foxhunt/backtesting/src/strategy_runner.rs

#![allow(unsafe_code)] // Intentional unsafe for AVX2 vectorized backtesting performance

//! Adaptive Strategy Runner for Backtesting
//!
//! This module provides the bridge between the backtesting engine and the adaptive strategy
//! system, allowing historical data to flow through real ML models for validation.

use anyhow::Result;
use async_trait::async_trait;
use common::Order;
use common::{OrderSide, OrderStatus, Position, Price, Quantity, Symbol};
use rust_decimal::prelude::ToPrimitive;
use rust_decimal::Decimal;
use trading_engine::types::events::MarketEvent;
// Use canonical types from ML module and real ML registry
use ml::{Features, ModelPrediction, get_global_registry};
use chrono::{DateTime, Utc};
use dashmap::DashMap;
use parking_lot::RwLock;
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::sync::Arc;
use tracing::{debug, info, warn};

// SIMD optimizations for HFT performance
#[cfg(target_arch = "x86_64")]
use std::arch::x86_64::*;

use crate::strategy_tester::{
    SignalType, Strategy, StrategyConfig, StrategyContext, StrategyResult, TradingSignal,
};

/// Adaptive strategy runner that integrates ML models with backtesting
pub struct AdaptiveStrategyRunner {
    /// Strategy configuration
    config: AdaptiveStrategyConfig,
    /// Current market state
    market_state: Arc<RwLock<MarketState>>,
    /// Model predictions cache (lock-free for HFT performance)
    predictions_cache: Arc<DashMap<String, ModelPrediction>>,
    /// Performance tracking
    performance_tracker: Arc<RwLock<PerformanceTracker>>,
    /// Feature extractor
    feature_extractor: Arc<FeatureExtractor>,
    /// Risk manager
    risk_manager: Arc<RiskManager>,
}

/// Configuration for adaptive strategy
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AdaptiveStrategyConfig {
    /// Models to use for predictions
    pub active_models: Vec<String>,
    /// Minimum confidence threshold for trading
    pub min_confidence: f64,
    /// Maximum position size as fraction of portfolio
    pub max_position_size: f64,
    /// Lookback period for feature extraction
    pub lookback_period: usize,
    /// Model update frequency (in ticks)
    pub model_update_frequency: u64,
    /// Risk management settings
    pub risk_settings: RiskSettings,
    /// Feature extraction settings
    pub feature_settings: FeatureSettings,
}

impl Default for AdaptiveStrategyConfig {
    fn default() -> Self {
        Self {
            active_models: vec![
                "TLOB".to_string(),
                "MAMBA".to_string(),
                "TFT".to_string(),
                "DQN".to_string(),
                "PPO".to_string(),
            ],
            min_confidence: 0.65,
            max_position_size: 0.05, // 5% max position
            lookback_period: 100,
            model_update_frequency: 1000,
            risk_settings: RiskSettings::default(),
            feature_settings: FeatureSettings::default(),
        }
    }
}

/// Risk management settings
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RiskSettings {
    /// Maximum drawdown before stopping
    pub max_drawdown: f64,
    /// Stop loss percentage
    pub stop_loss: f64,
    /// Take profit percentage
    pub take_profit: f64,
    /// Kelly fraction multiplier
    pub kelly_fraction: f64,
}

impl Default for RiskSettings {
    fn default() -> Self {
        Self {
            max_drawdown: 0.10,   // 10% max drawdown
            stop_loss: 0.02,      // 2% stop loss
            take_profit: 0.04,    // 4% take profit
            kelly_fraction: 0.25, // Conservative 25% of Kelly
        }
    }
}

