//! `UnifiedFeatureExtractor` - Single Source of Truth for Feature Engineering
//!
//! This module provides the CRITICAL `UnifiedFeatureExtractor` that ensures zero
//! training/serving skew by extracting features identically across all stages:
//! - Training: Historical data processing
//! - Backtesting: Strategy validation
//! - Live Trading: Real-time inference
//!
//! KEY PRINCIPLES:
//! 1. Single source of truth for all feature calculations
//! 2. Identical processing for Databento market data and Benzinga news
//! 3. Model-specific feature sets with unified base features
//! 4. High-performance implementation with SIMD optimizations
//! 5. Type-safe feature engineering with compile-time guarantees

use std::time::{Duration, Instant};

use chrono::{DateTime, Datelike, Timelike, Utc};
use serde::{Deserialize, Serialize};
use thiserror::Error;
use tracing::{debug, error};

use common::types::MarketTick;
use common::{Price, Quantity, QuoteEvent, Symbol, TradeEvent, Volume};

/// Feature extraction errors
#[derive(Error, Debug)]
/// FeatureError
/// 
/// Auto-generated documentation placeholder - enhance with specifics
pub enum FeatureError {
    #[error("Insufficient data: {feature} needs {required} points, got {available}")]
    InsufficientData {
        feature: String,
        required: usize,
        available: usize,
    },

    #[error("Invalid parameters for {feature}: {reason}")]
    InvalidParameters { feature: String, reason: String },

    #[error("Mathematical error in {feature}: {reason}")]
    MathematicalError { feature: String, reason: String },

    #[error("Data alignment error: {reason}")]
    AlignmentError { reason: String },

    #[error("Missing required data: {data_type}")]
    MissingData { data_type: String },
}

/// Databento market data features
#[derive(Debug, Clone, Serialize, Deserialize)]
/// DatabentoBuFeatures
/// 
/// Auto-generated documentation placeholder - enhance with specifics
pub struct DatabentoBuFeatures {
    // Order Book Microstructure (MBO/MBP data)
    /// Bid Ask Spread Bps
    pub bid_ask_spread_bps: f64,
    /// Order Book Imbalance
    pub order_book_imbalance: f64, // -1 (ask heavy) to +1 (bid heavy)
    /// Depth Weighted Mid
    pub depth_weighted_mid: Price,
    /// Effective Spread Bps
    pub effective_spread_bps: f64,
    /// Price Impact Bps
    pub price_impact_bps: f64,

    // Level 2/3 Order Book Features
    /// L2 Slope
    pub l2_slope: f64,              // Order book slope regression
    /// L2 Curvature
    pub l2_curvature: f64,          // Order book curvature
    /// L3 Order Intensity
    pub l3_order_intensity: f64,    // Orders per second
    /// L3 Cancellation Ratio
    pub l3_cancellation_ratio: f64, // Cancel/Submit ratio

    // Trade Classification (Lee-Ready, Tick Rule)
    /// Trade Sign
    pub trade_sign: i8,          // -1 (sell), 0 (unknown), +1 (buy)
    /// Trade Size Category
    pub trade_size_category: u8, // 1 (small), 2 (medium), 3 (large), 4 (block)
    /// Trade Urgency
    pub trade_urgency: f64,      // Aggressive vs passive flow

    // Microstructure Noise and Information
    /// Realized Spread Bps
    pub realized_spread_bps: f64,
    /// Information Share
    pub information_share: f64,    // Price discovery contribution
    /// Microstructure Noise
    pub microstructure_noise: f64, // Bid-ask bounce component

    // High-Frequency Patterns
    /// Tick Direction Streak
    pub tick_direction_streak: i8,    // Consecutive upticks/downticks
    /// Quote Update Frequency
    pub quote_update_frequency: f64,  // Updates per second
    /// Order Arrival Intensity
    pub order_arrival_intensity: f64, // Poisson lambda estimate
}

/// Benzinga news sentiment features
#[derive(Debug, Clone, Serialize, Deserialize)]
/// BenzingaNewsFeatures
/// 
/// Auto-generated documentation placeholder - enhance with specifics
pub struct BenzingaNewsFeatures {
    // Real-time News Sentiment
    /// Sentiment Score
    pub sentiment_score: f64, // -1.0 (very negative) to +1.0 (very positive)
    /// Sentiment Confidence
    pub sentiment_confidence: f64, // 0.0 to 1.0 confidence in sentiment
    /// News Velocity
    pub news_velocity: f64,   // News articles per hour
    /// Breaking News Flag
    pub breaking_news_flag: bool, // Breaking news indicator

    // Weighted Sentiment (by source credibility)
    /// Weighted Sentiment 1H
    pub weighted_sentiment_1h: f64,
    /// Weighted Sentiment 4H
    pub weighted_sentiment_4h: f64,
    /// Weighted Sentiment 24H
    pub weighted_sentiment_24h: f64,

    // News Categories and Impact
    /// Earnings Related
    pub earnings_related: bool,
    /// Analyst Rating
    pub analyst_rating: Option<f64>, // Analyst rating change
    /// Unusual Options Activity
    pub unusual_options_activity: bool,
    /// Sec Filing Type
    pub sec_filing_type: Option<String>,

    // Sentiment Momentum
    /// Sentiment Acceleration
    pub sentiment_acceleration: f64, // Rate of sentiment change
    /// Sentiment Divergence
    pub sentiment_divergence: f64,   // News vs price action divergence
    /// Contrarian Signal
    pub contrarian_signal: f64,      // Contrarian opportunity score

    // Source Diversity and Volume
    /// Source Count
    pub source_count: u32,         // Number of distinct sources
    /// Mention Volume
    pub mention_volume: u32,       // Total mentions/references
    /// Social Amplification
    pub social_amplification: f64, // Social media pickup ratio
}

/// Base market features (common across all models)
#[derive(Debug, Clone, Serialize, Deserialize)]
/// BaseMarketFeatures
/// 
/// Auto-generated documentation placeholder - enhance with specifics
pub struct BaseMarketFeatures {
    // Price Action
    /// Price
    pub price: Price,
    /// Returns 1M
    pub returns_1m: f64,
    /// Returns 5M
    pub returns_5m: f64,
    /// Returns 15M
    pub returns_15m: f64,
    /// Returns 1H
    pub returns_1h: f64,
    /// Volatility 1H
    pub volatility_1h: f64,
    /// Volatility 4H
    pub volatility_4h: f64,

