From 7022675d003b4a603e9a04769e8a91e7232421ea Mon Sep 17 00:00:00 2001
From: Charles Young <cy@unitive.tech>
Date: Mon, 8 Mar 2021 19:07:28 +0800
Subject: [PATCH] Resolve
 https://github.com/microsoft/qlib/pull/280\#discussion_r589169489

---
 qlib/portfolio/optimizer/enhanced_indexing.py | 40 +++++++++----------
 qlib/portfolio/optimizer/optimizer.py         | 25 ++++++------
 2 files changed, 31 insertions(+), 34 deletions(-)

diff --git a/qlib/portfolio/optimizer/enhanced_indexing.py b/qlib/portfolio/optimizer/enhanced_indexing.py
index 1f7de6cb4..5fdc1014d 100644
--- a/qlib/portfolio/optimizer/enhanced_indexing.py
+++ b/qlib/portfolio/optimizer/enhanced_indexing.py
@@ -21,15 +21,15 @@ class EnhancedIndexingOptimizer(BaseOptimizer):
     START_FROM_BENCH = "benchmark"
 
     def __init__(
-            self,
-            lamb: float = 10,
-            delta: float = 0.4,
-            bench_dev: float = 0.01,
-            inds_dev: float = None,
-            scale_alpha: bool = True,
-            verbose: bool = False,
-            warm_start: str = None,
-            max_iters: int = 10000,
+        self,
+        lamb: float = 10,
+        delta: float = 0.4,
+        bench_dev: float = 0.01,
+        inds_dev: float = None,
+        scale_alpha: bool = True,
+        verbose: bool = False,
+        warm_start: str = None,
+        max_iters: int = 10000,
     ):
         """
         Args:
@@ -56,11 +56,7 @@ class EnhancedIndexingOptimizer(BaseOptimizer):
         assert inds_dev is None or inds_dev >= 0, "industry deviation limit `inds_dev` should be positive or None."
         self.inds_dev = inds_dev
 
-        assert warm_start in [
-            None,
-            self.START_FROM_W0,
-            self.START_FROM_BENCH,
-        ], "illegal warm start option"
+        assert warm_start in [None, self.START_FROM_W0, self.START_FROM_BENCH,], "illegal warm start option"
         self.start_from_w0 = warm_start == self.START_FROM_W0
         self.start_from_bench = warm_start == self.START_FROM_BENCH
 
@@ -69,14 +65,14 @@ class EnhancedIndexingOptimizer(BaseOptimizer):
         self.max_iters = max_iters
 
     def __call__(
-            self,
-            u: Union[np.ndarray, pd.Series],
-            F: np.ndarray,
-            covB: np.ndarray,
-            varU: np.ndarray,
-            w0: np.ndarray,
-            w_bench: np.ndarray,
-            inds_onehot: np.ndarray = None,
+        self,
+        u: Union[np.ndarray, pd.Series],
+        F: np.ndarray,
+        covB: np.ndarray,
+        varU: np.ndarray,
+        w0: np.ndarray,
+        w_bench: np.ndarray,
+        inds_onehot: np.ndarray = None,
     ) -> Union[np.ndarray, pd.Series]:
         """
         Args:
diff --git a/qlib/portfolio/optimizer/optimizer.py b/qlib/portfolio/optimizer/optimizer.py
index 3daa98af3..54648a46a 100644
--- a/qlib/portfolio/optimizer/optimizer.py
+++ b/qlib/portfolio/optimizer/optimizer.py
@@ -30,13 +30,13 @@ class PortfolioOptimizer(BaseOptimizer):
     OPT_INV = "inv"
 
     def __init__(
-            self,
-            method: str = "inv",
-            lamb: float = 0,
-            delta: float = 0,
-            alpha: float = 0.0,
-            scale_alpha: bool = True,
-            tol: float = 1e-8,
+        self,
+        method: str = "inv",
+        lamb: float = 0,
+        delta: float = 0,
+        alpha: float = 0.0,
+        scale_alpha: bool = True,
+        tol: float = 1e-8,
     ):
         """
         Args:
@@ -63,10 +63,10 @@ class PortfolioOptimizer(BaseOptimizer):
         self.scale_alpha = scale_alpha
 
     def __call__(
-            self,
-            S: Union[np.ndarray, pd.DataFrame],
-            u: Optional[Union[np.ndarray, pd.Series]] = None,
-            w0: Optional[Union[np.ndarray, pd.Series]] = None,
+        self,
+        S: Union[np.ndarray, pd.DataFrame],
+        u: Optional[Union[np.ndarray, pd.Series]] = None,
+        w0: Optional[Union[np.ndarray, pd.Series]] = None,
     ) -> Union[np.ndarray, pd.Series]:
         """
         Args:
@@ -155,7 +155,7 @@ class PortfolioOptimizer(BaseOptimizer):
         return self._solve(len(S), self._get_objective_gmv(S), *self._get_constrains(w0))
 
     def _optimize_mvo(
-            self, S: np.ndarray, u: Optional[np.ndarray] = None, w0: Optional[np.ndarray] = None
+        self, S: np.ndarray, u: Optional[np.ndarray] = None, w0: Optional[np.ndarray] = None
     ) -> np.ndarray:
         """optimize mean-variance portfolio
 
@@ -251,6 +251,7 @@ class PortfolioOptimizer(BaseOptimizer):
         # add l2 regularization
         wrapped_obj = obj
         if self.alpha > 0:
+
             def opt_obj(x):
                 return obj(x) + self.alpha * np.sum(np.square(x))