Logging and analytics for carders: how to build a profit accounting and forecasting system

[Good Carder]

From a carder to carders. You've entered 1,000 cards, but you don't know which BINs give a 40% success rate and which only 5%. You don't remember which proxies burned out and which are still alive. You're wondering whether to buy a new batch from the vendor or replace it. Without a logging and analytics system, you're a casino player, not a carder. In 2026, the winner won't be the one who carding faster, but the one who analyzes smarter.

In this article, I'll show you how to build a system for tracking all attempts, how to accumulate data, how to automatically forecast profits, and how to make decisions based on metrics. You'll learn which fields are essential, how to organize storage (SQLite, PostgreSQL, Google Sheets), how to build dashboards (Grafana, Streamlit), and how to use ML to predict success. This isn't boring theory — it's a practical tool that will double your profits.

Part 1. Minimum set of log fields​

If you're not logging, you're blind. Here are the minimum fields you need to record for each attempt:

Field	Type	Example	Description
timestamp	ISO	2026-06-03 14:32:11	Time to try
bin	int	414720	The first 6 digits of the card
card_country	str	US	Country of issuer
proxy_ip	str	45.67.89.10	IP proxy
proxy_provider	str	webshare	Provider
antidetect_profile	str	dolphin_001	Profile ID
target_site	str	shopify.com/example	Target site
gateway	str	stripe	Gateway
amount	float	49.99	Check amount
status	str	success / fail	Result
decline_code	str	insufficient_funds	Cancellation code
response_time_ms	int	2350	Response time
notes	text	—	Comments

Keep this log in Google Sheets (for starters) or SQLite (for automation).

Part 2. Storage Organization: From Excel to Databases​

2.1. Google Sheets (to get started)​

Pros: Free, accessible from any device, API enabled. Cons: Slow for >10,000 rows.

How to connect Python to Google Sheets:

import gspread
from oauth2client.service_account import ServiceAccountCredentials

scope = ['https://spreadsheets.google.com/feeds']
creds = ServiceAccountCredentials.from_json_keyfile_name('creds.json', scope)
client = gspread.authorize(creds)
sheet = client.open('Carding Log').sheet1

def log_attempt(data):
row = [data['timestamp'], data['bin'], data['status'], ...]
sheet.append_row(row)

2.2. SQLite (lightweight database)​

Ideal for 10,000–100,000 records. No server required.

import sqlite3

conn = sqlite3.connect('carding.db')
c = conn.cursor()
c.execute('''CREATE TABLE IF NOT EXISTS attempts
             (timestamp TEXT, bin INTEGER, status TEXT, decline_code TEXT)''')
c.execute("INSERT INTO attempts VALUES (?,?,?,?)", (ts, bin, status, code))
conn.commit()

2.3. PostgreSQL (for scale)​

For 100,000+ records, use PostgreSQL (free on VPS).

Part 3. Automatic data collection from the checker and hit​

Don't enter data manually. Write scripts that automatically add records to the database.

Example after the checker:

def save_result(card, status, decline_code):
conn = sqlite3.connect('carding.db')
c = conn.cursor()
c.execute("INSERT INTO attempts VALUES (datetime('now'), ?, ?, ?)",
(card['bin'], status, decline_code))
conn.commit()

OpenBullet Integration: OpenBullet can save results to CSV. Write a script that uploads the CSV to the database once an hour.

Part 4. Analytics: What Metrics to Calculate​

4.1. Success by BIN​

SELECT bin, COUNT(*) as total, SUM(CASE WHEN status='success' THEN 1 ELSE 0 END) as success,
ROUND(100.0 * SUM(CASE WHEN status='success' THEN 1 ELSE 0 END) / COUNT(*), 2) as success_rate
FROM attempts
GROUP BY bin
ORDER BY success_rate DESC;

4.2. Success by proxy provider​

SELECT proxy_provider, COUNT(*), success_rate
FROM attempts
GROUP BY proxy_provider;

4.3. Gateway Success​

SELECT gateway, success_rate
FROM attempts
GROUP BY gateway;

4.4. Success by Time of Day​

SELECT strftime('%H', timestamp) as hour, success_rate
FROM attempts
GROUP BY hour;

Conclusion: Peak success often occurs between 2 and 5 am local time.