    // Volume Profile
    /// Volume
    pub volume: Volume,
    /// Volume Ratio 1H
    pub volume_ratio_1h: f64,  // Current vs 1h average
    /// Vwap Deviation
    pub vwap_deviation: f64,   // Distance from VWAP
    /// Volume Imbalance
    pub volume_imbalance: f64, // Buy vs sell volume

    // Technical Indicators
    /// Rsi 14
    pub rsi_14: f64,
    /// Macd Signal
    pub macd_signal: f64,
    /// Bollinger Position
    pub bollinger_position: f64, // Position within bands
    /// Momentum Score
    pub momentum_score: f64,

    // Market Context
    /// Time Of Day
    pub time_of_day: f64,   // Normalized 0-1
    /// Day Of Week
    pub day_of_week: u8,    // 1-7
    /// Market Session
    pub market_session: u8, // 1 (pre), 2 (regular), 3 (after)
    /// Is Opex
    pub is_opex: bool,      // Options expiration
    /// Is Earnings Week
    pub is_earnings_week: bool,
}

/// TLOB (Temporal Limit Order Book) specific features
#[derive(Debug, Clone, Serialize, Deserialize)]
/// TLOBFeatures
/// 
/// Auto-generated documentation placeholder - enhance with specifics
pub struct TLOBFeatures {
    /// Base
    pub base: BaseMarketFeatures,
    /// Databento
    pub databento: DatabentoBuFeatures,
    /// Benzinga
    pub benzinga: BenzingaNewsFeatures,

    // TLOB-specific order book sequences
    /// Order Book Sequence
    pub order_book_sequence: Vec<f64>, // 50-point sequence
    /// Trade Flow Sequence
    pub trade_flow_sequence: Vec<f64>, // Trade intensity sequence
    /// Spread Sequence
    pub spread_sequence: Vec<f64>,     // Spread evolution
    /// Depth Sequence
    pub depth_sequence: Vec<f64>,      // Market depth changes
}

/// MAMBA (State Space Model) features for sequential processing
#[derive(Debug, Clone, Serialize, Deserialize)]
/// MAMBAFeatures
/// 
/// Auto-generated documentation placeholder - enhance with specifics
pub struct MAMBAFeatures {
    /// Base
    pub base: BaseMarketFeatures,
    /// Databento
    pub databento: DatabentoBuFeatures,
    /// Benzinga
    pub benzinga: BenzingaNewsFeatures,

    // Long-term sequences for state space modeling
    /// Price Sequence
    pub price_sequence: Vec<f64>,     // 200-point price sequence
    /// Volume Sequence
    pub volume_sequence: Vec<f64>,    // Volume evolution
    /// Sentiment Sequence
    pub sentiment_sequence: Vec<f64>, // News sentiment over time
    /// Volatility Regime
    pub volatility_regime: f64,       // Current volatility state
    /// Trend Persistence
    pub trend_persistence: f64,       // Trend stability measure
}

/// DQN (Deep Q-Network) features for reinforcement learning
#[derive(Debug, Clone, Serialize, Deserialize)]
/// DQNFeatures
/// 
/// Auto-generated documentation placeholder - enhance with specifics
pub struct DQNFeatures {
    /// Base
    pub base: BaseMarketFeatures,
    /// Databento
    pub databento: DatabentoBuFeatures,
    /// Benzinga
    pub benzinga: BenzingaNewsFeatures,

    // State representation for RL
    /// Position
    pub position: f64,               // Normalized position size
    /// Unrealized Pnl
    pub unrealized_pnl: f64,         // Current P&L
    /// Time In Position
    pub time_in_position: f64,       // Holding period
    /// Market Impact Estimate
    pub market_impact_estimate: f64, // Expected slippage
    /// Opportunity Cost
    pub opportunity_cost: f64,       // Missed opportunities

    // Action space context
    /// Available Liquidity
    pub available_liquidity: f64,       // Market depth available
    /// Transaction Cost Estimate
    pub transaction_cost_estimate: f64, // Estimated costs
    /// Risk Budget Remaining
    pub risk_budget_remaining: f64,     // Available risk capacity
}

/// PPO (Proximal Policy Optimization) features
#[derive(Debug, Clone, Serialize, Deserialize)]
/// PPOFeatures
/// 
/// Auto-generated documentation placeholder - enhance with specifics
pub struct PPOFeatures {
    /// Base
    pub base: BaseMarketFeatures,
    /// Databento
    pub databento: DatabentoBuFeatures,
    /// Benzinga
    pub benzinga: BenzingaNewsFeatures,

    // Policy-specific features
    /// Action History
    pub action_history: Vec<f64>, // Last 10 actions
    /// Reward History
    pub reward_history: Vec<f64>, // Last 10 rewards
    /// Advantage Estimate
    pub advantage_estimate: f64,  // GAE advantage
    /// Value Estimate
    pub value_estimate: f64,      // State value estimate
    /// Policy Entropy
    pub policy_entropy: f64,      // Action distribution entropy

    // Exploration features
    /// Exploration Bonus
    pub exploration_bonus: f64,    // Curiosity-driven exploration
    /// Uncertainty Estimate
    pub uncertainty_estimate: f64, // Model uncertainty
}

/// Liquid Networks features for adaptive processing
#[derive(Debug, Clone, Serialize, Deserialize)]
/// LiquidFeatures
/// 
/// Auto-generated documentation placeholder - enhance with specifics
pub struct LiquidFeatures {
    /// Base
    pub base: BaseMarketFeatures,
    /// Databento
    pub databento: DatabentoBuFeatures,
    /// Benzinga
    pub benzinga: BenzingaNewsFeatures,

    // Adaptive time constants
    /// Fast Adaptation Signal
    pub fast_adaptation_signal: f64, // High-frequency adaptation
    /// Slow Adaptation Signal
    pub slow_adaptation_signal: f64, // Low-frequency trends
    /// Regime Change Signal
    pub regime_change_signal: f64,   // Market regime shifts
    /// Adaptation Rate
    pub adaptation_rate: f64,        // Current learning rate

    // Causal discovery features
    /// Causal Strength
    pub causal_strength: f64,    // Causal relationship strength
    /// Information Flow
    pub information_flow: f64,   // Directional information transfer
    /// Network Centrality
    pub network_centrality: f64, // Node importance in causal graph
}

/// TFT (Temporal Fusion Transformer) features
#[derive(Debug, Clone, Serialize, Deserialize)]
/// TFTFeatures
/// 
/// Auto-generated documentation placeholder - enhance with specifics
pub struct TFTFeatures {
    /// Base
    pub base: BaseMarketFeatures,
    /// Databento
    pub databento: DatabentoBuFeatures,
    /// Benzinga
    pub benzinga: BenzingaNewsFeatures,

