ZhuLinsen/daily_stock_analysis · warning · DataFetchError

[AlphaVantage] No data in date range for {symbol}

Error message

[AlphaVantage] No data in date range for {symbol}

What it means

A DataFetchError raised by AlphaVantageFetcher._fetch_raw_data when the time series parsed successfully but zero rows fall inside the requested [start_date, end_date] window. AV's 'compact' outputsize returns only the most recent ~100 trading points, so older date ranges yield no matching rows even though the symbol is valid.

Source

Thrown at data_provider/alphavantage_fetcher.py:88

            raise DataFetchError(f"[AlphaVantage] No time series data for {symbol}")

        rows = []
        start = datetime.strptime(start_date, '%Y-%m-%d').date()
        end = datetime.strptime(end_date, '%Y-%m-%d').date()
        for date_str, values in data[ts_key].items():
            row_date = datetime.strptime(date_str, '%Y-%m-%d').date()
            if start <= row_date <= end:
                rows.append({
                    'date': date_str,
                    '1. open': float(values.get('1. open', 0)),
                    '2. high': float(values.get('2. high', 0)),
                    '3. low': float(values.get('3. low', 0)),
                    '4. close': float(values.get('4. close', 0)),
                    '5. volume': float(values.get('5. volume', 0)),
                })

        if not rows:
            raise DataFetchError(f"[AlphaVantage] No data in date range for {symbol}")

        df = pd.DataFrame(rows)
        df.index = pd.to_datetime(df['date'])
        df.index.name = None  # 避免与 _normalize_data 重新添加的 'date' 列冲突
        return df.drop(columns=['date'])

    def _normalize_data(self, df: pd.DataFrame, stock_code: str) -> pd.DataFrame:
        if df.empty:
            return df

        df = df.copy()
        df['date'] = pd.to_datetime(df.index).date
        df = df.rename(columns={
            '1. open': 'open', '2. high': 'high', '3. low': 'low',
            '4. close': 'close', '5. volume': 'volume',
        })
        # AlphaVantage returns newest-first; sort ascending before computing pct_chg
        df = df.sort_values('date', ascending=True).reset_index(drop=True)

View on GitHub (pinned to 5159bd72e8)

Solutions

  1. If you need >100 days of history, request with outputsize='full' (requires editing the params in the fetcher or using a custom call).
  2. Verify start/end are within the last ~100 trading days when using compact mode.
  3. Check the symbol still trades; delisted ones have no recent rows.
  4. Fall back to Yfinance (free full history) for long backfills.

Example fix

# before (in alphavantage_fetcher.py params)
'outputsize': 'compact',  # only ~100 recent points

# after
'outputsize': 'full',  # full history; heavier response
Defensive patterns

Strategy: fallback

Validate before calling

from datetime import date, timedelta
compact_horizon = date.today() - timedelta(days=150)  # ~100 trading days
if date.fromisoformat(start_date) < compact_horizon:
    logger.info('range older than compact window; prefer a full-history source')

Try / catch

try:
    df = av_fetcher.fetch(sym, start, end)
except DataFetchError as e:
    if 'No data in date range' in str(e):
        df = yfinance_fetcher.fetch(sym, start, end)  # free full history
    else:
        raise

Prevention

When it happens

Trigger: Requesting a date range older than the last ~100 trading days (outputsize is hardcoded to 'compact'); requesting a range entirely in the future; weekends/holidays-only ranges for very recent windows; symbol whose recent data ends before start_date (delisted).

Common situations: Backfill jobs asking for years of history with a compact response; end_date accidentally formatted wrong (e.g. '2025-13-01' parsing failure happens earlier, but swapped day/month can silently shift the window); delisted symbols.

Related errors

AI-assisted analysis of ZhuLinsen/daily_stock_analysis@5159bd72e8 (2026-08-15). Data as JSON: /api/errors/7b278d96bb9bf266. Report an issue: GitHub.