"""Analytical and accounting checks; Python standard library only.""" import csv import math import unittest from uuid import uuid4 from datetime import date from pathlib import Path import study class StudyTests(unittest.TestCase): def setUp(self): self.test_directory = Path(__file__).resolve().parent / (".test-" + uuid4().hex) self.test_directory.mkdir() def tearDown(self): # Only files created inside this test's own directory are removed. for path in self.test_directory.iterdir(): path.unlink() self.test_directory.rmdir() def make_csv(self, rows): path = self.test_directory / (uuid4().hex + ".csv") with path.open("w", newline="", encoding="utf-8") as stream: writer = csv.writer(stream) writer.writerow(study.INPUT_COLUMNS) writer.writerows(rows) return path def test_known_sample_covariance_and_perfect_correlation(self): covariance = study.sample_covariance([[1, 2, 3], [2, 4, 6]]) self.assertEqual(covariance, [[1, 2], [2, 4]]) self.assertEqual(study.correlation_from_covariance(covariance), [[1, 1], [1, 1]]) def test_constant_series_has_undefined_correlation(self): correlation = study.correlation_from_covariance(study.sample_covariance([[0, 0, 0], [1, 2, 3]])) self.assertIsNone(correlation[0][0]) self.assertIsNone(correlation[0][1]) self.assertIsNone(correlation[1][0]) def test_equal_volatility_closed_form_and_perfect_correlation(self): for rho in [0, 0.5, 0.9, 1]: risk = study.portfolio_risk([1 / 3] * 3, study.covariance_from_volatility([0.01] * 3, rho)) expected = 0.01 * math.sqrt((1 + 2 * rho) / 3) self.assertAlmostEqual(risk["portfolio_daily_volatility"], expected, places=14) self.assertAlmostEqual(risk["diversification_ratio"], 0.01 / expected, places=14) def test_euler_components_sum_and_scale_with_risky_allocation(self): covariance = study.covariance_from_volatility([0.01, 0.02, 0.03], 0.3) full = study.portfolio_risk([0.4, 0.35, 0.25], covariance) cash = study.portfolio_risk([0.32, 0.28, 0.2], covariance) self.assertAlmostEqual(sum(full["component_daily_volatility"]), full["portfolio_daily_volatility"], places=14) self.assertAlmostEqual(sum(full["fraction_of_total_volatility"]), 1, places=14) self.assertAlmostEqual(cash["portfolio_daily_volatility"], 0.8 * full["portfolio_daily_volatility"], places=14) for actual, expected in zip(cash["fraction_of_total_volatility"], full["fraction_of_total_volatility"]): self.assertAlmostEqual(actual, expected, places=14) two_bot = study.portfolio_risk([0.5, 0.5], study.covariance_from_volatility([0.02, 0.01], 0)) self.assertAlmostEqual(two_bot["portfolio_daily_volatility"], math.sqrt(0.000125), places=14) self.assertAlmostEqual(two_bot["fraction_of_total_volatility"][0], 0.8, places=14) self.assertAlmostEqual(two_bot["fraction_of_total_volatility"][1], 0.2, places=14) def test_assumed_stress_dollars_and_cash_scaling(self): full = study.joint_stress(100000, [0.4, 0.35, 0.25], [-0.08, -0.06, -0.1]) cash = study.joint_stress(100000, [0.32, 0.28, 0.2], [-0.08, -0.06, -0.1]) self.assertAlmostEqual(full["portfolio_pnl"], -7800) self.assertAlmostEqual(sum(full["sleeve_pnl"]), full["portfolio_pnl"]) self.assertAlmostEqual(cash["portfolio_pnl"], -6240) self.assertAlmostEqual(cash["cash_weight"], 0.2) def test_no_rebalancing_accounting_and_nonadditive_drawdown(self): observations = [(date(2026, 1, 1), (0.1, -0.1, 0)), (date(2026, 1, 2), (-0.1, 0.1, 0))] rows, summary = study.equity_path(observations, 100000, [0.5, 0.5, 0]) self.assertAlmostEqual(rows[0]["portfolio_equity"], 100000) self.assertAlmostEqual(rows[1]["bot_a_opening_weight"], 0.55) self.assertAlmostEqual(rows[1]["portfolio_equity"], 99000) self.assertAlmostEqual(summary["daily_close_max_drawdown"], 0.01) self.assertAlmostEqual(summary["initial_weighted_average_of_sleeve_max_drawdowns"], 0.1) self.assertAlmostEqual(sum(summary["sleeve_final_equity"]) + summary["cash_equity"], summary["final_equity"]) def test_known_zero_valid_but_blank_and_nonfinite_rejected(self): valid = self.make_csv([["2026-01-01", 0, 0.01, 0.02], ["2026-01-02", 0.01, 0, -0.02]]) self.assertEqual(study.read_returns(valid)[0][1][0], 0) for invalid in ["", "NaN", "inf", "-inf", "-1.01"]: path = self.make_csv([["2026-01-01", 0, 0.01, 0.02], ["2026-01-02", invalid, 0, -0.02]]) with self.assertRaises(ValueError): study.read_returns(path) def test_missing_duplicate_and_reversed_dates_rejected(self): for second_date in ["2026-01-03", "2026-01-01", "2025-12-31"]: path = self.make_csv([["2026-01-01", 0, 0.01, 0.02], [second_date, 0.01, 0, -0.02]]) with self.assertRaises(ValueError): study.read_returns(path) def test_incomplete_alignment_and_impossible_weights_rejected(self): with self.assertRaises(ValueError): study.sample_covariance([[0, 1], [0]]) for weights in [[0.6, 0.6, 0], [-0.1, 0.5, 0.6], [float("nan"), 0.5, 0.5]]: with self.assertRaises(ValueError): study.check_weights(weights) with self.assertRaises(ValueError): study.covariance_from_volatility([0.01] * 3, -0.6) def test_fixture_complete_and_all_results_accounting_reconcile(self): source = Path(__file__).resolve().parent / "sleeve-returns.csv" result = study.run(source, self.test_directory) self.assertEqual(result["observations"], 24) self.assertEqual(result["start"], "2026-09-01") self.assertEqual(result["end"], "2026-09-24") equity = result["constructed_fixture_equity"] self.assertAlmostEqual(equity["net_pnl"], equity["final_equity"] - 100000) self.assertAlmostEqual(sum(equity["final_weights"]), 1) self.assertEqual(len(list(self.test_directory.glob("*.csv"))), 4) if __name__ == "__main__": unittest.main()