aprender-core 0.29.3

//! Core Monte Carlo simulation engine
//!
//! This module provides the foundation for Monte Carlo simulations,
//! including reproducible random number generation, variance reduction
//! techniques, and convergence diagnostics.

mod convergence;
mod rng;
mod types;
mod variance;

pub use convergence::{autocorrelation, calculate_ess, ess_autocorr, ConvergenceDiagnostics};
pub use rng::MonteCarloRng;
pub use types::{
    percentile, Budget, PathMetadata, Percentiles, SimulationPath, Statistics, TimeHorizon,
    TimeStep,
};
pub use variance::{empirical_variance_reduction, inverse_normal_cdf, VarianceReduction};

/// Trait for simulation models
pub trait SimulationModel: Send + Sync {
    /// Get the model name
    fn name(&self) -> &'static str;

    /// Generate a single simulation path
    fn generate_path(
        &self,
        rng: &mut MonteCarloRng,
        time_horizon: &TimeHorizon,
        path_id: usize,
    ) -> SimulationPath;

    /// Generate an antithetic path for variance reduction
    fn generate_antithetic_path(
        &self,
        rng: &mut MonteCarloRng,
        time_horizon: &TimeHorizon,
        path_id: usize,
    ) -> SimulationPath {
        // Default implementation just generates a regular path
        self.generate_path(rng, time_horizon, path_id)
    }
}

/// Monte Carlo simulation engine
#[derive(Debug, Clone)]
pub struct MonteCarloEngine {
    /// Number of simulation paths
    pub n_simulations: usize,

    /// Random seed for reproducibility
    pub seed: u64,

    /// Variance reduction technique
    pub variance_reduction: VarianceReduction,

    /// Target relative precision for convergence
    pub target_precision: f64,

    /// Maximum simulations before stopping
    pub max_simulations: usize,
}

impl MonteCarloEngine {
    /// Create a new engine with specified seed
    #[must_use]
    pub fn new(seed: u64) -> Self {
        Self {
            n_simulations: 10_000,
            seed,
            variance_reduction: VarianceReduction::Antithetic,
            target_precision: 0.01,
            max_simulations: 100_000,
        }
    }

    /// Create a reproducible engine with specific seed
    #[must_use]
    pub fn reproducible(seed: u64) -> Self {
        Self::new(seed)
    }

    /// Set number of simulations
    #[must_use]
    pub fn with_n_simulations(mut self, n: usize) -> Self {
        self.n_simulations = n;
        self
    }

    /// Set variance reduction technique
    #[must_use]
    pub fn with_variance_reduction(mut self, vr: VarianceReduction) -> Self {
        self.variance_reduction = vr;
        self
    }

    /// Set target precision
    #[must_use]
    pub fn with_target_precision(mut self, precision: f64) -> Self {
        self.target_precision = precision;
        self
    }

    /// Set maximum simulations
    #[must_use]
    pub fn with_max_simulations(mut self, max: usize) -> Self {
        self.max_simulations = max;
        self
    }

    /// Run simulation with given model
    pub fn simulate<M: SimulationModel>(
        &self,
        model: &M,
        time_horizon: &TimeHorizon,
    ) -> SimulationResult {
        let mut rng = MonteCarloRng::new(self.seed);
        let use_antithetic = matches!(self.variance_reduction, VarianceReduction::Antithetic);

        let effective_n = if use_antithetic {
            self.n_simulations / 2
        } else {
            self.n_simulations
        };

        let mut paths = Vec::with_capacity(self.n_simulations);
        let mut diagnostics = ConvergenceDiagnostics::new();

        for i in 0..effective_n {
            // Generate primary path
            let path = model.generate_path(&mut rng, time_horizon, i);
            paths.push(path);

            // Generate antithetic path if enabled
            if use_antithetic {
                let antithetic = model.generate_antithetic_path(&mut rng, time_horizon, i);
                paths.push(antithetic);
            }

            // Update diagnostics periodically
            if (i + 1) % 100 == 0 || i == effective_n - 1 {
                diagnostics.update(&paths);

                // Check for early convergence
                if diagnostics.is_converged(self.target_precision) && i >= 100 {
                    break;
                }
            }
        }

        SimulationResult {
            paths,
            diagnostics,
            model_name: model.name().to_string(),
            seed: self.seed,
        }
    }

