PERF: Use empyrical with ndarrays instead of Series

conda/empyrical/meta.yaml

@@ -1,11 +1,30 @@
 package:
   name: empyrical
-  version: "0.1.11"
+  version: "0.2.0"
 
 source:
-  fn: empyrical-0.1.11.tar.gz
-  url: https://pypi.python.org/packages/df/82/c4050b0fe341db97430b2de7ae736d89a2ddb78636d3e0235aef1b7499d5/empyrical-0.1.11.tar.gz
-  md5: fbd543416d204688146f13f325ca8731
+  fn: empyrical-0.2.0.tar.gz
+  url: https://pypi.python.org/packages/e0/e7/e6bf883c009993677f2f283d93c3efcf910c7e42ce0dcd4cf8abd465b070/empyrical-0.2.0.tar.gz
+  md5: 32a360c6a151b32872e5b6f56dfbe3e4
+#  patches:
+   # List any patch files here
+   # - fix.patch
+
+# build:
+  # noarch_python: True
+  # preserve_egg_dir: True
+  # entry_points:
+    # Put any entry points (scripts to be generated automatically) here. The
+    # syntax is module:function.  For example
+    #
+    # - empyrical = empyrical:main
+    #
+    # Would create an entry point called empyrical that calls empyrical.main()
+
+
+  # If this is a new build for the same version, increment the build
+  # number. If you do not include this key, it defaults to 0.
+  # number: 1
 
 requirements:
   build:
@@ -23,6 +42,21 @@ requirements:
     - scipy >=0.15.1
     - bottleneck >=1.0.0
 
+test:
+  # Python imports
+  imports:
+    - empyrical
+    - empyrical.tests
+
+  # commands:
+  #   - nosetests
+
+  # You can also put a file called run_test.py in the recipe that will be run
+  # at test time.
+
+  requires:
+    - nose >=1.3.7
+  #  - nose_parameterized >=0.5.0
 
 about:
   home: https://github.com/quantopian/empyrical

etc/requirements.txt

@@ -67,4 +67,4 @@ intervaltree==2.1.0
 lru-dict==1.1.4
 
 # For financial risk calculations
-empyrical==0.1.11
+empyrical==0.2.0

zipline/finance/risk/cumulative.py

@@ -28,15 +28,14 @@ from . risk import (
 )
 
 from empyrical import (
-    alpha,
+    alpha_beta_aligned,
     annual_volatility,
-    beta,
     cum_returns,
     downside_risk,
     information_ratio,
     max_drawdown,
     sharpe_ratio,
-    sortino_ratio
+    sortino_ratio,
 )
 
 log = logbook.Logger('Risk Cumulative')
@@ -152,7 +151,6 @@ class RiskMetricsCumulative(object):
 
         self.algorithm_returns_cont[dt_loc] = algorithm_returns
         self.algorithm_returns = self.algorithm_returns_cont[:dt_loc + 1]
-        algorithm_returns_series = pd.Series(self.algorithm_returns)
 
         self.num_trading_days = len(self.algorithm_returns)
 
@@ -161,8 +159,8 @@ class RiskMetricsCumulative(object):
                 self.algorithm_returns = np.append(0.0, self.algorithm_returns)
 
         self.algorithm_cumulative_returns[dt_loc] = cum_returns(
-            algorithm_returns_series
-        ).iloc[-1]
+            self.algorithm_returns
+        )[-1]
 
         algo_cumulative_returns_to_date = \
             self.algorithm_cumulative_returns[:dt_loc + 1]
@@ -186,14 +184,14 @@ class RiskMetricsCumulative(object):
 
         self.benchmark_returns_cont[dt_loc] = benchmark_returns
         self.benchmark_returns = self.benchmark_returns_cont[:dt_loc + 1]
-        benchmark_returns_series = pd.Series(self.benchmark_returns)
+
         if self.create_first_day_stats:
             if len(self.benchmark_returns) == 1:
                 self.benchmark_returns = np.append(0.0, self.benchmark_returns)
 
