From aab448f187a1c80f68d99f4832d51e310d8b90f2 Mon Sep 17 00:00:00 2001
From: Justin Lent <jlent@quantopian.com>
Date: Mon, 12 Oct 2015 16:05:12 -0700
Subject: [PATCH 01/11] ENH add ability to plot multiple stdev regions for the
 OOS cone in plotting.plot_rolling_returns. Update calling functions to use
 default of plotting these  multiple regions: 1.0, 1.5, 2.0 stdevs

---
 pyfolio/plotting.py | 36 +++++++++++++++++++++++++++++++-----
 pyfolio/tears.py    |  4 ++--
 2 files changed, 33 insertions(+), 7 deletions(-)

diff --git a/pyfolio/plotting.py b/pyfolio/plotting.py
index ac61e7e6..9adbc0dc 100644
--- a/pyfolio/plotting.py
+++ b/pyfolio/plotting.py
@@ -611,10 +611,18 @@ def plot_rolling_returns(
             label='Live', ax=ax, **kwargs)
 
         if cone_std is not None:
-            cone_df = timeseries.cone_rolling(
-                returns,
-                num_stdev=cone_std,
-                cone_fit_end_date=live_start_date)
+            # check to see if we're just rendering a single cone, or multiple
+            # cones at various stddevs, defined as elements of cone_std
+            if type(cone_std) is list:
+                cone_df = timeseries.cone_rolling(
+                    returns,
+                    num_stdev=cone_std[0],
+                    cone_fit_end_date=live_start_date)
+            else:
+                cone_df = timeseries.cone_rolling(
+                    returns,
+                    num_stdev=cone_std,
+                    cone_fit_end_date=live_start_date)
 
             cone_df_fit = cone_df[cone_df.index < live_start_date]
 
@@ -641,7 +649,25 @@ def plot_rolling_returns(
             ax.fill_between(cone_df_live.index,
                             cone_df_live.sd_down,
                             cone_df_live.sd_up,
-                            color='red', alpha=0.30)
+                            color='steelblue', alpha=0.25)
+
+            if type(cone_std) is list:
+                for cone_i in range(1, len(cone_std)):
+                    cone_df = timeseries.cone_rolling(
+                        returns,
+                        num_stdev=cone_std[cone_i],
+                        cone_fit_end_date=live_start_date)
+
+                    cone_df_fit = cone_df[cone_df.index < live_start_date]
+
+                    cone_df_live = cone_df[cone_df.index > live_start_date]
+                    cone_df_live = cone_df_live[cone_df_live.index
+                                                < returns.index[-1]]
+
+                    ax.fill_between(cone_df_live.index,
+                                    cone_df_live.sd_down,
+                                    cone_df_live.sd_up,
+                                    color='steelblue', alpha=0.25)
 
     ax.axhline(1.0, linestyle='--', color='black', lw=2)
     ax.set_ylabel('Cumulative returns')
diff --git a/pyfolio/tears.py b/pyfolio/tears.py
index 327ce08e..d8c70021 100644
--- a/pyfolio/tears.py
+++ b/pyfolio/tears.py
@@ -42,7 +42,7 @@ def create_full_tear_sheet(returns, positions=None, transactions=None,
                            benchmark_rets=None,
                            gross_lev=None,
                            live_start_date=None, bayesian=False,
-                           cone_std=1.0, set_context=True):
+                           cone_std=[1.0, 1.5, 2.0], set_context=True):
     """
     Generate a number of tear sheets that are useful
     for analyzing a strategy's performance.
@@ -140,7 +140,7 @@ def create_full_tear_sheet(returns, positions=None, transactions=None,
 
 @plotting_context
 def create_returns_tear_sheet(returns, live_start_date=None,
-                              cone_std=1.0,
+                              cone_std=[1.0, 1.5, 2.0],
                               benchmark_rets=None,
                               return_fig=False):
     """

