Canonicalize test units with @safetestset

Project.toml

@@ -16,6 +16,7 @@ DiffEqNoiseProcess = "5.13.0"
 Distributions = "0.25.100"
 Pkg = "1"
 RandomNumbers = "1.5.3"
+SafeTestsets = "0.1, 1"
 Statistics = "1"
 StochasticDiffEq = "6.65.0, 7"
 Test = "1"
@@ -24,9 +25,10 @@ julia = "1.10"
 [extras]
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
+SafeTestsets = "1bc83da4-3b8d-516f-aca4-4fe02f6d838f"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 StochasticDiffEq = "789caeaf-c7a9-5a7d-9973-96adeb23e2a0"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["Test", "Statistics", "StochasticDiffEq", "Distributions", "Pkg"]
+test = ["Test", "Statistics", "StochasticDiffEq", "Distributions", "Pkg", "SafeTestsets"]

test/analytical_tests.jl

@@ -0,0 +1,171 @@
+using DiffEqFinancial, Statistics, StochasticDiffEq, Test
+
+@testset "Analytical Solutions" begin
+    @testset "GBM analytical functions" begin
+        μ = 0.05
+        σ = 0.2
+        u0 = 100.0
+        t = 1.0
+
+        # Test at t=0 (should return initial value for mean, 0 for variance)
+        @test gbm_mean(μ, u0, 0.0) ≈ u0
+        @test gbm_variance(μ, σ, u0, 0.0) ≈ 0.0 atol = 1.0e-15
+
+        # Test analytical formulas
+        @test gbm_mean(μ, u0, t) ≈ u0 * exp(μ * t)
+        @test gbm_variance(μ, σ, u0, t) ≈ u0^2 * exp(2μ * t) * (exp(σ^2 * t) - 1)
+        @test gbm_std(μ, σ, u0, t) ≈ sqrt(gbm_variance(μ, σ, u0, t))
+
+        # Test broadcasting with array of times
+        times = [0.0, 0.5, 1.0, 2.0]
+        means = gbm_mean.(μ, u0, times)
+        @test length(means) == 4
+        @test means[1] ≈ u0
+
+        # Monte Carlo validation
+        prob = GeometricBrownianMotionProblem(μ, σ, u0, (0.0, t))
+        monte_prob = EnsembleProblem(prob)
+        sol = solve(monte_prob, EM(); dt = 0.01, trajectories = 10000)
+        final_values = [sol.u[i].u[end] for i in eachindex(sol.u)]
+        simulated_mean = mean(final_values)
+        simulated_var = var(final_values)
+
+        analytical_mean = gbm_mean(μ, u0, t)
+        analytical_var = gbm_variance(μ, σ, u0, t)
+
+        # Allow 5% relative error for Monte Carlo
+        @test abs(simulated_mean - analytical_mean) / analytical_mean < 0.05
+        @test abs(simulated_var - analytical_var) / analytical_var < 0.15
+    end
+
+    @testset "OU analytical functions" begin
+        a = 0.5
+        r = 1.0
+        σ = 0.3
+        u0 = 0.5
+        t = 2.0
+
+        # Test at t=0
+        @test ou_mean(a, r, u0, 0.0) ≈ u0
+        @test ou_variance(a, σ, 0.0) ≈ 0.0 atol = 1.0e-15
+
+        # Test analytical formulas
+        @test ou_mean(a, r, u0, t) ≈ r + (u0 - r) * exp(-a * t)
+        @test ou_variance(a, σ, t) ≈ (σ^2 / (2a)) * (1 - exp(-2a * t))
+        @test ou_std(a, σ, t) ≈ sqrt(ou_variance(a, σ, t))
+
+        # Test stationary values
+        @test ou_stationary_mean(r) == r
+        @test ou_stationary_variance(a, σ) ≈ σ^2 / (2a)
+
+        # Test convergence to stationary distribution
+        large_t = 100.0
+        @test ou_mean(a, r, u0, large_t) ≈ ou_stationary_mean(r) atol = 1.0e-10
+        @test ou_variance(a, σ, large_t) ≈ ou_stationary_variance(a, σ) atol = 1.0e-10
+
+        # Monte Carlo validation
+        prob = OrnsteinUhlenbeckProblem(a, r, σ, u0, (0.0, t))
+        monte_prob = EnsembleProblem(prob)
+        sol = solve(monte_prob, EM(); dt = 0.01, trajectories = 10000)
+        final_values = [sol.u[i].u[end] for i in eachindex(sol.u)]
+        simulated_mean = mean(final_values)
