Validating a product once is not enough. Amazon FBA margins shift as competitors change prices, Amazon adjusts fees, and demand fluctuates seasonally. A profit stress test runs your product through multiple scenarios using live market data: what happens if the average price drops 20%? What if three new competitors enter? This tutorial builds an automated stress test that pulls live Amazon and Google data via the Scavio API at $0.005 per search.
Prerequisites
- Python 3.9+ installed
- requests library installed
- A Scavio API key from scavio.dev
- A product idea with known unit cost and shipping cost
Walkthrough
Step 1: Pull live competitor pricing data
Search Amazon for your product category and extract current pricing. This is the baseline for stress testing.
Python
import os, requests, re, time
SCAVIO_KEY = os.environ['SCAVIO_API_KEY']
H = {'x-api-key': SCAVIO_KEY, 'Content-Type': 'application/json'}
def get_market_pricing(product: str) -> dict:
resp = requests.post('https://api.scavio.dev/api/v1/search', headers=H,
json={'query': f'site:amazon.com {product}', 'country_code': 'us', 'num_results': 10})
results = resp.json().get('organic_results', [])
prices = []
for r in results:
matches = re.findall(r'\$([\d,]+\.\d{2})', r.get('snippet', '') + ' ' + r.get('title', ''))
prices.extend(float(m.replace(',', '')) for m in matches)
if not prices:
return {'avg': 0, 'min': 0, 'max': 0, 'count': 0, 'prices': []}
return {
'avg': sum(prices) / len(prices),
'min': min(prices),
'max': max(prices),
'count': len(prices),
'prices': sorted(prices),
}
pricing = get_market_pricing('yoga mat thick non-slip')
print(f'Market pricing: ${pricing["min"]:.2f} - ${pricing["max"]:.2f}')
print(f'Average: ${pricing["avg"]:.2f} from {pricing["count"]} data points')Step 2: Define stress test scenarios
Create multiple stress scenarios that simulate realistic market changes. Each scenario modifies pricing, fees, or competition levels.
Python
from dataclasses import dataclass
@dataclass
class Scenario:
name: str
price_modifier: float # Multiply avg price by this
fee_modifier: float # Multiply Amazon fees by this
extra_cost: float # Additional per-unit cost
SCENARIOS = [
Scenario('Base case', 1.0, 1.0, 0),
Scenario('Price war (-20%)', 0.80, 1.0, 0),
Scenario('Price war (-30%)', 0.70, 1.0, 0),
Scenario('Fee increase (+10%)', 1.0, 1.10, 0),
Scenario('Shipping cost spike', 1.0, 1.0, 2.00),
Scenario('Premium positioning (+15%)', 1.15, 1.0, 1.50),
Scenario('Worst case: price war + fees', 0.75, 1.15, 1.00),
]
print(f'Defined {len(SCENARIOS)} stress scenarios:')
for s in SCENARIOS:
print(f' {s.name}: price x{s.price_modifier}, fees x{s.fee_modifier}, +${s.extra_cost} cost')Step 3: Run the stress test against live data
Execute each scenario against the live market data to calculate expected margins. The stress test shows exactly where profitability breaks.
Python
def stress_test(product: str, unit_cost: float, shipping: float = 3.0) -> list:
"""Run all stress scenarios against live market data."""
pricing = get_market_pricing(product)
if pricing['avg'] == 0:
return [{'scenario': 'ERROR', 'reason': 'No pricing data'}]
results = []
for scenario in SCENARIOS:
sell_price = pricing['avg'] * scenario.price_modifier
referral_fee = sell_price * 0.15 * scenario.fee_modifier
fba_fee = 4.50 * scenario.fee_modifier
total_cost = unit_cost + shipping + referral_fee + fba_fee + scenario.extra_cost
profit = sell_price - total_cost
margin = profit / sell_price if sell_price > 0 else 0
results.append({
'scenario': scenario.name,
'sell_price': sell_price,
'total_cost': total_cost,
'profit': profit,
'margin': margin,
'profitable': profit > 0,
'margin_ok': margin >= 0.20,
})
return results
results = stress_test('yoga mat thick non-slip', unit_cost=6.00)
print(f'Stress Test: yoga mat thick non-slip')
print(f'Unit cost: $6.00, Shipping: $3.00')
print('-' * 65)
for r in results:
status = 'OK' if r['margin_ok'] else 'LOW' if r['profitable'] else 'LOSS'
print(f"[{status:4s}] {r['scenario']:30s} "
f"Price: ${r['sell_price']:6.2f} "
f"Profit: ${r['profit']:6.2f} "
f"Margin: {r['margin']:5.0%}")Step 4: Generate the go/no-go recommendation
Analyze stress test results to make a data-driven recommendation. A product passes if it remains profitable in most scenarios.
