Get A-Share Real-Time Quotes with Python: Tencent Finance API Tutorial

Don't want to pay for market data? This tutorial shows you how to get A-share real-time quotes for free using Python. With a 15-minute delay, it's perfect for quantitative research and strategy validation.

Why Choose Tencent Finance API?

Free Market Data Comparison

Source	Delay	Cost	Stability	Quality
Tencent Finance	15 min	Free	⭐⭐⭐⭐	Good
Sina Finance	15 min	Free	⭐⭐⭐⭐	Good
TongdaXin VIP	Real-time	¥300/year	⭐⭐⭐⭐⭐	Excellent
JoinQuant	Real-time	¥3000+/year	⭐⭐⭐⭐⭐	Excellent
Exchange Official	Real-time	¥10000+/year	⭐⭐⭐⭐⭐	Best

Conclusion: Among free options, Tencent and Sina are most reliable. Tencent's response format is cleaner and easier to parse.

Complete Implementation

1. Core Fetcher

python

# -*- coding: utf-8 -*-
"""
Tencent Finance Real-Time Quote Fetcher

Supports:
- A-share real-time quotes (15-min delay)
- 5-level order book
- Real-time trades
- Intraday data

Data Source: Tencent Finance API (Free, 15-min delayed)
"""

import requests
import pandas as pd
from datetime import datetime
from typing import Optional, List, Dict


class TencentStockFetcher:
    """Tencent Finance Real-Time Quote Fetcher"""
    
    QUOTE_URL = "https://qt.gtimg.cn/q="
    
    def __init__(self):
        self.session = requests.Session()
        self.session.headers.update({
            'Referer': 'https://stockapp.finance.qq.com',
            'User-Agent': 'Mozilla/5.0'
        })
    
    def get_realtime_quote(self, symbols: List[str]) -> pd.DataFrame:
        """
        Get real-time quotes
        
        Args:
            symbols: Stock codes, e.g., ['sh600000', 'sz000001']
        
        Returns:
            DataFrame with real-time quote data
        """
        if not symbols:
            return pd.DataFrame()
        
        # Build code list (Tencent format)
        codes = []
        for symbol in symbols:
            if symbol.startswith('6') or symbol.startswith('9'):
                codes.append(f'sh{symbol}')
            else:
                codes.append(f'sz{symbol}')
        
        # Request quote data
        code_str = ','.join(codes)
        url = f"{self.QUOTE_URL}{code_str}"
        
        try:
            resp = self.session.get(url, timeout=10)
            resp.encoding = 'gbk'  # Tencent returns GBK encoding
            data = resp.text
            
            return self._parse_quote_data(data)
        except Exception as e:
            print(f"Failed to fetch quotes: {e}")
            return pd.DataFrame()
    
    def _parse_quote_data(self, data: str) -> pd.DataFrame:
        """Parse Tencent quote data"""
        results = []
        
        for line in data.strip().split('\n'):
            if not line.startswith('v_'):
                continue
            
            try:
                # Format: v_sh600000="51~浦发银行~600000~7.85~7.84~7.86~..."
                parts = line.split('~')
                if len(parts) < 50:
                    continue
                
                symbol = parts[2]
                name = parts[1]
                
                row = {
                    'symbol': symbol,
                    'name': name,
                    'price': float(parts[3]) if parts[3] else 0,
                    'open': float(parts[5]) if parts[5] else 0,
                    'high': float(parts[33]) if parts[33] else 0,
                    'low': float(parts[34]) if parts[34] else 0,
                    'close': float(parts[4]) if parts[4] else 0,
                    'volume': float(parts[6]) if parts[6] else 0,
                    'amount': float(parts[37]) if parts[37] else 0,
                    'bid': float(parts[11]) if parts[11] else 0,
                    'ask': float(parts[13]) if parts[13] else 0,
                    'bid_vol': float(parts[10]) if parts[10] else 0,
                    'ask_vol': float(parts[12]) if parts[12] else 0,
                    'change': float(parts[3]) - float(parts[4]) if parts[3] and parts[4] else 0,
                    'change_pct': float(parts[32]) if parts[32] else 0,
                    'timestamp': datetime.now()
                }
                
                results.append(row)
            except Exception as e:
                print(f"Failed to parse data: {e}")
                continue
        
        return pd.DataFrame(results)

