Fetching SGE Gold Price Data with Python POST Requests

The exchange provides real-time market data including open/high/low/close prices and trading volumes via its official API endpoints. This structured data is crucial for market participants analyzing price trends and making investment decisions.

Data Acquisition Process

Identify the target API endpoint for historical price data
Construct POST requests with appropriate authentication headers
Parse and store the returned JSON data in CSV format

Key Request Components

Headers: Include User-Agent and Referer fields for request validation
Payload: Contract identifier (e.g., 'Au99.99') as form data
Response: Structured JSON containing price records

Python Implementation

import requests
import csv
from datetime import datetime

# Configuration parameters
contract_id = "Au99.99"
output_file = f"SGE_{contract_id.replace('(', '_').replace(')', '_')}_{datetime.now().strftime('%Y%m%d')}.csv"

# API request setup
url = "https://www.sge.com.cn/graph/Dailyhq"
headers = {
    'User-Agent': 'Mozilla/5.0',
    'Referer': 'https://www.sge.com.cn/',
    'X-Requested-With': 'XMLHttpRequest'
}
payload = {'instid': contract_id}

# Data retrieval and storage
try:
    response = requests.post(url, headers=headers, data=payload, timeout=10)
    response.raise_for_status()
    price_data = response.json()

    with open(output_file, 'w', newline='', encoding='utf-8-sig') as f:
        writer = csv.writer(f)
        writer.writerow(['Date', 'Open', 'High', 'Low', 'Close'])
        writer.writerows(price_data['data'])

    print(f"Saved {len(price_data['data'])} records to {output_file}")

except Exception as e:
    print(f"Request failed: {e}")

Data Visualization

The following implementation uses ECharts to create an interactive HTML visualization of gold price trends:

import pandas as pd
import os

# Load and prepare data
file_path = 'SGE_Au99.99_20250818.csv'
price_df = pd.read_csv(file_path)
price_df['Date'] = pd.to_datetime(price_df['Date']).dt.strftime('%Y-%m-%d')
price_df.sort_values('Date', inplace=True)

# Generate HTML visualization
html_content = f"""
<html>
<head>
    <script src="https://cdn.jsdelivr.net/npm/echarts"></script>
    <style>.container {{ width: 90%; max-width: 1200px; margin: auto; }}</style>
</head>
<body>
    <div class="container">
        <h2>Au99.99 Price Trend ({price_df['Date'].iloc[0]} - {price_df['Date'].iloc[-1]})</h2>
        <div id="chart" style="height:500px;"></div>
    </div>
    <script>
        const chart = echarts.init(document.getElementById('chart'));
        chart.setOption({{
            tooltip: {{ trigger: 'axis' }},
            xAxis: {{ type: 'category', data: {json.dumps(price_df['Date'].tolist())} }},
            yAxis: {{ type: 'value' }},
            series: [{{
                name: 'Closing Price',
                type: 'line',
                data: {json.dumps(price_df['Close'].round(2).tolist())},
                smooth: true,
                itemStyle: {{ color: '#4a86e8' }}
            }}]
        }});
    </script>
</body>
</html>
"""

with open("gold_price_trend.html", "w", encoding="utf-8") as f:
    f.write(html_content)

Historical Price Anaylsis (2016-2025)

Long-term upward trend with 150% price increase
Key drivers include global economic uncertainty, monetary policy shifts, and geopolitical factors
Distinct market phases observed:
- 2016-2017: Stable base (~300 RMB/g)
- 2018-2019: Gradual ascent to 400 RMB/g
- 2020-2023: Volatile consolidation between 400-500 RMB/g
- 2023-2025: Sharp rise to 800 RMB/g peak

This analysis demonstrates the effectiveness of programmatic data collection and visualization for tracking precious metal price dynamics.

Tags: Python requests echarts CSV data processing financial data analysis

Posted on Mon, 03 Aug 2026 16:18:45 +0000 by zemerick

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