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data_simulator.py

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+import random
+import json
+import uuid
+from datetime import datetime, timedelta
+
+# Define laundering patterns
+PATTERNS = ["Fan-Out", "Fan-In", "Cycle", "Bipartite", "Stack", "Scatter Gather", "Gather Scatter", "Random"]
+TRANSACTION_TYPES = ["Credit", "Debit", "Wire", "Card", "Cash"]
+TIME_PERIODS = ["Morning", "Afternoon", "Night"]
+COUNTRIES = ["USA", "UK", "Canada", "Germany", "India", "China", "UAE", "Australia"]
+
+# Store transaction history
+transaction_history = {}
+
+def create_transaction(aml_flag=None):
+    """Generate a simulated transaction"""
+    sender = f"A{random.randint(1, 1000)}"
+    receiver = f"A{random.randint(1, 1000)}"
+    amount = random.randint(50, 10000)
+    transaction_id = str(uuid.uuid4())
+
+    # Random timestamp (past 6 months)
+    timestamp = (datetime.now() - timedelta(days=random.randint(0, 180))).isoformat()
+
+    currency = "USD"
+    transaction_type = random.choice(TRANSACTION_TYPES)
+    sender_ip = random.choice(COUNTRIES)
+    receiver_ip = random.choice(COUNTRIES)
+
+    # Risk Score & AML Flag (Ensuring 50% AML and 50% Normal Transactions)
+    if aml_flag is None:
+        risk_score = round(random.uniform(0, 1), 2)
+        aml_flag = 1 if risk_score > 0.7 else 0
+    else:
+        risk_score = round(random.uniform(0.8, 1), 2) if aml_flag == 1 else round(random.uniform(0, 0.3), 2)
+
+    # Track transaction history
+    sender_history = transaction_history.get(sender, [])
+    sender_history.append((amount, timestamp))
+    transaction_history[sender] = sender_history[-30:]  # Store last 30 transactions
+
+    # Compute Behavioral & Network Features
+    last_24h_tx = [tx for tx in sender_history if datetime.fromisoformat(tx[1]) > datetime.now() - timedelta(days=1)]
+    last_7d_tx = [tx for tx in sender_history if datetime.fromisoformat(tx[1]) > datetime.now() - timedelta(days=7)]
+    last_30d_tx = sender_history
+
+    daily_tx_count = len(last_24h_tx)
+    weekly_tx_count = len(last_7d_tx)
+    monthly_tx_count = len(last_30d_tx)
+    total_tx_volume = sum(tx[0] for tx in last_30d_tx)
+
+    avg_tx_amount = total_tx_volume / len(last_30d_tx) if last_30d_tx else amount
+    tx_amount_deviation = abs(amount - avg_tx_amount)
+
+    new_account_flag = 1 if len(sender_history) < 3 else 0
+    dormant_to_active_flag = 1 if random.random() < 0.05 else 0  # 5% chance
+
+    # Transaction Metadata
+    weekend_flag = 1 if datetime.fromisoformat(timestamp).weekday() >= 5 else 0
+    repeated_amount_flag = 1 if sum(1 for tx in sender_history if tx[0] == amount) > 1 else 0
+    time_of_day = random.choice(TIME_PERIODS)
+
+    return {
+        "TransactionID": transaction_id,
+        "Timestamp": timestamp,
+        "SenderAccount": sender,
+        "ReceiverAccount": receiver,
+        "Amount": amount,
+        "Currency": currency,
+        "TransactionType": transaction_type,
+        "AML_Flag": aml_flag,
+        "RiskScore": risk_score,
+        "DailyTransactionCount": daily_tx_count,
+        "WeeklyTransactionCount": weekly_tx_count,
+        "MonthlyTransactionCount": monthly_tx_count,
+        "TotalTransactionVolume": total_tx_volume,
+        "AverageTransactionAmount": avg_tx_amount,
+        "TransactionAmountDeviation": tx_amount_deviation,
+        "NewAccountFlag": new_account_flag,
+        "DormantToActiveFlag": dormant_to_active_flag,
+        "SenderIPLocation": sender_ip,
+        "ReceiverIPLocation": receiver_ip,
+        "WeekendFlag": weekend_flag,
+        "RepeatedAmountFlag": repeated_amount_flag,
+        "TimeOfDay": time_of_day
+    }
+
+# Generate 50% AML and 50% Normal Transactions
+aml_transactions = [create_transaction(aml_flag=1) for _ in range(5000)]  # 50% AML
+normal_transactions = [create_transaction(aml_flag=0) for _ in range(5000)]  # 50% Normal
+transactions = aml_transactions + normal_transactions
+random.shuffle(transactions)
+
+# Save to JSON file
+with open("simulated_transactions.json", "w") as f:
+    json.dump(transactions, f, indent=4)
+
+print("✅ Balanced simulated dataset created: simulated_transactions.json")