monitor initial version

examples/data/monitor.py

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+"""
+This script is the demonstrating the implementation of following requirements.
+
+
+NOTE: A lot of details is not considered in this script
+- Corner case that will raise error( std == 0)
+
+
+· Transformer:
+	1) Basic statistics on different slices of the DataFrame df:
+		§ The statistics include:
+			· STD, Mean, Skewnes, Kurtosis
+		§ The above statistics can be calculated on the following data slices:
+			· df.groupby(['datetime'])
+			· df.groupby(['datetime', 'industry' ])
+			· df.groupby(['instrument', 'factor'])
+			· df.apply("<expresion>").groupby([..]), in which [..] could be any one of the above slicing rules.
+	2) Advanced statistics on different slices of the DataFrame df:
+		§ Auto-correlation:
+			· Calculate corr(df.loc[t, :, :], df.loc[t-w, :, :]), w=1, 2, ….
+		§ Correlation between factors:
+			· For any pair of factors (i, j): calculate corr(df.loc[t, :, i], df.loc[t, :,  j]). The result is a correlation matrix with each element corresponds to a correlation value between a pair of factors.
+		§ The data slices are the same as those in 1).
+· Monitor:
+	1) Algorithms:
+		§ Basic checks:  NaN.
+		§ Point anomaly detection.
+		§ Segment anomaly detection.
+	2) Scenarios:
+		§ Online anomaly detection: monitoring streaming data.
+Offline anomaly detection: verifying whole historical data.
+"""
+
+# AUTO download data
+from qlib.utils import exists_qlib_data
+from qlib.tests.data import GetData
+from qlib.config import REG_CN
+
+provider_uri = "~/.qlib/qlib_data/cn_data"  # target_dir
+if not exists_qlib_data(provider_uri):
+    print(f"Qlib data is not found in {provider_uri}")
+    GetData().qlib_data(target_dir=provider_uri, region=REG_CN)
+
+import qlib
+
+qlib.init()
+import pandas as pd
+from qlib.contrib.data.handler import Alpha158
+from qlib.data.dataset.loader import QlibDataLoader
+from qlib.data.monitor.metric import format_conv
+from qlib.data.monitor.metric import MeanM, SkewM, KurtM, StdM, AutoCM, CorrM
+from qlib.data.monitor.detector import NDDetector, SWNDD, ThresholdD
+from qlib.data import D
+
+
+def get_factor_df(col_idx=0):
+    dh = Alpha158(instruments="csi300", infer_processors=[], learn_processors=[], start_time="20200101")
+    df = dh.fetch()
+
+    print(df.head())
+
+    # We don't have industries in dataframe, we generate the with fake data
+    industry = pd.Series(df.index.get_level_values("instrument").str.slice(stop=2).to_list(), index=df.index)
+
+    # select a factor
+    factor_df = format_conv(df.iloc[:, col_idx], industry=industry)
+    print(f"Selected metric: {df.columns[col_idx]}")
+
+    print(factor_df)
+    return factor_df
+
+
+def case_1_1():
+    factor_df = get_factor_df()
+    # 1) Extract metrics
+
+    # 1.1) df.groupby(["datetime"])
+    mtrc = MeanM()
+    m_mean = mtrc.extract(factor_df)
+    print(m_mean)
+
+    ndd = NDDetector()
+    ndd.fit(m_mean)  # use historical data to fit detector
+    check_res = ndd.check(m_mean)
+    print(check_res)  #  detecting on new data or historical data
+    print(check_res.value_counts())
+
+
+def case_1_2():
+    factor_df = get_factor_df()
+    # 1.2) df.groupby("datetime", "industry")
+    mtrc = MeanM(group=["industry"])
+    m_multi = mtrc.extract(factor_df)
+    print(m_multi)
+
+    for col_name, s in m_multi.iteritems():
+        print(col_name)
+        ndd = NDDetector()
+        ndd.fit(s)  # use historical data to fit detector
+        check_res = ndd.check(s)
+        print(check_res)  #  detecting on new data or historical data
+        print(check_res.value_counts())
+
+
+def case_1_3_1_4():
+    # case 1.3 and case 1.4
+    # factor_df = get_factor_df()
+    qdl = QlibDataLoader(config=(["$close/Ref($close, 1) - 1"], ["return"]))
+    df = qdl.load(instruments=["SH600519"], start_time="20200101")
+    df = format_conv(df)
+    s = df.iloc[:, 0]
+    print(s)
+    dtc = SWNDD(window=20)
+    dtc.fit(s)  # fit use historical data (TODO: updating will be supported in the future)
+    check_res = dtc.check(s)  #
+    print(check_res)
+    print(check_res.value_counts())
+    print(check_res[check_res])
+
+
+def case_2_1():
+    # · Calculate corr(df.loc[t, :, :], df.loc[t-w, :, :]), w=1, 2, ….
+    factor_df = get_factor_df()
+    acm = AutoCM()
+    mtrc = acm.extract(factor_df)
+    print(mtrc)
+
+    thd = ThresholdD(0.0, reverse=True)
+    check_res = thd.check(mtrc)
+
+    print(check_res)
+    print(check_res.value_counts())
+
+
+def case_2_2():
+    factor_df1, factor_df2 = get_factor_df(0), get_factor_df(1)
+
+    cm = CorrM()
+    mtrc = cm.extract(factor_df1, factor_df2)
+    print(mtrc)
+
+    thd = ThresholdD(0.0, reverse=True)
+    check_res = thd.check(mtrc)
+
+    print(check_res)
+    print(check_res.value_counts())
+
+
+if __name__ == "__main__":
+    case_1_1()
+    case_1_2()
+    case_1_3_1_4()
+    case_2_1()
+    case_2_2()