zwitschi
795a9f99f4
- 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.
159 lines
4.9 KiB
Python
159 lines
4.9 KiB
Python
from __future__ import annotations
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import math
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import numpy as np
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import pytest
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from services.financial import CashFlow, net_present_value
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from services.simulation import (
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CashFlowSpec,
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DistributionConfigError,
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DistributionSource,
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DistributionSpec,
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DistributionType,
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SimulationConfig,
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SimulationMetric,
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run_monte_carlo,
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)
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def test_run_monte_carlo_deterministic_matches_financial_helpers() -> None:
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base_flows = [
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CashFlow(amount=-1000.0, period_index=0),
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CashFlow(amount=600.0, period_index=1),
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CashFlow(amount=600.0, period_index=2),
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]
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specs = [CashFlowSpec(cash_flow=flow) for flow in base_flows]
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config = SimulationConfig(
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iterations=10,
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discount_rate=0.1,
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percentiles=(50,),
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seed=123,
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)
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result = run_monte_carlo(specs, config)
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summary = result.summaries[SimulationMetric.NPV]
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expected = net_present_value(0.1, base_flows)
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assert summary.sample_size == config.iterations
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assert summary.failed_runs == 0
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assert summary.mean == pytest.approx(expected, rel=1e-6)
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assert summary.std_dev == 0.0
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assert summary.percentiles[50] == pytest.approx(expected, rel=1e-6)
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def test_run_monte_carlo_normal_distribution_uses_seed_for_reproducibility() -> None:
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base_flows = [
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CashFlow(amount=-100.0, period_index=0),
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CashFlow(amount=0.0, period_index=1),
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CashFlow(amount=0.0, period_index=2),
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]
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revenue_flow = CashFlowSpec(
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cash_flow=CashFlow(amount=120.0, period_index=1),
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distribution=DistributionSpec(
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type=DistributionType.NORMAL,
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parameters={"mean": 120.0, "std_dev": 10.0},
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),
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)
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specs = [CashFlowSpec(cash_flow=base_flows[0]), revenue_flow]
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config = SimulationConfig(
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iterations=1000,
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discount_rate=0.0,
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percentiles=(5.0, 50.0, 95.0),
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seed=42,
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)
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result = run_monte_carlo(specs, config)
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summary = result.summaries[SimulationMetric.NPV]
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assert summary.sample_size == config.iterations
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assert summary.failed_runs == 0
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# With zero discount rate the expected mean NPV equals mean sampled value minus investment.
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assert summary.mean == pytest.approx(20.0, abs=1.0)
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assert summary.std_dev == pytest.approx(10.0, abs=1.0)
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assert summary.percentiles[50.0] == pytest.approx(summary.mean, abs=1.0)
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def test_run_monte_carlo_supports_scenario_field_source() -> None:
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base_flow = CashFlow(amount=0.0, period_index=1)
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spec = CashFlowSpec(
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cash_flow=base_flow,
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distribution=DistributionSpec(
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type=DistributionType.NORMAL,
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parameters={"std_dev": 0.0},
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source=DistributionSource.SCENARIO_FIELD,
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source_key="salvage_mean",
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),
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)
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config = SimulationConfig(iterations=1, discount_rate=0.0, seed=7)
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result = run_monte_carlo(
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[CashFlowSpec(cash_flow=CashFlow(
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amount=-100.0, period_index=0)), spec],
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config,
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scenario_context={"salvage_mean": 150.0},
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)
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summary = result.summaries[SimulationMetric.NPV]
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assert summary.sample_size == 1
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assert summary.mean == pytest.approx(50.0)
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def test_run_monte_carlo_records_failed_metrics_when_not_defined() -> None:
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base_flows = [CashFlow(amount=100.0, period_index=0)]
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specs = [CashFlowSpec(cash_flow=flow) for flow in base_flows]
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config = SimulationConfig(
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iterations=5,
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discount_rate=0.1,
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metrics=(SimulationMetric.IRR,),
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seed=5,
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)
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result = run_monte_carlo(specs, config)
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summary = result.summaries[SimulationMetric.IRR]
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assert summary.sample_size == 0
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assert summary.failed_runs == config.iterations
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assert math.isnan(summary.mean)
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def test_run_monte_carlo_distribution_missing_context_raises() -> None:
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spec = DistributionSpec(
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type=DistributionType.NORMAL,
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parameters={"std_dev": 1.0},
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source=DistributionSource.SCENARIO_FIELD,
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source_key="unknown",
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)
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cash_flow_spec = CashFlowSpec(
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cash_flow=CashFlow(amount=0.0, period_index=0),
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distribution=spec,
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)
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config = SimulationConfig(iterations=1, discount_rate=0.0)
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with pytest.raises(DistributionConfigError):
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run_monte_carlo([cash_flow_spec], config, scenario_context={})
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def test_run_monte_carlo_can_return_samples() -> None:
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base_flow = CashFlow(amount=50.0, period_index=1)
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specs = [
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CashFlowSpec(cash_flow=CashFlow(amount=-40.0, period_index=0)),
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CashFlowSpec(cash_flow=base_flow),
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]
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config = SimulationConfig(
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iterations=3,
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discount_rate=0.0,
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metrics=(SimulationMetric.NPV,),
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return_samples=True,
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seed=11,
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)
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result = run_monte_carlo(specs, config)
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assert result.samples is not None
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assert SimulationMetric.NPV in result.samples
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samples = result.samples[SimulationMetric.NPV]
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assert isinstance(samples, np.ndarray)
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assert samples.shape == (config.iterations,)
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