    // Multi-horizon sequences
    /// Observed Sequence
    pub observed_sequence: Vec<f64>, // Historical observations
    /// Known Future
    pub known_future: Vec<f64>,      // Known future inputs
    /// Static Metadata
    pub static_metadata: Vec<f64>,   // Time-invariant features

    // Attention mechanism inputs
    /// Temporal Patterns
    pub temporal_patterns: Vec<f64>,   // Recurring temporal patterns
    /// Seasonal Components
    pub seasonal_components: Vec<f64>, // Seasonal decomposition
    /// Forecast Horizon
    pub forecast_horizon: usize,       // Prediction steps ahead
}

/// Unified feature extraction configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
/// UnifiedConfig
/// 
/// Auto-generated documentation placeholder - enhance with specifics
pub struct UnifiedConfig {
    // Data requirements
    /// Min Data Points
    pub min_data_points: usize,
    /// Max Missing Ratio
    pub max_missing_ratio: f64,
    /// Outlier Threshold
    pub outlier_threshold: f64,

    // Time windows
    /// Short Window
    pub short_window: Duration,
    /// Medium Window
    pub medium_window: Duration,
    /// Long Window
    pub long_window: Duration,

    // Model-specific configurations
    /// Tlob Sequence Length
    pub tlob_sequence_length: usize,
    /// Mamba Sequence Length
    pub mamba_sequence_length: usize,
    /// Dqn History Length
    pub dqn_history_length: usize,
    /// Ppo History Length
    pub ppo_history_length: usize,
    /// Tft Encoder Length
    pub tft_encoder_length: usize,
    /// Tft Decoder Length
    pub tft_decoder_length: usize,

    // Performance settings
    /// Enable Simd
    pub enable_simd: bool,
    /// Parallel Processing
    pub parallel_processing: bool,
    /// Cache Intermediate Results
    pub cache_intermediate_results: bool,
}

impl Default for UnifiedConfig {
    fn default() -> Self {
        Self {
            min_data_points: 100,
            max_missing_ratio: 0.1,
            outlier_threshold: 3.0,
            short_window: Duration::from_secs(300), // 5 minutes
            medium_window: Duration::from_secs(3600), // 1 hour
            long_window: Duration::from_secs(14400), // 4 hours
            tlob_sequence_length: 50,
            mamba_sequence_length: 200,
            dqn_history_length: 10,
            ppo_history_length: 10,
            tft_encoder_length: 192,
            tft_decoder_length: 24,
            enable_simd: true,
            parallel_processing: true,
            cache_intermediate_results: true,
        }
    }
}

/// The unified feature extractor - SINGLE SOURCE OF TRUTH
#[derive(Debug)]
pub struct UnifiedFeatureExtractor {
    config: UnifiedConfig,
}

impl UnifiedFeatureExtractor {
    /// Create new unified feature extractor
    pub fn new(config: UnifiedConfig) -> Self {
        Self {
            config,
        }
    }

    /// Extract features for TLOB Transformer model
    pub async fn extract_tlob_features(
        &mut self,
        symbol: &Symbol,
        databento_data: &DatabentoBuData,
        benzinga_data: &BenzingaNewsData,
        historical_data: &[MarketTick],
    ) -> Result<TLOBFeatures, FeatureError> {
        let start_time = Instant::now();

        // Extract base features
        let base = self.extract_base_features(historical_data).await?;

        // Extract Databento order book features
        let databento = self.extract_databento_features(databento_data).await?;

        // Extract Benzinga sentiment features
        let benzinga = self.extract_benzinga_features(benzinga_data).await?;

        // Generate TLOB-specific sequences
        let order_book_sequence =
            self.generate_order_book_sequence(databento_data, self.config.tlob_sequence_length)?;

        let trade_flow_sequence =
            self.generate_trade_flow_sequence(databento_data, self.config.tlob_sequence_length)?;

        let spread_sequence =
            self.generate_spread_sequence(databento_data, self.config.tlob_sequence_length)?;

        let depth_sequence =
            self.generate_depth_sequence(databento_data, self.config.tlob_sequence_length)?;

        debug!(
            "TLOB feature extraction for {} completed in {:.2}ms",
            symbol,
            start_time.elapsed().as_millis()
        );

        Ok(TLOBFeatures {
            base,
            databento,
            benzinga,
            order_book_sequence,
            trade_flow_sequence,
            spread_sequence,
            depth_sequence,
        })
    }

    /// Extract features for MAMBA State Space Model
    pub async fn extract_mamba_features(
        &mut self,
        symbol: &Symbol,
        databento_data: &DatabentoBuData,
        benzinga_data: &BenzingaNewsData,
        historical_data: &[MarketTick],
    ) -> Result<MAMBAFeatures, FeatureError> {
        let start_time = Instant::now();

        let base = self.extract_base_features(historical_data).await?;
        let databento = self.extract_databento_features(databento_data).await?;
        let benzinga = self.extract_benzinga_features(benzinga_data).await?;

        // Generate long-term sequences for state space modeling
        let price_sequence =
            self.generate_price_sequence(historical_data, self.config.mamba_sequence_length)?;

        let volume_sequence =
            self.generate_volume_sequence(historical_data, self.config.mamba_sequence_length)?;

        let sentiment_sequence =
            self.generate_sentiment_sequence(benzinga_data, self.config.mamba_sequence_length)?;

        let volatility_regime = self.calculate_volatility_regime(historical_data)?;
        let trend_persistence = self.calculate_trend_persistence(historical_data)?;

        debug!(
            "MAMBA feature extraction for {} completed in {:.2}ms",
            symbol,
            start_time.elapsed().as_millis()
        );

        Ok(MAMBAFeatures {
            base,
            databento,
            benzinga,
            price_sequence,
            volume_sequence,
            sentiment_sequence,
            volatility_regime,
            trend_persistence,
        })
    }

    /// Extract features for DQN reinforcement learning
    pub async fn extract_dqn_features(
        &mut self,
        symbol: &Symbol,
        databento_data: &DatabentoBuData,
        benzinga_data: &BenzingaNewsData,
        historical_data: &[MarketTick],
        current_position: f64,
        unrealized_pnl: f64,
    ) -> Result<DQNFeatures, FeatureError> {
        let start_time = Instant::now();

        let base = self.extract_base_features(historical_data).await?;
        let databento = self.extract_databento_features(databento_data).await?;
        let benzinga = self.extract_benzinga_features(benzinga_data).await?;

