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Frequently Asked Questions (FAQ)

🔧 Installation & Setup

Q: How do I resolve dependency conflicts?

A: Use a virtual environment to isolate dependencies:

python -m venv quantdb_env
# Linux/Mac
source quantdb_env/bin/activate
# Windows (PowerShell)
quantdb_env\Scripts\Activate.ps1
pip install quantdb

Q: How can I upgrade to the latest version?

A:

pip install --upgrade quantdb

Q: Which Python versions are supported?

A: Python 3.8 and above. We recommend Python 3.9+ for best performance.

📊 Data Fetching

Q: Why is the data sometimes not up-to-date?

A: Due to caching. You can:

  • Clear cache: qdb.clear_cache()
  • Use fresh fetch where available: qdb.get_realtime_data(symbol, force_refresh=True)
  • Note: TTL is managed internally in this version; there are no set_cache_expire / disable_cache functions.

Q: How do I fetch more historical data?

A: Use date parameters:

data = qdb.stock_zh_a_hist(
    symbol="000001",
    start_date="20200101",
    end_date="20241231"
)

Q: Which markets are supported?

A: Currently focusing on:

  • Mainland China A-shares (SSE/SZSE)
  • Hong Kong market
  • US market (partial support)

⚡ Performance

Q: How can I speed up data fetching?

A: Tips:

  1. Keep cache enabled (default)
  2. Use reasonable intervals when fetching in batch
  3. Warm up cache for frequently used symbols
  4. Periodically purge expired cache

Q: Cache database grows too large, what can I do?

A:

  • Clear cache periodically: qdb.clear_cache()
  • Manually delete the cache DB file if needed (default: in your qdb cache dir)
  • Note: TTL is managed internally; there is no set_cache_expire() function in this version

Q: How to inspect cache usage?

A:

stats = qdb.cache_stats()
print(stats)  # e.g. {'cache_dir': '...', 'cache_size_mb': 12.34, 'initialized': True, 'status': 'Running'}

🐛 Errors & Troubleshooting

Q: Network errors?

A: Check:

  1. Network connectivity
  2. Firewall/Proxy constraints
  3. Data source availability
  4. Consider using a proxy or VPN if needed

Q: Unexpected data format?

A: Possible reasons:

  • Invalid symbol format (e.g., use "000001" not "1")
  • Wrong date format (use "YYYYMMDD")
  • Temporary data source changes

Q: It runs slow, how to diagnose?

A:

  1. First run downloads data — subsequent runs will be faster
  2. Ensure cache is enabled
  3. Check network speed
  4. Reduce time range

🔄 Keeping data fresh

Q: How to ensure latest data?

A:

# Option 1: Force refresh where supported
rt = qdb.get_realtime_data("000001", force_refresh=True)

# Option 2: Clear all cache (symbol-level clearing not yet implemented in simplified mode)
qdb.clear_cache()

# Option 3: Bypass cache by using a narrower date range if needed
hist = qdb.stock_zh_a_hist("000001", start_date="20250101", end_date="20250131")

Note: TTL is managed internally in this version.

Q: Update frequency?

A:

  • Realtime quotes: often delayed ~15 minutes
  • Daily data: updated after market close
  • Financials: quarterly updates

🛠️ Integration

Q: Production usage best practices?

A:

  1. Use a dedicated database path
  2. Tune TTL to your workload
  3. Add retry logic
  4. Monitor cache usage regularly

Q: Can I combine with other data sources?

A: Yes. QuantDB is primarily a cache layer for AKShare, but you can:

  • Combine multiple sources
  • Validate and clean data
  • Build your own data pipelines

Q: How to contribute or report issues?

A:

📚 More help

If you didn’t find your answer:

  1. See the User Guide
  2. See the API Reference
  3. Open an Issue on GitHub

🔗 Links