feat: Enhance currency handling and validation across scenarios

- Updated form template to prefill currency input with default value and added help text for clarity.
- Modified integration tests to assert more descriptive error messages for invalid currency codes.
- Introduced new tests for currency normalization and validation in various scenarios, including imports and exports.
- Added comprehensive tests for pricing calculations, ensuring defaults are respected and overrides function correctly.
- Implemented unit tests for pricing settings repository, ensuring CRUD operations and default settings are handled properly.
- Enhanced scenario pricing evaluation tests to validate currency handling and metadata defaults.
- Added simulation tests to ensure Monte Carlo runs are accurate and handle various distribution scenarios.

tests/test_simulation.py

@@ -0,0 +1,158 @@
+from __future__ import annotations
+
+import math
+
+import numpy as np
+import pytest
+
+from services.financial import CashFlow, net_present_value
+from services.simulation import (
+    CashFlowSpec,
+    DistributionConfigError,
+    DistributionSource,
+    DistributionSpec,
+    DistributionType,
+    SimulationConfig,
+    SimulationMetric,
+    run_monte_carlo,
+)
+
+
+def test_run_monte_carlo_deterministic_matches_financial_helpers() -> None:
+    base_flows = [
+        CashFlow(amount=-1000.0, period_index=0),
+        CashFlow(amount=600.0, period_index=1),
+        CashFlow(amount=600.0, period_index=2),
+    ]
+    specs = [CashFlowSpec(cash_flow=flow) for flow in base_flows]
+    config = SimulationConfig(
+        iterations=10,
+        discount_rate=0.1,
+        percentiles=(50,),
+        seed=123,
+    )
+
+    result = run_monte_carlo(specs, config)
+    summary = result.summaries[SimulationMetric.NPV]
+    expected = net_present_value(0.1, base_flows)
+
+    assert summary.sample_size == config.iterations
+    assert summary.failed_runs == 0
+    assert summary.mean == pytest.approx(expected, rel=1e-6)
+    assert summary.std_dev == 0.0
+    assert summary.percentiles[50] == pytest.approx(expected, rel=1e-6)
+
+
+def test_run_monte_carlo_normal_distribution_uses_seed_for_reproducibility() -> None:
+    base_flows = [
+        CashFlow(amount=-100.0, period_index=0),
+        CashFlow(amount=0.0, period_index=1),
+        CashFlow(amount=0.0, period_index=2),
+    ]
+    revenue_flow = CashFlowSpec(
+        cash_flow=CashFlow(amount=120.0, period_index=1),
+        distribution=DistributionSpec(
+            type=DistributionType.NORMAL,
+            parameters={"mean": 120.0, "std_dev": 10.0},
+        ),
+    )
+    specs = [CashFlowSpec(cash_flow=base_flows[0]), revenue_flow]
+    config = SimulationConfig(
+        iterations=1000,
+        discount_rate=0.0,
+        percentiles=(5.0, 50.0, 95.0),
+        seed=42,
+    )
+
+    result = run_monte_carlo(specs, config)
+    summary = result.summaries[SimulationMetric.NPV]
+
+    assert summary.sample_size == config.iterations
+    assert summary.failed_runs == 0
+    # With zero discount rate the expected mean NPV equals mean sampled value minus investment.
+    assert summary.mean == pytest.approx(20.0, abs=1.0)
+    assert summary.std_dev == pytest.approx(10.0, abs=1.0)
+    assert summary.percentiles[50.0] == pytest.approx(summary.mean, abs=1.0)
+
+
+def test_run_monte_carlo_supports_scenario_field_source() -> None:
+    base_flow = CashFlow(amount=0.0, period_index=1)
+    spec = CashFlowSpec(
+        cash_flow=base_flow,
+        distribution=DistributionSpec(
+            type=DistributionType.NORMAL,
+            parameters={"std_dev": 0.0},
+            source=DistributionSource.SCENARIO_FIELD,
+            source_key="salvage_mean",
+        ),
+    )
+    config = SimulationConfig(iterations=1, discount_rate=0.0, seed=7)
+
+    result = run_monte_carlo(
+        [CashFlowSpec(cash_flow=CashFlow(
+            amount=-100.0, period_index=0)), spec],
+        config,
+        scenario_context={"salvage_mean": 150.0},
+    )
+
+    summary = result.summaries[SimulationMetric.NPV]
+    assert summary.sample_size == 1
+    assert summary.mean == pytest.approx(50.0)
+
+
+def test_run_monte_carlo_records_failed_metrics_when_not_defined() -> None:
+    base_flows = [CashFlow(amount=100.0, period_index=0)]
+    specs = [CashFlowSpec(cash_flow=flow) for flow in base_flows]
+    config = SimulationConfig(
+        iterations=5,
+        discount_rate=0.1,
+        metrics=(SimulationMetric.IRR,),
+        seed=5,
+    )
+
+    result = run_monte_carlo(specs, config)
+    summary = result.summaries[SimulationMetric.IRR]
+
+    assert summary.sample_size == 0
+    assert summary.failed_runs == config.iterations
+    assert math.isnan(summary.mean)
+
+
+def test_run_monte_carlo_distribution_missing_context_raises() -> None:
+    spec = DistributionSpec(
+        type=DistributionType.NORMAL,
+        parameters={"std_dev": 1.0},
+        source=DistributionSource.SCENARIO_FIELD,
+        source_key="unknown",
+    )
+    cash_flow_spec = CashFlowSpec(
+        cash_flow=CashFlow(amount=0.0, period_index=0),
+        distribution=spec,
+    )
+    config = SimulationConfig(iterations=1, discount_rate=0.0)
+
+    with pytest.raises(DistributionConfigError):
+        run_monte_carlo([cash_flow_spec], config, scenario_context={})
+
+
+def test_run_monte_carlo_can_return_samples() -> None:
+    base_flow = CashFlow(amount=50.0, period_index=1)
+    specs = [
+        CashFlowSpec(cash_flow=CashFlow(amount=-40.0, period_index=0)),
+        CashFlowSpec(cash_flow=base_flow),
+    ]
+    config = SimulationConfig(
+        iterations=3,
+        discount_rate=0.0,
+        metrics=(SimulationMetric.NPV,),
+        return_samples=True,
+        seed=11,
+    )
+
+    result = run_monte_carlo(specs, config)
+
+    assert result.samples is not None
+    assert SimulationMetric.NPV in result.samples
+    samples = result.samples[SimulationMetric.NPV]
+    assert isinstance(samples, np.ndarray)
+    assert samples.shape == (config.iterations,)