2. Real-Time Monitor

python

# -*- coding: utf-8 -*-
"""
Tencent Finance Real-Time Monitor

Usage:
    python monitor.py                  # Monitor default stocks
    python monitor.py 600000,000001    # Monitor specific stocks
"""

import argparse
import time
from datetime import datetime
from tencent_fetcher import TencentStockFetcher


def main():
    parser = argparse.ArgumentParser(description='Tencent Finance Real-Time Monitor')
    parser.add_argument('symbols', nargs='?', default='600000,000001,300750',
                       help='Stock codes, comma-separated')
    parser.add_argument('--interval', type=int, default=60,
                       help='Refresh interval in seconds, default 60')
    
    args = parser.parse_args()
    
    fetcher = TencentStockFetcher()
    symbols = args.symbols.split(',')
    
    print(f"\nMonitoring: {symbols}")
    print("Data delay: ~15 minutes")
    print("Press Ctrl+C to stop\n")
    
    try:
        while True:
            df = fetcher.get_realtime_quote(symbols)
            
            if df.empty:
                print("Failed to fetch data, retrying in 5s...")
                time.sleep(5)
                continue
            
            # Clear screen
            print("\033[2J\033[H", end='')
            print(f"Updated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n")
            
            # Display quotes
            for _, row in df.iterrows():
                icon = '📈' if row['change'] > 0 else '📉' if row['change'] < 0 else '➖'
                print(f"{icon} {row['symbol']} {row['name']:<10} "
                      f"¥{row['price']:.2f} "
                      f"({row['change_pct']:+.2f}%) "
                      f"Change: {row['change']:.2f}")
            
            print(f"\nNext update: {args.interval}s")
            time.sleep(args.interval)
            
    except KeyboardInterrupt:
        print("\nMonitoring stopped")


if __name__ == "__main__":
    main()

Usage Examples

Install Dependencies

bash

pip install requests pandas

Get Single Stock Quote

python

from tencent_fetcher import TencentStockFetcher

fetcher = TencentStockFetcher()

# Get real-time quotes
df = fetcher.get_realtime_quote(['600000', '000001', '300750'])

print(df[['symbol', 'name', 'price', 'change_pct']])

Output:

  symbol    name   price  change_pct
0  600000  浦发银行  7.85      +1.28
1  000001  平安银行  10.23     -0.58
2  300750  宁德时代  180.50    +0.00

Monitor Multiple Stocks

bash

# Monitor default stocks (refresh every 60s)
python monitor.py

# Monitor specific stocks
python monitor.py 600000,000001,300750

# Custom refresh interval (30s)
python monitor.py 600000,000001 --interval 30

Integrate into Your Project

python

from tencent_fetcher import TencentStockFetcher

fetcher = TencentStockFetcher()

# Get stock price
df = fetcher.get_realtime_quote(['600000'])
current_price = df.iloc[0]['price']
change_pct = df.iloc[0]['change_pct']

# Check if price is up
if change_pct > 0:
    print(f"📈 Up {change_pct}%")
elif change_pct < 0:
    print(f"📉 Down {abs(change_pct)}%")
else:
    print("➖ Unchanged")

Data Fields

Field	Description	Example
symbol	Stock code	600000
name	Stock name	浦发银行
price	Current price	7.85
open	Open price	7.80
high	High price	7.92
low	Low price	7.78
close	Previous close	7.75
volume	Volume (lots)	123456
amount	Turnover (CNY)	98765432
bid	Bid price (buy 1)	7.84
ask	Ask price (sell 1)	7.86
bid_vol	Bid volume	1000
ask_vol	Ask volume	1500
change	Price change	+0.10
change_pct	Change %	+1.28

Important Notes

1. Data Delay

Free API, ~15 minute delay
Suitable for: quantitative research, strategy validation, offline analysis
NOT suitable for: live trading, high-frequency trading

2. Trading Hours

Real-time data only available during trading hours (9:30-15:00 on trading days):

Morning: 9:30 - 11:30
Afternoon: 13:00 - 15:00

Outside trading hours, returns closing price.

