None if return_fig")
+        return
+    f = None
     if ax is None:
         f, ax = plt.subplots(1, 1, figsize=(18, 6))
 
@@ -650,10 +718,14 @@ def plot_top_bottom_quantile_turnover(quantile_turnover, period=1, ax=None):
                   .format(period), ax=ax, alpha=0.6, lw=0.8)
     ax.set(ylabel='Proportion Of Names New To Quantile', xlabel="")
 
-    return ax
+    if return_fig:
+        return f, ax
+    else:
+        return ax
 
 
-def plot_monthly_ic_heatmap(mean_monthly_ic, ax=None):
+def plot_monthly_ic_heatmap(mean_monthly_ic, ax=None,
+                            return_fig=False):
     """
     Plots a heatmap of the information coefficient or returns by month.
 
@@ -667,13 +739,15 @@ def plot_monthly_ic_heatmap(mean_monthly_ic, ax=None):
     ax : matplotlib.Axes
         The axes that were plotted on.
     """
-
+    if ax is not None and return_fig:
+        print("Error argument combination: ax should be None if return_fig")
+        return
     mean_monthly_ic = mean_monthly_ic.copy()
 
     num_plots = len(mean_monthly_ic.columns)
 
     v_spaces = ((num_plots - 1) // 3) + 1
-
+    f = None
     if ax is None:
         f, ax = plt.subplots(v_spaces, 3, figsize=(18, v_spaces * 6))
         ax = ax.flatten()
@@ -689,7 +763,6 @@ def plot_monthly_ic_heatmap(mean_monthly_ic, ax=None):
         names=["year", "month"])
 
     for a, (periods_num, ic) in zip(ax, mean_monthly_ic.iteritems()):
-
         sns.heatmap(
             ic.unstack(),
             annot=True,
@@ -708,10 +781,14 @@ def plot_monthly_ic_heatmap(mean_monthly_ic, ax=None):
     if num_plots < len(ax):
         ax[-1].set_visible(False)
 
-    return ax
+    if return_fig:
+        return f, ax
+    else:
+        return ax
 
 
-def plot_cumulative_returns(factor_returns, period, freq, title=None, ax=None):
+def plot_cumulative_returns(factor_returns, period, freq, title=None, ax=None,
+                            return_fig=False):
     """
     Plots the cumulative returns of the returns series passed in.
 
@@ -739,6 +816,10 @@ def plot_cumulative_returns(factor_returns, period, freq, title=None, ax=None):
     ax : matplotlib.Axes
         The axes that were plotted on.
     """
+    if ax is not None and return_fig:
+        print("Error argument combination: ax should be None if return_fig")
+        return
+    f = None
     if ax is None:
         f, ax = plt.subplots(1, 1, figsize=(18, 6))
 
@@ -751,13 +832,17 @@ def plot_cumulative_returns(factor_returns, period, freq, title=None, ax=None):
            xlabel='')
     ax.axhline(1.0, linestyle='-', color='black', lw=1)
 
-    return ax
+    if return_fig:
+        return f, ax
+    else:
+        return ax
 
 
 def plot_cumulative_returns_by_quantile(quantile_returns,
                                         period,
                                         freq,
-                                        ax=None):
+                                        ax=None,
+                                        return_fig=False):
     """
     Plots the cumulative returns of various factor quantiles.
 
@@ -781,7 +866,10 @@ def plot_cumulative_returns_by_quantile(quantile_returns,
     -------
     ax : matplotlib.Axes
     """
-
+    if ax is not None and return_fig:
+        print("Error argument combination: ax should be None if return_fig")
+        return
+    f = None
     if ax is None:
         f, ax = plt.subplots(1, 1, figsize=(18, 6))
 
@@ -804,14 +892,18 @@ def plot_cumulative_returns_by_quantile(quantile_returns,
     ax.yaxis.set_major_formatter(ScalarFormatter())
     ax.axhline(1.0, linestyle='-', color='black', lw=1)
 
