Cómo construir un escáner de precios de dropshipping multiplataforma

import os, requests, json, re
from datetime import datetime

API_KEY = os.environ['SCAVIO_API_KEY']
SH = {'x-api-key': API_KEY, 'Content-Type': 'application/json'}

def search_product(query, platform):
    data = requests.post('https://api.scavio.dev/api/v1/search',
        headers=SH, json={'query': query, 'platform': platform, 'country_code': 'us'}).json()
    results = data.get('organic_results', data.get('shopping_results', []))[:3]
    parsed = []
    for r in results:
        price_str = str(r.get('price', '')).replace('$', '').replace(',', '')
        try: price = float(re.search(r'[\d.]+', price_str).group())
        except: price = None
        parsed.append({'title': r.get('title', '')[:60], 'price': price,
                       'rating': r.get('rating', 'N/A'), 'link': r.get('link', '')})
    return parsed

def multi_platform_search(product):
    results = {}
    for platform in ['amazon', 'walmart', 'google']:
        results[platform] = search_product(product, platform)
    return results

results = multi_platform_search('wireless earbuds')
for platform, items in results.items():
    if items and items[0]['price']:
        print(f'  {platform:10}: ${items[0]["price"]:.2f} - {items[0]["title"][:40]}')

def find_margins(product):
    results = multi_platform_search(product)
    prices = {}
    for platform, items in results.items():
        if items and items[0]['price']:
            prices[platform] = {'price': items[0]['price'], 'title': items[0]['title']}
    if len(prices) < 2:
        return None
    lowest = min(prices.items(), key=lambda x: x[1]['price'])
    highest = min(prices.items(), key=lambda x: x[1]['price'])
    margins = []
    platforms = list(prices.keys())
    for i in range(len(platforms)):
        for j in range(i+1, len(platforms)):
            p1, p2 = platforms[i], platforms[j]
            diff = abs(prices[p1]['price'] - prices[p2]['price'])
            if diff > 0:
                buy_from = p1 if prices[p1]['price'] < prices[p2]['price'] else p2
                sell_on = p2 if buy_from == p1 else p1
                margin_pct = diff / prices[buy_from]['price'] * 100
                margins.append({'product': product, 'buy_from': buy_from,
                    'buy_price': prices[buy_from]['price'],
                    'sell_on': sell_on, 'sell_price': prices[sell_on]['price'],
                    'margin': round(diff, 2), 'margin_pct': round(margin_pct, 1)})
    return margins

margins = find_margins('wireless earbuds')
if margins:
    for m in margins:
        print(f'  Buy on {m["buy_from"]:10} ${m["buy_price"]:7.2f} -> Sell on {m["sell_on"]:10} ${m["sell_price"]:7.2f} | Margin: ${m["margin"]} ({m["margin_pct"]}%)')

import sqlite3

db = sqlite3.connect('price_tracker.db')
db.execute('''CREATE TABLE IF NOT EXISTS prices (
    product TEXT, platform TEXT, price REAL, checked_at TEXT
)''')
db.commit()

def track_price(product, platform, price):
    db.execute('INSERT INTO prices VALUES (?,?,?,?)',
        (product, platform, price, datetime.now().isoformat()))
    db.commit()

def price_change(product, platform):
    rows = db.execute(
        'SELECT price, checked_at FROM prices WHERE product=? AND platform=? ORDER BY checked_at DESC LIMIT 2',
        (product, platform)).fetchall()
    if len(rows) < 2: return None
    curr, prev = rows[0][0], rows[1][0]
    if curr == prev: return None
    change = ((curr - prev) / prev) * 100
    return {'product': product, 'platform': platform, 'prev': prev, 'curr': curr, 'change': round(change, 1)}

def daily_scan(products):
    alerts = []
    for product in products:
        results = multi_platform_search(product)
        for platform, items in results.items():
            if items and items[0]['price']:
                track_price(product, platform, items[0]['price'])
                change = price_change(product, platform)
                if change and abs(change['change']) > 5:
                    alerts.append(change)
    cost = len(products) * 3 * 0.005  # 3 platforms each
    print(f'Scanned {len(products)} products x 3 platforms. Cost: ${cost:.3f}')
    for a in alerts:
        print(f'  ALERT: {a["product"]} on {a["platform"]}: ${a["prev"]} -> ${a["curr"]} ({a["change"]:+.1f}%)')

daily_scan(['wireless earbuds', 'portable charger', 'phone case'])

def margin_report(products):
    print(f'\n=== Dropshipping Margin Report - {datetime.now().strftime("%Y-%m-%d")} ===')
    opportunities = []
    total_cost = 0
    for product in products:
        margins = find_margins(product)
        total_cost += 3 * 0.005  # 3 platform searches
        if margins:
            for m in margins:
                if m['margin_pct'] > 10:  # Only significant margins
                    opportunities.append(m)
    opportunities.sort(key=lambda x: x['margin_pct'], reverse=True)
    print(f'\nOpportunities (>{10}% margin):')
    for o in opportunities[:10]:
        print(f'  {o["product"]:25} | Buy: {o["buy_from"]:8} ${o["buy_price"]:6.2f} | Sell: {o["sell_on"]:8} ${o["sell_price"]:6.2f} | {o["margin_pct"]}%')
    print(f'\nTotal cost: ${total_cost:.3f}')
    print(f'Products scanned: {len(products)}')
    print(f'Opportunities found: {len(opportunities)}')

margin_report(['wireless earbuds', 'portable charger', 'phone case', 'laptop stand', 'ring light'])