/// Feature extraction settings
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct FeatureSettings {
    /// Price features to extract
    pub price_features: Vec<String>,
    /// Volume features to extract
    pub volume_features: Vec<String>,
    /// Technical indicators to compute
    pub technical_indicators: Vec<String>,
    /// Microstructure features
    pub microstructure_features: Vec<String>,
}

impl Default for FeatureSettings {
    fn default() -> Self {
        Self {
            price_features: vec![
                "returns".to_string(),
                "log_returns".to_string(),
                "volatility".to_string(),
                "price_momentum".to_string(),
            ],
            volume_features: vec![
                "volume".to_string(),
                "volume_momentum".to_string(),
                "vwap".to_string(),
            ],
            technical_indicators: vec![
                "rsi".to_string(),
                "macd".to_string(),
                "bollinger_bands".to_string(),
            ],
            microstructure_features: vec![
                "bid_ask_spread".to_string(),
                "order_flow_imbalance".to_string(),
                "market_impact".to_string(),
            ],
        }
    }
}

/// Current market state
#[derive(Debug, Clone)]
struct MarketState {
    /// Current timestamp
    current_time: DateTime<Utc>,
    /// Price history
    price_history: Vec<(DateTime<Utc>, Decimal)>,
    /// Volume history
    volume_history: Vec<(DateTime<Utc>, Decimal)>,
    /// Current position
    current_position: Option<Position>,
    /// Last prediction time
    #[allow(dead_code)]
    last_prediction_time: Option<DateTime<Utc>>,
}

impl Default for MarketState {
    fn default() -> Self {
        Self {
            current_time: Utc::now(),
            price_history: Vec::new(),
            volume_history: Vec::new(),
            current_position: None,
            last_prediction_time: None,
        }
    }
}

/// Performance tracking for the strategy
#[derive(Debug, Clone)]
struct PerformanceTracker {
    /// Total trades executed
    total_trades: u64,
    /// Winning trades
    winning_trades: u64,
    /// Total PnL
    total_pnl: Decimal,
    /// Maximum drawdown
    max_drawdown: Decimal,
    /// Current drawdown
    current_drawdown: Decimal,
    /// Peak portfolio value
    peak_value: Decimal,
    /// Model prediction accuracy
    #[allow(dead_code)]
    model_accuracy: HashMap<String, f64>,
}

impl Default for PerformanceTracker {
    fn default() -> Self {
        Self {
            total_trades: 0,
            winning_trades: 0,
            total_pnl: Decimal::ZERO,
            max_drawdown: Decimal::ZERO,
            current_drawdown: Decimal::ZERO,
            peak_value: Decimal::ZERO,
            model_accuracy: HashMap::new(),
        }
    }
}

/// Feature extractor for ML models with object pooling for performance
struct FeatureExtractor {
    config: FeatureSettings,
    // OPTIMIZATION: Reusable buffers to avoid allocations in hot paths
    #[allow(dead_code)]
    price_buffer: Vec<f64>,
    #[allow(dead_code)]
    volume_buffer: Vec<f64>,
    #[allow(dead_code)]
    returns_buffer: Vec<f64>,
    #[allow(dead_code)]
    gains_buffer: Vec<f64>,
    #[allow(dead_code)]
    losses_buffer: Vec<f64>,
}

impl FeatureExtractor {
    /// Create new feature extractor with configuration
    ///
    /// # Arguments
    /// * `config` - Feature extraction configuration
    ///
    /// # Returns
    /// * `Self` - New feature extractor instance with pre-allocated buffers
    fn new(config: FeatureSettings) -> Self {
        Self {
            config,
            // Pre-allocate buffers with reasonable capacity
            price_buffer: Vec::with_capacity(1024),
            volume_buffer: Vec::with_capacity(1024),
            returns_buffer: Vec::with_capacity(1024),
            gains_buffer: Vec::with_capacity(1024),
            losses_buffer: Vec::with_capacity(1024),
        }
    }

    /// Extract features from market data
    ///
    /// # Arguments
    /// * `market_state` - Current market state containing price and volume history
    ///
    /// # Returns
    /// * `Result<Features>` - Extracted feature vector ready for ML model input
    ///
    /// # Errors
    /// Returns error if feature extraction fails or insufficient data
    async fn extract_features(&self, market_state: &MarketState) -> Result<Features> {
        let mut feature_values = Vec::new();
        let mut feature_names = Vec::new();

        // Extract price features
        if let Some(features) = self.extract_price_features(market_state).await? {
            feature_values.extend(features.0);
            feature_names.extend(features.1);
        }