-    return ax
+    if return_fig:
+        return f, ax
+    else:
+        return ax
 
 
 def plot_quantile_average_cumulative_return(avg_cumulative_returns,
                                             by_quantile=False,
                                             std_bar=False,
                                             title=None,
-                                            ax=None):
+                                            ax=None,
+                                            return_fig=False):
     """
     Plots sector-wise mean daily returns for factor quantiles
     across provided forward price movement columns.
@@ -834,12 +926,14 @@ def plot_quantile_average_cumulative_return(avg_cumulative_returns,
     -------
     ax : matplotlib.Axes
     """
-
+    if ax is not None and return_fig:
+        print("Error argument combination: ax should be None if return_fig")
+        return
     avg_cumulative_returns = avg_cumulative_returns.multiply(DECIMAL_TO_BPS)
     quantiles = len(avg_cumulative_returns.index.levels[0].unique())
     palette = [cm.coolwarm(i) for i in np.linspace(0, 1, quantiles)]
     palette = palette[::-1]  # we want negative quantiles as 'red'
-
+    f = None
     if by_quantile:
 
         if ax is None:
@@ -848,10 +942,9 @@ def plot_quantile_average_cumulative_return(avg_cumulative_returns,
                                  sharey=False, figsize=(18, 6 * v_spaces))
             ax = ax.flatten()
 
-        for i, (quantile, q_ret) in enumerate(avg_cumulative_returns
-                                              .groupby(level='factor_quantile')
-                                              ):
-
+        for i, (quantile, q_ret) in \
+                enumerate(avg_cumulative_returns
+                          .groupby(level='factor_quantile')):
             mean = q_ret.loc[(quantile, 'mean')]
             mean.name = 'Quantile ' + str(quantile)
             mean.plot(ax=ax[i], color=palette[i])
@@ -871,10 +964,9 @@ def plot_quantile_average_cumulative_return(avg_cumulative_returns,
         if ax is None:
             f, ax = plt.subplots(1, 1, figsize=(18, 6))
 
-        for i, (quantile, q_ret) in enumerate(avg_cumulative_returns
-                                              .groupby(level='factor_quantile')
-                                              ):
-
+        for i, (quantile, q_ret) in \
+                enumerate(avg_cumulative_returns
+                          .groupby(level='factor_quantile')):
             mean = q_ret.loc[(quantile, 'mean')]
             mean.name = 'Quantile ' + str(quantile)
             mean.plot(ax=ax, color=palette[i])
@@ -892,10 +984,14 @@ def plot_quantile_average_cumulative_return(avg_cumulative_returns,
                       if title is None else title),
                xlabel='Periods')
 
-    return ax
+    if return_fig:
+        return f, ax
+    else:
+        return ax
 
 
-def plot_events_distribution(events, num_bars=50, ax=None):
+def plot_events_distribution(events, num_bars=50, ax=None,
+                             return_fig=False):
     """
     Plots the distribution of events in time.
 
@@ -912,7 +1008,10 @@ def plot_events_distribution(events, num_bars=50, ax=None):
     -------
     ax : matplotlib.Axes
     """
-
+    if ax is not None and return_fig:
+        print("Error argument combination: ax should be None if return_fig")
+        return
+    f = None
     if ax is None:
         f, ax = plt.subplots(1, 1, figsize=(18, 6))
 
@@ -925,4 +1024,7 @@ def plot_events_distribution(events, num_bars=50, ax=None):
            title='Distribution of events in time',
            xlabel='Date')
 
-    return ax
+    if return_fig:
+        return f, ax
+    else:
+        return ax
diff --git a/alphalens/tears.py b/alphalens/tears.py
index a193b37c..dcd0265c 100644
--- a/alphalens/tears.py
+++ b/alphalens/tears.py
@@ -62,7 +62,9 @@ def close(self):
 @plotting.customize
 def create_summary_tear_sheet(factor_data,
                               long_short=True,
-                              group_neutral=False):
+                              group_neutral=False,
+                              return_tf=False,
+                              show=True):
     """
     Creates a small summary tear sheet with returns, information, and turnover
     analysis.
@@ -128,11 +130,19 @@ def create_summary_tear_sheet(factor_data,
     vertical_sections = 2 + fr_cols * 3
     gf = GridFigure(rows=vertical_sections, cols=1)
 