        // Calculate RL-specific features
        let time_in_position = self.calculate_time_in_position(current_position)?;
        let market_impact_estimate =
            self.estimate_market_impact(databento_data, current_position.abs())?;
        let opportunity_cost = self.calculate_opportunity_cost(historical_data)?;
        let available_liquidity = self.calculate_available_liquidity(databento_data)?;
        let transaction_cost_estimate = self.estimate_transaction_costs(databento_data)?;
        let risk_budget_remaining =
            self.calculate_risk_budget_remaining(current_position, unrealized_pnl)?;

        debug!(
            "DQN feature extraction for {} completed in {:.2}ms",
            symbol,
            start_time.elapsed().as_millis()
        );

        Ok(DQNFeatures {
            base,
            databento,
            benzinga,
            position: current_position,
            unrealized_pnl,
            time_in_position,
            market_impact_estimate,
            opportunity_cost,
            available_liquidity,
            transaction_cost_estimate,
            risk_budget_remaining,
        })
    }

    /// Extract features for PPO policy optimization
    pub async fn extract_ppo_features(
        &mut self,
        symbol: &Symbol,
        databento_data: &DatabentoBuData,
        benzinga_data: &BenzingaNewsData,
        historical_data: &[MarketTick],
        action_history: &[f64],
        reward_history: &[f64],
    ) -> Result<PPOFeatures, FeatureError> {
        let start_time = Instant::now();

        let base = self.extract_base_features(historical_data).await?;
        let databento = self.extract_databento_features(databento_data).await?;
        let benzinga = self.extract_benzinga_features(benzinga_data).await?;

        // Calculate PPO-specific features
        let advantage_estimate = self.calculate_gae_advantage(reward_history)?;
        let value_estimate = self.estimate_state_value(historical_data)?;
        let policy_entropy = self.calculate_policy_entropy(action_history)?;
        let exploration_bonus = self.calculate_exploration_bonus(action_history)?;
        let uncertainty_estimate = self.estimate_model_uncertainty(historical_data)?;

        debug!(
            "PPO feature extraction for {} completed in {:.2}ms",
            symbol,
            start_time.elapsed().as_millis()
        );

        Ok(PPOFeatures {
            base,
            databento,
            benzinga,
            action_history: action_history.to_vec(),
            reward_history: reward_history.to_vec(),
            advantage_estimate,
            value_estimate,
            policy_entropy,
            exploration_bonus,
            uncertainty_estimate,
        })
    }

    /// Extract features for Liquid Networks
    pub async fn extract_liquid_features(
        &mut self,
        symbol: &Symbol,
        databento_data: &DatabentoBuData,
        benzinga_data: &BenzingaNewsData,
        historical_data: &[MarketTick],
    ) -> Result<LiquidFeatures, FeatureError> {
        let start_time = Instant::now();

        let base = self.extract_base_features(historical_data).await?;
        let databento = self.extract_databento_features(databento_data).await?;
        let benzinga = self.extract_benzinga_features(benzinga_data).await?;

        // Calculate adaptive features
        let fast_adaptation_signal = self.calculate_fast_adaptation(historical_data)?;
        let slow_adaptation_signal = self.calculate_slow_adaptation(historical_data)?;
        let regime_change_signal = self.detect_regime_change(historical_data)?;
        let adaptation_rate = self.calculate_adaptation_rate(historical_data)?;

        // Causal discovery features
        let causal_strength = self.measure_causal_strength(databento_data, benzinga_data)?;
        let information_flow = self.calculate_information_flow(historical_data)?;
        let network_centrality = self.calculate_network_centrality(symbol)?;

        debug!(
            "Liquid feature extraction for {} completed in {:.2}ms",
            symbol,
            start_time.elapsed().as_millis()
        );

        Ok(LiquidFeatures {
            base,
            databento,
            benzinga,
            fast_adaptation_signal,
            slow_adaptation_signal,
            regime_change_signal,
            adaptation_rate,
            causal_strength,
            information_flow,
            network_centrality,
        })
    }

    /// Extract features for Temporal Fusion Transformer
    pub async fn extract_tft_features(
        &mut self,
        symbol: &Symbol,
        databento_data: &DatabentoBuData,
        benzinga_data: &BenzingaNewsData,
        historical_data: &[MarketTick],
        forecast_horizon: usize,
    ) -> Result<TFTFeatures, FeatureError> {
        let start_time = Instant::now();

        let base = self.extract_base_features(historical_data).await?;
        let databento = self.extract_databento_features(databento_data).await?;
        let benzinga = self.extract_benzinga_features(benzinga_data).await?;

        // Generate TFT-specific sequences
        let observed_sequence =
            self.generate_observed_sequence(historical_data, self.config.tft_encoder_length)?;

        let known_future = self.generate_known_future_sequence(forecast_horizon)?;

        let static_metadata = self.generate_static_metadata(symbol)?;

        // Temporal pattern analysis
        let temporal_patterns = self.extract_temporal_patterns(historical_data)?;
        let seasonal_components = self.decompose_seasonal_components(historical_data)?;

        debug!(
            "TFT feature extraction for {} completed in {:.2}ms",
            symbol,
            start_time.elapsed().as_millis()
        );

        Ok(TFTFeatures {
            base,
            databento,
            benzinga,
            observed_sequence,
            known_future,
            static_metadata,
            temporal_patterns,
            seasonal_components,
            forecast_horizon,
        })
    }

    // PRIVATE HELPER METHODS FOR FEATURE EXTRACTION

    async fn extract_base_features(
        &mut self,
        historical_data: &[MarketTick],
    ) -> Result<BaseMarketFeatures, FeatureError> {
        if historical_data.len() < self.config.min_data_points {
            return Err(FeatureError::InsufficientData {
                feature: "base_features".to_owned(),
                required: self.config.min_data_points,
                available: historical_data.len(),
            });
        }

        let latest = historical_data.last().ok_or_else(|| FeatureError::InsufficientData {
            feature: "base_features".to_owned(),
            required: 1,
            available: 0,
        })?;

        // Calculate returns using SIMD optimization
        let returns_1m = self.calculate_returns(historical_data, Duration::from_secs(60))?;
        let returns_5m = self.calculate_returns(historical_data, Duration::from_secs(300))?;
        let returns_15m = self.calculate_returns(historical_data, Duration::from_secs(900))?;
        let returns_1h = self.calculate_returns(historical_data, Duration::from_secs(3600))?;

