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mirror of https://github.com/microsoft/qlib.git synced 2026-07-17 17:34:35 +08:00

update format

This commit is contained in:
Alex Wang
2021-03-22 15:37:54 +08:00
parent 1ad237f89f
commit 3bf6c7f95f
2 changed files with 24 additions and 16 deletions

View File

@@ -7,8 +7,11 @@ 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 :
@@ -23,13 +26,12 @@ 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)
@@ -45,7 +47,7 @@ def calc_prec(pred: pd.Series, label: pd.Series, date_col="datetime", quantile:
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]:

View File

@@ -160,6 +160,7 @@ 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