From b81e207a15108c644d64b85f4d34874fe00cdc18 Mon Sep 17 00:00:00 2001
From: Justin Lent <jlent@quantopian.com>
Date: Mon, 12 Oct 2015 16:20:47 -0700
Subject: [PATCH 02/11] STY PEP8

---
 pyfolio/plotting.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/pyfolio/plotting.py b/pyfolio/plotting.py
index 9adbc0dc..9f1049cf 100644
--- a/pyfolio/plotting.py
+++ b/pyfolio/plotting.py
@@ -661,8 +661,8 @@ def plot_rolling_returns(
                     cone_df_fit = cone_df[cone_df.index < live_start_date]
 
                     cone_df_live = cone_df[cone_df.index > live_start_date]
-                    cone_df_live = cone_df_live[cone_df_live.index
-                                                < returns.index[-1]]
+                    temp_keep_days = cone_df_live.index < returns.index[-1]
+                    cone_df_live = cone_df_live[temp_keep_days]
 
                     ax.fill_between(cone_df_live.index,
                                     cone_df_live.sd_down,

From 3cffef4f408b28b1bae1d959f1227fcee127c080 Mon Sep 17 00:00:00 2001
From: Justin Lent <jlent@quantopian.com>
Date: Mon, 12 Oct 2015 17:09:57 -0700
Subject: [PATCH 03/11] STY Python3 compat

---
 pyfolio/plotting.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyfolio/plotting.py b/pyfolio/plotting.py
index 9f1049cf..cce2473b 100644
--- a/pyfolio/plotting.py
+++ b/pyfolio/plotting.py
@@ -652,7 +652,7 @@ def plot_rolling_returns(
                             color='steelblue', alpha=0.25)
 
             if type(cone_std) is list:
-                for cone_i in range(1, len(cone_std)):
+                for cone_i in list(range(1, len(cone_std))):
                     cone_df = timeseries.cone_rolling(
                         returns,
                         num_stdev=cone_std[cone_i],

From efcf5ae92fcc109d0f0e41f34f76b28ff11cf641 Mon Sep 17 00:00:00 2001
From: Justin Lent <jlent@quantopian.com>
Date: Tue, 13 Oct 2015 18:06:21 -0700
Subject: [PATCH 04/11] ENH in plotting.plot_rolling_returns, factor out cone
 drawing into local function. Turn off cones completely if plotting volatility
 match

---
 pyfolio/plotting.py | 68 +++++++++++++++++++--------------------------
 1 file changed, 29 insertions(+), 39 deletions(-)

diff --git a/pyfolio/plotting.py b/pyfolio/plotting.py
index cce2473b..688cb8d5 100644
--- a/pyfolio/plotting.py
+++ b/pyfolio/plotting.py
@@ -573,6 +573,24 @@ def plot_rolling_returns(
         The axes that were plotted on.
 
 """
+    def draw_cone(returns, num_stdev, live_start_date, ax):
+        cone_df = timeseries.cone_rolling(
+            returns,
+            num_stdev=num_stdev,
+            cone_fit_end_date=live_start_date)
+
+        cone_df_fit = cone_df[cone_df.index < live_start_date]
+
+        cone_df_live = cone_df[cone_df.index > live_start_date]
+        temp_keep_days = cone_df_live.index < returns.index[-1]
+        cone_df_live = cone_df_live[temp_keep_days]
+
+        ax.fill_between(cone_df_live.index,
+                        cone_df_live.sd_down,
+                        cone_df_live.sd_up,
+                        color='steelblue', alpha=0.25)
+
+        return cone_df_fit, cone_df_live
 
     if ax is None:
         ax = plt.gca()
@@ -610,24 +628,19 @@ def plot_rolling_returns(
             lw=4, color='red', alpha=0.6,
             label='Live', ax=ax, **kwargs)
 
