#854 added support for qlib's data format, implemented factor check, …

…reformatted summary

scripts/check_data_health.py

@@ -5,8 +5,11 @@
 
 import fire
 import pandas as pd
+import qlib
 from tqdm import tqdm
 
+from qlib.data import D
+
 
 class DataHealthChecker:
     """Checks a dataset for data completeness and correctness. The data will be converted to a pd.DataFrame and checked for the following problems:
@@ -47,8 +50,16 @@ def __init__(
                 self.data[filename] = df
 
         elif qlib_dir:
-            # todo: add support for qlib_dir
-            pass
+            qlib.init(provider_uri=qlib_dir)
+            self.load_qlib_data()
+
+    def load_qlib_data(self):
+        instruments = D.instruments(market="all")
+        instrument_list = D.list_instruments(instruments=instruments, as_list=True)
+        required_fields = ["$open", "$close", "$low", "$high", "$volume"]
+        for instrument in instrument_list:
+            df = D.features([instrument], required_fields, freq="day")
+            self.data[instrument] = df
 
     def check_missing_data(
         self, filename: str, df: pd.DataFrame
@@ -102,8 +113,17 @@ def check_required_columns(
     def check_missing_factor(
         self, filename: str, df: pd.DataFrame
     ) -> Optional[Tuple[DataProblem, List[str]]]:
-        # todo
-        pass
+        """Check if the 'factor' column is missing in the DataFrame."""
+        if "factor" not in df.columns:
+            logging.warning(
+                f"{filename}: Missing 'factor' column, trading unit will be disabled."
+            )
+            return self.DataProblem.MISSING_FACTOR, ["factor"]
+        elif df["factor"].isnull().any():
+            logging.warning(
+                f"{filename}: Missing factor data, trading unit may be incorrectly adjusted."
+            )
+            return self.DataProblem.MISSING_FACTOR, ["factor"]
 
     def check_data(self):
         checks = [
@@ -130,20 +150,37 @@ def _print_report(self, problems: Dict[str, List[Tuple[DataProblem, str]]]):
                     )
                     stats["count"] += 1
                     stats["affected_columns"].update(affected_columns)
-            print("\n-----------------------------")
-            print("Summary of data health check:")
-            print(f"Files checked: {len(self.data)}")
-            padding = max(len(problem.name) for problem in self.DataProblem)
+            print(f"\nSummary of data health check ({len(self.data)} files checked):")
+            print("-----------------------")
+            padding_between_columns = 2
+            padding_problem_name = (
+                max(len(problem.name) for problem in self.DataProblem)
+                + padding_between_columns
+            )
+            padding_count = (
+                max(
+                    len(str(stats["count"])) for stats in problem_stats_by_type.values()
+                )
+                + padding_between_columns
+            )
+            print(
+                "Problem".ljust(padding_problem_name),
+                "Count".ljust(padding_count),
+                "Affected columns",
+            )
             for problem in self.DataProblem:
-                padded_name = problem.name.ljust(padding + 2, " ")
-                print(f"• {padded_name}", end="")
-                if problem in problem_stats_by_type:
-                    print(f"{problem_stats_by_type[problem]['count']}")
-                    print(
-                        f"  affected columns{' ' * max(padding - 14, 0)}{problem_stats_by_type[problem]['affected_columns']}"
-                    )
-                else:
-                    print("0")
+                padded_name = problem.name.ljust(padding_problem_name)
+                padded_count = str(
+                    problem_stats_by_type[problem]["count"]
+                    if problem in problem_stats_by_type
+                    else 0
+                ).ljust(padding_count)
+                affected_columns = (
+                    problem_stats_by_type[problem]["affected_columns"]
+                    if problem in problem_stats_by_type
+                    else "-"
+                )
+                print(padded_name, padded_count, affected_columns)
         else:
             logging.info("Data check passed. No problems found.")