//! Neural network-based derivative pricing
//!
//! This module implements deep learning approaches for pricing complex derivatives,
//! calibrating models, and solving high-dimensional pricing problems.
//!
//! # Features
//! - Deep neural networks for option pricing
//! - Physics-informed neural networks (PINNs)
//! - Generative models for scenario simulation
//! - Neural calibration of pricing models
//! - High-dimensional American option pricing
//! - Model-agnostic pricing frameworks

use crate::error::Result;

/// Neural pricing model interface
pub trait NeuralPricer {
    // TODO: Define interface methods
}

// TODO: Implement neural pricing models
// - DeepPricingNetwork with custom architectures
// - PINNPricer enforcing PDE constraints
// - GenerativePricer using VAEs/GANs
// - NeuralCalibrator for model parameters
// - AmericanOptionNN for early exercise
// - UniversalPricer for model-agnostic pricing
// - DeepHedging for optimal hedging strategies

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

    // TODO: Add tests for neural pricing models
}