-        if cone_std is not None:
-            # check to see if we're just rendering a single cone, or multiple
-            # cones at various stddevs, defined as elements of cone_std
-            if type(cone_std) is list:
-                cone_df = timeseries.cone_rolling(
-                    returns,
-                    num_stdev=cone_std[0],
-                    cone_fit_end_date=live_start_date)
+        if cone_std is not None and not volatility_match:
+            # check to see if cone_std was passed as a single value and,
+            # if so, just convert to list automatically
+            if isinstance(cone_std, float):
+                cone_std_list = [cone_std]
             else:
-                cone_df = timeseries.cone_rolling(
-                    returns,
-                    num_stdev=cone_std,
-                    cone_fit_end_date=live_start_date)
-
-            cone_df_fit = cone_df[cone_df.index < live_start_date]
+                cone_std_list = cone_std
 
-            cone_df_live = cone_df[cone_df.index > live_start_date]
-            cone_df_live = cone_df_live[cone_df_live.index < returns.index[-1]]
+            for cone_i in cone_std_list:
+                cone_df_fit, cone_df_live = draw_cone(returns,
+                                                      cone_i,
+                                                      live_start_date,
+                                                      ax)
 
             cone_df_fit['line'].plot(
                 ax=ax,
@@ -646,29 +659,6 @@ def plot_rolling_returns(
                 alpha=0.7,
                 **kwargs)
 
-            ax.fill_between(cone_df_live.index,
-                            cone_df_live.sd_down,
-                            cone_df_live.sd_up,
-                            color='steelblue', alpha=0.25)
-
-            if type(cone_std) is list:
-                for cone_i in list(range(1, len(cone_std))):
-                    cone_df = timeseries.cone_rolling(
-                        returns,
-                        num_stdev=cone_std[cone_i],
-                        cone_fit_end_date=live_start_date)
-
-                    cone_df_fit = cone_df[cone_df.index < live_start_date]
-
-                    cone_df_live = cone_df[cone_df.index > live_start_date]
-                    temp_keep_days = cone_df_live.index < returns.index[-1]
-                    cone_df_live = cone_df_live[temp_keep_days]
-
-                    ax.fill_between(cone_df_live.index,
-                                    cone_df_live.sd_down,
-                                    cone_df_live.sd_up,
-                                    color='steelblue', alpha=0.25)
-
     ax.axhline(1.0, linestyle='--', color='black', lw=2)
     ax.set_ylabel('Cumulative returns')
     ax.set_title('Cumulative Returns')

From 8da21929e8a1f056ef6080346bd9365910cbca06 Mon Sep 17 00:00:00 2001
From: Justin Lent <jlent@quantopian.com>
Date: Tue, 13 Oct 2015 18:09:38 -0700
Subject: [PATCH 05/11] DOC update docstring

---
 pyfolio/plotting.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/pyfolio/plotting.py b/pyfolio/plotting.py
index 688cb8d5..cf7a5bb4 100644
--- a/pyfolio/plotting.py
+++ b/pyfolio/plotting.py
@@ -552,8 +552,9 @@ def plot_rolling_returns(
     live_start_date : datetime, optional
         The point in time when the strategy began live trading, after
         its backtest period.
-    cone_std : float, optional
-        The standard deviation to use for the cone plots.
+    cone_std : float, or list, optional
+        If float, The standard deviation to use for the cone plots.
+        If list, A list of standard deviation values to use for the cone plots
          - The cone is a normal distribution with this standard deviation
              centered around a linear regression.
     legend_loc : matplotlib.loc, optional

From 8300be51608a6968d62d13bff2f8800c32fb036d Mon Sep 17 00:00:00 2001
From: Justin Lent <jlent@quantopian.com>
Date: Wed, 14 Oct 2015 10:34:14 -0700
Subject: [PATCH 06/11] STY PEP8 and code cleanup from CR recommendations

---
 pyfolio/plotting.py | 46 +++++++++++++++++++++++----------------------
 1 file changed, 24 insertions(+), 22 deletions(-)

diff --git a/pyfolio/plotting.py b/pyfolio/plotting.py
index cf7a5bb4..89a5c93e 100644
--- a/pyfolio/plotting.py
+++ b/pyfolio/plotting.py
@@ -580,18 +580,17 @@ def draw_cone(returns, num_stdev, live_start_date, ax):
             num_stdev=num_stdev,
             cone_fit_end_date=live_start_date)
 
