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microsoft · you-n-g · Mar 10, 2022 · Mar 9, 2021 · Mar 9, 2021 · Mar 10, 2021
commit 71d5640036bce2cad685a0b69a4ef50e7595b53c
diff --git a/scripts/data_collector/pit/collector.py b/scripts/data_collector/pit/collector.py
@@ -18,6 18,7 @@
 from data_collector.base import BaseCollector, BaseRun
 from data_collector.utils import get_calendar_list, get_hs_stock_symbols
 
 
 class PitCollector(BaseCollector):
 
  DEFAULT_START_DATETIME_QUARTER = pd.Timestamp("2000-01-01")
@@ -76,103 77,109 @@ def normalize_symbol(self, symbol):
  symbol_s = symbol.split(".")
  symbol = f"sh{symbol_s[0]}" if symbol_s[-1] == "ss" else f"sz{symbol_s[0]}"
  return symbol
- 
 
  def get_instrument_list(self):
  logger.info("get cn stock symbols......")
  symbols = get_hs_stock_symbols()
  logger.info(f"get {len(symbols)} symbols.")
  return symbols
- 
 
  def _get_data_from_baostock(self, symbol, interval, start_datetime, end_datetime):
  error_msg = f"{symbol}-{interval}-{start_datetime}-{end_datetime}"
  try:
  symbol = f"{symbol[7:]}.{symbol[:6]}"
  print(symbol)
- rs_report = bs.query_performance_express_report(code=symbol, start_date=str(start_datetime.date()), end_date=str(end_datetime.date()))
  rs_report = bs.query_performance_express_report(
  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():
  while (rs_report.error_code == "0") & rs_report.next():
  report_list.append(rs_report.get_row_data())
  # 获取一条记录，将记录合并在一起
  df_report = pd.DataFrame(report_list, columns=rs_report.fields)
  if not df_report.empty:
- df_report = df_report[['performanceExpPubDate', 'performanceExpStatDate', 'performanceExpressROEWa']]
  df_report = df_report[["performanceExpPubDate", "performanceExpStatDate", "performanceExpressROEWa"]]
  df_report.rename(
- columns = {
- "performanceExpPubDate": "date", 
  columns={
  "performanceExpPubDate": "date",
  "performanceExpStatDate": "period",
- "performanceExpressROEWa":"value"
- }, 
  "performanceExpressROEWa": "value",
  },
  inplace=True,
  )
- df_report['value'] = df_report['value'].astype('float32') / 100.0
- df_report['field'] = "roeWa"
  df_report["value"] = df_report["value"].astype("float32") / 100.0
  df_report["field"] = "roeWa"
 
  profit_list = []
  for year in range(start_datetime.year - 1, end_datetime.year 1):
  for q_num in range(0, 4):
  rs_profit = bs.query_profit_data(code=symbol, year=year, quarter=q_num 1)
- while (rs_profit.error_code == '0') & rs_profit.next():
  while (rs_profit.error_code == "0") & rs_profit.next():
  row_data = rs_profit.get_row_data()
- pub_date = pd.Timestamp(row_data[rs_profit.fields.index('pubDate')])
  pub_date = pd.Timestamp(row_data[rs_profit.fields.index("pubDate")])
  if pub_date >= start_datetime and pub_date <= end_datetime:
  profit_list.append(row_data)
- 
 
  df_profit = pd.DataFrame(profit_list, columns=rs_profit.fields)
  if not df_profit.empty:
- df_profit = df_profit[['pubDate', 'statDate', 'roeAvg']]
  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'].astype('float32')
- df_profit['field'] = "roeWa"
  df_profit["value"] = df_profit["value"].astype("float32")
  df_profit["field"] = "roeWa"
 
  forecast_list = []
- rs_forecast = bs.query_forecast_report(code=symbol, start_date=str(start_datetime.date()), end_date=str(end_datetime.date()))
-
- while (rs_forecast.error_code == '0') & rs_forecast.next():
  rs_forecast = bs.query_forecast_report(
  code=symbol, start_date=str(start_datetime.date()), end_date=str(end_datetime.date())
  )
 
