El monitoreo de precios multiplataforma compara el mismo producto en Amazon y Google Shopping para encontrar la mejor oferta y detectar cambios de precios. En lugar de crear raspadores separados para cada plataforma, la API de Scavio proporciona datos de productos de Amazon y resultados de Google Shopping a través de un único punto final. Este tutorial crea un canal de monitoreo que rastrea productos en todas las plataformas, almacena precios históricos y alerta cuando los precios caen por debajo de un umbral. Cada cheque de plataforma cuesta $0,005.
Requisitos previos
- Python 3.9+ instalado
- solicita biblioteca instalada
- Una clave API de Scavio de scavio.dev
- Una lista de productos a monitorear
Guia paso a paso
Paso 1: Buscar productos en ambas plataformas
Crear funciones para buscar en Amazon y Google Shopping un mismo producto. Ambos utilizan la API de búsqueda de Scavio con diferentes parámetros de plataforma.
Python
import requests, os
API_KEY = os.environ['SCAVIO_API_KEY']
ENDPOINT = 'https://api.scavio.dev/api/v1/search'
def search_amazon(query: str) -> list:
resp = requests.post(ENDPOINT,
headers={'x-api-key': API_KEY, 'Content-Type': 'application/json'},
json={'platform': 'amazon', 'query': query, 'marketplace': 'US'})
return resp.json().get('products', [])
def search_google_shopping(query: str) -> list:
resp = requests.post(ENDPOINT,
headers={'x-api-key': API_KEY, 'Content-Type': 'application/json'},
json={'query': query, 'country_code': 'us', 'type': 'shopping'})
return resp.json().get('shopping_results', [])Paso 2: Normalizar los precios en todas las plataformas
Amazon y Google Shopping devuelven precios en diferentes formatos. Normalícelos a valores flotantes para compararlos.
Python
import re
def parse_price(price_str) -> float:
if not price_str:
return 0.0
if isinstance(price_str, (int, float)):
return float(price_str)
cleaned = re.sub(r'[^0-9.]', '', str(price_str))
try:
return float(cleaned)
except ValueError:
return 0.0
def compare_prices(query: str) -> dict:
amazon = search_amazon(query)
google = search_google_shopping(query)
amazon_prices = [{'title': p.get('title', '')[:60], 'price': parse_price(p.get('price')),
'platform': 'amazon', 'link': p.get('link', '')}
for p in amazon if parse_price(p.get('price')) > 0]
google_prices = [{'title': p.get('title', '')[:60], 'price': parse_price(p.get('price')),
'platform': 'google_shopping', 'link': p.get('link', '')}
for p in google if parse_price(p.get('price')) > 0]
all_prices = sorted(amazon_prices + google_prices, key=lambda x: x['price'])
return {'query': query, 'total_listings': len(all_prices),
'cheapest': all_prices[0] if all_prices else None,
'all': all_prices[:10]}
result = compare_prices('Sony WH-1000XM5')
print(f'Cheapest: ${result["cheapest"]["price"]} on {result["cheapest"]["platform"]}')Paso 3: Historial de precios de la tienda en JSON
Guarde instantáneas de precios diarias para que pueda realizar un seguimiento de las tendencias y detectar caídas. Cada producto obtiene una serie temporal de precios por plataforma.
Python
import json
from datetime import date
HISTORY_FILE = 'price_history.json'
def load_history() -> dict:
try:
with open(HISTORY_FILE) as f:
return json.load(f)
except FileNotFoundError:
return {}
def save_snapshot(query: str, prices: list) -> None:
history = load_history()
today = date.today().isoformat()
if query not in history:
history[query] = []
history[query].append({
'date': today,
'prices': [{'platform': p['platform'], 'price': p['price'],
'title': p['title'][:40]} for p in prices[:5]]
})
with open(HISTORY_FILE, 'w') as f:
json.dump(history, f, indent=2)
# Save today's data:
result = compare_prices('Sony WH-1000XM5')
save_snapshot('Sony WH-1000XM5', result['all'])
print(f'Saved snapshot with {len(result["all"])} listings')Paso 4: Detectar caídas de precios a partir del historial
Compare los precios de hoy con la instantánea anterior. Alerta cuando un producto cae por debajo de un umbral o muestra una disminución porcentual significativa.