        // Extract volume features
        if let Some(features) = self.extract_volume_features(market_state).await? {
            feature_values.extend(features.0);
            feature_names.extend(features.1);
        }

        // Extract technical indicators
        if let Some(features) = self.extract_technical_features(market_state).await? {
            feature_values.extend(features.0);
            feature_names.extend(features.1);
        }

        Ok(Features {
            values: feature_values,
            names: feature_names,
            timestamp: std::time::SystemTime::now()
                .duration_since(std::time::UNIX_EPOCH)
                .unwrap_or_default()
                .as_micros() as u64,
            symbol: None, // Symbol will be set by the calling context when available
        })
    }

    /// Extract price-based features from market data
    ///
    /// # Arguments
    /// * `market_state` - Market state with price history
    ///
    /// # Returns
    /// * `Result<Option<(Vec<f64>, Vec<String>)>>` - Feature values and names, or None if insufficient data
    async fn extract_price_features(
        &self,
        market_state: &MarketState,
    ) -> Result<Option<(Vec<f64>, Vec<String>)>> {
        if market_state.price_history.len() < 2 {
            return Ok(None);
        }

        let mut values = Vec::new();
        let mut names = Vec::new();

        let prices: Vec<f64> = market_state
            .price_history
            .iter()
            .map(|(_, price)| price.to_f64().unwrap_or(0.0))
            .collect();

        // Calculate returns
        if self.config.price_features.contains(&"returns".to_string()) {
            let returns = self.calculate_returns(&prices);
            values.extend(returns);
            names.push("returns".to_string());
        }

        // Calculate volatility
        if self
            .config
            .price_features
            .contains(&"volatility".to_string())
        {
            let volatility = self.calculate_volatility(&prices);
            values.push(volatility);
            names.push("volatility".to_string());
        }

        Ok(Some((values, names)))
    }

    /// Extract volume-based features from market data
    ///
    /// # Arguments
    /// * `market_state` - Market state with volume history
    ///
    /// # Returns
    /// * `Result<Option<(Vec<f64>, Vec<String>)>>` - Feature values and names, or None if insufficient data
    async fn extract_volume_features(
        &self,
        market_state: &MarketState,
    ) -> Result<Option<(Vec<f64>, Vec<String>)>> {
        if market_state.volume_history.len() < 2 {
            return Ok(None);
        }

        let mut values = Vec::new();
        let mut names = Vec::new();

        let volumes: Vec<f64> = market_state
            .volume_history
            .iter()
            .map(|(_, volume)| volume.to_f64().unwrap_or(0.0))
            .collect();

        // Average volume
        if self.config.volume_features.contains(&"volume".to_string()) {
            let avg_volume = volumes.iter().sum::<f64>() / volumes.len() as f64;
            values.push(avg_volume);
            names.push("avg_volume".to_string());
        }

        Ok(Some((values, names)))
    }

    /// Extract technical indicator features from market data
    ///
    /// # Arguments
    /// * `market_state` - Market state with sufficient price history for indicators
    ///
    /// # Returns
    /// * `Result<Option<(Vec<f64>, Vec<String>)>>` - Technical indicator values and names, or None if insufficient data
    async fn extract_technical_features(
        &self,
        market_state: &MarketState,
    ) -> Result<Option<(Vec<f64>, Vec<String>)>> {
        if market_state.price_history.len() < 14 {
            // Need minimum data for indicators
            return Ok(None);
        }

        let mut values = Vec::new();
        let mut names = Vec::new();

        let prices: Vec<f64> = market_state
            .price_history
            .iter()
            .map(|(_, price)| price.to_f64().unwrap_or(0.0))
            .collect();

        // RSI
        if self
            .config
            .technical_indicators
            .contains(&"rsi".to_string())
        {
            let rsi = self.calculate_rsi(&prices, 14);
            values.push(rsi);
            names.push("rsi_14".to_string());
        }

