Code Examples

Red Flag Detection

from datetime import datetime

# Example transaction data
transactions = [
    {'amount': 500, 'wallet': '0xABC', 'timestamp': '2024-12-01T12:00:00Z'},
    {'amount': 500, 'wallet': '0xABC', 'timestamp': '2024-12-01T12:01:00Z'},
    {'amount': 1000, 'wallet': '0xDEF', 'timestamp': '2024-12-01T12:02:00Z'},
]

def detect_red_flags(transactions):
    red_flags = []

    # Check for rapid transactions from a single wallet
    wallet_activity = {}
    for tx in transactions:
        wallet = tx['wallet']
        wallet_activity[wallet] = wallet_activity.get(wallet, 0) + 1

    for wallet, count in wallet_activity.items():
        if count > 2:
            red_flags.append(f"Wallet {wallet} has {count} rapid transactions.")

    return red_flags

flags = detect_red_flags(transactions)
print(flags)

Predictive Model (Simplified)

from sklearn.linear_model import LogisticRegression

# Training data (simplified example)
features = [
    [0.1, 0.2, 0.7],  # Low risk
    [0.8, 0.7, 0.9],  # High risk
    [0.3, 0.4, 0.5],  # Medium risk
]
labels = [0, 1, 0]  # 0 = Low Risk, 1 = High Risk

# Train model
model = LogisticRegression()
model.fit(features, labels)

# Predict risk
new_token = [[0.6, 0.5, 0.8]]  # Example new token
prediction = model.predict(new_token)
print("High Risk" if prediction[0] == 1 else "Low/Medium Risk")

Risk Scoring Algorithm

import numpy as np

def calculate_risk_score(token_data):
    # Example weights for different factors
    weights = {
        'holder_concentration': 0.4,
        'transaction_anomalies': 0.3,
        'social_presence': 0.2,
        'historical_performance': 0.1
    }

    # Normalize factors to a 0-1 scale
    normalized_factors = {
        key: value / max_value
        for key, (value, max_value) in token_data.items()
    }

    # Calculate weighted risk score
    risk_score = sum(
        weights[key] * normalized_factors[key]
        for key in weights.keys()
    ) * 100  # Scale to 0-100

    return round(risk_score, 2)

# Example token data
sample_token_data = {
    'holder_concentration': (80, 100),
    'transaction_anomalies': (70, 100),
    'social_presence': (10, 100),
    'historical_performance': (50, 100)
}

risk_score = calculate_risk_score(sample_token_data)
print(f"Risk Score: {risk_score}")