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Maximize returns with robust optimization models that account for risk, volatility, and constraints.
Run sophisticated risk scenarios and stress tests on large financial datasets.
Quickly model and optimize pricing strategies, derivatives, and other financial instruments.
Maximize returns with robust optimization models that account for risk, volatility, and constraints.
Run sophisticated risk scenarios and stress tests on large financial datasets.
Quickly model and optimize pricing strategies, derivatives, and other financial instruments.
ampl model sample
# Simple Portfolio Optimization in AMPL
set ASSETS; # Set of assets
param mean_return{ASSETS}; # Expected returns of the assets
param variance{ASSETS}; # Variance of each asset
param covar{ASSETS, ASSETS}; # Covariance between the assets
param min_return; # Minimum required return
param total_investment = 1; # Total investment, normalized to 1 (100%)
param upper_bound{ASSETS}; # Upper bound on investment in each asset
var x{ASSETS} >= 0, <= upper_bound; # Fraction invested in each asset
# Objective: Minimize portfolio variance (risk)
minimize risk: sum{i in ASSETS, j in ASSETS} x[i] * covar[i,j] * x[j];
# Constraint: Achieve at least the minimum required return
subject to return_constraint:
sum{i in ASSETS} x[i] * mean_return[i] >= min_return;
# Constraint: The total sum of investments should equal 1 (100%)
subject to budget_constraint:
sum{i in ASSETS} x[i] = total_investment;
*For illustrative purposes only – used to gain understanding of AMPL syntax in action.
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