remove sklearn dependency

Author: brian-from-quantrocket

pyfolio/timeseries.py

@@ -22,7 +22,6 @@
 import pandas as pd
 import scipy as sp
 import scipy.stats as stats
-from sklearn import linear_model
 
 from .deprecate import deprecated
 from .interesting_periods import PERIODS
@@ -547,62 +546,6 @@ def rolling_beta(returns, factor_returns,
 
         return out
 
-
-def rolling_regression(returns, factor_returns,
-                       rolling_window=APPROX_BDAYS_PER_MONTH * 6,
-                       nan_threshold=0.1):
-    """
-    Computes rolling factor betas using a multivariate linear regression
-    (separate linear regressions is problematic because the factors may be
-    confounded).
-
-    Parameters
-    ----------
-    returns : pd.Series
-        Daily returns of the strategy, noncumulative.
-         - See full explanation in tears.create_full_tear_sheet.
-    factor_returns : pd.DataFrame
-        Daily noncumulative returns of the benchmark factor to which betas are
-        computed. Usually a benchmark such as market returns.
-         - Computes rolling beta for each column.
-         - This is in the same style as returns.
-    rolling_window : int, optional
-        The days window over which to compute the beta. Defaults to 6 months.
-    nan_threshold : float, optional
-        If there are more than this fraction of NaNs, the rolling regression
-        for the given date will be skipped.
-
-    Returns
-    -------
-    pandas.DataFrame
-        DataFrame containing rolling beta coefficients to SMB, HML and UMD
-    """
-
-    # We need to drop NaNs to regress
-    ret_no_na = returns.dropna()
-
-    columns = ['alpha'] + factor_returns.columns.tolist()
-    rolling_risk = pd.DataFrame(columns=columns,
-                                index=ret_no_na.index)
-
-    rolling_risk.index.name = 'dt'
-
-    for beg, end in zip(ret_no_na.index[:-rolling_window],
-                        ret_no_na.index[rolling_window:]):
-        returns_period = ret_no_na[beg:end]
-        factor_returns_period = factor_returns.loc[returns_period.index]
-
-        if np.all(factor_returns_period.isnull().mean()) < nan_threshold:
-            factor_returns_period_dnan = factor_returns_period.dropna()
-            reg = linear_model.LinearRegression(fit_intercept=True).fit(
-                factor_returns_period_dnan,
-                returns_period.loc[factor_returns_period_dnan.index])
-            rolling_risk.loc[end, factor_returns.columns] = reg.coef_
-            rolling_risk.loc[end, 'alpha'] = reg.intercept_
-
-    return rolling_risk
-
-
 def gross_lev(positions):
     """
     Calculates the gross leverage of a strategy.

setup.py

@@ -48,7 +48,6 @@
     'pandas>=0.18.1',
     'pytz>=2014.10',
     'scipy>=0.14.0',
-    'scikit-learn>=0.16.1',
     'seaborn>=0.10.1',
     'empyrical>=0.5.0',
     'quantrocket-moonchart>=1.8.0'