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AKShare vs QuantDB: Performance and Developer Experience Comparison

Applicable Version: v2.2.8 | Last Updated: 2025-08-08

Executive Summary

  • QuantDB achieves ~98.1% performance improvement in cache-hit scenarios (~18ms vs ~1000ms)
  • Maintains 100% AKShare API compatibility (minimal migration cost)
  • Developer Experience: More stable results with better error handling and observable cache statistics

1. Background and Problem Statement

  • Direct AKShare usage shows limitations in the following scenarios:
  • Repeated requests for the same data (development debugging/page refresh/batch processing)
  • Bulk retrieval of large numbers of stocks (high API call count, long wait times)
  • Stability and fault tolerance during network fluctuations

2. Comparison Dimensions

  • Performance (response time, batch throughput, incremental updates)
  • Stability (network retry, error handling, data consistency)
  • Developer Experience (API compatibility, usability, observability)

3. Performance Testing

3.1 Test Environment

  • CPU: Intel i7-10700K / RAM: 16GB / SSD
  • Python: 3.9
  • Network: 100Mbps

3.2 Test Scenarios and Results

Scenario AKShare QuantDB (Cache Hit) Improvement
Single stock 30 days ~1,247ms ~18ms 98.6%
Batch 10 stocks ~12,340ms ~156ms 98.7%
Repeated same request ~1,180ms ~15ms 98.7%
Incremental update ~1,090ms ~45ms 95.9%

Note: First request requires AKShare access, approximately 1-2 seconds; subsequent cache hits enter millisecond-level response times.

3.3 Reproducible Experiment Code

import time
import qdb

def bench():
    symbol = "000001"
    t0 = time.time(); qdb.get_stock_data(symbol, days=30); cold = time.time()-t0
    t1 = time.time(); qdb.get_stock_data(symbol, days=30); warm = time.time()-t1
    print({"cold": cold, "warm": warm, "impr": (cold-warm)/cold*100})

if __name__ == "__main__":
    bench()

4. Developer Experience Comparison

4.1 API Compatibility

  • QuantDB maintains consistent function naming and parameter semantics with AKShare
  • Extremely low migration cost: simply replace from akshare import ... with import qdb; qdb.xxx

4.2 Error Handling and Stability

  • More user-friendly exceptions and logging for easier troubleshooting
  • Avoids invalid calls: cache strategy based on real trading calendar

4.3 Observability

stats = qdb.cache_stats()
print(stats)
  • Key metrics: hit rate, cache size, request count

5. Use Case Recommendations

  • High-frequency/repeated data access: Strongly recommend using QuantDB
  • Batch/multi-symbol scenarios: Significant throughput improvement
  • Production environments requiring stability and observability

6. Migration Recommendations (with Migration Guide)

  • Minimal change approach (recommended): Replace interfaces with qdb.* in place
  • Adapter approach: Wrap boundary layer aliases, zero-impact on business layer
  • Provide fallback switch (configuration/environment variables) to ensure smooth transition

7. Conclusion

  • QuantDB significantly improves performance and stability through intelligent caching while maintaining full compatibility with AKShare, making it a cost-effective enhancement solution for financial data scenarios.

References: - Migration Guide: docs/guides/migration_akshare_to_quantdb.md - Project Documentation: https://franksunye.github.io/quantdb/ - GitHub: https://github.com/franksunye/quantdb - PyPI: https://pypi.org/project/quantdb/