Part 5. Profit Forecasting Based on Logs​

With the log, you can predict how much profit the next batch of cards will bring.
Formula: Expected profit = (Number of cards × Success rate × Average check) – (Number of cards × Card cost) – Fixed costs.
Example: You buy 100 cards BIN 414720 with an expected success rate of 25% (from the logs). The average check is $500, the card costs $35, fixed costs $100.
Expected profit = 100 × 0.25 × 500 – 100 × 35 – 100 = 12,500 – 3,500 – 100 = $8,900

Part 6. Real-Time Dashboard (Streamlit)​

import streamlit as st
import sqlite3
import pandas as pd

conn = sqlite3.connect('carding.db')
df = pd.read_sql_query("SELECT * FROM attempts", conn)

st.title("Carding Analytics Dashboard")
st.metric("Total attempts", len(df))
st.metric("Success rate", f"{df['status'].value_counts(normalize=True).get('success', 0)*100:.1f}%")

# Graph by BIN
bin_success = df.groupby('bin')['status'].apply(lambda x: (x=='success').mean()).sort_values(ascending=False).head(10)
st.bar_chart(bin_success)

# Schedule by hours
df['hour'] = pd.to_datetime(df['timestamp']).dt.hour
hourly = df.groupby('hour')['status'].apply(lambda x: (x=='success').mean())
st.line_chart(hourly)

Run: streamlit run dashboard.py - and you will see analytics in the browser.

Part 7. Using ML for Forecasting​

If you have a lot of data (>10,000 records), you can train a simple model that predicts success based on BIN, proxy, gateway, and time of day.

from sklearn.ensemble import RandomForestClassifier
import pandas as pd

df = pd.read_sql_query("SELECT bin, proxy_provider, gateway, hour, status FROM attempts", conn)
df['success'] = (df['status'] == 'success').astype(int)
features = pd.get_dummies(df[['bin', 'proxy_provider', 'gateway', 'hour']])
X = features
y = df['success']

model = RandomForestClassifier()
model.fit(X, y)

# Prediction for a new card
new_card = pd.DataFrame([[414720, 'webshare', 'stripe', 3]], columns=['bin','proxy_provider','gateway','hour'])
new_card_encoded = pd.get_dummies(new_card).reindex(columns=features.columns, fill_value=0)
pred = model.predict_proba(new_card_encoded)[0][1]
print(f"Probability of success: {pred*100:.1f}%")

Part 8. Logging Errors​

Mistake 1. Not recording refusals. Record both successes and failures. Only then will you see the full picture.
Mistake 2. Ignoring timings. Response time helps distinguish between a BIN block and a card failure.
Mistake 3. Not clearing duplicates. The same card can be checked multiple times — keep this in mind.
Mistake 4. Storing logs in plaintext. Logs are evidence. Encrypt them (VeraCrypt, AES-256) and store them for no longer than 3-6 months.

Part 9. Analytics System Implementation Checklist​

• Create a table in Google Sheets or a database with the required fields.
• Automate adding records from checkers and hit scripts.
• Set up a regular collection (for example, once an hour).
• Calculate basic metrics (success by BIN, proxy, gateway).
• Build a dashboard in Streamlit or Google Data Studio.
• Use prediction to select cards.
• Once a week, analyze trends and adjust your strategy.
• Encrypt and periodically destroy old logs.

Summary​

Logging and analytics aren't bureaucracy, they're weapons. Without them, you're a casino player; with them, you're a carder managing profits. Start with Google Sheets and a simple script, then move on to SQLite and dashboards. Use ML for forecasting. And remember: data is your fuel. The more high-quality logs, the higher your ROI.

A quick one-line reminder:
"Logs are your compass: BIN, proxy, gateway, amount, rejection code, timing." SQLite stores thousands of records, Streamlit shows trends. ML predicts success. 10,000 attempts → an accurate model. Analyze, predict, scale.