Struct AvellanedaLotSize 

pub struct AvellanedaLotSize {
    pub gamma: f64,
    pub sigma: f64,
    pub kappa: f64,
    pub a: f64,
    pub lot_size: i32,
}

Avellaneda-Stoikov with lot size lot_size on the inventory dimension.

Fields

gamma: f64

Risk aversion.

sigma: f64

Mid-price volatility.

kappa: f64

Fill-intensity decay.

a: f64

Base order arrival intensity.

lot_size: i32

Number of inventory units moved per fill (positive integer).

Implementations

impl AvellanedaLotSize

pub fn new(gamma: f64, sigma: f64, kappa: f64, a: f64, lot_size: i32) -> Self

Creates the model.

Panics

Panics if lot_size is not positive.

Examples

use solver::models::avellaneda_lot_size::AvellanedaLotSize;
let model = AvellanedaLotSize::new(0.5, 0.5, 1.5, 140.0, 2);
assert_eq!(model.lot_size, 2);

Trait Implementations

impl Clone for AvellanedaLotSize

fn clone(&self) -> AvellanedaLotSize

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl ControlProblem<2> for AvellanedaLotSize

type Control = MarketMakingControl

The action type. For Merton this is a scalar portfolio fraction; for market making it is a pair of bid/ask intensities.

fn optimize( &self, _t: f64, _state: &[f64; 2], derivs: &StateDerivatives<2>, ) -> Self::Control

Returns the control that maximizes the driver at (t, state).

fn running_reward( &self, _t: f64, _state: &[f64; 2], control: &Self::Control, ) -> f64

Returns the running reward f(t,x,u).

fn bsde_driver( &self, t: f64, state: &[f64; 2], control: &Self::Control, derivs: &StateDerivatives<2>, _dt: f64, ) -> f64

Returns the backward driver consumed by the BSDE regression solver.

fn generator( &self, _t: f64, state: &[f64; 2], _control: &Self::Control, _derivs: &StateDerivatives<2>, ) -> f64

Returns the infinitesimal generator L^u V for the given control and derivative bundle.

fn terminal(&self, _state: &[f64; 2]) -> f64

Terminal value g(x) at the horizon.

fn discount_rate(&self, _state: &[f64; 2]) -> f64

Discount rate r(t,x). Defaults to zero.

fn constant_discount_rate(&self) -> Option<f64>

Constant discount rate hint. Defaults to None (state dependent).

fn next_step( &self, _t: f64, state: &[f64; 2], dt: f64, noise: &[f64; 2], ) -> [f64; 2]

Advances the state one step under the optimal control at forward time t.

fn next_step_controlled( &self, _t: f64, state: &[f64; 2], control: &Self::Control, dt: f64, noise: &[f64; 2], ) -> [f64; 2]

Advances the state one step under an explicitly supplied control.

fn is_reduced_value(&self) -> bool

Whether the value this problem solves is a reduced value.

fn is_diffusion_dimension(&self, dim: usize) -> bool

Whether a dimension is driven by Brownian diffusion.

fn gradient_step(&self, dim: usize) -> f64

Physical finite-difference step for a dimension, used by mesh-free gradient stencils.

fn driver( &self, t: f64, state: &[f64; N], control: &Self::Control, derivs: &StateDerivatives<N>, ) -> f64

Returns the full HJB driver f(t,x,u) + L^u V.

fn apply_constraint(&self, _state: &[f64; N], value: f64) -> f64

Optional pointwise constraint on the value, e.g. the early-exercise obstacle V >= payoff for an American option.

impl Copy for AvellanedaLotSize

impl Debug for AvellanedaLotSize

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl PdeProblem<2> for AvellanedaLotSize

fn dimension_kind(&self, dim: usize) -> DimensionKind

Discretization kind for dim.

fn transport( &self, _t: f64, state: &[f64; 2], control: &Self::Control, derivs: &StateDerivatives<2>, ) -> Transport<2>

Transport and local source for the given control and derivatives.

fn jump_kernel( &self, _t: f64, _state: &[f64; 2], control: &Self::Control, _derivs: &StateDerivatives<2>, _transport: &Transport<2>, ) -> JumpKernel<2>

Jump kernel for arbitrary-amplitude jump dimensions.