MAINT: Test fixes + miscellaneous cleanup

alphalens/examples/tear_sheet_walk_through.ipynb

@@ -1674,7 +1674,7 @@
    "outputs": [],
    "source": [
     "quantile_factor = factor_data['factor_quantile']\n",
-    "turnover_period = '1D'"
+    "turnover_period = 1"
    ]
   },
   {

alphalens/performance.py

@@ -578,6 +578,7 @@ def quantile_turnover(quantile_factor, quantile, period=1):
         Quantile on which to perform turnover analysis.
     period: int, optional
         Number of days over which to calculate the turnover.
+
     Returns
     -------
     quant_turnover : pd.Series
@@ -616,14 +617,13 @@ def factor_rank_autocorrelation(factor_data, period=1):
         - See full explanation in utils.get_clean_factor_and_forward_returns
     period: int, optional
         Number of days over which to calculate the turnover.
+
     Returns
     -------
     autocorr : pd.Series
         Rolling 1 period (defined by time_rule) autocorrelation of
         factor values.
-
     """
-
     grouper = [factor_data.index.get_level_values('date')]
 
     ranks = factor_data.groupby(grouper)['factor'].rank()
@@ -658,17 +658,18 @@ def common_start_returns(factor,
     factor : pd.DataFrame
         DataFrame with at least date and equity as index, the columns are
         irrelevant
-    prices : pd.DataFrame
-        A wide form Pandas DataFrame indexed by date with assets
-        in the columns. Pricing data should span the factor
-        analysis time period plus/minus an additional buffer window
-        corresponding to after/before period parameters.
+    returns : pd.DataFrame
+        A wide form Pandas DataFrame indexed by date with assets in the
+        columns. Returns data should span the factor analysis time period
+        plus/minus an additional buffer window corresponding to after/before
+        period parameters.
     before:
         How many returns to load before factor date
     after:
         How many returns to load after factor date
     cumulative: bool, optional
-        Return cumulative returns
+        Whether or not the given returns are cumulative. If False the given
+        returns are assumed to be daily.
     mean_by_date: bool, optional
         If True, compute mean returns for each date and return that
         instead of a return series for each asset
@@ -684,7 +685,6 @@ def common_start_returns(factor,
         Dataframe containing returns series for each factor aligned to the same
         index: -before to after
     """
-
     if not cumulative:
         returns = returns.apply(cumulative_returns, axis=0)
 
@@ -714,9 +714,6 @@ def common_start_returns(factor,
         series.index = range(starting_index - day_zero_index,
                              ending_index - day_zero_index)
 
-        if cumulative:
-            series = (series / series.loc[0, :]) - 1
-
         if demean_by is not None:
             mean = series.loc[:, demean_equities].mean(axis=1)
             series = series.loc[:, equities]
@@ -749,11 +746,11 @@ def average_cumulative_return_by_quantile(factor_data,
         each period, the factor quantile/bin that factor value belongs to, and
         (optionally) the group the asset belongs to.
         - See full explanation in utils.get_clean_factor_and_forward_returns
-    prices : pd.DataFrame
-        A wide form Pandas DataFrame indexed by date with assets
-        in the columns. Pricing data should span the factor
-        analysis time period plus/minus an additional buffer window
-        corresponding to periods_after/periods_before parameters.
+    returns : pd.DataFrame
+        A wide form Pandas DataFrame indexed by date with assets in the
+        columns. Returns data should span the factor analysis time period
+        plus/minus an additional buffer window corresponding to periods_after/
+        periods_before parameters.
     periods_before : int, optional
         How many periods before factor to plot
     periods_after  : int, optional
@@ -765,6 +762,7 @@ def average_cumulative_return_by_quantile(factor_data,
         neutral portfolio)
     by_group : bool
         If True, compute cumulative returns separately for each group
+
     Returns
     -------
     cumulative returns and std deviation : pd.DataFrame
@@ -791,10 +789,15 @@ def average_cumulative_return_by_quantile(factor_data,
     """
 
     def cumulative_return_around_event(q_fact, demean_by):
-        return common_start_returns(q_fact, returns,
-                                    periods_before,
-                                    periods_after,
-                                    True, True, demean_by)
+        return common_start_returns(
+            q_fact,
+            returns,
+            periods_before,
+            periods_after,
+            cumulative=True,
+            mean_by_date=True,
+            demean_by=demean_by,
+        )
 
     def average_cumulative_return(q_fact, demean_by):
         q_returns = cumulative_return_around_event(q_fact, demean_by)

alphalens/plotting.py

@@ -629,7 +629,7 @@ def plot_top_bottom_quantile_turnover(quantile_turnover, period=1, ax=None):
     quantile_turnover: pd.Dataframe
         Quantile turnover (each DataFrame column a quantile).
     period: int, optional
-        Period over which to calculate the turnover
+        Period over which to calculate the turnover.
     ax : matplotlib.Axes, optional
         Axes upon which to plot.
 