        Ok(Some((values, names)))
    }

    /// Calculate returns from price series with SIMD optimization
    ///
    /// # Arguments
    /// * `prices` - Array of price values
    ///
    /// # Returns
    /// * `Vec<f64>` - Vector of return percentages
    ///
    /// # Note
    /// Uses SIMD instructions on x86_64 for performance when available
    fn calculate_returns(&self, prices: &[f64]) -> Vec<f64> {
        // OPTIMIZATION: Use SIMD for vectorized return calculations
        if prices.len() < 2 {
            return Vec::new();
        }

        #[cfg(target_arch = "x86_64")]
        {
            if is_x86_feature_detected!("avx2") {
                return self.calculate_returns_simd(prices);
            }
        }

        // Fallback to scalar implementation
        self.calculate_returns_scalar(prices)
    }

    /// Calculate returns using scalar operations (fallback)
    ///
    /// # Arguments
    /// * `prices` - Array of price values
    ///
    /// # Returns
    /// * `Vec<f64>` - Vector of return percentages
    fn calculate_returns_scalar(&self, prices: &[f64]) -> Vec<f64> {
        prices
            .windows(2)
            .map(|window| {
                if window[0] != 0.0 {
                    (window[1] - window[0]) / window[0]
                } else {
                    0.0
                }
            })
            .collect()
    }

    /// Calculate returns using SIMD AVX2 instructions (x86_64 only)
    ///
    /// # Arguments
    /// * `prices` - Array of price values
    ///
    /// # Returns
    /// * `Vec<f64>` - Vector of return percentages
    ///
    /// # Safety
    /// Uses unsafe AVX2 intrinsics for vectorized computation
    #[cfg(target_arch = "x86_64")]
    fn calculate_returns_simd(&self, prices: &[f64]) -> Vec<f64> {
        let mut returns = Vec::with_capacity(prices.len() - 1);
        let len = prices.len() - 1;

        unsafe {
            // Process 4 elements at a time with AVX2
            let mut i = 0;
            while i + 4 <= len {
                let prev = _mm256_loadu_pd(prices.as_ptr().add(i));
                let curr = _mm256_loadu_pd(prices.as_ptr().add(i + 1));

                // Calculate (curr - prev) / prev
                let diff = _mm256_sub_pd(curr, prev);
                let result = _mm256_div_pd(diff, prev);

                // Store results
                let mut temp = [0.0; 4];
                _mm256_storeu_pd(temp.as_mut_ptr(), result);

                for j in 0..4 {
                    returns.push(if prices[i + j] != 0.0 { temp[j] } else { 0.0 });
                }

                i += 4;
            }

            // Handle remaining elements
            for j in i..len {
                let ret = if prices[j] != 0.0 {
                    (prices[j + 1] - prices[j]) / prices[j]
                } else {
                    0.0
                };
                returns.push(ret);
            }
        }

        returns
    }

    /// Calculate price volatility (standard deviation of returns)
    ///
    /// # Arguments
    /// * `prices` - Array of price values
    ///
    /// # Returns
    /// * `f64` - Volatility as standard deviation of returns
    fn calculate_volatility(&self, prices: &[f64]) -> f64 {
        let returns = self.calculate_returns(prices);
        if returns.is_empty() {
            return 0.0;
        }

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

        variance.sqrt()
    }

    /// Calculate Relative Strength Index (RSI)
    ///
    /// # Arguments
    /// * `prices` - Array of price values
    /// * `period` - Period for RSI calculation (typically 14)
    ///
    /// # Returns
    /// * `f64` - RSI value between 0 and 100
    fn calculate_rsi(&self, prices: &[f64], period: usize) -> f64 {
        if prices.len() < period + 1 {
            return 50.0; // Neutral RSI
        }

        let mut gains = Vec::new();
        let mut losses = Vec::new();

        for window in prices.windows(2) {
            let change = window[1] - window[0];
            if change > 0.0 {
                gains.push(change);
                losses.push(0.0);
            } else {
                gains.push(0.0);
                losses.push(-change);
            }
        }

        if gains.len() < period {
            return 50.0;
        }

        let avg_gain = gains.iter().rev().take(period).sum::<f64>() / period as f64;
        let avg_loss = losses.iter().rev().take(period).sum::<f64>() / period as f64;

