From 3bf6c7f95f5cc77d4025358e618d5f688138f5cc Mon Sep 17 00:00:00 2001
From: Alex Wang <haouyw2@gmail.com>
Date: Mon, 22 Mar 2021 15:37:54 +0800
Subject: [PATCH] update format

---
 qlib/contrib/eva/alpha.py    | 24 +++++++++++++-----------
 qlib/workflow/record_temp.py | 16 +++++++++++-----
 2 files changed, 24 insertions(+), 16 deletions(-)

diff --git a/qlib/contrib/eva/alpha.py b/qlib/contrib/eva/alpha.py
index e2beafc13..8078dd4ed 100644
--- a/qlib/contrib/eva/alpha.py
+++ b/qlib/contrib/eva/alpha.py
@@ -7,15 +7,18 @@ import pandas as pd
 
 from typing import Tuple
 
-def calc_prec(pred: pd.Series, label: pd.Series, date_col="datetime", quantile: float = 0.2, dropna=False, is_alpha=False) -> Tuple[pd.Series, pd.Series]:
-    """ calculate the precision
+
+def calc_prec(
+    pred: pd.Series, label: pd.Series, date_col="datetime", quantile: float = 0.2, dropna=False, is_alpha=False
+) -> Tuple[pd.Series, pd.Series]:
+    """calculate the precision
     pred :
         pred
     label :
         label
     date_col :
         date_col
-    
+
     Returns
     -------
     (pd.Series, pd.Series)
@@ -23,29 +26,28 @@ def calc_prec(pred: pd.Series, label: pd.Series, date_col="datetime", quantile:
     """
     if is_alpha:
         label = label - label.mean(level=0)
-    if int(1/quantile) >= len(label.index.get_level_values(1).unique()):
+    if int(1 / quantile) >= len(label.index.get_level_values(1).unique()):
         raise ValueError("Need more instruments to calculate precision")
 
-    
     df = pd.DataFrame({"pred": pred, "label": label})
     if dropna:
-        df.dropna(inplace = True)
-        
+        df.dropna(inplace=True)
+
     group = df.groupby(level=date_col)
-    
+
     N = lambda x: int(len(x) * quantile)
     # find the top/low quantile of prediction and treat them as long and short target
     long = group.apply(lambda x: x.nlargest(N(x), columns="pred").label).reset_index(level=0, drop=True)
     short = group.apply(lambda x: x.nsmallest(N(x), columns="pred").label).reset_index(level=0, drop=True)
-    
+
     groupll = long.groupby(date_col)
     ll_ration = groupll.apply(lambda x: x > 0)
     ll_c = groupll.count()
-    
+
     groups = short.groupby(date_col)
     s_ration = groups.apply(lambda x: x < 0)
     s_c = groups.count()
-    return (ll_ration.groupby(date_col).sum()/ll_c), (s_ration.groupby(date_col).sum()/s_c)
+    return (ll_ration.groupby(date_col).sum() / ll_c), (s_ration.groupby(date_col).sum() / s_c)
 
 
 def calc_ic(pred: pd.Series, label: pd.Series, date_col="datetime", dropna=False) -> Tuple[pd.Series, pd.Series]:
diff --git a/qlib/workflow/record_temp.py b/qlib/workflow/record_temp.py
index 8ab8405a5..c47b999f3 100644
--- a/qlib/workflow/record_temp.py
+++ b/qlib/workflow/record_temp.py
@@ -154,12 +154,13 @@ class SignalRecord(RecordTemp):
 
     def load(self, name="pred.pkl"):
         return super().load(name)
-    
-    
+
+
 class HFSignalRecord(SignalRecord):
     """
     This is the Signal Analysis Record class that generates the analysis results such as IC and IR. This class inherits the ``RecordTemp`` class.
     """
+
     artifact_path = "hg_sig_analysis"
 
     def __init__(self, recorder, **kwargs):
@@ -169,7 +170,7 @@ class HFSignalRecord(SignalRecord):
         pred = self.load("pred.pkl")
         raw_label = self.load("label.pkl")
 
-        long_pre, short_pre = calc_prec(pred.iloc[:, 0], raw_label.iloc[:, 0], is_alpha = True)
+        long_pre, short_pre = calc_prec(pred.iloc[:, 0], raw_label.iloc[:, 0], is_alpha=True)
         ic, ric = calc_ic(pred.iloc[:, 0], raw_label.iloc[:, 0])
         metrics = {
             "IC": ic.mean(),
@@ -177,7 +178,7 @@ class HFSignalRecord(SignalRecord):
             "Rank IC": ric.mean(),
             "Rank ICIR": ric.mean() / ric.std(),
             "Long precision": long_pre.mean(),
-            "Short precision": short_pre.mean()
+            "Short precision": short_pre.mean(),
         }
         objects = {"ic.pkl": ic, "ric.pkl": ric}
         objects.update({"long_pre.pkl": long_pre, "short_pre.pkl": short_pre})
@@ -199,7 +200,12 @@ class HFSignalRecord(SignalRecord):
         pprint(metrics)
 
     def list(self):
-        paths = [self.get_path("ic.pkl"), self.get_path("ric.pkl"), self.get_path("long_pre.pkl"), self.get_path("short_pre.pkl")]
+        paths = [
+            self.get_path("ic.pkl"),
+            self.get_path("ric.pkl"),
+            self.get_path("long_pre.pkl"),
+            self.get_path("short_pre.pkl"),
+        ]
         paths.extend([self.get_path("long_short_r.pkl"), self.get_path("long_avg_r.pkl")])
         return paths