alphalens/tears.py

@@ -124,6 +124,7 @@ def create_summary_tear_sheet(
     )
 
     periods = utils.get_forward_returns_columns(factor_data.columns)
+    periods = list(map(lambda p: pd.Timedelta(p).days, periods))
 
     fr_cols = len(periods)
     vertical_sections = 2 + fr_cols * 3
@@ -430,9 +431,11 @@ def create_turnover_tear_sheet(factor_data, turnover_periods=None):
     if turnover_periods is None:
         input_periods = utils.get_forward_returns_columns(
             factor_data.columns, require_exact_day_multiple=True,
-        )
-        turnover_periods = list(
-            map((lambda x: pd.Timedelta(x).days), input_periods.get_values())
+        ).get_values()
+        turnover_periods = utils.timedelta_strings_to_integers(input_periods)
+    else:
+        turnover_periods = utils.timedelta_strings_to_integers(
+            turnover_periods,
         )
 
     quantile_factor = factor_data["factor_quantile"]
@@ -483,9 +486,10 @@ def create_turnover_tear_sheet(factor_data, turnover_periods=None):
 
 
 @plotting.customize
-def create_full_tear_sheet(
-    factor_data, long_short=True, group_neutral=False, by_group=False
-):
+def create_full_tear_sheet(factor_data,
+                           long_short=True,
+                           group_neutral=False,
+                           by_group=False):
     """
     Creates a full tear sheet for analysis and evaluating single
     return predicting (alpha) factor.
@@ -523,15 +527,13 @@ def create_full_tear_sheet(
 
 
 @plotting.customize
-def create_event_returns_tear_sheet(
-    factor_data,
-    returns,
-    avgretplot=(5, 15),
-    long_short=True,
-    group_neutral=False,
-    std_bar=True,
-    by_group=False,
-):
+def create_event_returns_tear_sheet(factor_data,
+                                    returns,
+                                    avgretplot=(5, 15),
+                                    long_short=True,
+                                    group_neutral=False,
+                                    std_bar=True,
+                                    by_group=False):
     """
     Creates a tear sheet to view the average cumulative returns for a
     factor within a window (pre and post event).
@@ -544,9 +546,9 @@ def create_event_returns_tear_sheet(
         quantile/bin that factor value belongs to and (optionally) the group
         the asset belongs to.
         - See full explanation in utils.get_clean_factor_and_forward_returns
-    prices : pd.DataFrame
-        A DataFrame indexed by date with assets in the columns containing the
-        pricing data.
+    returns : pd.DataFrame
+        A DataFrame indexed by date with assets in the columns containing daily
+        returns.
         - See full explanation in utils.get_clean_factor_and_forward_returns
     avgretplot: tuple (int, int) - (before, after)
         If not None, plot quantile average cumulative returns
@@ -631,9 +633,11 @@ def create_event_returns_tear_sheet(
 
 
 @plotting.customize
-def create_event_study_tear_sheet(
-    factor_data, returns, avgretplot=(5, 15), rate_of_ret=True, n_bars=50
-):
+def create_event_study_tear_sheet(factor_data,
+                                  returns,
+                                  avgretplot=(5, 15),
+                                  rate_of_ret=True,
+                                  n_bars=50):
     """
     Creates an event study tear sheet for analysis of a specific event.
 
@@ -644,9 +648,9 @@ def create_event_study_tear_sheet(
         containing the values for a single event, forward returns for each
         period, the factor quantile/bin that factor value belongs to, and
         (optionally) the group the asset belongs to.
-    prices : pd.DataFrame, required only if 'avgretplot' is provided
-        A DataFrame indexed by date with assets in the columns containing the
-        pricing data.
+    returns : pd.DataFrame, required only if 'avgretplot' is provided
+        A DataFrame indexed by date with assets in the columns containing daily
+        returns.
         - See full explanation in utils.get_clean_factor_and_forward_returns
     avgretplot: tuple (int, int) - (before, after), optional
         If not None, plot event style average cumulative returns within a