-        cone_df_fit = cone_df[cone_df.index < live_start_date]
-
-        cone_df_live = cone_df[cone_df.index > live_start_date]
-        temp_keep_days = cone_df_live.index < returns.index[-1]
-        cone_df_live = cone_df_live[temp_keep_days]
-
-        ax.fill_between(cone_df_live.index,
-                        cone_df_live.sd_down,
-                        cone_df_live.sd_up,
+        cone_in_sample = cone_df[cone_df.index < live_start_date]
+        cone_out_of_sample = cone_df[cone_df.index > live_start_date]
+        cone_out_of_sample = cone_out_of_sample[
+            cone_out_of_sample.index < returns.index[-1]]
+
+        ax.fill_between(cone_out_of_sample.index,
+                        cone_out_of_sample.sd_down,
+                        cone_out_of_sample.sd_up,
                         color='steelblue', alpha=0.25)
 
-        return cone_df_fit, cone_df_live
+        return cone_in_sample, cone_out_of_sample
 
     if ax is None:
         ax = plt.gca()
@@ -633,17 +632,20 @@ def draw_cone(returns, num_stdev, live_start_date, ax):
             # check to see if cone_std was passed as a single value and,
             # if so, just convert to list automatically
             if isinstance(cone_std, float):
-                cone_std_list = [cone_std]
-            else:
-                cone_std_list = cone_std
-
-            for cone_i in cone_std_list:
-                cone_df_fit, cone_df_live = draw_cone(returns,
-                                                      cone_i,
-                                                      live_start_date,
-                                                      ax)
-
-            cone_df_fit['line'].plot(
+                #cone_std_list = [cone_std]
+                cone_std = [cone_std]
+            #else:
+            #    cone_std_list = cone_std
+
+            #for cone_i in cone_std_list:
+            for cone_i in cone_std:
+                cone_in_sample, cone_out_of_sample = draw_cone(
+                    returns,
+                    cone_i,
+                    live_start_date,
+                    ax)
+
+            cone_in_sample['line'].plot(
                 ax=ax,
                 ls='--',
                 label='Backtest trend',
@@ -651,7 +653,7 @@ def draw_cone(returns, num_stdev, live_start_date, ax):
                 color='forestgreen',
                 alpha=0.7,
                 **kwargs)
-            cone_df_live['line'].plot(
+            cone_out_of_sample['line'].plot(
                 ax=ax,
                 ls='--',
                 label='Predicted trend',

From b3c221549a3c20f51b150921b980e6567cc5a99d Mon Sep 17 00:00:00 2001
From: Justin Lent <jlent@quantopian.com>
Date: Wed, 14 Oct 2015 10:44:53 -0700
Subject: [PATCH 07/11] FIX reverse hardcoding of turning off cone in
 volatility matched plot in ploting.plot_rolling_retuns(), and specify it
 turned off in the calling functions tears.create_returns_tear_sheet()

---
 pyfolio/plotting.py | 2 +-
 pyfolio/tears.py    | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/pyfolio/plotting.py b/pyfolio/plotting.py
index 89a5c93e..92e40a0c 100644
--- a/pyfolio/plotting.py
+++ b/pyfolio/plotting.py
@@ -628,7 +628,7 @@ def draw_cone(returns, num_stdev, live_start_date, ax):
             lw=4, color='red', alpha=0.6,
             label='Live', ax=ax, **kwargs)
 