+        simulated_var = var(final_values)
+
+        analytical_mean = ou_mean(a, r, u0, t)
+        analytical_var = ou_variance(a, σ, t)
+
+        @test abs(simulated_mean - analytical_mean) < 0.05
+        @test abs(simulated_var - analytical_var) / analytical_var < 0.15
+    end
+
+    @testset "CIR analytical functions" begin
+        κ = 0.5
+        θ = 0.04
+        σ = 0.1
+        u0 = 0.03
+        t = 2.0
+
+        # Verify Feller condition for test parameters
+        @test 2κ * θ >= σ^2  # Should satisfy Feller condition
+
+        # Test at t=0
+        @test cir_mean(κ, θ, u0, 0.0) ≈ u0
+        @test cir_variance(κ, θ, σ, u0, 0.0) ≈ 0.0 atol = 1.0e-15
+
+        # Test analytical formulas
+        exp_κt = exp(-κ * t)
+        exp_2κt = exp(-2κ * t)
+        expected_mean = θ + (u0 - θ) * exp_κt
+        expected_var = u0 * (σ^2 / κ) * (exp_κt - exp_2κt) +
+            (θ * σ^2 / (2κ)) * (1 - exp_κt)^2
+
+        @test cir_mean(κ, θ, u0, t) ≈ expected_mean
+        @test cir_variance(κ, θ, σ, u0, t) ≈ expected_var
+        @test cir_std(κ, θ, σ, u0, t) ≈ sqrt(expected_var)
+
+        # Test stationary values
+        @test cir_stationary_mean(θ) == θ
+        @test cir_stationary_variance(κ, θ, σ) ≈ θ * σ^2 / (2κ)
+
+        # Test convergence to stationary distribution
+        large_t = 100.0
+        @test cir_mean(κ, θ, u0, large_t) ≈ cir_stationary_mean(θ) atol = 1.0e-10
+
+        # Monte Carlo validation
+        prob = CIRProblem(κ, θ, σ, u0, (0.0, t))
+        monte_prob = EnsembleProblem(prob)
+        sol = solve(monte_prob, EM(); dt = 0.001, trajectories = 10000)
+        final_values = [sol.u[i].u[end] for i in eachindex(sol.u)]
+        simulated_mean = mean(final_values)
+        simulated_var = var(final_values)
+
+        analytical_mean = cir_mean(κ, θ, u0, t)
+        analytical_var = cir_variance(κ, θ, σ, u0, t)
+
+        @test abs(simulated_mean - analytical_mean) / analytical_mean < 0.05
+        @test abs(simulated_var - analytical_var) / analytical_var < 0.2
+    end
+
+    @testset "Black-Scholes log-price functions" begin
+        r = 0.05
+        σ = 0.2
+        u0 = log(100.0)  # log of initial price
+        t = 1.0
+
+        # Test at t=0
+        @test bs_log_mean(r, σ, u0, 0.0) ≈ u0
+        @test bs_log_variance(σ, 0.0) ≈ 0.0 atol = 1.0e-15
+
+        # Test analytical formulas
+        @test bs_log_mean(r, σ, u0, t) ≈ u0 + (r - σ^2 / 2) * t
+        @test bs_log_variance(σ, t) ≈ σ^2 * t
+        @test bs_log_std(σ, t) ≈ σ * sqrt(t)
+
+        # Test broadcasting
+        times = [0.25, 0.5, 1.0]
+        variances = bs_log_variance.(σ, times)
+        @test variances ≈ σ^2 .* times
+    end
+
+    @testset "Heston analytical functions" begin
+        μ = 0.05
+        κ = 2.0
+        Θ = 0.04
+        σ = 0.3
+        u0_S = 100.0
+        u0_v = 0.04
+        t = 1.0
+
+        # Test Heston mean (should match GBM mean since volatility has zero mean increment)
+        @test heston_mean(μ, u0_S, t) ≈ u0_S * exp(μ * t)
+        @test heston_mean(μ, u0_S, 0.0) ≈ u0_S
+
+        # Test variance process mean (should match CIR mean)
+        @test heston_variance_mean(κ, Θ, u0_v, t) ≈ cir_mean(κ, Θ, u0_v, t)
+        @test heston_variance_mean(κ, Θ, u0_v, 0.0) ≈ u0_v
+
+        # Test variance of variance process (should match CIR variance)
+        @test heston_variance_variance(κ, Θ, σ, u0_v, t) ≈
+            cir_variance(κ, Θ, σ, u0_v, t)
+    end
+end

