Fix pit download_data script TypeError (#978) (#979)

* Fix pit download_data script TypeError (#978)

* Format pit collector with black

* Format pit collector with black

scripts/data_collector/base.py

@@ -369,8 +369,6 @@ class BaseRun(abc.ABC):
             default 2
         delay: float
             time.sleep(delay), default 0
-        interval: str
-            freq, value from [1min, 1d], default 1d
         start: str
             start datetime, default "2000-01-01"
         end: str

scripts/data_collector/pit/collector.py

@@ -2,10 +2,8 @@
 # Licensed under the MIT License.
 
 import re
-import abc
 import sys
 import datetime
-from abc import ABC
 from pathlib import Path
 
 import fire
@@ -114,15 +112,27 @@ class PitCollector(BaseCollector):
             market = {"ss": "sh"}.get(market, market)  # baostock's API naming is different from default symbol list
             symbol = f"{market}.{code}"
             rs_report = bs.query_performance_express_report(
-                code=symbol, start_date=str(start_datetime.date()), end_date=str(end_datetime.date())
+                code=symbol,
+                start_date=str(start_datetime.date()),
+                end_date=str(end_datetime.date()),
             )
             report_list = []
             while (rs_report.error_code == "0") & rs_report.next():
                 report_list.append(rs_report.get_row_data())
 
             df_report = pd.DataFrame(report_list, columns=rs_report.fields)
-            if {"performanceExpPubDate", "performanceExpStatDate", "performanceExpressROEWa"} <= set(rs_report.fields):
-                df_report = df_report[["performanceExpPubDate", "performanceExpStatDate", "performanceExpressROEWa"]]
+            if {
+                "performanceExpPubDate",
+                "performanceExpStatDate",
+                "performanceExpressROEWa",
+            } <= set(rs_report.fields):
+                df_report = df_report[
+                    [
+                        "performanceExpPubDate",
+                        "performanceExpStatDate",
+                        "performanceExpressROEWa",
+                    ]
+                ]
                 df_report.rename(
                     columns={
                         "performanceExpPubDate": "date",
@@ -149,7 +159,11 @@ class PitCollector(BaseCollector):
             if {"pubDate", "statDate", "roeAvg"} <= set(rs_profit.fields):
                 df_profit = df_profit[["pubDate", "statDate", "roeAvg"]]
                 df_profit.rename(
-                    columns={"pubDate": "date", "statDate": "period", "roeAvg": "value"},
+                    columns={
+                        "pubDate": "date",
+                        "statDate": "period",
+                        "roeAvg": "value",
+                    },
                     inplace=True,
                 )
                 df_profit["value"] = df_profit["value"].apply(_str_to_float)
@@ -157,7 +171,9 @@ class PitCollector(BaseCollector):
 
             forecast_list = []
             rs_forecast = bs.query_forecast_report(
-                code=symbol, start_date=str(start_datetime.date()), end_date=str(end_datetime.date())
+                code=symbol,
+                start_date=str(start_datetime.date()),
+                end_date=str(end_datetime.date()),
             )
 
             while (rs_forecast.error_code == "0") & rs_forecast.next():
@@ -192,7 +208,11 @@ class PitCollector(BaseCollector):
                     df_forecast["profitForcastChgPctUp"] + df_forecast["profitForcastChgPctDwn"]
                 ) / 200
                 df_forecast["field"] = "YOYNI"
-                df_forecast.drop(["profitForcastChgPctUp", "profitForcastChgPctDwn"], axis=1, inplace=True)
+                df_forecast.drop(
+                    ["profitForcastChgPctUp", "profitForcastChgPctDwn"],
+                    axis=1,
+                    inplace=True,
+                )
 
             growth_list = []
             for year in range(start_datetime.year - 1, end_datetime.year + 1):
@@ -240,7 +260,11 @@ class PitCollector(BaseCollector):
             logger.warning(f"{error_msg}:{e}")
 
     def get_data(
-        self, symbol: str, interval: str, start_datetime: pd.Timestamp, end_datetime: pd.Timestamp
+        self,
+        symbol: str,
+        interval: str,
+        start_datetime: pd.Timestamp,
+        end_datetime: pd.Timestamp,
     ) -> [pd.DataFrame]:
 
         if interval == self.INTERVAL_quarterly:
@@ -266,8 +290,6 @@ class Run(BaseRun):
         ----------
         source_dir: str
             The directory where the raw data collected from the Internet is saved, default "Path(__file__).parent/source"
-        normalize_dir: str
-            Directory for normalize data, default "Path(__file__).parent/normalize"
         max_workers: int
             Concurrent number, default is 4
         interval: str
@@ -289,7 +311,6 @@ class Run(BaseRun):
         delay=0,
         start=None,
         end=None,
-        interval="quarterly",
         check_data_length=False,
         limit_nums=None,
         **kwargs,
@@ -302,8 +323,6 @@ class Run(BaseRun):
             default 2
         delay: float
             time.sleep(delay), default 0
-        interval: str
-            freq, value from [quarterly, annual], default 1d
         start: str
             start datetime, default "2000-01-01"
         end: str
@@ -320,7 +339,13 @@ class Run(BaseRun):
         """
 
         super(Run, self).download_data(
-            max_collector_count, delay, start, end, interval, check_data_length, limit_nums, **kwargs
+            max_collector_count,
+            delay,
+            start,
+            end,
+            check_data_length,
+            limit_nums,
+            **kwargs,
         )
 
     def normalize_class_name(self):