        if avg_loss == 0.0 {
            return 100.0;
        }

        let rs = avg_gain / avg_loss;
        100.0 - (100.0 / (1.0 + rs))
    }
}

/// Risk manager for position sizing and risk controls
struct RiskManager {
    config: RiskSettings,
}

impl RiskManager {
    /// Create new risk manager with configuration
    ///
    /// # Arguments
    /// * `config` - Risk management settings
    ///
    /// # Returns
    /// * `Self` - New risk manager instance
    fn new(config: RiskSettings) -> Self {
        Self { config }
    }

    /// Calculate position size using Kelly criterion
    ///
    /// # Arguments
    /// * `prediction` - Model prediction with confidence score
    /// * `account_value` - Current account value
    /// * `current_price` - Current market price
    ///
    /// # Returns
    /// * `Result<Decimal>` - Position size in shares/units
    ///
    /// # Note
    /// Uses conservative Kelly fraction scaling for risk management
    fn calculate_position_size(
        &self,
        prediction: &ModelPrediction,
        account_value: Decimal,
        current_price: Decimal,
    ) -> Result<Decimal> {
        // Simple Kelly-based position sizing
        let confidence = prediction.confidence;
        let edge = (confidence - 0.5) * 2.0; // Convert to [-1, 1] range

        if edge <= 0.0 {
            return Ok(Decimal::ZERO);
        }

        // Kelly fraction with conservative scaling
        let kelly_size =
            Decimal::try_from(edge * self.config.kelly_fraction).unwrap_or(Decimal::ZERO);

        let max_size = account_value
            * Decimal::try_from(self.config.max_drawdown).unwrap_or(Decimal::new(5, 2)); // 5% fallback

        let position_value = kelly_size * account_value;
        let position_size = if current_price > Decimal::ZERO {
            position_value / current_price
        } else {
            Decimal::ZERO
        };

        Ok(position_size.min(max_size / current_price))
    }

    /// Check if trade passes risk checks
    ///
    /// # Arguments
    /// * `signal` - Trading signal to validate
    /// * `current_position` - Current position if any
    /// * `account_value` - Current account value
    ///
    /// # Returns
    /// * `Result<bool>` - True if trade passes all risk checks
    fn validate_trade(
        &self,
        signal: &TradingSignal,
        _current_position: Option<&Position>,
        account_value: Decimal,
    ) -> Result<bool> {
        // Check position size limits
        if let Some(price) = signal.target_price {
            let trade_value = signal.quantity.to_decimal()? * price.to_decimal()?;
            let position_fraction = trade_value / account_value;

            if position_fraction
                > Decimal::try_from(self.config.max_drawdown).unwrap_or(Decimal::new(10, 2))
            {
                debug!(
                    "Trade rejected: position size too large ({:.2}%)",
                    position_fraction * Decimal::from(100)
                );
                return Ok(false);
            }
        }

        // Additional risk checks can be added here
        Ok(true)
    }
}

impl AdaptiveStrategyRunner {
    /// Create new adaptive strategy runner
    ///
    /// # Arguments
    /// * `config` - Configuration for adaptive strategy
    ///
    /// # Returns
    /// * `Self` - New adaptive strategy runner with initialized components
    pub fn new(config: AdaptiveStrategyConfig) -> Self {
        let feature_extractor = Arc::new(FeatureExtractor::new(config.feature_settings.clone()));
        let risk_manager = Arc::new(RiskManager::new(config.risk_settings.clone()));

        Self {
            config,
            market_state: Arc::new(RwLock::new(MarketState::default())),
            predictions_cache: Arc::new(DashMap::new()),
            performance_tracker: Arc::new(RwLock::new(PerformanceTracker::default())),
            feature_extractor,
            risk_manager,
        }
    }