-        if cone_std is not None and not volatility_match:
+        if cone_std is not None:
             # check to see if cone_std was passed as a single value and,
             # if so, just convert to list automatically
             if isinstance(cone_std, float):
diff --git a/pyfolio/tears.py b/pyfolio/tears.py
index d8c70021..c5ba90c0 100644
--- a/pyfolio/tears.py
+++ b/pyfolio/tears.py
@@ -230,7 +230,7 @@ def create_returns_tear_sheet(returns, live_start_date=None,
         returns,
         factor_returns=benchmark_rets,
         live_start_date=live_start_date,
-        cone_std=cone_std,
+        cone_std=None,
         volatility_match=True,
         ax=ax_rolling_returns_vol_match)
     ax_rolling_returns_vol_match.set_title(

From 2a0c49c4c77e70bc504e6f6368b8423ee05391a9 Mon Sep 17 00:00:00 2001
From: Justin Lent <jlent@quantopian.com>
Date: Wed, 14 Oct 2015 10:50:43 -0700
Subject: [PATCH 08/11] Added TO-DO comment/question in
 plotting.plot_rolling_returns() for how to properly address Issue #152

---
 pyfolio/plotting.py | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/pyfolio/plotting.py b/pyfolio/plotting.py
index 92e40a0c..ef21a3bc 100644
--- a/pyfolio/plotting.py
+++ b/pyfolio/plotting.py
@@ -600,6 +600,11 @@ def draw_cone(returns, num_stdev, live_start_date, ax):
                          'factor_returns.')
     elif volatility_match and factor_returns is not None:
         bmark_vol = factor_returns.loc[returns.index].std()
+        # TO-DO: @tweicki 'returns' probably needs to get updated to:
+        # (returns / returns.std()) * bmark_vol if we want to plot
+        # the cone on this later on.
+        # Will we need a temp variable to do this? Or can we just re-assign as
+        # returns = (returns / returns.std()) * bmark_vol
         df_cum_rets = timeseries.cum_returns(
             (returns / returns.std()) * bmark_vol,
             1.0

From cf83da207f43e1014bf9034d20bc2544afa4eee6 Mon Sep 17 00:00:00 2001
From: Justin Lent <jlent@quantopian.com>
Date: Wed, 14 Oct 2015 10:59:40 -0700
Subject: [PATCH 09/11] STY delete stale comments leftover from debugging

---
 pyfolio/plotting.py | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/pyfolio/plotting.py b/pyfolio/plotting.py
index ef21a3bc..d991b174 100644
--- a/pyfolio/plotting.py
+++ b/pyfolio/plotting.py
@@ -637,12 +637,8 @@ def draw_cone(returns, num_stdev, live_start_date, ax):
             # check to see if cone_std was passed as a single value and,
             # if so, just convert to list automatically
             if isinstance(cone_std, float):
-                #cone_std_list = [cone_std]
                 cone_std = [cone_std]
-            #else:
-            #    cone_std_list = cone_std
 
-            #for cone_i in cone_std_list:
             for cone_i in cone_std:
                 cone_in_sample, cone_out_of_sample = draw_cone(
                     returns,

From 50540ff55c21eb779f5806ec9873ab0d5eae2f35 Mon Sep 17 00:00:00 2001
From: Justin Lent <jlent@quantopian.com>
Date: Fri, 16 Oct 2015 08:07:08 -0700
Subject: [PATCH 10/11] STY change input cone_std in
 plotting.plot_rolling_returns to be tuple

---
 pyfolio/plotting.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/pyfolio/plotting.py b/pyfolio/plotting.py
index d991b174..5c605387 100644
--- a/pyfolio/plotting.py
+++ b/pyfolio/plotting.py
@@ -552,9 +552,9 @@ def plot_rolling_returns(
     live_start_date : datetime, optional
         The point in time when the strategy began live trading, after
         its backtest period.
-    cone_std : float, or list, optional
+    cone_std : float, or tuple, optional
         If float, The standard deviation to use for the cone plots.
-        If list, A list of standard deviation values to use for the cone plots
+        If tuple, Tuple of standard deviation values to use for the cone plots
          - The cone is a normal distribution with this standard deviation
              centered around a linear regression.
     legend_loc : matplotlib.loc, optional

