ThotDjehuty
e67b0f8376
New Rust portfolio_optimization module with PyO3 bindings: - CARA/CRRA utility maximization via projected gradient descent - General-purpose convex objective solver on simplex (ProjectedGradientSolver) - Mean-variance optimization (max Sharpe, target return, min variance) - Equal Risk Contribution (ERC) portfolio allocation - Python bindings: cara_optimal_weights, mean_variance_optimal_weights, min_variance_weights, erc_weights - 6/6 unit tests passing Convergence fix: removed gradient-norm criterion on simplex boundary (projected gradient never vanishes at constrained optimum). Default learning rate increased from 0.005 to 0.1.
76 lines
2.1 KiB
Rust
76 lines
2.1 KiB
Rust
//! Core traits for portfolio utility and convex optimisation.
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use crate::core::OptimizrError;
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/// A utility function U(x) mapping wealth → utility.
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pub trait UtilityFunction: Send + Sync {
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/// U(x) — the utility of wealth level x.
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fn utility(&self, x: f64) -> f64;
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/// U'(x) — first derivative (marginal utility).
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fn marginal_utility(&self, x: f64) -> f64;
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/// (U')^{-1}(y) — inverse marginal utility (used in duality).
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fn inverse_marginal(&self, y: f64) -> f64;
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/// Risk-aversion coefficient A(x) = -U''(x) / U'(x).
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fn risk_aversion(&self, x: f64) -> f64;
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/// Name identifier for logging / serialisation.
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fn name(&self) -> &str;
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}
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/// Convex objective f(w) over portfolio weights w ∈ ℝ^n.
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///
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/// Used by convex solvers (projected gradient, ADMM, etc.).
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pub trait ConvexObjective: Send + Sync {
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/// f(w) — objective value.
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fn value(&self, w: &[f64]) -> f64;
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/// ∇f(w) — gradient vector.
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fn gradient(&self, w: &[f64]) -> Vec<f64>;
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/// Dimension of the weight vector.
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fn dim(&self) -> usize;
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}
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/// Convex constraint g(w) ≤ 0.
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pub trait ConvexConstraint: Send + Sync {
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/// g(w) — constraint value (feasible when ≤ 0).
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fn value(&self, w: &[f64]) -> f64;
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/// ∇g(w) — gradient of constraint function.
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fn gradient(&self, w: &[f64]) -> Vec<f64>;
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}
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/// Result of a portfolio optimisation.
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#[derive(Debug, Clone)]
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pub struct PortfolioResult {
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pub weights: Vec<f64>,
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pub utility: f64,
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pub expected_return: f64,
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pub portfolio_variance: f64,
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pub iterations: usize,
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pub converged: bool,
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}
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impl PortfolioResult {
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pub fn sharpe_ratio(&self, risk_free: f64) -> f64 {
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let vol = self.portfolio_variance.sqrt();
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if vol < 1e-15 {
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return 0.0;
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}
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(self.expected_return - risk_free) / vol
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}
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}
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/// Generic portfolio optimiser trait.
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pub trait PortfolioOptimizer: Send + Sync {
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fn optimize(
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&self,
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mu: &[f64],
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cov: &[Vec<f64>],
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max_weight: f64,
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) -> Result<PortfolioResult, OptimizrError>;
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}
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