What causes "Pytdx 获取数据失败: {e}" in ZhuLinsen/daily_stock_analysis?

Inspect the chained cause (raise ... from e — log e.__cause__) to identify whether it is a parsing, protocol, or data-shape failure.

ZhuLinsen/daily_stock_analysis · error · DataFetchError

Pytdx 获取数据失败: {e}

Error message

Pytdx 获取数据失败: {e}

What it means

Catch-all DataFetchError wrapping any non-DataFetchError exception raised inside the pytdx fetch loop (to_df conversion, pandas datetime parsing, date filtering, or TDX API errors). The 'from e' chain preserves the original exception for diagnosis.

Source

Thrown at data_provider/pytdx_fetcher.py:374

                    count=count
                )
                
                if data is None or len(data) == 0:
                    raise DataFetchError(f"Pytdx 未查询到 {stock_code} 的数据")
                
                # 转换为 DataFrame
                df = api.to_df(data)
                
                # 过滤日期范围
                df['datetime'] = pd.to_datetime(df['datetime'])
                df = df[(df['datetime'] >= start_date) & (df['datetime'] <= end_date)]
                
                return df
                
            except Exception as e:
                if isinstance(e, DataFetchError):
                    raise
                raise DataFetchError(f"Pytdx 获取数据失败: {e}") from e
    
    def _normalize_data(self, df: pd.DataFrame, stock_code: str) -> pd.DataFrame:
        """
        标准化 Pytdx 数据
        
        Pytdx 返回的列名：
        datetime, open, high, low, close, vol, amount
        
        需要映射到标准列名：
        date, open, high, low, close, volume, amount, pct_chg
        """
        df = df.copy()
        
        # 列名映射
        column_mapping = {
            'datetime': 'date',
            'vol': 'volume',
        }

View on GitHub (pinned to 5159bd72e8)

Solutions

Inspect the chained cause (raise ... from e — log e.__cause__) to identify whether it is a parsing, protocol, or data-shape failure.
Pin/upgrade pytdx to a version whose to_df output matches the normalization code.
Wrap at the manager level with provider fallback so a single pytdx parse failure does not kill the analysis run.

Example fix

# before
try:
    df = pytdx_fetcher.get_stock_data(code, start, end)
except DataFetchError as e:
    raise  # cause hidden, run aborts

# after
try:
    df = pytdx_fetcher.get_stock_data(code, start, end)
except DataFetchError as e:
    logger.warning(f"pytdx fetch failed: {e}; cause={e.__cause__}")
    df = akshare_fetcher.get_stock_data(code, start, end)

Defensive patterns

Strategy: try-catch

Try / catch

try:
    df = pytdx_fetcher.get_stock_data(code, start, end)
except DataFetchError as e:
    logger.warning(f"pytdx failure, cause: {e.__cause__!r}")
    df = akshare_fetcher.get_stock_data(code, start, end)  # or re-raise for non-fetch contexts

Prevention

Always log the chained cause (__cause__) for wrapper errors before deciding on a fix.
Pin the pytdx version so to_df output columns stay stable.
Do not share one TDX session across threads; open a session per request via _pytdx_session().

When it happens

Trigger: api.to_df(data) failing on malformed payloads; pandas errors while parsing 'datetime' strings; KeyError from unexpected DataFrame columns; low-level TDX protocol errors surfacing mid-request.

Common situations: pytdx version changes altering returned columns; partially received network payloads; float precision / NaN issues in vol/amount columns breaking to_df; concurrent use of a non-thread-safe api session.

Related errors

AI-assisted analysis of ZhuLinsen/daily_stock_analysis@5159bd72e8 (2026-08-15). Data as JSON: /api/errors/225382fc9db3e0bc. Report an issue: GitHub.