    /// Run simulation with custom budget
    pub fn simulate_with_budget<M: SimulationModel>(
        &self,
        model: &M,
        time_horizon: &TimeHorizon,
        budget: &Budget,
    ) -> SimulationResult {
        let mut rng = MonteCarloRng::new(self.seed);
        let use_antithetic = matches!(self.variance_reduction, VarianceReduction::Antithetic);

        let max_n = budget.max_simulations();
        let effective_n = if use_antithetic { max_n / 2 } else { max_n };

        let mut paths = Vec::with_capacity(max_n);
        let mut diagnostics = ConvergenceDiagnostics::new();
        let mut converged = false;

        for i in 0..effective_n {
            let path = model.generate_path(&mut rng, time_horizon, i);
            paths.push(path);

            if use_antithetic {
                let antithetic = model.generate_antithetic_path(&mut rng, time_horizon, i);
                paths.push(antithetic);
            }

            // Check budget-specific stopping criteria
            if let Budget::Convergence {
                patience,
                min_delta,
                ..
            } = &budget
            {
                if (i + 1) % 100 == 0 {
                    diagnostics.update(&paths);

                    // Check convergence with patience
                    if diagnostics.running_mean_history.len() >= *patience {
                        let recent = &diagnostics.running_mean_history
                            [diagnostics.running_mean_history.len() - patience..];

                        let range = recent.iter().copied().fold(f64::NEG_INFINITY, f64::max)
                            - recent.iter().copied().fold(f64::INFINITY, f64::min);

                        if range < *min_delta {
                            converged = true;
                            break;
                        }
                    }
                }
            }
        }

        if !converged {
            diagnostics.update(&paths);
        }

        SimulationResult {
            paths,
            diagnostics,
            model_name: model.name().to_string(),
            seed: self.seed,
        }
    }
}

impl Default for MonteCarloEngine {
    fn default() -> Self {
        Self::new(42)
    }
}

/// Result of a Monte Carlo simulation
#[derive(Debug, Clone)]
pub struct SimulationResult {
    /// All simulation paths
    pub paths: Vec<SimulationPath>,

    /// Convergence diagnostics
    pub diagnostics: ConvergenceDiagnostics,

    /// Model name
    pub model_name: String,

    /// Seed used for simulation
    pub seed: u64,
}

impl SimulationResult {
    /// Get number of paths
    #[must_use]
    pub fn n_paths(&self) -> usize {
        self.paths.len()
    }

    /// Get final values from all paths
    #[must_use]
    pub fn final_values(&self) -> Vec<f64> {
        self.paths
            .iter()
            .filter_map(SimulationPath::final_value)
            .collect()
    }

    /// Get total returns from all paths
    #[must_use]
    pub fn total_returns(&self) -> Vec<f64> {
        self.paths
            .iter()
            .filter_map(SimulationPath::total_return)
            .collect()
    }

    /// Calculate statistics for final values
    #[must_use]
    pub fn final_value_statistics(&self) -> Statistics {
        Statistics::from_values(&self.final_values())
    }

    /// Calculate statistics for returns
    #[must_use]
    pub fn return_statistics(&self) -> Statistics {
        Statistics::from_values(&self.total_returns())
    }

    /// Get values at specific time index across all paths
    #[must_use]
    pub fn values_at_time(&self, time_idx: usize) -> Vec<f64> {
        self.paths
            .iter()
            .filter_map(|p| p.values.get(time_idx).copied())
            .collect()
    }

    /// Calculate statistics at each time point
    #[must_use]
    pub fn statistics_over_time(&self) -> Vec<Statistics> {
        if self.paths.is_empty() {
            return Vec::new();
        }

        let n_times = self.paths.first().map_or(0, |p| p.values.len());

        (0..n_times)
            .map(|t| Statistics::from_values(&self.values_at_time(t)))
            .collect()
    }

    /// Get percentiles of final values
    #[must_use]
    pub fn final_value_percentiles(&self) -> Percentiles {
        Percentiles::from_values(&self.final_values())
    }

    /// Get percentiles of returns
    #[must_use]
    pub fn return_percentiles(&self) -> Percentiles {
        Percentiles::from_values(&self.total_returns())
    }
}

#[cfg(test)]
#[path = "engine_tests.rs"]
mod tests;