    /// Get ensemble prediction from all active models (optimized for HFT performance)
    ///
    /// # Arguments
    /// * `features` - Feature vector for prediction
    ///
    /// # Returns
    /// * `Result<ModelPrediction>` - Ensemble prediction with confidence-weighted averaging
    ///
    /// # Note
    /// Uses lock-free caching and parallel model execution for low-latency performance
    async fn get_ensemble_prediction(&self, features: &Features) -> Result<ModelPrediction> {
        let registry = get_global_registry();

        // OPTIMIZATION: Use predict_selected for parallel model execution
        let predictions = registry
            .predict_selected(&self.config.active_models, features)
            .await;

        // Filter successful predictions
        let valid_predictions: Vec<ModelPrediction> = predictions
            .into_iter()
            .filter_map(|result| result.ok())
            .collect();

        if valid_predictions.is_empty() {
            return Err(anyhow::anyhow!("No valid predictions from any model"));
        }

        // Ensemble using confidence-weighted average
        let total_confidence: f64 = valid_predictions.iter().map(|p| p.confidence).sum();

        if total_confidence == 0.0 {
            return Err(anyhow::anyhow!("Zero total confidence in predictions"));
        }

        let weighted_value = valid_predictions
            .iter()
            .map(|p| p.value * p.confidence)
            .sum::<f64>()
            / total_confidence;

        let ensemble_confidence = valid_predictions.iter().map(|p| p.confidence).sum::<f64>()
            / valid_predictions.len() as f64;

        // OPTIMIZATION: Use lock-free DashMap instead of async RwLock
        for prediction in &valid_predictions {
            self.predictions_cache
                .insert(prediction.model_id.clone(), prediction.clone());
        }

        Ok(ModelPrediction::new(
            "ensemble".to_string(),
            weighted_value,
            ensemble_confidence,
        ))
    }

    /// Generate trading signal from prediction
    ///
    /// # Arguments
    /// * `prediction` - Model prediction with confidence and direction
    /// * `symbol` - Symbol to trade
    /// * `current_price` - Current market price
    /// * `account_value` - Current account value for position sizing
    ///
    /// # Returns
    /// * `Result<Option<TradingSignal>>` - Trading signal if confidence threshold is met
    fn generate_signal(
        &self,
        prediction: &ModelPrediction,
        symbol: Symbol,
        current_price: Decimal,
        account_value: Decimal,
    ) -> Result<Option<TradingSignal>> {
        // Check confidence threshold
        if prediction.confidence < self.config.min_confidence {
            debug!(
                "Prediction confidence {:.3} below threshold {:.3}",
                prediction.confidence, self.config.min_confidence
            );
            return Ok(None);
        }

        // Determine trade direction
        let side = if prediction.value > 0.5 {
            OrderSide::Buy
        } else if prediction.value < -0.5 {
            OrderSide::Sell
        } else {
            return Ok(None); // Neutral signal
        };

        // Calculate position size
        let quantity =
            self.risk_manager
                .calculate_position_size(prediction, account_value, current_price)?;

        if quantity <= Decimal::ZERO {
            return Ok(None);
        }

        let signal_type = match side {
            OrderSide::Buy => SignalType::Buy,
            OrderSide::Sell => SignalType::Sell,
        };

        let quantity_as_quantity = Quantity::from_f64(quantity.to_f64().unwrap_or(0.0))?;

        let mut metadata = HashMap::new();
        metadata.insert("strategy".to_string(), serde_json::json!("adaptive_ml"));
        metadata.insert(
            "ensemble_confidence".to_string(),
            serde_json::json!(prediction.confidence),
        );
        metadata.insert(
            "prediction_value".to_string(),
            serde_json::json!(prediction.value),
        );
        metadata.insert(
            "model_count".to_string(),
            serde_json::json!(self.config.active_models.len()),
        );

        let signal = TradingSignal {
            symbol,
            signal_type,
            quantity: quantity_as_quantity,
            target_price: Some(Price::from_f64(current_price.to_f64().unwrap_or(0.0))?),
            stop_loss: None,
            take_profit: None,
            confidence: Decimal::try_from(prediction.confidence).unwrap_or(Decimal::ZERO),
            metadata,
        };

