get_adjust_factor_data

Fetches adjustment factor data for A-share stocks to calculate accurate historical prices by applying Baostock's price adjustment algorithms for specified date ranges.

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.

Input Schema

TableJSON Schema

Name	Required	Default
`code`	Yes
`start_date`	Yes
`end_date`	Yes
`limit`	No
`format`	No	markdown

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}")

A-Share MCP Server