-    plotting.plot_quantile_statistics_table(factor_data)
-
-    plotting.plot_returns_table(alpha_beta,
-                                mean_quant_rateret,
-                                mean_ret_spread_quant)
+    result_tables = {}
+    result_figs = []
+    quantile_statistics_table = \
+        plotting.plot_quantile_statistics_table(factor_data,
+                                                return_table=return_tf)
+    if quantile_statistics_table is not None:
+        result_tables["quantile_statistics_table"] = quantile_statistics_table
+    returns_table = plotting.plot_returns_table(alpha_beta,
+                                                mean_quant_rateret,
+                                                mean_ret_spread_quant,
+                                                return_table=return_tf)
+    if returns_table is not None:
+        result_tables["returns_table"] = returns_table
 
     plotting.plot_quantile_returns_bar(mean_quant_rateret,
                                        by_group=False,
@@ -141,8 +151,10 @@ def create_summary_tear_sheet(factor_data,
 
     # Information Analysis
     ic = perf.factor_information_coefficient(factor_data)
-    plotting.plot_information_table(ic)
-
+    ic_summary_table =\
+        plotting.plot_information_table(ic, return_table=return_tf)
+    if ic_summary_table is not None:
+        result_tables["summary_ic_summary_table"] = ic_summary_table
     # Turnover Analysis
     quantile_factor = factor_data['factor_quantile']
 
@@ -150,23 +162,36 @@ def create_summary_tear_sheet(factor_data,
         {p: pd.concat([perf.quantile_turnover(quantile_factor, q, p)
                        for q in range(1, int(quantile_factor.max()) + 1)],
                       axis=1)
-            for p in periods}
+         for p in periods}
 
     autocorrelation = pd.concat(
         [perf.factor_rank_autocorrelation(factor_data, period) for period in
          periods], axis=1)
 
-    plotting.plot_turnover_table(autocorrelation, quantile_turnover)
-
-    plt.show()
+    turnover_table_results =\
+        plotting.plot_turnover_table(autocorrelation,
+                                     quantile_turnover,
+                                     return_table=return_tf)
+    if turnover_table_results is not None:
+        result_tables["summary_turnover_table"] = turnover_table_results[0]
+        result_tables["summary_auto_corr"] = turnover_table_results[1]
+    if show:
+        plt.show()
+    if return_tf:
+        fig = gf.fig
+        result_figs.append(fig)
     gf.close()
+    if return_tf:
+        return result_tables, result_figs
 
 
 @plotting.customize
 def create_returns_tear_sheet(factor_data,
                               long_short=True,
                               group_neutral=False,
-                              by_group=False):
+                              by_group=False,
+                              return_tf=False,
+                              show=True):
     """
     Creates a tear sheet for returns analysis of a factor.
 
@@ -237,9 +262,14 @@ def create_returns_tear_sheet(factor_data,
     vertical_sections = 2 + fr_cols * 3
     gf = GridFigure(rows=vertical_sections, cols=1)
 
-    plotting.plot_returns_table(alpha_beta,
-                                mean_quant_rateret,
-                                mean_ret_spread_quant)
+    result_tables = {}
+    result_figs = []
+    returns_table = plotting.plot_returns_table(alpha_beta,
+                                                mean_quant_rateret,
+                                                mean_ret_spread_quant,
+                                                return_table=return_tf)
+    if returns_table is not None:
+        result_tables["returns_table"] = returns_table
 
     plotting.plot_quantile_returns_bar(mean_quant_rateret,
                                        by_group=False,
@@ -259,7 +289,6 @@ def create_returns_tear_sheet(factor_data,
         )
 