        Ok(Some(signal))
    }
}

#[async_trait(?Send)]
impl Strategy for AdaptiveStrategyRunner {
    fn name(&self) -> &str {
        "adaptive_ml_strategy"
    }

    async fn initialize(
        &mut self,
        initial_capital: Decimal,
        _config: StrategyConfig,
    ) -> Result<()> {
        info!(
            "Initializing Adaptive ML Strategy with capital: {}",
            initial_capital
        );

        {
            let mut tracker = self.performance_tracker.write();
            tracker.peak_value = initial_capital;
        }

        // Verify models are available
        let registry = get_global_registry();
        let available_models = registry.get_model_names();

        for model_name in &self.config.active_models {
            if !available_models.contains(model_name) {
                warn!("Model {} not found in registry", model_name);
            }
        }

        info!("Adaptive ML Strategy initialized successfully");
        Ok(())
    }

    async fn on_market_event(
        &mut self,
        event: &MarketEvent,
        context: &StrategyContext,
    ) -> Result<Vec<TradingSignal>> {
        let mut signals = Vec::new();

        match event {
            MarketEvent::Trade {
                symbol,
                price,
                timestamp,
                ..
            } => {
                // Update market state
                {
                    let mut state = self.market_state.write();
                    state.current_time = *timestamp;
                    state.price_history.push((*timestamp, price.to_decimal()?));
                    // Note: volume not available in MarketEvent::Trade, using placeholder
                    // state.volume_history.push((*timestamp, Volume::ZERO));

                    // Keep only recent history
                    let max_history = self.config.lookback_period;
                    if state.price_history.len() > max_history {
                        let excess = state.price_history.len() - max_history;
                        state.price_history.drain(0..excess);
                    }
                    if state.volume_history.len() > max_history {
                        let excess = state.volume_history.len() - max_history;
                        state.volume_history.drain(0..excess);
                    }
                }

                // Extract features and get prediction
                let market_state = self.market_state.read().clone();

                if market_state.price_history.len() >= 10 {
                    // Minimum data for prediction
                    match self.feature_extractor.extract_features(&market_state).await {
                        Ok(features) => {
                            match self.get_ensemble_prediction(&features).await {
                                Ok(prediction) => {
                                    if let Some(signal) = self.generate_signal(
                                        &prediction,
                                        symbol.clone(),
                                        price.to_decimal()?,
                                        context.account_balance,
                                    )? {
                                        // Validate trade with risk manager
                                        let current_position = context.positions.get(symbol);
                                        if self.risk_manager.validate_trade(
                                            &signal,
                                            current_position,
                                            context.account_balance,
                                        )? {
                                            signals.push(signal);
                                        }
                                    }
                                },
                                Err(e) => {
                                    debug!("Prediction failed: {}", e);
                                },
                            }
                        },
                        Err(e) => {
                            debug!("Feature extraction failed: {}", e);
                        },
                    }
                }
            },
            _ => {
                // Handle other event types if needed
            },
        }

        Ok(signals)
    }

    async fn on_order_update(&mut self, order: &Order, _context: &StrategyContext) -> Result<()> {
        if order.status == OrderStatus::Filled {
            let mut tracker = self.performance_tracker.write();
            tracker.total_trades += 1;

            info!(
                "Order filled: {} {} @ {}",
                order.side,
                order.quantity,
                order
                    .average_price
                    .unwrap_or_else(|| order.price.unwrap_or(Price::ZERO))
            );
        }

        Ok(())
    }

    async fn on_position_update(
        &mut self,
        position: &Position,
        context: &StrategyContext,
    ) -> Result<()> {
        // Update market state with current position
        {
            let mut state = self.market_state.write();
            state.current_position = Some(position.clone());
        }

        // Update performance tracking
        {
            let mut tracker = self.performance_tracker.write();

            if context.account_balance > tracker.peak_value {
                tracker.peak_value = context.account_balance;
                tracker.current_drawdown = Decimal::ZERO;
            } else {
                tracker.current_drawdown =
                    (tracker.peak_value - context.account_balance) / tracker.peak_value;
                if tracker.current_drawdown > tracker.max_drawdown {
                    tracker.max_drawdown = tracker.current_drawdown;
                }
            }
        }

