Atlas Agent Documentation

Introduction

Atlas Agent Agents are advanced AI-driven systems designed to analyze tokens listed on Pump.fun. They assess the potential of these tokens and identify red flags that may indicate scams, high risk, or other concerns. Atlas Agent leverages state-of-the-art machine learning and blockchain analytics to ensure accurate and actionable insights.

Key Features

    1. Real-Time Token Analysis

      • Continuous monitoring of Pump.fun token listings.

      • Instant assessment of token metadata, transaction patterns, and community metrics.

    2. Red Flag Detection

      • Identifies warning signs such as abnormal trading volume, high slippage, or anomalous wallet activity.

      • Alerts users to potential rug pulls, honeypots, and other malicious activities.

    3. Predictive Insights

      • Uses predictive models to gauge the likelihood of price pumps or crashes.

      • Assigns a risk score to each token based on historical data and live metrics.

    4. Customizable Alerts

      • Users can configure alerts for specific criteria, such as price thresholds, volume changes, or risk levels.

    5. Comprehensive Reporting

      • Generates detailed reports for each token, including key metrics and AI-driven recommendations.

How Atlas Agent Works

Data Collection

Atlas Agent collects data from:

  • Pump.fun token metadata

  • Blockchain explorers

  • Social media and community platforms

  • Historical price and volume data

Analysis Process

  1. Feature Extraction

    • Extracts key features such as transaction frequency, wallet distribution, and price volatility.

  2. Machine Learning Models

    • Applies AI models trained on historical data to predict token behavior and detect anomalies.

  3. Risk Scoring

    • Aggregates insights into a single risk score, with detailed explanations for transparency.

  4. Report Generation

    • Compiles findings into a user-friendly report format with actionable recommendations.

Example Use Case

Scenario

A user wants to analyze a newly listed token on Pump.fun: $TOKENXYZ.

CRONEX AI Output

  • Risk Score: 85 (High Risk)

  • Red Flags:

    • 80% of tokens held by a single wallet.

    • Suspiciously high transaction fees.

    • No verifiable social media presence.

  • Recommendations: Avoid investing until token distribution improves and transparency increases.

Code Examples

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}")

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")

API Integration

CRONEX AI provides an API for developers to integrate its functionalities into their own platforms.

Endpoints

GET /analyze

  • Input: Token address

  • Output: Risk score, red flags, recommendations

  • POST /configure-alerts

    • Input: Alert criteria

    • Output: Confirmation of alert configuration

Example API Call

curl -X GET "https://api.cronex.ai/analyze?token_address=0x123" \
-H "Authorization: Bearer YOUR_API_KEY"

Conclusion

Atlas Agent Agents provide invaluable insights for token analysis on Pump.fun. By leveraging advanced AI models and blockchain analytics, they empower users to make informed decisions and avoid high-risk investments.

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