2. Temporal Analysis Framework

LSTM Implementation Architecture

The Temporal Analysis Framework implements a sophisticated deep learning architecture utilizing Long Short-Term Memory networks for time series prediction. The system employs a multi-layer approach with attention mechanisms for enhanced feature extraction.

Neural Network Configuration

LSTM_CONFIG = {
    'hidden_layers': 3,
    'hidden_size': 128,
    'dropout': 0.2,
    'sequence_length': 24,  # hours
    'feature_count': 15,
    'batch_size': 32,
    'learning_rate': 0.001
}

Volatility Prediction Mechanisms

The volatility calculator implements advanced statistical methods for market volatility estimation. The system utilizes a hybrid approach combining both parametric and non-parametric methods:

class VolatilityPredictor:
    def __init__(
        self,
        sequence_length: int = 100,
        prediction_window: int = 24,
        confidence_threshold: float = 0.7
    ):
        self.model = LSTMModel(
            input_size=15,
            hidden_size=64,
            num_layers=2,
            output_size=1
        )
        self.pattern_windows = defaultdict(
            lambda: deque(maxlen=1000)
        )

Feature Engineering Pipeline

The system implements sophisticated feature extraction mechanisms:

async def _calculate_market_impact(
    self,
    pattern: AccumulationPattern,
    market_cap: float
) -> Dict:
    """Calculate market impact metrics.
    
    Args:
        pattern: Detected accumulation pattern
        market_cap: Current market capitalization
        
    Returns:
        Dict containing impact metrics
    """
    return {
        "price_impact": pattern.price_impact,
        "market_share": pattern.market_share,
        "supply_share": pattern.current_price / total_supply,
        "cap_impact": (pattern.total_volume / market_cap),
        "manipulation_risk": self._calculate_manipulation_risk(
            pattern,
            market_cap
        )
    }

Flash Crash Detection Algorithms

The flash crash detector employs real-time monitoring with multi-faceted analysis:

Detection Parameters

FLASH_CRASH_THRESHOLDS = {
    'price_drop': 0.15,  # 15% drop
    'time_window': 300,  # 5 minutes
    'volume_spike': 2.5,  # 2.5x average volume
    'liquidity_impact': 0.3
}

Performance Metrics

The system maintains strict performance characteristics:

Processing Latency:
    Real-time Analysis: <50ms
    Pattern Recognition: <100ms
    Alert Generation: <10ms

Resource Utilization:
    GPU Memory: 4GB recommended
    CUDA Cores: 2000+ for optimal performance
    Batch Processing: 64 samples/batch

Model Performance:
    RMSE: <0.08 for 1-hour predictions
    MAE: <0.05 for volume predictions
    R² Score: >0.85 for price predictions

Monitoring Integration

The framework exposes comprehensive metrics through Prometheus endpoints:

Monitoring Metrics:
    - prediction_accuracy_rolling_window
    - model_inference_time_seconds
    - prediction_confidence_distribution
    - gpu_memory_utilization
    - batch_processing_duration

Alert Configurations:
    - AccuracyDegradation: <0.8 accuracy
    - HighLatency: >100ms inference time
    - ResourceSaturation: >90% GPU utilization
    - PredictionDivergence: >20% error rate

Training Pipeline

The system implements automated model retraining with the following characteristics:

TRAINING_CONFIG = {
    'epochs': 100,
    'batch_size': 32,
    'validation_split': 0.2,
    'early_stopping_patience': 10,
    'learning_rate_scheduler': {
        'factor': 0.5,
        'patience': 5,
        'min_lr': 1e-6
    }
}

Model Versioning

Version Control:
    Strategy: Time-based versioning
    Retention: Rolling 5 versions
    Fallback: Automatic to last stable
    Validation: Cross-epoch performance
    
Deployment:
    Method: Blue-Green deployment
    Warmup: 1000 inference cycles
    Rollback: Automatic on accuracy drop
    Monitoring: Continuous accuracy tracking

The framework maintains comprehensive model versioning and deployment strategies, ensuring continuous service availability during updates and maintaining strict performance characteristics throughout the model lifecycle.