update high freq demo

qlib/contrib/eva/alpha.py

@@ -7,6 +7,46 @@ 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
+    pred :
+        pred
+    label :
+        label
+    date_col :
+        date_col
+    
+    Returns
+    -------
+    (pd.Series, pd.Series)
+        long precision and short precision in time level
+    """
+    if is_alpha:
+        label = label - label.mean(level=0)
+    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)
+        
+    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)
+
 
 def calc_ic(pred: pd.Series, label: pd.Series, date_col="datetime", dropna=False) -> Tuple[pd.Series, pd.Series]:
     """calc_ic.