AliExpress Product Data API: Prices, Ratings, and Orders in Python

AliExpress is the largest source of dropshipping product data on the internet. Getting that data programmatically is harder than it should be. Here is the practical guide.

The official API problem

AliExpress has an Open Platform API, but it requires applying for the affiliate program and waiting for approval. Even after approval, it only covers products that are part of the affiliate catalog — a subset of what is actually listed. For full product search and price tracking, scraping is the only option.

What data you can pull

Each product listing on AliExpress contains:

• Product title (English and localised)
• Current price and original price (for discounted items)
• Discount percentage
• Star rating (0-5) and total review count
• Total order count (this is the key demand signal)
• Seller store name and rating
• Primary product image URL
• Product URL

Using the Apify actor

import apify_client

client = apify_client.ApifyClient('YOUR_APIFY_TOKEN')

run_input = {
    "searchQuery": "wireless earbuds",
    "maxResults": 100,
    "sortBy": "orders",       # sort by most ordered
    "minPrice": 5,
    "maxPrice": 50,
    "minRating": 4.0,
    "minOrders": 1000,
}

run = client.actor('themineworks/aliexpress-products').call(run_input=run_input)

for product in client.dataset(run['defaultDatasetId']).iterate_items():
    print(f"{product['title'][:60]}")
    print(f"  Price: ${product['price']}  Orders: {product['orders']}  Rating: {product['rating']}")

Finding winning dropshipping products

The standard dropshipping research formula on AliExpress:

results = list(client.dataset(run['defaultDatasetId']).iterate_items())

# Filter for products with strong demand signals
winners = [p for p in results if
    p.get('orders', 0) > 5000 and      # proven demand
    p.get('rating', 0) >= 4.2 and       # quality signal
    p.get('price', 0) < 20              # room for retail markup
]

winners.sort(key=lambda x: x['orders'], reverse=True)
for p in winners[:10]:
    print(p['title'], p['orders'], 'orders')

Price tracking over time

Schedule weekly runs and compare prices to detect seasonal patterns and restocking cycles:

import json
from datetime import datetime

# Store each run with a timestamp
run_date = datetime.now().strftime('%Y-%m-%d')
results = list(client.dataset(run['defaultDatasetId']).iterate_items())

with open(f'aliexpress_prices_{run_date}.json', 'w') as f:
    json.dump(results, f)

Category browsing

Instead of keyword search, browse by category ID for comprehensive coverage of a product niche:

run_input = {
    "categoryId": "200000528",  # Phones & Accessories
    "sortBy": "orders",
    "maxResults": 500,
}

Pricing

Pay $0.0025 per product returned. Zero charge on empty searches.