Style

Author: Gerry Manoim

alphalens/performance.py

@@ -346,13 +346,12 @@ def cumulative_returns(returns):
             2015-01-05   1.001310
             2015-01-06   1.000805
             2015-01-07   1.001092
-            2015-01-08   0.999200    
+            2015-01-08   0.999200
     """
 
     return ep.cum_returns(returns, starting_value=1)
 
 
-
 def positions(weights, period, freq=None):
     """
     Builds net position values time series, the portfolio percentage invested
@@ -845,7 +844,9 @@ def average_cumulative_return(q_fact, demean_by):
             all_returns = []
             for group, g_data in factor_data.groupby('group'):
                 g_fq = g_data['factor_quantile']
-                avgcumret = g_fq.groupby(g_fq).apply(cumulative_return, g_fq)
+                avgcumret = g_fq.groupby(g_fq).apply(
+                    cumulative_return_around_event, g_fq
+                )
                 all_returns.append(avgcumret)
             q_returns = pd.concat(all_returns, axis=1)
             q_returns = pd.DataFrame({'mean': q_returns.mean(axis=1),

alphalens/plotting.py

@@ -711,10 +711,10 @@ def plot_monthly_ic_heatmap(mean_monthly_ic, ax=None):
     return ax
 
 
-def plot_cumulative_returns(factor_returns, 
+def plot_cumulative_returns(factor_returns,
                             period,
-                            freq=None, 
-                            title=None, 
+                            freq=None,
+                            title=None,
                             ax=None):
     """
     Plots the cumulative returns of the returns series passed in.
@@ -793,7 +793,6 @@ def plot_cumulative_returns_by_quantile(quantile_returns,
 
     cum_ret = ret_wide.apply(perf.cumulative_returns)
 
-
     cum_ret = cum_ret.loc[:, ::-1]  # we want negative quantiles as 'red'
 
     cum_ret.plot(lw=2, ax=ax, cmap=cm.coolwarm)