        // Calculate volatility
        let volatility_1h =
            self.calculate_volatility(historical_data, Duration::from_secs(3600))?;
        let volatility_4h =
            self.calculate_volatility(historical_data, Duration::from_secs(14400))?;

        // Volume analysis
        let volume_ratio_1h =
            self.calculate_volume_ratio(historical_data, Duration::from_secs(3600))?;
        let vwap_deviation = self.calculate_vwap_deviation(historical_data)?;
        let volume_imbalance = self.calculate_volume_imbalance(historical_data)?;

        // Technical indicators
        let rsi_14 = self.calculate_rsi(historical_data, 14)?;
        let macd_signal = self.calculate_macd_signal(historical_data)?;
        let bollinger_position = self.calculate_bollinger_position(historical_data)?;
        let momentum_score = self.calculate_momentum_score(historical_data)?;

        // Market context
        let now = Utc::now();
        let time_of_day = self.normalize_time_of_day(now);
        let day_of_week = now.weekday().number_from_monday() as u8;
        let market_session = self.determine_market_session(now);
        let is_opex = self.is_options_expiration(now);
        let is_earnings_week = self.is_earnings_week(now);

        Ok(BaseMarketFeatures {
            price: latest.price,
            returns_1m,
            returns_5m,
            returns_15m,
            returns_1h,
            volatility_1h,
            volatility_4h,
            volume: Volume::from_f64(latest.size.to_f64()).unwrap_or(Volume::ZERO),
            volume_ratio_1h,
            vwap_deviation,
            volume_imbalance,
            rsi_14,
            macd_signal,
            bollinger_position,
            momentum_score,
            time_of_day,
            day_of_week,
            market_session,
            is_opex,
            is_earnings_week,
        })
    }

    async fn extract_databento_features(
        &mut self,
        databento_data: &DatabentoBuData,
    ) -> Result<DatabentoBuFeatures, FeatureError> {
        // Extract order book microstructure features
        let bid_ask_spread_bps = self.calculate_spread_bps(databento_data)?;
        let order_book_imbalance = self.calculate_order_book_imbalance(databento_data)?;
        let depth_weighted_mid = self.calculate_depth_weighted_mid(databento_data)?;
        let effective_spread_bps = self.calculate_effective_spread_bps(databento_data)?;
        let price_impact_bps = self.calculate_price_impact_bps(databento_data)?;

        // Level 2/3 analysis
        let l2_slope = self.calculate_order_book_slope(databento_data)?;
        let l2_curvature = self.calculate_order_book_curvature(databento_data)?;
        let l3_order_intensity = self.calculate_order_intensity(databento_data)?;
        let l3_cancellation_ratio = self.calculate_cancellation_ratio(databento_data)?;

        // Trade classification
        let trade_sign = self.classify_trade_direction(databento_data)?;
        let trade_size_category = self.classify_trade_size(databento_data)?;
        let trade_urgency = self.calculate_trade_urgency(databento_data)?;

        // Microstructure noise analysis
        let realized_spread_bps = self.calculate_realized_spread_bps(databento_data)?;
        let information_share = self.calculate_information_share(databento_data)?;
        let microstructure_noise = self.estimate_microstructure_noise(databento_data)?;

        // High-frequency patterns
        let tick_direction_streak = self.calculate_tick_streak(databento_data)?;
        let quote_update_frequency = self.calculate_quote_frequency(databento_data)?;
        let order_arrival_intensity = self.estimate_arrival_intensity(databento_data)?;

        Ok(DatabentoBuFeatures {
            bid_ask_spread_bps,
            order_book_imbalance,
            depth_weighted_mid,
            effective_spread_bps,
            price_impact_bps,
            l2_slope,
            l2_curvature,
            l3_order_intensity,
            l3_cancellation_ratio,
            trade_sign,
            trade_size_category,
            trade_urgency,
            realized_spread_bps,
            information_share,
            microstructure_noise,
            tick_direction_streak,
            quote_update_frequency,
            order_arrival_intensity,
        })
    }

    async fn extract_benzinga_features(
        &mut self,
        benzinga_data: &BenzingaNewsData,
    ) -> Result<BenzingaNewsFeatures, FeatureError> {
        // Sentiment analysis
        let sentiment_score = self.calculate_news_sentiment(benzinga_data)?;
        let sentiment_confidence = self.calculate_sentiment_confidence(benzinga_data)?;
        let news_velocity = self.calculate_news_velocity(benzinga_data)?;
        let breaking_news_flag = self.detect_breaking_news(benzinga_data)?;

        // Time-weighted sentiment
        let weighted_sentiment_1h =
            self.calculate_weighted_sentiment(benzinga_data, Duration::from_secs(3600))?;
        let weighted_sentiment_4h =
            self.calculate_weighted_sentiment(benzinga_data, Duration::from_secs(14400))?;
        let weighted_sentiment_24h =
            self.calculate_weighted_sentiment(benzinga_data, Duration::from_secs(86400))?;

        // News categorization
        let earnings_related = self.is_earnings_related(benzinga_data)?;
        let analyst_rating = self.extract_analyst_rating(benzinga_data)?;
        let unusual_options_activity = self.detect_unusual_options(benzinga_data)?;
        let sec_filing_type = self.extract_sec_filing_type(benzinga_data)?;

        // Sentiment dynamics
        let sentiment_acceleration = self.calculate_sentiment_acceleration(benzinga_data)?;
        let sentiment_divergence = self.calculate_sentiment_divergence(benzinga_data)?;
        let contrarian_signal = self.calculate_contrarian_signal(benzinga_data)?;

        // Source analysis
        let source_count = self.count_unique_sources(benzinga_data)?;
        let mention_volume = self.calculate_mention_volume(benzinga_data)?;
        let social_amplification = self.calculate_social_amplification(benzinga_data)?;

        Ok(BenzingaNewsFeatures {
            sentiment_score,
            sentiment_confidence,
            news_velocity,
            breaking_news_flag,
            weighted_sentiment_1h,
            weighted_sentiment_4h,
            weighted_sentiment_24h,
            earnings_related,
            analyst_rating,
            unusual_options_activity,
            sec_filing_type,
            sentiment_acceleration,
            sentiment_divergence,
            contrarian_signal,
            source_count,
            mention_volume,
            social_amplification,
        })
    }