  while (rs_forecast.error_code == "0") & rs_forecast.next():
  forecast_list.append(rs_forecast.get_row_data())
 
  df_forecast = pd.DataFrame(forecast_list, columns=rs_forecast.fields)
  if not df_forecast.empty:
- df_forecast = df_forecast[['profitForcastExpPubDate', 'profitForcastExpStatDate', 'profitForcastChgPctUp', 'profitForcastChgPctDwn']]
  df_forecast = df_forecast[
  [
  "profitForcastExpPubDate",
  "profitForcastExpStatDate",
  "profitForcastChgPctUp",
  "profitForcastChgPctDwn",
  ]
  ]
  df_forecast.rename(
- columns = {
- "profitForcastExpPubDate": "date", 
  columns={
  "profitForcastExpPubDate": "date",
  "profitForcastExpStatDate": "period",
- }, 
  },
  inplace=True,
  )
- df_forecast['value'] = (df_forecast['profitForcastChgPctUp'].astype('float32') df_forecast['profitForcastChgPctDwn'].astype('float32')) / 200
- df_forecast['field'] = "YOYNI"
- df_forecast.drop(['profitForcastChgPctUp', 'profitForcastChgPctDwn'], axis=1, inplace=True)
  df_forecast["value"] = (
  df_forecast["profitForcastChgPctUp"].astype("float32")
  df_forecast["profitForcastChgPctDwn"].astype("float32")
  ) / 200
  df_forecast["field"] = "YOYNI"
  df_forecast.drop(["profitForcastChgPctUp", "profitForcastChgPctDwn"], axis=1, inplace=True)
 
  growth_list = []
  for year in range(start_datetime.year - 1, end_datetime.year 1):
  for q_num in range(0, 4):
  rs_growth = bs.query_growth_data(code=symbol, year=year, quarter=q_num 1)
- while (rs_growth.error_code == '0') & rs_growth.next():
  while (rs_growth.error_code == "0") & rs_growth.next():
  row_data = rs_growth.get_row_data()
- pub_date = pd.Timestamp(row_data[rs_growth.fields.index('pubDate')])
  pub_date = pd.Timestamp(row_data[rs_growth.fields.index("pubDate")])
  if pub_date >= start_datetime and pub_date <= end_datetime:
  growth_list.append(row_data)
- df_growth = pd.DataFrame(growth_list, columns=rs_growth.fields)[['pubDate', 'statDate', 'YOYNI']]
  df_growth = pd.DataFrame(growth_list, columns=rs_growth.fields)[["pubDate", "statDate", "YOYNI"]]
  if not df_growth.empty:
- df_growth = df_growth[['pubDate', 'statDate', 'YOYNI']]
  df_growth = df_growth[["pubDate", "statDate", "YOYNI"]]
  df_growth.rename(
- columns = {
- "pubDate": "date", 
- "statDate": "period",
- "YOYNI":"value"
- }, 
  columns={"pubDate": "date", "statDate": "period", "YOYNI": "value"},
  inplace=True,
  )
- df_growth['value'] = df_growth['value'].astype('float32')
- df_growth['field'] = "YOYNI"
  df_growth["value"] = df_growth["value"].astype("float32")
  df_growth["field"] = "YOYNI"
 
  df_merge = df_report.append([df_profit, df_forecast, df_growth])
  return df_merge
@@ -193,6 200,7 @@ def get_data(
  def min_numbers_trading(self):
  pass
 
 
 class Run(BaseRun):
  def __init__(self, source_dir=None, max_workers=4, interval="quarterly"):
  """
@@ -258,6 266,7 @@ def download_data(
  def normalize_class_name(self):
  pass
 
 
 if __name__ == "__main__":
  bs.login()
  fire.Fire(Run)