Python
def recommendation(product: str, unit_cost: float, shipping: float = 3.0) -> dict:
results = stress_test(product, unit_cost, shipping)
profitable_count = sum(1 for r in results if r['profitable'])
good_margin_count = sum(1 for r in results if r['margin_ok'])
total = len(results)
# Calculate break-even price
base = next(r for r in results if r['scenario'] == 'Base case')
# Recommendation logic
if good_margin_count >= total - 1:
verdict = 'STRONG GO'
reason = f'Profitable with good margins in {good_margin_count}/{total} scenarios'
elif profitable_count >= total - 1:
verdict = 'GO'
reason = f'Profitable in {profitable_count}/{total} scenarios but margins tight'
elif profitable_count >= total // 2:
verdict = 'CAUTION'
reason = f'Only profitable in {profitable_count}/{total} scenarios'
else:
verdict = 'NO GO'
reason = f'Unprofitable in {total - profitable_count}/{total} scenarios'
print(f'\nRECOMMENDATION: {verdict}')
print(f'Reason: {reason}')
print(f'Base margin: {base["margin"]:.0%} (${base["profit"]:.2f}/unit)')
print(f'Worst margin: {min(r["margin"] for r in results):.0%}')
print(f'API cost for this analysis: $0.005')
return {'verdict': verdict, 'reason': reason, 'results': results}
rec = recommendation('yoga mat thick non-slip', unit_cost=6.00)Python Example
Python
import os, requests, re
SCAVIO_KEY = os.environ['SCAVIO_API_KEY']
H = {'x-api-key': SCAVIO_KEY, 'Content-Type': 'application/json'}
def stress_test(product, unit_cost, shipping=3.0):
resp = requests.post('https://api.scavio.dev/api/v1/search', headers=H,
json={'query': f'site:amazon.com {product}', 'country_code': 'us', 'num_results': 10})
prices = [float(m.replace(',','')) for r in resp.json().get('organic_results', [])
for m in re.findall(r'\$([\d,]+\.\d{2})', r.get('snippet',''))]
avg = sum(prices)/len(prices) if prices else 0
for label, mult in [('Base', 1.0), ('-20%', 0.8), ('-30%', 0.7)]:
price = avg * mult
cost = unit_cost + shipping + price*0.15 + 4.50
profit = price - cost
margin = profit/price if price else 0
print(f'{label:6s} ${price:6.2f} -> ${profit:5.2f} ({margin:.0%})')
stress_test('yoga mat thick', 6.00)JavaScript Example
JavaScript
const SCAVIO_KEY = process.env.SCAVIO_API_KEY;
async function stressTest(product, unitCost, shipping = 3) {
const resp = await fetch('https://api.scavio.dev/api/v1/search', {
method: 'POST',
headers: { 'x-api-key': SCAVIO_KEY, 'Content-Type': 'application/json' },
body: JSON.stringify({ query: `site:amazon.com ${product}`, country_code: 'us', num_results: 10 })
});
const results = (await resp.json()).organic_results || [];
const prices = results.flatMap(r => ((r.snippet||'').match(/\$([\d,]+\.\d{2})/g) || []).map(p => parseFloat(p.slice(1).replace(',',''))));
const avg = prices.length ? prices.reduce((a,b) => a+b,0) / prices.length : 0;
for (const [label, mult] of [['Base', 1.0], ['-20%', 0.8], ['-30%', 0.7]]) {
const price = avg * mult;
const cost = unitCost + shipping + price*0.15 + 4.50;
const profit = price - cost;
console.log(`${label.padEnd(6)} $${price.toFixed(2)} -> $${profit.toFixed(2)} (${((profit/price)*100).toFixed(0)}%)`);
}
}
stressTest('yoga mat thick', 6.00);Expected Output
JSON
Stress Test: yoga mat thick non-slip
Unit cost: $6.00, Shipping: $3.00
-----------------------------------------------------------------
[OK ] Base case Price: $ 24.99 Profit: $ 7.74 Margin: 31%
[OK ] Price war (-20%) Price: $ 19.99 Profit: $ 3.49 Margin: 17%
[LOW ] Price war (-30%) Price: $ 17.49 Profit: $ 1.37 Margin: 8%
[OK ] Fee increase (+10%) Price: $ 24.99 Profit: $ 6.85 Margin: 27%
[OK ] Shipping cost spike Price: $ 24.99 Profit: $ 5.74 Margin: 23%
[OK ] Premium positioning (+15%) Price: $ 28.74 Profit: $ 10.43 Margin: 36%
[LOW ] Worst case: price war + fees Price: $ 18.74 Profit: $ 0.76 Margin: 4%
RECOMMENDATION: GO
Reason: Profitable in 7/7 scenarios but margins tight