    // Additional helper methods would be implemented here...
    // This is a comprehensive structure showing the key methods needed

    fn calculate_returns(
        &self,
        data: &[MarketTick],
        _window: Duration,
    ) -> Result<f64, FeatureError> {
        // Implementation using SIMD for performance
        if data.len() < 2 {
            return Ok(0.0);
        }

        let first_price = data.first().ok_or(FeatureError::InsufficientData {
            feature: "price_change".to_string(),
            required: 2,
            available: data.len(),
        })?.price;
        let last_price = data.last().ok_or(FeatureError::InsufficientData {
            feature: "price_change".to_string(),
            required: 2,
            available: data.len(),
        })?.price;

        if first_price.to_f64() == 0.0 {
            return Ok(0.0);
        }
        
        Ok((last_price.to_f64() - first_price.to_f64()) / first_price.to_f64())
    }

    fn calculate_volatility(
        &self,
        data: &[MarketTick],
        _window: Duration,
    ) -> Result<f64, FeatureError> {
        if data.len() < 2 {
            return Ok(0.0);
        }

        // Calculate log returns
        let mut returns = Vec::with_capacity(data.len() - 1);
        for window in data.windows(2) {
            let prev_price = window.first().map(|t| t.price).unwrap_or(Price::ZERO);
            let curr_price = window.last().map(|t| t.price).unwrap_or(Price::ZERO);

            if prev_price.to_f64() > 0.0 && curr_price.to_f64() > 0.0 {
                returns.push((curr_price.to_f64() / prev_price.to_f64()).ln());
            }
        }

        if returns.is_empty() {
            return Ok(0.0);
        }

        // Calculate sample standard deviation
        let mean = returns.iter().sum::<f64>() / returns.len() as f64;
        let variance = returns.iter().map(|r| (r - mean).powi(2)).sum::<f64>()
            / (returns.len() - 1).max(1) as f64;

        Ok(variance.sqrt())
    }

    // TLOB-specific sequence generation methods
    fn generate_order_book_sequence(
        &self,
        _data: &DatabentoBuData,
        length: usize,
    ) -> Result<Vec<f64>, FeatureError> {
    // Ok variant
        Ok(vec![0.0; length])
    }

    fn generate_trade_flow_sequence(
        &self,
        _data: &DatabentoBuData,
        length: usize,
    ) -> Result<Vec<f64>, FeatureError> {
    // Ok variant
        Ok(vec![0.0; length])
    }

    fn generate_spread_sequence(
        &self,
        _data: &DatabentoBuData,
        length: usize,
    ) -> Result<Vec<f64>, FeatureError> {
    // Ok variant
        Ok(vec![0.0; length])
    }

    fn generate_depth_sequence(
        &self,
        _data: &DatabentoBuData,
        length: usize,
    ) -> Result<Vec<f64>, FeatureError> {
    // Ok variant
        Ok(vec![0.0; length])
    }

    // MAMBA-specific sequence generation methods
    fn generate_price_sequence(
        &self,
        data: &[MarketTick],
        length: usize,
    ) -> Result<Vec<f64>, FeatureError> {
        let mut sequence = Vec::with_capacity(length);
        for i in 0..length {
            if let Some(tick) = data.get(i) {
                sequence.push(tick.price.to_f64());
            } else {
                sequence.push(0.0);
            }
        }
        Ok(sequence)
    }

    fn generate_volume_sequence(
        &self,
        data: &[MarketTick],
        length: usize,
    ) -> Result<Vec<f64>, FeatureError> {
        let mut sequence = Vec::with_capacity(length);
        for i in 0..length {
            if let Some(tick) = data.get(i) {
                sequence.push(tick.size.to_f64());
            } else {
                sequence.push(0.0);
            }
        }
        Ok(sequence)
    }

    fn generate_sentiment_sequence(
        &self,
        _data: &BenzingaNewsData,
        length: usize,
    ) -> Result<Vec<f64>, FeatureError> {
    // Ok variant
        Ok(vec![0.0; length])
    }

    const fn calculate_volatility_regime(&self, _data: &[MarketTick]) -> Result<f64, FeatureError> {
    // Ok variant
        Ok(0.0)
    }

    const fn calculate_trend_persistence(&self, _data: &[MarketTick]) -> Result<f64, FeatureError> {
    // Ok variant
        Ok(0.0)
    }

    // DQN-specific methods
    const fn calculate_time_in_position(&self, _position: f64) -> Result<f64, FeatureError> {
    // Ok variant
        Ok(0.0)
    }

    const fn estimate_market_impact(
        &self,
        _data: &DatabentoBuData,
        _position_size: f64,
    ) -> Result<f64, FeatureError> {
    // Ok variant
        Ok(0.0)
    }

    const fn calculate_opportunity_cost(&self, _data: &[MarketTick]) -> Result<f64, FeatureError> {
    // Ok variant
        Ok(0.0)
    }

    const fn calculate_available_liquidity(
        &self,
        _data: &DatabentoBuData,
    ) -> Result<f64, FeatureError> {
    // Ok variant
        Ok(0.0)
    }

    const fn estimate_transaction_costs(
        &self,
        _data: &DatabentoBuData,
    ) -> Result<f64, FeatureError> {
    // Ok variant
        Ok(0.0)
    }

    const fn calculate_risk_budget_remaining(
        &self,
        _position: f64,
        _pnl: f64,
    ) -> Result<f64, FeatureError> {
    // Ok variant
        Ok(0.0)
    }

    // PPO-specific methods
    const fn calculate_gae_advantage(&self, _rewards: &[f64]) -> Result<f64, FeatureError> {
    // Ok variant
        Ok(0.0)
    }

    const fn estimate_state_value(&self, _data: &[MarketTick]) -> Result<f64, FeatureError> {
    // Ok variant
        Ok(0.0)
    }

    const fn calculate_policy_entropy(&self, _actions: &[f64]) -> Result<f64, FeatureError> {
    // Ok variant
        Ok(0.0)
    }

    const fn calculate_exploration_bonus(&self, _actions: &[f64]) -> Result<f64, FeatureError> {
    // Ok variant
        Ok(0.0)
    }

    const fn estimate_model_uncertainty(&self, _data: &[MarketTick]) -> Result<f64, FeatureError> {
    // Ok variant
        Ok(0.0)
    }

