From e579ddd33d6ccf612e7ce53cbb1c242978d726e2 Mon Sep 17 00:00:00 2001
From: devin stevenson <devin@madroneco.com>
Date: Sun, 31 Jan 2016 23:32:42 -0800
Subject: [PATCH 1/2] ENH: vectorize max_drawdown

approx 4X speedup
---
 pyfolio/timeseries.py | 15 +++------------
 1 file changed, 3 insertions(+), 12 deletions(-)

diff --git a/pyfolio/timeseries.py b/pyfolio/timeseries.py
index aafcdfef..dec1f041 100644
--- a/pyfolio/timeseries.py
+++ b/pyfolio/timeseries.py
@@ -76,18 +76,9 @@ def max_drawdown(returns):
         return np.nan
 
     df_cum_rets = cum_returns(returns, starting_value=100)
+    cum_max_return = df_cum_rets.cummax()
 
-    MDD = 0
-    DD = 0
-    peak = -99999
-    for value in df_cum_rets:
-        if (value > peak):
-            peak = value
-        else:
-            DD = (peak - value) / peak
-        if (DD > MDD):
-            MDD = DD
-    return -1 * MDD
+    return df_cum_rets.sub(cum_max_return).div(cum_max_return).min()
 
 
 def annual_return(returns, period=DAILY):
@@ -925,7 +916,7 @@ def calc_distribution_stats(x):
 
 
 def get_max_drawdown_underwater(underwater):
-    """Determines peak, valley, and recovery dates given and 'underwater'
+    """Determines peak, valley, and recovery dates given an 'underwater'
     DataFrame.
 
     An underwater DataFrame is a DataFrame that has precomputed

From 977069f19b327e31c8cb10b525afe574403c771b Mon Sep 17 00:00:00 2001
From: devin stevenson <devin@madroneco.com>
Date: Sun, 31 Jan 2016 23:33:13 -0800
Subject: [PATCH 2/2] BUG: fix docs on model_returns[t|normal]

---
 pyfolio/bayesian.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/pyfolio/bayesian.py b/pyfolio/bayesian.py
index 549d8f03..23a25d73 100644
--- a/pyfolio/bayesian.py
+++ b/pyfolio/bayesian.py
@@ -106,11 +106,7 @@ def model_returns_t_alpha_beta(data, bmark, samples=2000):
 
 
 def model_returns_normal(data, samples=500):
-    """Run Bayesian model assuming returns are Student-T distributed.
-
-    Compared with the normal model, this model assumes returns be
-    T-distributed and thus has a 3rd parameter (nu) that controls the
-    mass in the tails.
+    """Run Bayesian model assuming returns are normally distributed.
 
     Parameters
     ----------
@@ -149,7 +145,11 @@ def model_returns_normal(data, samples=500):
 
 
 def model_returns_t(data, samples=500):
-    """Run Bayesian model assuming returns are normally distributed.
+    """Run Bayesian model assuming returns are Student-T distributed.
+
+    Compared with the normal model, this model assumes returns are
+    T-distributed and thus have a 3rd parameter (nu) that controls the
+    mass in the tails.
 
     Parameters
     ----------