test/core_tests.jl

@@ -0,0 +1,31 @@
+using DiffEqFinancial, Statistics, StochasticDiffEq, Test
+
+@testset "Core Tests" begin
+    u0 = [1.0; 0.5]
+    σ = 0.25
+    prob = HestonProblem(1.0, 1.0, σ, 1.0, 1.0, u0, (0.0, 1.0))
+    sol = solve(prob, SRIW1(), adaptive = false, dt = 1 / 100)
+
+    prob = BlackScholesProblem((t) -> t^2, (t, u) -> 1.0, σ, 0.5, (0.0, 1.0))
+    sol = solve(prob, SRIW1())
+
+    r = 0.03
+    sigma = 0.2
+    S0 = 100.0
+    t = 0.0
+    T = 1.0
+    days = 252
+    dt = 1 / days
+
+    prob = GeometricBrownianMotionProblem(r, sigma, S0, (t, T))
+    sol = solve(prob, EM(); dt = dt)
+    monte_prob = EnsembleProblem(prob)
+    sol = solve(monte_prob, EM(); dt = dt, trajectories = 1000000)
+    us = [sol.u[i].u for i in eachindex(sol.u)]
+    simulated = mean(us)
+
+    tsteps = collect(0:dt:T)
+    expected = S0 * exp.(r * tsteps)
+    testerr = sum(abs2.(simulated .- expected))
+    @test testerr < 2.5e-1
+end

test/qa/Project.toml

@@ -3,6 +3,7 @@ AllocCheck = "9b6a8646-10ed-4001-bbdc-1d2f46dfbb1a"
 DiffEqFinancial = "5a0ffddc-d203-54b0-88ba-2c03c0fc2e67"
 ExplicitImports = "7d51a73a-1435-4ff3-83d9-f097790105c7"
 JET = "c3a54625-cd67-489e-a8e7-0a5a0ff4e31b"
+SafeTestsets = "1bc83da4-3b8d-516f-aca4-4fe02f6d838f"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [sources]
@@ -13,5 +14,6 @@ AllocCheck = "0.2"
 DiffEqFinancial = "2.10.0"
 ExplicitImports = "1.14"
 JET = "0.9, 0.10, 0.11"
+SafeTestsets = "0.1, 1"
 Test = "1.10"
 julia = "1.10"

test/qa/qa.jl

@@ -1,9 +1,11 @@
-@testset "Explicit Imports" begin
+using SafeTestsets
+
+@safetestset "Explicit Imports" begin
     include("explicit_imports.jl")
 end
-@testset "JET Tests" begin
+@safetestset "JET Tests" begin
     include("jet_tests.jl")
 end
-@testset "AllocCheck - Zero Allocations" begin
+@safetestset "AllocCheck - Zero Allocations" begin
     include("alloc_tests.jl")
 end