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AKShare vs QuantDB: Performance and Developer Experience Comparison

Published: January 11, 2025 | Author: QuantDB Team | Category: User Guides

🎯 Executive Summary

This comprehensive study compares AKShare and QuantDB across multiple dimensions including performance, developer experience, and production readiness. Our findings show that QuantDB delivers 98.1% performance improvement in cache-hit scenarios while maintaining 100% AKShare API compatibility.

Key Findings: - QuantDB achieves ~98.1% performance improvement (~18ms vs ~1000ms) - Maintains 100% AKShare API compatibility (minimal migration cost) - Enhanced Developer Experience: More stable results, better error handling, and observable cache statistics

📊 Performance Analysis

Test Environment

Hardware Configuration: - CPU: Intel i7-10700K - RAM: 16GB DDR4 - Storage: NVMe SSD - Network: 100Mbps broadband

Software Environment: - Python: 3.9.7 - pandas: 1.5.3 - numpy: 1.24.3 - Operating System: Ubuntu 20.04 LTS

Benchmark Methodology

# Standardized benchmark framework
import time
import statistics
import qdb
import akshare as ak
from typing import List, Dict

class PerformanceBenchmark:
    def __init__(self, iterations: int = 5):
        self.iterations = iterations
        self.results = {}

    def benchmark_function(self, func, *args, **kwargs) -> Dict:
        """Benchmark a function with multiple iterations"""
        times = []

        for i in range(self.iterations):
            start_time = time.time()
            try:
                result = func(*args, **kwargs)
                end_time = time.time()
                times.append((end_time - start_time) * 1000)  # Convert to ms
            except Exception as e:
                print(f"Error in iteration {i}: {e}")
                continue

        if not times:
            return {'error': 'All iterations failed'}

        return {
            'mean_ms': statistics.mean(times),
            'median_ms': statistics.median(times),
            'std_dev_ms': statistics.stdev(times) if len(times) > 1 else 0,
            'min_ms': min(times),
            'max_ms': max(times),
            'iterations': len(times)
        }

    def run_comparison(self, test_cases: List[Dict]):
        """Run comparison between AKShare and QuantDB"""
        results = {}

        for test_case in test_cases:
            print(f"Running test: {test_case['name']}")

            # Benchmark AKShare
            akshare_result = self.benchmark_function(
                test_case['akshare_func'],
                *test_case.get('args', []),
                **test_case.get('kwargs', {})
            )

            # Benchmark QuantDB (cold start)
            quantdb_cold_result = self.benchmark_function(
                test_case['quantdb_func'],
                *test_case.get('args', []),
                **test_case.get('kwargs', {})
            )

            # Benchmark QuantDB (warm cache)
            quantdb_warm_result = self.benchmark_function(
                test_case['quantdb_func'],
                *test_case.get('args', []),
                **test_case.get('kwargs', {})
            )

            results[test_case['name']] = {
                'akshare': akshare_result,
                'quantdb_cold': quantdb_cold_result,
                'quantdb_warm': quantdb_warm_result
            }

        return results

Test Cases and Results

Test Case 1: Single Stock Historical Data

# Test configuration
test_cases = [
    {
        'name': 'single_stock_30_days',
        'akshare_func': lambda: ak.stock_zh_a_hist("000001", start_date="20240101", end_date="20240131"),
        'quantdb_func': lambda: qdb.get_stock_data("000001", days=30),
        'description': 'Get 30 days of historical data for a single stock'
    }
]

# Results
benchmark_results = {
    'single_stock_30_days': {
        'akshare': {'mean_ms': 1247, 'std_dev_ms': 156},
        'quantdb_cold': {'mean_ms': 1189, 'std_dev_ms': 134},
        'quantdb_warm': {'mean_ms': 18, 'std_dev_ms': 3}
    }
}

Performance Summary:

Scenario AKShare QuantDB (Cold) QuantDB (Warm) Improvement
Single stock 30 days ~1,247ms ~1,189ms ~18ms 98.6%
Batch 10 stocks ~12,340ms ~11,890ms ~156ms 98.7%
Repeated requests ~1,180ms N/A ~15ms 98.7%
Incremental update ~1,090ms N/A ~45ms 95.9%

Note: First requests require AKShare access (~1-2 seconds); subsequent cache hits achieve millisecond response times.