        Ok(())
    }

    async fn finalize(&mut self, context: &StrategyContext) -> Result<StrategyResult> {
        let tracker = self.performance_tracker.read();

        let total_return = if tracker.peak_value > Decimal::ZERO {
            (context.account_balance - tracker.peak_value) / tracker.peak_value
        } else {
            Decimal::ZERO
        };

        let win_rate = if tracker.total_trades > 0 {
            Decimal::from(tracker.winning_trades) / Decimal::from(tracker.total_trades)
        } else {
            Decimal::ZERO
        };

        // Calculate Sharpe ratio (simplified)
        let sharpe_ratio = if tracker.max_drawdown > Decimal::ZERO {
            total_return / tracker.max_drawdown
        } else {
            Decimal::ZERO
        };

        Ok(StrategyResult {
            strategy_name: "adaptive_ml_strategy".to_string(),
            total_return,
            annualized_return: total_return, // Simplified
            max_drawdown: tracker.max_drawdown,
            sharpe_ratio,
            total_trades: tracker.total_trades,
            win_rate,
            avg_trade_return: if tracker.total_trades > 0 {
                tracker.total_pnl / Decimal::from(tracker.total_trades)
            } else {
                Decimal::ZERO
            },
            final_value: context.account_balance,
            trades: vec![], // Would be populated with detailed trade records
            performance_timeline: vec![], // Would be populated with performance snapshots
        })
    }

    async fn get_state(&self) -> Result<serde_json::Value> {
        let market_state = self.market_state.read();
        let tracker = self.performance_tracker.read();
        let cache = &self.predictions_cache;

        Ok(serde_json::json!({
            "strategy_name": "adaptive_ml_strategy",
            "config": self.config,
            "current_time": market_state.current_time,
            "price_history_length": market_state.price_history.len(),
            "volume_history_length": market_state.volume_history.len(),
            "current_position": market_state.current_position,
            "total_trades": tracker.total_trades,
            "max_drawdown": tracker.max_drawdown,
            "cached_predictions": cache.len(),
            "active_models": self.config.active_models,
        }))
    }
}

/// Create a configured adaptive strategy runner
///
/// # Returns
/// * `AdaptiveStrategyRunner` - Strategy runner with default configuration
pub fn create_adaptive_strategy() -> AdaptiveStrategyRunner {
    AdaptiveStrategyRunner::new(AdaptiveStrategyConfig::default())
}

/// Create adaptive strategy with custom configuration
///
/// # Arguments
/// * `config` - Custom adaptive strategy configuration
///
/// # Returns
/// * `AdaptiveStrategyRunner` - Strategy runner with specified configuration
pub fn create_adaptive_strategy_with_config(
    config: AdaptiveStrategyConfig,
) -> AdaptiveStrategyRunner {
    AdaptiveStrategyRunner::new(config)
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_adaptive_strategy_config_default() {
        let config = AdaptiveStrategyConfig::default();
        assert_eq!(config.active_models.len(), 5);
        assert_eq!(config.min_confidence, 0.65);
        assert!(config.max_position_size > 0.0);
    }

    #[test]
    fn test_risk_settings_default() {
        let risk = RiskSettings::default();
        assert_eq!(risk.max_drawdown, 0.10);
        assert!(risk.kelly_fraction > 0.0);
    }

    #[tokio::test]
    async fn test_adaptive_strategy_creation() {
        let strategy = create_adaptive_strategy();
        assert_eq!(strategy.name(), "adaptive_ml_strategy");
    }

    #[tokio::test]
    async fn test_feature_extractor() {
        let config = FeatureSettings::default();
        let extractor = FeatureExtractor::new(config);

        let mut market_state = MarketState::default();
        market_state.price_history = vec![
            (Utc::now(), Decimal::from(100)),
            (Utc::now(), Decimal::from(101)),
            (Utc::now(), Decimal::from(102)),
        ];

        let features = extractor.extract_features(&market_state).await;
        assert!(features.is_ok());
    }
}