PERF: Risk-adjust returns series once before calling empyrical

This prevents empyrical from having to subtract
the risk-free rate from the returns in each
individual method.

zipline/finance/risk/cumulative.py

@@ -257,6 +257,9 @@ algorithm_returns ({algo_count}) in range {start} : {end} on {dt}"
         self.excess_returns[dt_loc] = (
             self.algorithm_cumulative_returns[dt_loc] -
             self.treasury_period_return)
+
+        risk_adj_returns = algorithm_returns_series - benchmark_returns_series
+
         self.beta[dt_loc] = beta(
             algorithm_returns_series,
             benchmark_returns_series
@@ -267,16 +270,13 @@ algorithm_returns ({algo_count}) in range {start} : {end} on {dt}"
             _beta=self.beta[dt_loc]
         )
         self.sharpe[dt_loc] = sharpe_ratio(
-            algorithm_returns_series,
-            benchmark_returns_series
+            risk_adj_returns
         )
         self.downside_risk[dt_loc] = downside_risk(
-            algorithm_returns_series,
-            benchmark_returns_series
+            risk_adj_returns
         )
         self.sortino[dt_loc] = sortino_ratio(
-            algorithm_returns_series,
-            benchmark_returns_series,
+            risk_adj_returns,
             _downside_risk=self.downside_risk[dt_loc]
         )
         self.information[dt_loc] = information_ratio(

zipline/finance/risk/period.py

@@ -123,13 +123,12 @@ class RiskMetricsPeriod(object):
         # In the meantime, convert nan values to 0.0
         if pd.isnull(self.sharpe):
             self.sharpe = 0.0
+        risk_adj_returns = self.algorithm_returns - self.benchmark_returns
         self.downside_risk = downside_risk(
-            self.algorithm_returns,
-            self.benchmark_returns
+            risk_adj_returns
         )
         self.sortino = sortino_ratio(
-            self.algorithm_returns,
-            self.benchmark_returns,
+            risk_adj_returns,
             _downside_risk=self.downside_risk
         )
         self.information = information_ratio(