3. Rate Limits

Recommended refresh interval ≥ 30 seconds
Avoid大量 requests in short time (may get IP blocked)
Monitoring 10-20 stocks is fine

4. Encoding

Tencent returns GBK encoding, handled in code:

python

resp.encoding = 'gbk'

5. Stock Code Format

Tencent format:

Shanghai: sh600000
Shenzhen: sz000001

Auto-converted in the code.

Advanced Usage

python

def get_stock_list(self) -> pd.DataFrame:
    """Get A-share stock list"""
    all_stocks = []
    
    for i in range(1, 100):
        url = f"http://vip.stock.finance.sina.com.cn/quotes_service/api/json_v2.php/Market_Center.getHQNodeData?page={i}&num=80&sort=symbol&asc=1&node=hs_a"
        resp = self.session.get(url, timeout=10)
        data = resp.json()
        if not data:
            break
        all_stocks.extend(data)
    
    return pd.DataFrame(all_stocks)

2. Combine with SimTradeData

python

# SimTradeData for historical data
from simtradedata.fetchers.baostock_fetcher import BaoStockFetcher

# Tencent for real-time data
from tencent_fetcher import TencentStockFetcher

# Historical + Real-time = Complete data pipeline
historical = baostock_fetcher.fetch_daily('600000', '2026-01-01', '2026-03-10')
realtime = tencent_fetcher.get_realtime_quote(['600000'])

3. Data Persistence

python

import sqlite3

# Save to database
conn = sqlite3.connect('stock_data.db')
df.to_sql('realtime_quotes', conn, if_exists='append', index=False)

How It Works

Tencent Quote API

https://qt.gtimg.cn/q=sh600000,sz000001

Response format (GBK encoded):

v_sh600000="51~浦发银行~600000~7.85~7.84~7.86~7.80~7.92~7.78~..."

Fields separated by ~, parsed by position.

API Features

No API Key required - Direct access
Batch support - Up to 100 stocks per request
Fast response - Typically < 500ms
Stable - Official Tencent API

Summary

Pros

✅ Completely free ✅ No registration required ✅ Good data quality ✅ Stable API ✅ Batch queries supported

Cons

❌ 15-minute delay ❌ Unofficial API (may change) ❌ Not suitable for live trading

Use Cases

✅ Quantitative strategy research
✅ Historical backtesting validation
✅ Stock monitoring tools
✅ Learning projects

Full code available: https://github.com/your-username/tencent-realtime

Related Projects:

SimTradeData - A-share historical data downloader
SimTradeLab - Quantitative backtesting framework

If this article helped you, please like, bookmark, and share!

Questions? Email me: cnyfdr@gmail.com 🐶

Get A-Share Real-Time Quotes with Python: Tencent Finance API Tutorial ​

Why Choose Tencent Finance API? ​

Free Market Data Comparison ​

Complete Implementation ​

1. Core Fetcher ​

2. Real-Time Monitor ​

Usage Examples ​

Install Dependencies ​

Get Single Stock Quote ​

Monitor Multiple Stocks ​

Integrate into Your Project ​

Data Fields ​

Important Notes ​

1. Data Delay ​

2. Trading Hours ​

3. Rate Limits ​

4. Encoding ​

5. Stock Code Format ​

Advanced Usage ​

1. Get All A-Share List ​

2. Combine with SimTradeData ​

3. Data Persistence ​

How It Works ​

Tencent Quote API ​

API Features ​

Summary ​

Pros ​

Cons ​

Use Cases ​

Get A-Share Real-Time Quotes with Python: Tencent Finance API Tutorial

Why Choose Tencent Finance API?

Free Market Data Comparison

Complete Implementation

1. Core Fetcher

2. Real-Time Monitor

Usage Examples

Install Dependencies

Get Single Stock Quote

Monitor Multiple Stocks

Integrate into Your Project

Data Fields

Important Notes

1. Data Delay

2. Trading Hours

3. Rate Limits

4. Encoding

5. Stock Code Format

Advanced Usage

1. Get All A-Share List

2. Combine with SimTradeData

3. Data Persistence

How It Works

Tencent Quote API

API Features

Summary

Pros

Cons

Use Cases