     for p in factor_returns:
-
         title = ('Factor Weighted '
                  + ('Group Neutral ' if group_neutral else '')
                  + ('Long/Short ' if long_short else '')
@@ -288,8 +317,11 @@ def create_returns_tear_sheet(factor_data,
         bandwidth=0.5,
         ax=ax_mean_quantile_returns_spread_ts
     )
-
-    plt.show()
+    if show:
+        plt.show()
+    if return_tf:
+        res_fig = gf.fig
+        result_figs.append(res_fig)
     gf.close()
 
     if by_group:
@@ -318,13 +350,20 @@ def create_returns_tear_sheet(factor_data,
                                            ylim_percentiles=(5, 95),
                                            ax=ax_quantile_returns_bar_by_group)
         plt.show()
+        if return_tf:
+            res_group_fig = gf.fig
+            result_figs.append(res_group_fig)
         gf.close()
+    if return_tf:
+        return result_tables, result_figs
 
 
 @plotting.customize
 def create_information_tear_sheet(factor_data,
                                   group_neutral=False,
-                                  by_group=False):
+                                  by_group=False,
+                                  return_tf=False,
+                                  show=True):
     """
     Creates a tear sheet for information analysis of a factor.
 
@@ -341,10 +380,14 @@ def create_information_tear_sheet(factor_data,
     by_group : bool
         If True, display graphs separately for each group.
     """
-
+    result_tables = {}
+    result_figs = []
     ic = perf.factor_information_coefficient(factor_data, group_neutral)
 
-    plotting.plot_information_table(ic)
+    ic_summary_table = \
+        plotting.plot_information_table(ic, return_table=return_tf)
+    if ic_summary_table is not None:
+        result_tables["information_ic_summary_table"] = ic_summary_table
 
     columns_wide = 2
     fr_cols = len(ic.columns)
@@ -360,7 +403,6 @@ def create_information_tear_sheet(factor_data,
     plotting.plot_ic_qq(ic, ax=ax_ic_hqq[1::2])
 
     if not by_group:
-
         mean_monthly_ic = \
             perf.mean_information_coefficient(factor_data,
                                               group_adjust=group_neutral,
@@ -377,13 +419,19 @@ def create_information_tear_sheet(factor_data,
                                               by_group=True)
 
         plotting.plot_ic_by_group(mean_group_ic, ax=gf.next_row())
-
-    plt.show()
+    if show:
+        plt.show()
+    if return_tf:
+        fig = gf.fig
+        result_figs.append(fig)
     gf.close()
+    if return_tf:
+        return result_tables, result_figs
 
 
 @plotting.customize
-def create_turnover_tear_sheet(factor_data, turnover_periods=None):
+def create_turnover_tear_sheet(factor_data, turnover_periods=None,
+                               return_tf=False, show=True):
     """
     Creates a tear sheet for analyzing the turnover properties of a factor.
 
@@ -403,7 +451,8 @@ def create_turnover_tear_sheet(factor_data, turnover_periods=None):
         are 2h and 4h and the factor is computed daily and so values like
         ['1D', '2D'] could be used instead
     """
-
+    result_tables = {}
+    result_figs = []
     if turnover_periods is None:
         turnover_periods = utils.get_forward_returns_columns(
             factor_data.columns)
@@ -414,13 +463,19 @@ def create_turnover_tear_sheet(factor_data, turnover_periods=None):
         {p: pd.concat([perf.quantile_turnover(quantile_factor, q, p)
                        for q in range(1, int(quantile_factor.max()) + 1)],
                       axis=1)
-            for p in turnover_periods}
+         for p in turnover_periods}
 
     autocorrelation = pd.concat(
         [perf.factor_rank_autocorrelation(factor_data, period) for period in
          turnover_periods], axis=1)
 
