//! ML-enhanced portfolio optimization
//!
//! This module implements machine learning approaches to portfolio optimization including
//! deep reinforcement learning, neural portfolio policies, and adaptive allocation strategies.
//!
//! # Features
//! - Deep reinforcement learning for portfolio management
//! - Neural network-based allocation strategies
//! - Adaptive risk parity with ML
//! - Factor modeling with deep learning
//! - Transaction cost optimization
//! - Market regime-aware allocation

use crate::error::Result;

/// ML-enhanced portfolio optimizer interface
pub trait MLPortfolioOptimizer {
    // TODO: Define interface methods
}

// TODO: Implement ML portfolio optimization
// - DeepRLPortfolio using PPO/A3C algorithms
// - NeuralPortfolioPolicy with direct optimization
// - AdaptiveRiskParity with regime detection
// - DeepFactorModel for latent factor extraction
// - TransactionCostNN for impact modeling
// - RegimeAwareAllocator with market state detection
// - OnlinePortfolioSelection with regret bounds

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

    // TODO: Add tests for ML portfolio optimization
}
