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Instructions

    Fetches adjustment factor data for a given stock code and date range.
    Uses Baostock's "涨跌幅复权算法" factors. Useful for calculating adjusted prices.

    Args:
        code: The stock code in Baostock format (e.g., 'sh.600000', 'sz.000001').
        start_date: Start date in 'YYYY-MM-DD' format.
        end_date: End date in 'YYYY-MM-DD' format.

    Returns:
        Adjustment factors table.
    

Implementation Reference

• src/tools/stock_market.py:140-165 (handler)

  The MCP tool handler function for 'get_adjust_factor_data'. Decorated with @app.tool() for automatic registration. Handles input, logging, error handling via run_tool_with_handling, and delegates to the use case fetch function.

  @app.tool()
  def get_adjust_factor_data(code: str, start_date: str, end_date: str, limit: int = 250, format: str = "markdown") -> str:
      """
      Fetches adjustment factor data for a given stock code and date range.
      Uses Baostock's "涨跌幅复权算法" factors. Useful for calculating adjusted prices.
  
      Args:
          code: The stock code in Baostock format (e.g., 'sh.600000', 'sz.000001').
          start_date: Start date in 'YYYY-MM-DD' format.
          end_date: End date in 'YYYY-MM-DD' format.
  
      Returns:
          Adjustment factors table.
      """
      logger.info(f"Tool 'get_adjust_factor_data' called for {code} ({start_date} to {end_date})")
      return run_tool_with_handling(
          lambda: fetch_adjust_factor_data(
              active_data_source,
              code=code,
              start_date=start_date,
              end_date=end_date,
              limit=limit,
              format=format,
          ),
          context=f"get_adjust_factor_data:{code}",
      )

• src/use_cases/stock_market.py:82-94 (helper)

  Use case helper function that fetches adjustment factor data from the data source, adds metadata, and formats the output table.

  def fetch_adjust_factor_data(
      data_source: FinancialDataSource,
      *,
      code: str,
      start_date: str,
      end_date: str,
      limit: int = 250,
      format: str = "markdown",
  ) -> str:
      validate_output_format(format)
      df = data_source.get_adjust_factor_data(code=code, start_date=start_date, end_date=end_date)
      meta = {"code": code, "start_date": start_date, "end_date": end_date}
      return format_table_output(df, format=format, max_rows=limit, meta=meta)

• src/data_source_interface.py:128-131 (schema)

  Interface definition specifying the method signature and documentation for the data source's get_adjust_factor_data method, serving as schema for the underlying data fetch.

  @abstractmethod
  def get_adjust_factor_data(self, code: str, start_date: str, end_date: str) -> pd.DataFrame:
      """Fetches adjustment factor data used for price adjustments."""
      pass

• src/baostock_data_source.py:369-411 (helper)

  Concrete implementation in BaostockDataSource class that queries the Baostock API for adjustment factor data, handles errors, and returns a DataFrame.

  def get_adjust_factor_data(self, code: str, start_date: str, end_date: str) -> pd.DataFrame:
      """Fetches adjustment factor data using Baostock."""
      logger.info(
          f"Fetching adjustment factor data for {code} ({start_date} to {end_date})")
      try:
          with baostock_login_context():
              rs = bs.query_adjust_factor(
                  code=code, start_date=start_date, end_date=end_date)
  
              if rs.error_code != '0':
                  logger.error(
                      f"Baostock API error (Adjust Factor) for {code}: {rs.error_msg} (code: {rs.error_code})")
                  if "no record found" in rs.error_msg.lower() or rs.error_code == '10002':
                      raise NoDataFoundError(
                          f"No adjustment factor data found for {code} in the specified range. Baostock msg: {rs.error_msg}")
                  else:
                      raise DataSourceError(
                          f"Baostock API error fetching adjust factor data: {rs.error_msg} (code: {rs.error_code})")
  
              data_list = []
              while rs.next():
                  data_list.append(rs.get_row_data())
  
              if not data_list:
                  logger.warning(
                      f"No adjustment factor data found for {code} in range (empty result set from Baostock).")
                  raise NoDataFoundError(
                      f"No adjustment factor data found for {code} in the specified range (empty result set).")
  
              result_df = pd.DataFrame(data_list, columns=rs.fields)
              logger.info(
                  f"Retrieved {len(result_df)} adjustment factor records for {code}.")
              return result_df
  
      except (LoginError, NoDataFoundError, DataSourceError, ValueError) as e:
          logger.warning(
              f"Caught known error fetching adjust factor data for {code}: {type(e).__name__}")
          raise e
      except Exception as e:
          logger.exception(
              f"Unexpected error fetching adjust factor data for {code}: {e}")
          raise DataSourceError(
              f"Unexpected error fetching adjust factor data for {code}: {e}")