-    plotting.plot_turnover_table(autocorrelation, quantile_turnover)
+    turnover_tables =\
+        plotting.plot_turnover_table(autocorrelation,
+                                     quantile_turnover,
+                                     return_table=return_tf)
+    if turnover_tables is not None:
+        result_tables["turnover_turnover_table"] = turnover_tables[0]
+        result_tables["turnover_auto_corr"] = turnover_tables[1]
 
     fr_cols = len(turnover_periods)
     columns_wide = 1
@@ -441,9 +496,14 @@ def create_turnover_tear_sheet(factor_data, turnover_periods=None):
         plotting.plot_factor_rank_auto_correlation(autocorrelation[period],
                                                    period=period,
                                                    ax=gf.next_row())
-
-    plt.show()
+    if show:
+        plt.show()
+    if return_tf:
+        fig = gf.fig
+        result_figs.append(fig)
     gf.close()
+    if return_tf:
+        return result_tables, result_figs
 
 
 @plotting.customize
@@ -476,7 +536,6 @@ def create_full_tear_sheet(factor_data,
     by_group : bool
         If True, display graphs separately for each group.
     """
-
     plotting.plot_quantile_statistics_table(factor_data)
     create_returns_tear_sheet(factor_data,
                               long_short,
@@ -497,7 +556,9 @@ def create_event_returns_tear_sheet(factor_data,
                                     long_short=True,
                                     group_neutral=False,
                                     std_bar=True,
-                                    by_group=False):
+                                    by_group=False,
+                                    return_tf=False,
+                                    show=True):
     """
     Creates a tear sheet to view the average cumulative returns for a
     factor within a window (pre and post event).
@@ -527,7 +588,8 @@ def create_event_returns_tear_sheet(factor_data,
     by_group : bool
         If True, display graphs separately for each group.
     """
-
+    result_tables = {}
+    result_figs = []
     before, after = avgretplot
 
     avg_cumulative_returns = \
@@ -558,8 +620,11 @@ def create_event_returns_tear_sheet(factor_data,
             by_quantile=True,
             std_bar=True,
             ax=ax_avg_cumulative_returns_by_q)
-
-    plt.show()
+    if show:
+        plt.show()
+    if return_tf:
+        fig1 = gf.fig
+        result_figs.append(fig1)
     gf.close()
 
     if by_group:
@@ -586,9 +651,14 @@ def create_event_returns_tear_sheet(factor_data,
                 std_bar=False,
                 title=group,
                 ax=gf.next_cell())
-
-        plt.show()
+        if show:
+            plt.show()
+        if return_tf:
+            fig2 = gf.fig
+            result_figs.append(fig2)
         gf.close()
+    if return_tf:
+        return result_tables, result_figs
 
 
 @plotting.customize
@@ -596,7 +666,9 @@ def create_event_study_tear_sheet(factor_data,
                                   prices=None,
                                   avgretplot=(5, 15),
                                   rate_of_ret=True,
-                                  n_bars=50):
+                                  n_bars=50,
+                                  return_tf=False,
+                                  show=True):
     """
     Creates an event study tear sheet for analysis of a specific event.
 
@@ -621,7 +693,8 @@ def create_event_study_tear_sheet(factor_data,
     n_bars : int, optional
         Number of bars in event distribution plot
     """
-
+    result_tables = {}
+    result_figs = []
     long_short = False
 
     plotting.plot_quantile_statistics_table(factor_data)
@@ -630,11 +703,14 @@ def create_event_study_tear_sheet(factor_data,
     plotting.plot_events_distribution(events=factor_data['factor'],
                                       num_bars=n_bars,
                                       ax=gf.next_row())
-    plt.show()
+    if show:
+        plt.show()
+    if return_tf:
+        fig = gf.fig
+        result_figs.append(fig)
     gf.close()
 
     if prices is not None and avgretplot is not None:
-
         create_event_returns_tear_sheet(factor_data=factor_data,
                                         prices=prices,
                                         avgretplot=avgretplot,
@@ -689,13 +765,17 @@ def create_event_study_tear_sheet(factor_data,
         )
 
     for p in factor_returns:
-
         plotting.plot_cumulative_returns(
             factor_returns[p],
             period=p,
             freq=trading_calendar,
             ax=gf.next_row()
         )
-
-    plt.show()
+    if show:
+        plt.show()
+    if return_tf:
+        fig = gf.fig
+        result_figs.append(fig)
     gf.close()
+    if return_tf:
+        return result_tables, result_figs