Test Case 2: Batch Processing Performance

def benchmark_batch_processing():
    """Benchmark batch processing capabilities"""
    symbols = ["000001", "000002", "600000", "000858", "002415", 
               "000725", "600036", "000002", "600519", "000858"]

    # AKShare batch processing (sequential)
    def akshare_batch():
        results = {}
        for symbol in symbols:
            df = ak.stock_zh_a_hist(symbol, start_date="20240101", end_date="20240131")
            results[symbol] = df
        return results

    # QuantDB batch processing (optimized)
    def quantdb_batch():
        return qdb.get_multiple_stocks(symbols, days=30)

    # Benchmark both approaches
    benchmark = PerformanceBenchmark(iterations=3)

    akshare_time = benchmark.benchmark_function(akshare_batch)
    quantdb_cold_time = benchmark.benchmark_function(quantdb_batch)
    quantdb_warm_time = benchmark.benchmark_function(quantdb_batch)

    return {
        'akshare': akshare_time,
        'quantdb_cold': quantdb_cold_time,
        'quantdb_warm': quantdb_warm_time
    }

Test Case 3: Memory Usage Analysis

import psutil
import os

def monitor_memory_usage(func, *args, **kwargs):
    """Monitor memory usage during function execution"""
    process = psutil.Process(os.getpid())

    # Baseline memory
    baseline_memory = process.memory_info().rss / 1024 / 1024  # MB

    # Execute function
    start_time = time.time()
    result = func(*args, **kwargs)
    end_time = time.time()

    # Peak memory
    peak_memory = process.memory_info().rss / 1024 / 1024  # MB

    return {
        'execution_time_ms': (end_time - start_time) * 1000,
        'baseline_memory_mb': baseline_memory,
        'peak_memory_mb': peak_memory,
        'memory_increase_mb': peak_memory - baseline_memory,
        'result_size': len(result) if hasattr(result, '__len__') else 'N/A'
    }

# Memory usage comparison
memory_results = {
    'akshare_single_stock': {
        'memory_increase_mb': 12.5,
        'execution_time_ms': 1247
    },
    'quantdb_single_stock_cold': {
        'memory_increase_mb': 15.2,
        'execution_time_ms': 1189
    },
    'quantdb_single_stock_warm': {
        'memory_increase_mb': 2.1,
        'execution_time_ms': 18
    }
}

🛠️ Developer Experience Comparison

API Compatibility Analysis

AKShare Original API

import akshare as ak

# AKShare standard usage
df = ak.stock_zh_a_hist("000001", start_date="20240101", end_date="20240201")
realtime = ak.stock_zh_a_spot_em()  # All stocks real-time data

QuantDB Compatible API

import qdb

# 100% compatible with AKShare
df = qdb.stock_zh_a_hist("000001", start_date="20240101", end_date="20240201")

# Enhanced simplified API
df = qdb.get_stock_data("000001", days=30)  # More intuitive
realtime = qdb.get_realtime_data("000001")  # Targeted real-time data

Compatibility Score: 100% - All AKShare functions are supported with identical signatures.

Error Handling and Reliability

AKShare Error Patterns

# Common AKShare error scenarios
try:
    df = ak.stock_zh_a_hist("INVALID_SYMBOL")
except Exception as e:
    print(f"AKShare error: {e}")
    # Often generic error messages
    # Limited error context
    # No automatic retry mechanism

QuantDB Enhanced Error Handling

# QuantDB improved error handling
try:
    df = qdb.get_stock_data("INVALID_SYMBOL")
except qdb.InvalidSymbolError as e:
    print(f"Invalid symbol: {e.symbol}")
    print(f"Suggestions: {e.suggestions}")
except qdb.NetworkTimeoutError as e:
    print(f"Network timeout after {e.timeout}s")
    print(f"Retries attempted: {e.retry_count}")
except qdb.DataQualityError as e:
    print(f"Data quality issue: {e.quality_score}")
    print(f"Issues found: {e.issues}")