Python
def detect_drops(query: str, drop_threshold_pct: float = 10.0) -> list:
history = load_history()
snapshots = history.get(query, [])
if len(snapshots) < 2:
return []
prev = {p['title']: p['price'] for p in snapshots[-2]['prices']}
current = {p['title']: p['price'] for p in snapshots[-1]['prices']}
drops = []
for title, curr_price in current.items():
if title in prev and prev[title] > 0:
pct_change = ((curr_price - prev[title]) / prev[title]) * 100
if pct_change < -drop_threshold_pct:
drops.append({
'product': title,
'old_price': prev[title],
'new_price': curr_price,
'drop_pct': round(abs(pct_change), 1)
})
return drops
drops = detect_drops('Sony WH-1000XM5')
for d in drops:
print(f'PRICE DROP: {d["product"]} ${d["old_price"]} -> ${d["new_price"]} (-{d["drop_pct"]}%)')Paso 5: Realizar un seguimiento diario de todos los productos
Cree un script principal que monitoree múltiples productos e informe todos los hallazgos. Programelo con cron para ejecución diaria.
Python
def daily_monitor(products: list) -> None:
print(f'Price monitor: {len(products)} products, {date.today()}')
all_drops = []
for product in products:
result = compare_prices(product)
save_snapshot(product, result['all'])
cheapest = result.get('cheapest')
if cheapest:
print(f' {product}: ${cheapest["price"]} ({cheapest["platform"]})')
drops = detect_drops(product)
all_drops.extend(drops)
if all_drops:
print(f'\n{len(all_drops)} price drops detected:')
for d in all_drops:
print(f' {d["product"]}: ${d["old_price"]} -> ${d["new_price"]} (-{d["drop_pct"]}%)')
cost = len(products) * 2 * 0.005 # 2 API calls per product
print(f'\nAPI cost: ${cost:.2f} ({len(products) * 2} credits)')
if __name__ == '__main__':
daily_monitor(['Sony WH-1000XM5', 'Apple AirPods Pro', 'Samsung Galaxy Buds'])Ejemplo en Python
Python
import os, requests, re, json
from datetime import date
API_KEY = os.environ['SCAVIO_API_KEY']
EP = 'https://api.scavio.dev/api/v1/search'
def search(body):
return requests.post(EP, headers={'x-api-key': API_KEY, 'Content-Type': 'application/json'}, json=body).json()
def parse_price(p):
try: return float(re.sub(r'[^0-9.]', '', str(p or '0')))
except: return 0.0
def compare(query):
amazon = search({'platform': 'amazon', 'query': query, 'marketplace': 'US'}).get('products', [])
google = search({'query': query, 'country_code': 'us', 'type': 'shopping'}).get('shopping_results', [])
all_p = [{'title': p.get('title','')[:50], 'price': parse_price(p.get('price')), 'src': 'amazon'} for p in amazon]
all_p += [{'title': p.get('title','')[:50], 'price': parse_price(p.get('price')), 'src': 'google'} for p in google]
return sorted([p for p in all_p if p['price'] > 0], key=lambda x: x['price'])
for q in ['Sony WH-1000XM5', 'AirPods Pro']:
results = compare(q)
if results:
print(f'{q}: ${results[0]["price"]} ({results[0]["src"]})')Ejemplo en JavaScript
JavaScript
const API_KEY = process.env.SCAVIO_API_KEY;
const EP = 'https://api.scavio.dev/api/v1/search';
async function search(body) {
const r = await fetch(EP, {
method: 'POST',
headers: { 'x-api-key': API_KEY, 'Content-Type': 'application/json' },
body: JSON.stringify(body)
});
return r.json();
}
function parsePrice(p) {
return parseFloat(String(p || '0').replace(/[^0-9.]/g, '')) || 0;
}
async function compare(query) {
const [amazon, google] = await Promise.all([
search({ platform: 'amazon', query, marketplace: 'US' }),
search({ query, country_code: 'us', type: 'shopping' })
]);
const all = [
...(amazon.products || []).map(p => ({ title: p.title, price: parsePrice(p.price), src: 'amazon' })),
...(google.shopping_results || []).map(p => ({ title: p.title, price: parsePrice(p.price), src: 'google' }))
].filter(p => p.price > 0).sort((a, b) => a.price - b.price);
return all;
}
compare('Sony WH-1000XM5').then(r => {
if (r.length) console.log(`Cheapest: $${r[0].price} (${r[0].src})`);
});Salida esperada
JSON
Price monitor: 3 products, 2026-05-13
Sony WH-1000XM5: $278.00 (amazon)
Apple AirPods Pro: $189.99 (google_shopping)
Samsung Galaxy Buds: $99.99 (amazon)
1 price drops detected:
Apple AirPods Pro: $199.99 -> $189.99 (-5.0%)
API cost: $0.03 (6 credits)