ABC-MART Popular Sneaker Price History Analysis (Python Visualization)

In the sneaker market, price fluctuations are an important consideration. For consumers looking to purchase their favorite sneakers, understanding price history and predicting future price trends can help them buy at the best time and avoid paying too much.

This article will combine ABC-MART sneaker price data, using Python for data scraping, analysis, and visualization, to help you understand the price trends of popular sneakers.

1️⃣ How to Obtain ABC-MART Sneaker Price Data?

Method 1: Using Luckdata API (Recommended ✅)

If you want to quickly get ABC-MART price data, you can use the Luckdata API, Luckdata's Sneaker API can directly return product price, inventory, images, etc.

import requests
API_KEY = "your_key"  # Replace with your API Key
product_url = "https://www.abc-mart.com.tw/product/uwrpdfcb"
api_url = f"https://luckdata.io/api/sneaker-API/get_vpx1?url={product_url}"
headers = {"X-Luckdata-Api-Key": API_KEY}
response = requests.get(api_url, headers=headers)
if response.status_code == 200:
data = response.json()
print(f"Product Name: {data.get('name', 'N/A')}")
print(f"Current Price: {data.get('price', 'N/A')}")
else:
print(f"Request Failed: {response.status_code}")

✅ Pros: No need for complex scraping logic, direct access to data
❌ Cons: Requires API subscription

Method 2: Scraping ABC-MART Website with Python (Suitable for personal analysis)

If you don't want to use an API, you can use BeautifulSoup + Selenium to scrape price data directly from the ABC-MART website.

import requests
from bs4 import BeautifulSoup
url = "https://www.abc-mart.net/shop/g/g12345678/"
headers = {"User-Agent": "Mozilla/5.0"}
response = requests.get(url, headers=headers)
soup = BeautifulSoup(response.text, "html.parser")
product_name = soup.find("h1", class_="product-title").text.strip()
price = soup.find("span", class_="price").text.strip()
print(f"Product Name: {product_name}")
print(f"Current Price: {price}")

✅ Pros: Free, not limited by API quotas
❌ Cons: May need to bypass anti-scraping mechanisms, data storage is more complex

2️⃣ Collect Data Over Time and Build a Price Trend

To analyze price changes, we need to periodically scrape data, for example, record data once a day for 1-2 months.

Automatically Record Data (Scrape Once Every 24 Hours)

You can use schedule to automatically run the scraper and save the data to a CSV file.

import csv
import schedule
import time
def fetch_price():
response = requests.get(url, headers=headers)
soup = BeautifulSoup(response.text, "html.parser")
price = soup.find("span", class_="price").text.strip()
with open("sneaker_prices.csv", "a", newline="", encoding="utf-8") as file:
writer = csv.writer(file)
writer.writerow([time.strftime("%Y-%m-%d"), price])
print(f"Successfully Recorded: {time.strftime('%Y-%m-%d')} Price: {price}")
# Set up scheduled task (run once at 10:00 AM daily)
schedule.every().day.at("10:00").do(fetch_price)
while True:
schedule.run_pending()
time.sleep(60)  # Check every minute

✅ CSV File Example:

3️⃣ Data Visualization: Plotting the Price Trend

Once we have collected enough historical data, we can use Matplotlib and Pandas to plot the price trend.

Plotting a Line Chart for Price Trend Using Matplotlib

import pandas as pd
import matplotlib.pyplot as plt
# Read data
df = pd.read_csv("sneaker_prices.csv", names=["Date", "Price"], parse_dates=["Date"])
# Convert price to numeric value
df["Price"] = df["Price"].str.replace("NT$", "").astype(float)
# Plot the line chart
plt.figure(figsize=(10, 5))
plt.plot(df["Date"], df["Price"], marker="o", linestyle="-", color="b", label="Price Trend")
# Add title and labels
plt.title("ABC-MART Popular Sneaker Price Trend")
plt.xlabel("Date")
plt.ylabel("Price (NTD)")
plt.legend()
plt.grid()
# Rotate x-axis labels for better readability
plt.xticks(rotation=45)
# Display the chart
plt.show()

4️⃣ Price Analysis & Buying Suggestions

By analyzing the price trend, we can gain the following market insights:

✅ Is the price showing a downward trend?
If the price has been decreasing over the past month, it may be wise to wait for an even lower price before making a purchase.

✅ Are there cyclical promotions?
If certain time periods (like weekends, holidays, Black Friday) show lower prices, it's recommended to buy during these times.

✅ The relationship between inventory and price changes
If inventory decreases, prices may rise. You can combine Luckdata API to monitor inventory and make a quick purchase when restocked.

Price Forecasting

We can use Simple Moving Average (SMA) or Machine Learning (e.g., Prophet) to predict future price trends.

df["SMA_7"] = df["Price"].rolling(window=7).mean()
plt.plot(df["Date"], df["Price"], marker="o", label="Actual Price")
plt.plot(df["Date"], df["SMA_7"], linestyle="--", color="r", label="7-day Moving Average")
plt.legend()
plt.title("ABC-MART Sneaker Price Trend + Forecast")
plt.show()

✅ If the moving average is downward, it suggests the price may continue to drop
✅ If the moving average is upward, it indicates the price may start to rise again

5️⃣ Conclusion & Summary

✅ By periodically scraping ABC-MART popular sneaker price data, we can build historical price curves, helping users better decide when to buy sneakers.
✅ Combining Luckdata API or Python scrapers, we can obtain price, inventory, and other information for smart shopping.
✅ With data visualization & trend forecasting, we can judge market conditions and seize the best time to buy at the lowest price.