         self.benchmark_cumulative_returns[dt_loc] = cum_returns(
-            benchmark_returns_series
-        ).iloc[-1]
+            self.benchmark_returns
+        )[-1]
 
         benchmark_cumulative_returns_to_date = \
             self.benchmark_cumulative_returns[:dt_loc + 1]
@@ -234,10 +232,10 @@ algorithm_returns ({algo_count}) in range {start} : {end} on {dt}"
 
         self.update_current_max()
         self.benchmark_volatility[dt_loc] = annual_volatility(
-            benchmark_returns_series
+            self.benchmark_returns
         )
         self.algorithm_volatility[dt_loc] = annual_volatility(
-            algorithm_returns_series
+            self.algorithm_returns
         )
 
         # caching the treasury rates for the minutely case is a
@@ -258,31 +256,26 @@ algorithm_returns ({algo_count}) in range {start} : {end} on {dt}"
             self.algorithm_cumulative_returns[dt_loc] -
             self.treasury_period_return)
 
-        self.beta[dt_loc] = beta(
-            algorithm_returns_series,
-            benchmark_returns_series
-        )
-        self.alpha[dt_loc] = alpha(
-            algorithm_returns_series,
-            benchmark_returns_series,
-            _beta=self.beta[dt_loc]
+        self.alpha[dt_loc], self.beta[dt_loc] = alpha_beta_aligned(
+            self.algorithm_returns,
+            self.benchmark_returns,
         )
         self.sharpe[dt_loc] = sharpe_ratio(
-            algorithm_returns_series
+            self.algorithm_returns,
         )
         self.downside_risk[dt_loc] = downside_risk(
-            algorithm_returns_series
+            self.algorithm_returns
         )
         self.sortino[dt_loc] = sortino_ratio(
-            algorithm_returns_series,
+            self.algorithm_returns,
             _downside_risk=self.downside_risk[dt_loc]
         )
         self.information[dt_loc] = information_ratio(
-            algorithm_returns_series,
-            benchmark_returns_series
+            self.algorithm_returns,
+            self.benchmark_returns,
         )
         self.max_drawdown = max_drawdown(
-            algorithm_returns_series
+            self.algorithm_returns
         )
         self.max_drawdowns[dt_loc] = self.max_drawdown
         self.max_leverage = self.calculate_max_leverage()

zipline/finance/risk/period.py

@@ -25,9 +25,8 @@ from . import risk
 from . risk import check_entry
 
 from empyrical import (
-    alpha,
+    alpha_beta_aligned,
     annual_volatility,
-    beta,
     cum_returns,
     downside_risk,
     information_ratio,
@@ -124,28 +123,23 @@ class RiskMetricsPeriod(object):
         if pd.isnull(self.sharpe):
             self.sharpe = 0.0
         self.downside_risk = downside_risk(
-            self.algorithm_returns
+            self.algorithm_returns.values
         )
         self.sortino = sortino_ratio(
-            self.algorithm_returns,
-            _downside_risk=self.downside_risk
+            self.algorithm_returns.values,
+            _downside_risk=self.downside_risk,
         )
         self.information = information_ratio(
-            self.algorithm_returns,
-            self.benchmark_returns
+            self.algorithm_returns.values,
+            self.benchmark_returns.values,
         )
-        self.beta = beta(
-            self.algorithm_returns,
-            self.benchmark_returns
-        )
-        self.alpha = alpha(
-            self.algorithm_returns,
-            self.benchmark_returns,
-            _beta=self.beta
+        self.alpha, self.beta = alpha_beta_aligned(
+            self.algorithm_returns.values,
+            self.benchmark_returns.values,
         )
         self.excess_return = self.algorithm_period_returns - \
             self.treasury_period_return
-        self.max_drawdown = max_drawdown(self.algorithm_returns)
+        self.max_drawdown = max_drawdown(self.algorithm_returns.values)
         self.max_leverage = self.calculate_max_leverage()
 
     def to_dict(self):