
import unittest
import user_code
import ast
import re   
import importlib
import csv
import unittest
import importlib
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt

class TestTask(unittest.TestCase):
    def setUp(self):
        # Prepare reference data
        np.random.seed(42)
        self.asset_returns = np.random.normal(loc=[0.0005, 0.0007, 0.0003], scale=[0.01, 0.012, 0.008], size=(252, 3))
        self.portfolio_weights = np.array([0.4, 0.4, 0.2])
        self.correct_portfolio_returns = np.dot(self.asset_returns, self.portfolio_weights)
        self.correct_mean = np.mean(self.correct_portfolio_returns)
        self.correct_std = np.std(self.correct_portfolio_returns)

    def test_portfolio_returns_correct(self):
        import user_code
        portfolio_returns = getattr(user_code, 'portfolio_returns', None)
        _dynamic_test(
            self,
            portfolio_returns is not None and isinstance(portfolio_returns, np.ndarray) and np.allclose(portfolio_returns, self.correct_portfolio_returns, atol=1e-8),
            'Portfolio returns calculated correctly.',
            f'Portfolio returns not calculated correctly or missing. Got: {portfolio_returns}'
        )

    def test_mean_and_std(self):
        import user_code
        mean_return = getattr(user_code, 'mean_return', None)
        std_return = getattr(user_code, 'std_return', None)
        _dynamic_test(
            self,
            mean_return is not None and np.isclose(mean_return, self.correct_mean, atol=1e-8),
            'Mean of portfolio returns is correct.',
            f'Expected mean: {self.correct_mean}, got: {mean_return}'
        )
        _dynamic_test(
            self,
            std_return is not None and np.isclose(std_return, self.correct_std, atol=1e-8),
            'Standard deviation of portfolio returns is correct.',
            f'Expected std: {self.correct_std}, got: {std_return}'
        )

    def test_histogram_is_plotted(self):
        import user_code
        # Check if plt.hist was called with correct data
        # Monkeypatch plt.hist to intercept calls
        called = {'hist': False}
        orig_hist = plt.hist
        def fake_hist(data, *args, **kwargs):
            if isinstance(data, np.ndarray) and np.allclose(data, self.correct_portfolio_returns, atol=1e-8):
                called['hist'] = True
            return orig_hist(data, *args, **kwargs)
        plt.hist = fake_hist
        try:
            importlib.reload(user_code)
        finally:
            plt.hist = orig_hist
        _dynamic_test(
            self,
            called['hist'],
            'Histogram of portfolio returns is plotted.',
            'Histogram of portfolio returns was not plotted using correct data.'
        )

def _dynamic_test(test_case, condition, success_message, failure_message):
    if condition:
        test_case._testMethodName = success_message
        test_case.assertTrue(True, success_message)
    else:
        test_case._testMethodName = failure_message
        test_case.fail(failure_message)

def normalize_text(text):
    text = text.lower()
    text = re.sub(r"\\s{2,}", " ", text)
    text = re.sub(r"\\s*([,:?])\\s*", r"\\1 ", text)
    return text.strip()

def change_var(code: str, var_name: str, value: str) -> str:
    tree = ast.parse(code)
    lines = code.splitlines()
    changed = False
    # Collect all assignment nodes to modify
    assign_nodes = [
        (i, node)
        for i, node in enumerate(tree.body)
        if isinstance(node, ast.Assign)
        and any(isinstance(target, ast.Name) and target.id == var_name for target in node.targets)
    ]

    # If nothing to change, return unmodified code
    if not assign_nodes:
        return code

    # Perform replacements for all matching assignments (from last to first to not break line offsets)
    for i, node in reversed(assign_nodes):
        start_line = node.lineno - 1
        line = lines[start_line]
        indent = ' ' * (len(line) - len(line.lstrip()))
        lines[start_line] = f"{indent}{var_name} = {value}"
        next_line = len(lines)
        for next_node in tree.body[i+1:]:
            if hasattr(next_node, 'lineno'):
                next_line = next_node.lineno - 1
                break
        if next_line > start_line + 1:
            lines[start_line+1:next_line] = []
        changed = True

    return '\\n'.join(lines) if changed else code

if __name__ == "__main__":
    unittest.main()


test_main.py

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Challenge: Simulate and Visualize Portfolio Diversification

Lösung

Challenge: Simulate and Visualize Portfolio Diversification

Lösung