Addressed Luca's feedback.

alphalens/performance.py

@@ -568,7 +568,7 @@ def compute_mean_returns_spread(mean_returns,
 
 def quantile_turnover(quantile_factor, quantile, period=1):
     """
-    Computes the daily proportion of names in a factor quantile that were
+    Computes the proportion of names in a factor quantile that were
     not in that quantile in the previous period.
 
     Parameters

alphalens/tears.py

@@ -701,21 +701,5 @@ def create_event_study_tear_sheet(factor_data,
             UserWarning
         )
 
-
-    if '1D' in factor_returns:
-        plotting.plot_cumulative_returns(
-            factor_returns['1D'],
-            period='1D',
-            freq=trading_calendar,
-            ax=gf.next_row(),
-        )
-
-        plotting.plot_cumulative_returns(
-            factor_returns['1D'],
-            period='1D',
-            freq=trading_calendar,
-            ax=gf.next_row(),
-        )
-
     plt.show()
     gf.close()

alphalens/utils.py

@@ -1016,168 +1016,3 @@ def diff_custom_calendar_timedeltas(start, end, freq):
     timediff = end - start
     delta_days = timediff.components.days - actual_days
     return timediff - pd.Timedelta(days=delta_days)
-
-def subportfolio_cumulative_returns(returns, period, freq=None):
-    """
-    Builds cumulative returns from 'period' returns. This function simulates
-    the cumulative effect that a series of gains or losses (the 'returns')
-    have on an original amount of capital over a period of time.
-
-    if F is the frequency at which returns are computed (e.g. 1 day if
-    'returns' contains daily values) and N is the period for which the retuns
-    are computed (e.g. returns after 1 day, 5 hours or 3 days) then:
-    - if N <= F the cumulative retuns are trivially computed as Compound Return
-    - if N > F (e.g. F 1 day, and N is 3 days) then the returns overlap and the
-      cumulative returns are computed building and averaging N interleaved sub
-      portfolios (started at subsequent periods 1,2,..,N) each one rebalancing
-      every N periods. This correspond to an algorithm which trades the factor
-      every single time it is computed, which is statistically more robust and
-      with a lower volatity compared to an algorithm that trades the factor
-      every N periods and whose returns depend on the specific starting day of
-      trading.
-
-    Also note that when the factor is not computed at a specific frequency, for
-    exaple a factor representing a random event, it is not efficient to create
-    multiples sub-portfolios as it is not certain when the factor will be
-    traded and this would result in an underleveraged portfolio. In this case
-    the simulated portfolio is fully invested whenever an event happens and if
-    a subsequent event occur while the portfolio is still invested in a
-    previous event then the portfolio is rebalanced and split equally among the
-    active events.
-
-    Parameters
-    ----------
-    returns: pd.Series
-        pd.Series containing factor 'period' forward returns, the index
-        contains timestamps at which the trades are computed and the values
-        correspond to returns after 'period' time
-    period: pandas.Timedelta or string
-        Length of period for which the returns are computed (1 day, 2 mins,
-        3 hours etc). It can be a Timedelta or a string in the format accepted
-        by Timedelta constructor ('1 days', '1D', '30m', '3h', '1D1h', etc)
-    freq : pandas DateOffset, optional
-        Used to specify a particular trading calendar. If not present
-        returns.index.freq will be used
-
-    Returns
-    -------
-    Cumulative returns series : pd.Series
-        Example:
-            2015-07-16 09:30:00  -0.012143
-            2015-07-16 12:30:00   0.012546
-            2015-07-17 09:30:00   0.045350
-            2015-07-17 12:30:00   0.065897
-            2015-07-20 09:30:00   0.030957
-    """
-
-    if not isinstance(period, pd.Timedelta):
-        period = pd.Timedelta(period)
-
-    if freq is None:
-        freq = returns.index.freq
-
-    if freq is None:
-        freq = BDay()
-        warnings.warn("'freq' not set, using business day calendar",
-                      UserWarning)
-
-    #
-    # returns index contains factor computation timestamps, then add returns
-    # timestamps too (factor timestamps + period) and save them to 'full_idx'
-    # Cumulative returns will use 'full_idx' index,because we want a cumulative
-    # returns value for each entry in 'full_idx'
-    #
-    trades_idx = returns.index.copy()
-    returns_idx = utils.add_custom_calendar_timedelta(trades_idx, period, freq)
-    full_idx = trades_idx.union(returns_idx)
-
-    #
-    # Build N sub_returns from the single returns Series. Each sub_retuns
-    # stream will contain non-overlapping returns.
-    # In the next step we'll compute the portfolio returns averaging the
-    # returns happening on those overlapping returns streams
-    #
-    sub_returns = []
-    print(returns.shape)
-    while len(trades_idx) > 0:
-
-        #
-        # select non-overlapping returns starting with first timestamp in index
-        #
-        sub_index = []
-        next = trades_idx.min()
-        while next <= trades_idx.max():
-            sub_index.append(next)
-            next = utils.add_custom_calendar_timedelta(next, period, freq)
-            # make sure to fetch the next available entry after 'period'
-            try:
-                i = trades_idx.get_loc(next, method='bfill')
-                next = trades_idx[i]
-            except KeyError:
-                break
-
-        sub_index = pd.DatetimeIndex(sub_index, tz=full_idx.tz)
-        subret = returns[sub_index]
-
-        # make the index to have all entries in 'full_idx'
-        subret = subret.reindex(full_idx)
-
-        #
-        # compute intermediate returns values for each index in subret that are
-        # in between the timestaps at which the factors are computed and the
-        # timestamps at which the 'period' returns actually happen
-        #
-        for pret_idx in reversed(sub_index):
-
-            pret = subret[pret_idx]
-
-            # get all timestamps between factor computation and period returns
-            pret_end_idx = \
-                utils.add_custom_calendar_timedelta(pret_idx, period, freq)
-            slice = subret[(subret.index > pret_idx) & (
-                subret.index <= pret_end_idx)].index
-
-            if pd.isnull(pret):
-                continue
-
-            def rate_of_returns(ret, period):
-                return ((np.nansum(ret) + 1)**(1. / period)) - 1
-
-            # compute intermediate 'period' returns values, note that this also
-            # moves the final 'period' returns value from trading timestamp to
-            # trading timestamp + 'period'
-            for slice_idx in slice:
-                sub_period = utils.diff_custom_calendar_timedeltas(
-                    pret_idx, slice_idx, freq)
-                subret[slice_idx] = rate_of_returns(pret, period / sub_period)
-
-            subret[pret_idx] = np.nan
-
-            # transform returns as percentage change from previous value
-            subret[slice[1:]] = (subret[slice] + 1).pct_change()[slice[1:]]
-
-        sub_returns.append(subret)
-        trades_idx = trades_idx.difference(sub_index)
-
-    #
-    # Compute portfolio cumulative returns averaging the returns happening on
-    # overlapping returns streams.
-    #
-    sub_portfolios = pd.concat(sub_returns, axis=1)
-    portfolio = pd.Series(index=sub_portfolios.index)
-
-    for i, (index, row) in enumerate(sub_portfolios.iterrows()):
-
-        # check the active portfolios, count() returns non-nans elements
-        active_subfolios = row.count()
-
-        # fill forward portfolio value
-        portfolio.iloc[i] = portfolio.iloc[i - 1] if i > 0 else 1.
-
-        if active_subfolios <= 0:
-            continue
-
-        # current portfolio is the average of active sub_portfolios
-        portfolio.iloc[i] *= (row + 1).mean(skipna=True)
-
-    return portfolio