From 62958f576b8b0e3b883d3d5ad4405e9381bba107 Mon Sep 17 00:00:00 2001
From: Justin Lent <jlent@quantopian.com>
Date: Fri, 16 Oct 2015 08:40:21 -0700
Subject: [PATCH 11/11] FIX update all callers of plotting.plot_rolling_returns
 to pass tuples. Fix cone rendering in volatility weighted case

---
 pyfolio/plotting.py | 14 +++-----------
 pyfolio/tears.py    | 14 ++++++++------
 2 files changed, 11 insertions(+), 17 deletions(-)

diff --git a/pyfolio/plotting.py b/pyfolio/plotting.py
index 5c605387..f779b476 100644
--- a/pyfolio/plotting.py
+++ b/pyfolio/plotting.py
@@ -600,17 +600,9 @@ def draw_cone(returns, num_stdev, live_start_date, ax):
                          'factor_returns.')
     elif volatility_match and factor_returns is not None:
         bmark_vol = factor_returns.loc[returns.index].std()
-        # TO-DO: @tweicki 'returns' probably needs to get updated to:
-        # (returns / returns.std()) * bmark_vol if we want to plot
-        # the cone on this later on.
-        # Will we need a temp variable to do this? Or can we just re-assign as
-        # returns = (returns / returns.std()) * bmark_vol
-        df_cum_rets = timeseries.cum_returns(
-            (returns / returns.std()) * bmark_vol,
-            1.0
-        )
-    else:
-        df_cum_rets = timeseries.cum_returns(returns, 1.0)
+        returns = (returns / returns.std()) * bmark_vol
+
+    df_cum_rets = timeseries.cum_returns(returns, 1.0)
 
     y_axis_formatter = FuncFormatter(utils.one_dec_places)
     ax.yaxis.set_major_formatter(FuncFormatter(y_axis_formatter))
diff --git a/pyfolio/tears.py b/pyfolio/tears.py
index c5ba90c0..58cce5f2 100644
--- a/pyfolio/tears.py
+++ b/pyfolio/tears.py
@@ -42,7 +42,7 @@ def create_full_tear_sheet(returns, positions=None, transactions=None,
                            benchmark_rets=None,
                            gross_lev=None,
                            live_start_date=None, bayesian=False,
-                           cone_std=[1.0, 1.5, 2.0], set_context=True):
+                           cone_std=(1.0, 1.5, 2.0), set_context=True):
     """
     Generate a number of tear sheets that are useful
     for analyzing a strategy's performance.
@@ -94,8 +94,9 @@ def create_full_tear_sheet(returns, positions=None, transactions=None,
         after its backtest period.
     bayesian: boolean, optional
         If True, causes the generation of a Bayesian tear sheet.
-    cone_std : float, optional
-        The standard deviation to use for the cone plots.
+    cone_std : float, or tuple, optional
+        If float, The standard deviation to use for the cone plots.
+        If tuple, Tuple of standard deviation values to use for the cone plots
          - The cone is a normal distribution with this standard deviation
              centered around a linear regression.
     set_context : boolean, optional
@@ -140,7 +141,7 @@ def create_full_tear_sheet(returns, positions=None, transactions=None,
 
 @plotting_context
 def create_returns_tear_sheet(returns, live_start_date=None,
-                              cone_std=[1.0, 1.5, 2.0],
+                              cone_std=(1.0, 1.5, 2.0),
                               benchmark_rets=None,
                               return_fig=False):
     """
@@ -162,8 +163,9 @@ def create_returns_tear_sheet(returns, live_start_date=None,
     live_start_date : datetime, optional
         The point in time when the strategy began live trading,
         after its backtest period.
-    cone_std : float, optional
-        The standard deviation to use for the cone plots.
+    cone_std : float, or tuple, optional
+        If float, The standard deviation to use for the cone plots.
+        If tuple, Tuple of standard deviation values to use for the cone plots
          - The cone is a normal distribution with this standard deviation
              centered around a linear regression.
     benchmark_rets : pd.Series, optional