Analytical Methods

Statistical Analysis

Time Series Analysis

Price Action Analysis

• Moving average convergence/divergence

• Volatility clustering detection

• Trend strength measurement

• Support/resistance identification

Volume Analysis Framework

import pandas as pd
import numpy as np

def analyze_volume_patterns(volume_data):
    """
    Comprehensive volume analysis for manipulation detection
    """
    # Calculate volume moving averages
    volume_ma_7 = volume_data.rolling(window=7).mean()
    volume_ma_30 = volume_data.rolling(window=30).mean()
    
    # Detect volume spikes
    volume_spikes = volume_data > (volume_ma_30 * 3)
    
    # Calculate volume distribution
    volume_distribution = {
        'median': volume_data.median(),
        'std': volume_data.std(),
        'skewness': volume_data.skew(),
        'kurtosis': volume_data.kurtosis()
    }
    
    return {
        'spikes': volume_spikes.sum(),
        'distribution': volume_distribution,
        'manipulation_score': calculate_volume_manipulation_score(volume_data)
    }

Correlation Analysis

Cross-Asset Correlation

• Correlation with major cryptocurrencies

• Sector-specific correlation patterns

• Market cap correlation analysis

• Liquidity correlation assessment

Pattern Recognition

Machine Learning Models

Behavioral Pattern Detection

• Transaction pattern classification

• Wallet behavior clustering

• Social sentiment pattern recognition

• Market cycle identification

Feature Engineering

def extract_features(project_data):
    """
    Extract features for ML model input
    """
    features = {
        # Technical features
        'price_volatility': calculate_volatility(project_data.prices),
        'volume_trend': calculate_volume_trend(project_data.volumes),
        'liquidity_ratio': project_data.liquidity / project_data.market_cap,
        
        # Social features
        'sentiment_score': project_data.social_sentiment,
        'engagement_rate': project_data.social_engagement,
        'viral_coefficient': project_data.viral_metrics,
        
        # On-chain features
        'holder_concentration': calculate_concentration(project_data.holders),
        'transaction_diversity': analyze_tx_patterns(project_data.transactions),
        'contract_complexity': assess_contract_complexity(project_data.contract)
    }
    return features

Pattern Categories

Legitimate Growth Patterns:

• Organic user adoption curves

• Sustainable development activity

• Natural community growth

• Balanced market participation

Suspicious Patterns:

• Artificial pump sequences

• Coordinated social campaigns

• Manipulated trading volumes

• Sudden liquidity withdrawals

Anomaly Detection

Detection Methodologies

Statistical Anomaly Detection

• Z-score based outlier identification

• Isolation forest algorithms

• Local outlier factor analysis

• Time-series anomaly detection

Behavioral Anomaly Detection

from sklearn.ensemble import IsolationForest
import numpy as np

class BehaviorAnomalyDetector:
    def __init__(self):
        self.model = IsolationForest(contamination=0.1, random_state=42)
        
    def detect_anomalies(self, behavior_features):
        """
        Detect anomalous behavior patterns
        """
        # Normalize features
        normalized_features = self.normalize_features(behavior_features)
        
        # Fit model and predict anomalies
        anomaly_scores = self.model.fit_predict(normalized_features)
        
        # Calculate anomaly confidence
        confidence_scores = self.model.score_samples(normalized_features)
        
        return {
            'anomalies': anomaly_scores == -1,
            'confidence': confidence_scores,
            'anomaly_count': np.sum(anomaly_scores == -1)
        }

Real-time Monitoring

Alert Triggers:

• Sudden behavior changes (>3 standard deviations)

• Unusual transaction patterns

• Abnormal social activity spikes

• Unexpected liquidity movements

Predictive Modeling

Model Architecture

Ensemble Prediction Framework

• Gradient boosting models for risk prediction

• Neural networks for pattern recognition

• Time series forecasting for trend prediction

• Ensemble methods for improved accuracy

Model Training Pipeline

class KaizenPredictiveModel:
    def __init__(self):
        self.models = {
            'risk_classifier': GradientBoostingClassifier(),
            'price_predictor': LSTMModel(),
            'sentiment_analyzer': TransformerModel(),
            'anomaly_detector': AutoEncoder()
        }
    
    def train_ensemble(self, training_data):
        """
        Train ensemble of models for comprehensive prediction
        """
        for model_name, model in self.models.items():
            # Prepare model-specific features
            features = self.prepare_features(training_data, model_name)
            
            # Train model
            model.fit(features['X'], features['y'])
            
            # Validate performance
            self.validate_model(model, features['X_val'], features['y_val'])
    
    def predict_risk(self, project_features):
        """
        Generate comprehensive risk prediction
        """
        predictions = {}
        for model_name, model in self.models.items():
            pred = model.predict(project_features[model_name])
            predictions[model_name] = pred
        
        # Ensemble prediction
        final_prediction = self.ensemble_predict(predictions)
        return final_prediction

Prediction Categories

Short-term Predictions (1-7 days):

• Price movement direction

• Volume trend changes

• Social sentiment shifts

• Immediate risk events

Medium-term Predictions (1-4 weeks):

• Project sustainability assessment

• Community growth projections

• Development milestone predictions

• Market position forecasts

Long-term Predictions (1-6 months):

• Project viability assessment

• Ecosystem integration potential

• Competition analysis

• Technology adoption forecasts

Performance Metrics

Model Performance Evaluation

Classification Metrics

• Precision, Recall, F1-Score for risk classification

• ROC-AUC for binary risk prediction

• Matthews Correlation Coefficient for balanced assessment

• Confusion matrix analysis for error patterns

Regression Metrics

def evaluate_prediction_performance(y_true, y_pred):
    """
    Comprehensive performance evaluation for predictions
    """
    metrics = {
        'mse': mean_squared_error(y_true, y_pred),
        'mae': mean_absolute_error(y_true, y_pred),
        'r2': r2_score(y_true, y_pred),
        'mape': mean_absolute_percentage_error(y_true, y_pred)
    }
    
    # Calculate custom metrics
    metrics['directional_accuracy'] = calculate_directional_accuracy(y_true, y_pred)
    metrics['risk_adjusted_accuracy'] = calculate_risk_adjusted_accuracy(y_true, y_pred)
    
    return metrics

Business Impact Metrics

User Protection Metrics:

• False positive rate (legitimate projects flagged as risky)

• False negative rate (risky projects not detected)

• Early warning effectiveness (detection before major events)

• User satisfaction with risk assessments

Platform Performance:

• Scoring accuracy over time

• Model drift detection

• Response time for new projects

• Coverage of blockchain ecosystems

Continuous Improvement Framework

Model Monitoring:

• Real-time performance tracking

• Data drift detection

• Concept drift identification

• Automated retraining triggers

Feedback Integration:

• User feedback incorporation

• Expert review integration

• Market outcome validation

• Community input processing