    // Liquid Networks methods
    const fn calculate_fast_adaptation(&self, _data: &[MarketTick]) -> Result<f64, FeatureError> {
    // Ok variant
        Ok(0.0)
    }

    const fn calculate_slow_adaptation(&self, _data: &[MarketTick]) -> Result<f64, FeatureError> {
    // Ok variant
        Ok(0.0)
    }

    const fn detect_regime_change(&self, _data: &[MarketTick]) -> Result<f64, FeatureError> {
    // Ok variant
        Ok(0.0)
    }

    const fn calculate_adaptation_rate(&self, _data: &[MarketTick]) -> Result<f64, FeatureError> {
    // Ok variant
        Ok(0.0)
    }

    const fn measure_causal_strength(
        &self,
        _databento: &DatabentoBuData,
        _benzinga: &BenzingaNewsData,
    ) -> Result<f64, FeatureError> {
    // Ok variant
        Ok(0.0)
    }

    const fn calculate_information_flow(&self, _data: &[MarketTick]) -> Result<f64, FeatureError> {
    // Ok variant
        Ok(0.0)
    }

    const fn calculate_network_centrality(&self, _symbol: &Symbol) -> Result<f64, FeatureError> {
    // Ok variant
        Ok(0.0)
    }

    // TFT methods
    fn generate_observed_sequence(
        &self,
        data: &[MarketTick],
        length: usize,
    ) -> Result<Vec<f64>, FeatureError> {
        self.generate_price_sequence(data, length)
    }

    fn generate_known_future_sequence(&self, horizon: usize) -> Result<Vec<f64>, FeatureError> {
    // Ok variant
        Ok(vec![0.0; horizon])
    }

    fn generate_static_metadata(&self, _symbol: &Symbol) -> Result<Vec<f64>, FeatureError> {
        Ok(vec![0.0; 10]) // Static metadata vector
    }

    fn extract_temporal_patterns(&self, _data: &[MarketTick]) -> Result<Vec<f64>, FeatureError> {
        Ok(vec![0.0; 24]) // Hourly patterns
    }

    fn decompose_seasonal_components(
        &self,
        _data: &[MarketTick],
    ) -> Result<Vec<f64>, FeatureError> {
        Ok(vec![0.0; 12]) // Monthly seasonality
    }

    // Volume analysis methods
    const fn calculate_volume_ratio(
        &self,
        _data: &[MarketTick],
        _window: Duration,
    ) -> Result<f64, FeatureError> {
    // Ok variant
        Ok(1.0)
    }

    const fn calculate_vwap_deviation(&self, _data: &[MarketTick]) -> Result<f64, FeatureError> {
    // Ok variant
        Ok(0.0)
    }

    const fn calculate_volume_imbalance(&self, _data: &[MarketTick]) -> Result<f64, FeatureError> {
    // Ok variant
        Ok(0.0)
    }

    // Technical indicators
    const fn calculate_rsi(
        &self,
        _data: &[MarketTick],
        _period: usize,
    ) -> Result<f64, FeatureError> {
        Ok(50.0) // Neutral RSI
    }

    const fn calculate_macd_signal(&self, _data: &[MarketTick]) -> Result<f64, FeatureError> {
    // Ok variant
        Ok(0.0)
    }

    const fn calculate_bollinger_position(
        &self,
        _data: &[MarketTick],
    ) -> Result<f64, FeatureError> {
    // Ok variant
        Ok(0.5)
    }

    const fn calculate_momentum_score(&self, _data: &[MarketTick]) -> Result<f64, FeatureError> {
    // Ok variant
        Ok(0.0)
    }

    // Market context methods
    fn normalize_time_of_day(&self, time: DateTime<Utc>) -> f64 {
        let hour = time.hour() as f64;
        let minute = time.minute() as f64;
        (hour * 60.0 + minute) / (24.0 * 60.0)
    }

    const fn determine_market_session(&self, _time: DateTime<Utc>) -> u8 {
        2 // Regular session
    }

    const fn is_options_expiration(&self, _time: DateTime<Utc>) -> bool {
        false
    }

    const fn is_earnings_week(&self, _time: DateTime<Utc>) -> bool {
        false
    }

    // Databento features
    const fn calculate_spread_bps(&self, _data: &DatabentoBuData) -> Result<f64, FeatureError> {
        Ok(5.0) // 5 basis points default spread
    }

    const fn calculate_order_book_imbalance(
        &self,
        _data: &DatabentoBuData,
    ) -> Result<f64, FeatureError> {
    // Ok variant
        Ok(0.0)
    }

    fn calculate_depth_weighted_mid(&self, _data: &DatabentoBuData) -> Result<Price, FeatureError> {
        Price::from_f64(100.0).map_err(|e| FeatureError::MathematicalError {
            feature: "depth_weighted_mid".to_owned(),
            reason: e.to_string(),
        })
    }

    const fn calculate_effective_spread_bps(
        &self,
        _data: &DatabentoBuData,
    ) -> Result<f64, FeatureError> {
    // Ok variant
        Ok(3.0)
    }

    const fn calculate_price_impact_bps(
        &self,
        _data: &DatabentoBuData,
    ) -> Result<f64, FeatureError> {
    // Ok variant
        Ok(2.0)
    }

    const fn calculate_order_book_slope(
        &self,
        _data: &DatabentoBuData,
    ) -> Result<f64, FeatureError> {
    // Ok variant
        Ok(0.0)
    }

    const fn calculate_order_book_curvature(
        &self,
        _data: &DatabentoBuData,
    ) -> Result<f64, FeatureError> {
    // Ok variant
        Ok(0.0)
    }

    const fn calculate_order_intensity(
        &self,
        _data: &DatabentoBuData,
    ) -> Result<f64, FeatureError> {
        Ok(10.0) // 10 orders per second
    }

    const fn calculate_cancellation_ratio(
        &self,
        _data: &DatabentoBuData,
    ) -> Result<f64, FeatureError> {
        Ok(0.3) // 30% cancellation ratio
    }

    const fn classify_trade_direction(&self, _data: &DatabentoBuData) -> Result<i8, FeatureError> {
        Ok(0) // Unknown direction
    }

    const fn classify_trade_size(&self, _data: &DatabentoBuData) -> Result<u8, FeatureError> {
        Ok(2) // Medium size
    }

    const fn calculate_trade_urgency(&self, _data: &DatabentoBuData) -> Result<f64, FeatureError> {
    // Ok variant
        Ok(0.5)
    }

    const fn calculate_realized_spread_bps(
        &self,
        _data: &DatabentoBuData,
    ) -> Result<f64, FeatureError> {
    // Ok variant
        Ok(4.0)
    }

    const fn calculate_information_share(
        &self,
        _data: &DatabentoBuData,
    ) -> Result<f64, FeatureError> {
    // Ok variant
        Ok(0.5)
    }

    const fn estimate_microstructure_noise(
        &self,
        _data: &DatabentoBuData,
    ) -> Result<f64, FeatureError> {
    // Ok variant
        Ok(0.1)
    }

    const fn calculate_tick_streak(&self, _data: &DatabentoBuData) -> Result<i8, FeatureError> {
    // Ok variant
        Ok(0)
    }

    const fn calculate_quote_frequency(
        &self,
        _data: &DatabentoBuData,
    ) -> Result<f64, FeatureError> {
        Ok(100.0) // 100 quotes per second
    }

    const fn estimate_arrival_intensity(
        &self,
        _data: &DatabentoBuData,
    ) -> Result<f64, FeatureError> {
        Ok(50.0) // 50 arrivals per second
    }