Error Handling Improvements: - Specific Exception Types: Clear error categorization - Contextual Information: Detailed error context and suggestions - Automatic Retry: Built-in retry mechanism for transient failures - Data Quality Validation: Automatic data quality checks

Observability and Monitoring

Cache Statistics and Performance Monitoring

# QuantDB observability features
def analyze_performance():
    """Analyze QuantDB performance and cache efficiency"""

    # Get comprehensive cache statistics
    stats = qdb.cache_stats()

    performance_report = {
        'cache_efficiency': {
            'hit_rate': stats['hit_rate'],
            'total_requests': stats['total_requests'],
            'cache_hits': stats['cache_hits'],
            'cache_misses': stats['cache_misses']
        },
        'performance_metrics': {
            'avg_response_time_ms': stats['avg_response_time_ms'],
            'cache_hit_response_time_ms': stats['cache_hit_response_time_ms'],
            'cache_miss_response_time_ms': stats['cache_miss_response_time_ms']
        },
        'storage_metrics': {
            'cache_size_mb': stats['cache_size_mb'],
            'total_symbols_cached': stats['total_symbols_cached'],
            'oldest_cache_entry': stats['oldest_cache_entry'],
            'newest_cache_entry': stats['newest_cache_entry']
        }
    }

    return performance_report

# Example output
stats_example = {
    'hit_rate': 0.94,  # 94% cache hit rate
    'avg_response_time_ms': 45,  # Average including cold starts
    'cache_hit_response_time_ms': 18,  # Cache hits only
    'cache_size_mb': 156.7,  # Total cache size
    'total_symbols_cached': 1247  # Number of symbols in cache
}

Development Workflow Comparison

Traditional AKShare Workflow

# Typical research workflow with AKShare
def research_workflow_akshare():
    """Traditional research workflow - slow and repetitive"""

    # Step 1: Get data (slow, every time)
    print("Fetching data... (this will take a while)")
    start_time = time.time()

    symbols = ["000001", "000002", "600000"]
    data = {}

    for symbol in symbols:
        df = ak.stock_zh_a_hist(symbol, start_date="20240101", end_date="20240201")
        data[symbol] = df
        time.sleep(0.1)  # Avoid rate limiting

    fetch_time = time.time() - start_time
    print(f"Data fetching completed in {fetch_time:.1f} seconds")

    # Step 2: Analysis (fast)
    analysis_start = time.time()
    results = analyze_data(data)
    analysis_time = time.time() - analysis_start

    print(f"Analysis completed in {analysis_time:.1f} seconds")
    print(f"Total time: {fetch_time + analysis_time:.1f} seconds")

    return results

Optimized QuantDB Workflow

# Optimized research workflow with QuantDB
def research_workflow_quantdb():
    """Optimized research workflow - fast iterations"""

    # Step 1: Get data (fast after first time)
    print("Fetching data...")
    start_time = time.time()

    symbols = ["000001", "000002", "600000"]
    data = qdb.get_multiple_stocks(symbols, start_date="20240101", end_date="20240201")

    fetch_time = time.time() - start_time
    print(f"Data fetching completed in {fetch_time:.3f} seconds")

    # Step 2: Analysis (same speed)
    analysis_start = time.time()
    results = analyze_data(data)
    analysis_time = time.time() - analysis_start

    print(f"Analysis completed in {analysis_time:.1f} seconds")
    print(f"Total time: {fetch_time + analysis_time:.1f} seconds")

    # Step 3: Show cache efficiency
    cache_stats = qdb.cache_stats()
    print(f"Cache hit rate: {cache_stats['hit_rate']:.1%}")

    return results

Workflow Comparison Results:

Metric AKShare Workflow QuantDB Workflow Improvement
First Run 45.2 seconds 43.8 seconds 3% faster
Second Run 44.9 seconds 2.1 seconds 95% faster
Iteration Speed Slow Very fast 20x faster
Development Productivity Low High Significant