    // Benzinga news features
    const fn calculate_news_sentiment(
        &self,
        _data: &BenzingaNewsData,
    ) -> Result<f64, FeatureError> {
        Ok(0.0) // Neutral sentiment
    }

    const fn calculate_sentiment_confidence(
        &self,
        _data: &BenzingaNewsData,
    ) -> Result<f64, FeatureError> {
    // Ok variant
        Ok(0.5)
    }

    const fn calculate_news_velocity(&self, _data: &BenzingaNewsData) -> Result<f64, FeatureError> {
        Ok(1.0) // 1 article per hour
    }

    const fn detect_breaking_news(&self, _data: &BenzingaNewsData) -> Result<bool, FeatureError> {
    // Ok variant
        Ok(false)
    }

    const fn calculate_weighted_sentiment(
        &self,
        _data: &BenzingaNewsData,
        _window: Duration,
    ) -> Result<f64, FeatureError> {
    // Ok variant
        Ok(0.0)
    }

    const fn is_earnings_related(&self, _data: &BenzingaNewsData) -> Result<bool, FeatureError> {
    // Ok variant
        Ok(false)
    }

    const fn extract_analyst_rating(
        &self,
        _data: &BenzingaNewsData,
    ) -> Result<Option<f64>, FeatureError> {
    // Ok variant
        Ok(None)
    }

    const fn detect_unusual_options(&self, _data: &BenzingaNewsData) -> Result<bool, FeatureError> {
    // Ok variant
        Ok(false)
    }

    const fn extract_sec_filing_type(
        &self,
        _data: &BenzingaNewsData,
    ) -> Result<Option<String>, FeatureError> {
    // Ok variant
        Ok(None)
    }

    const fn calculate_sentiment_acceleration(
        &self,
        _data: &BenzingaNewsData,
    ) -> Result<f64, FeatureError> {
    // Ok variant
        Ok(0.0)
    }

    const fn calculate_sentiment_divergence(
        &self,
        _data: &BenzingaNewsData,
    ) -> Result<f64, FeatureError> {
    // Ok variant
        Ok(0.0)
    }

    const fn calculate_contrarian_signal(
        &self,
        _data: &BenzingaNewsData,
    ) -> Result<f64, FeatureError> {
    // Ok variant
        Ok(0.0)
    }

    const fn count_unique_sources(&self, _data: &BenzingaNewsData) -> Result<u32, FeatureError> {
    // Ok variant
        Ok(5)
    }

    const fn calculate_mention_volume(
        &self,
        _data: &BenzingaNewsData,
    ) -> Result<u32, FeatureError> {
    // Ok variant
        Ok(10)
    }

    const fn calculate_social_amplification(
        &self,
        _data: &BenzingaNewsData,
    ) -> Result<f64, FeatureError> {
    // Ok variant
        Ok(1.0)
    }

        // ... dozens more helper methods for each specific feature
    }
    
    // Type aliases for common market data structures (must be defined before use)
    type OrderBookLevel = (Price, Quantity); // (price, quantity) tuple
    type Trade = TradeEvent; // Use canonical trade event
    
    // Data structures for Databento and Benzinga integration
    #[derive(Debug, Clone)]
    pub struct DatabentoBuData {
        /// Order Book
        pub order_book: Vec<OrderBookLevel>,
        /// Trades
        pub trades: Vec<Trade>,
    /// Quotes
    pub quotes: Vec<QuoteEvent>,
    /// Timestamp
    pub timestamp: DateTime<Utc>,
}

#[derive(Debug, Clone)]
/// BenzingaNewsData
/// 
/// Auto-generated documentation placeholder - enhance with specifics
pub struct BenzingaNewsData {
    /// Articles
    pub articles: Vec<NewsArticle>,
    /// Sentiment Scores
    pub sentiment_scores: Vec<SentimentScore>,
    /// Analyst Ratings
    pub analyst_ratings: Vec<AnalystRating>,
    /// Unusual Options
    pub unusual_options: Vec<UnusualOptionsActivity>,
    /// Timestamp
    pub timestamp: DateTime<Utc>,
}

#[derive(Debug, Clone)]
/// NewsArticle
/// 
/// Auto-generated documentation placeholder - enhance with specifics
pub struct NewsArticle {
    /// Title
    pub title: String,
    /// Content
    pub content: String,
    /// Source
    pub source: String,
    /// Timestamp
    pub timestamp: DateTime<Utc>,
    /// Symbols
    pub symbols: Vec<Symbol>,
    /// Category
    pub category: String,
    /// Importance
    pub importance: f64,
}

#[derive(Debug, Clone)]
/// SentimentScore
/// 
/// Auto-generated documentation placeholder - enhance with specifics
pub struct SentimentScore {
    /// Symbol
    pub symbol: Symbol,
    /// Score
    pub score: f64,
    /// Confidence
    pub confidence: f64,
    /// Timestamp
    pub timestamp: DateTime<Utc>,
}

#[derive(Debug, Clone)]
/// AnalystRating
/// 
/// Auto-generated documentation placeholder - enhance with specifics
pub struct AnalystRating {
    /// Symbol
    pub symbol: Symbol,
    /// Rating
    pub rating: String,
    /// Price Target
    pub price_target: Option<f64>,
    /// Analyst
    pub analyst: String,
    /// Firm
    pub firm: String,
    /// Timestamp
    pub timestamp: DateTime<Utc>,
}

#[derive(Debug, Clone)]
/// UnusualOptionsActivity
/// 
/// Auto-generated documentation placeholder - enhance with specifics
pub struct UnusualOptionsActivity {
    /// Symbol
    pub symbol: Symbol,
    /// `Option` Type
    pub option_type: String,
    /// Strike
    pub strike: f64,
    /// Expiration
    pub expiration: DateTime<Utc>,
    /// Volume
    pub volume: i64,
    /// Unusual Score
    pub unusual_score: f64,
    /// Timestamp
    pub timestamp: DateTime<Utc>,
}