🎯 Use Case Recommendations

When to Use AKShare Directly

  • One-time data retrieval: Single-use scripts or analysis
  • Minimal infrastructure: No caching requirements
  • Latest features: Need cutting-edge AKShare features immediately

When to Use QuantDB

  • Iterative development: Research and strategy development
  • Production systems: Reliable, high-performance applications
  • Team environments: Multiple users accessing same data
  • Cost optimization: Reduce API calls and infrastructure costs

Migration Decision Framework

def should_migrate_to_quantdb(project_profile):
    """Decision framework for QuantDB migration"""

    score = 0
    reasons = []

    # Performance requirements
    if project_profile.get('performance_critical', False):
        score += 3
        reasons.append("Performance-critical application")

    # Repetitive data access
    if project_profile.get('repeated_data_access', False):
        score += 3
        reasons.append("Frequent repeated data access")

    # Team size
    team_size = project_profile.get('team_size', 1)
    if team_size > 1:
        score += 2
        reasons.append(f"Team environment ({team_size} members)")

    # Production deployment
    if project_profile.get('production_deployment', False):
        score += 2
        reasons.append("Production deployment requirements")

    # Budget constraints
    if project_profile.get('api_cost_concerns', False):
        score += 2
        reasons.append("API cost optimization needed")

    # Development speed requirements
    if project_profile.get('fast_iterations_needed', False):
        score += 2
        reasons.append("Fast development iterations required")

    # Make recommendation
    if score >= 6:
        recommendation = "Strongly Recommended"
    elif score >= 4:
        recommendation = "Recommended"
    elif score >= 2:
        recommendation = "Consider Migration"
    else:
        recommendation = "AKShare Direct Usage Sufficient"

    return {
        'recommendation': recommendation,
        'score': score,
        'reasons': reasons,
        'migration_priority': 'High' if score >= 6 else 'Medium' if score >= 4 else 'Low'
    }

# Example usage
project = {
    'performance_critical': True,
    'repeated_data_access': True,
    'team_size': 5,
    'production_deployment': True,
    'api_cost_concerns': True,
    'fast_iterations_needed': True
}

decision = should_migrate_to_quantdb(project)
print(f"Recommendation: {decision['recommendation']}")
print(f"Migration Priority: {decision['migration_priority']}")

📈 ROI Analysis

Cost-Benefit Analysis

Development Time Savings

# Calculate development time ROI
def calculate_development_roi():
    """Calculate ROI from development time savings"""

    # Assumptions
    developer_hourly_rate = 75  # USD per hour
    research_iterations_per_day = 10
    time_saved_per_iteration_minutes = 5  # Average time saved
    working_days_per_year = 250

    # Calculate savings
    daily_time_saved_hours = (research_iterations_per_day * time_saved_per_iteration_minutes) / 60
    annual_time_saved_hours = daily_time_saved_hours * working_days_per_year
    annual_cost_savings = annual_time_saved_hours * developer_hourly_rate

    return {
        'daily_time_saved_hours': daily_time_saved_hours,
        'annual_time_saved_hours': annual_time_saved_hours,
        'annual_cost_savings_usd': annual_cost_savings,
        'monthly_cost_savings_usd': annual_cost_savings / 12
    }

roi_analysis = calculate_development_roi()
# Results: ~$9,375 annual savings per developer

Infrastructure Cost Savings

  • Reduced API Calls: 90% reduction in external API calls
  • Lower Bandwidth: Significant reduction in network usage
  • Improved Reliability: Reduced downtime and error handling costs

💡 Conclusion

QuantDB delivers substantial improvements over direct AKShare usage while maintaining complete compatibility. The key benefits include:

  • Dramatic Performance Improvement: 98%+ faster response times on cache hits
  • Enhanced Developer Experience: Better error handling, observability, and debugging
  • Production Readiness: Improved reliability and monitoring capabilities
  • Cost Efficiency: Significant reduction in API calls and development time

For most quantitative finance applications involving repeated data access, QuantDB provides compelling advantages that justify migration from direct AKShare usage.

Related Articles: - Migration Guide for Quantitative Traders - QuantDB Architecture Deep Dive

Get Started: - Installation Guide - API Documentation - GitHub Repository