
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):
        self.returns = np.array([
            0.001, -0.002, 0.003, -0.004, 0.002, 0.001, -0.003, 0.004, -0.002, 0.003,
            0.002, -0.001, 0.001, -0.002, 0.003, -0.003, 0.002, 0.001, -0.001, 0.002
        ])
        self.confidence_level = 0.95
        self.expected_var = abs(np.percentile(self.returns, 100 * (1 - self.confidence_level)))

    def test_var_calculation_correctness(self):
        import user_code
        importlib.reload(user_code)
        var = user_code.calculate_var(self.returns, self.confidence_level)
        _dynamic_test(
            self,
            isinstance(var, float) or isinstance(var, np.floating),
            "VaR is returned as a float value.",
            f"VaR is not float. Got type: {type(var)}"
        )
        _dynamic_test(
            self,
            np.isclose(var, self.expected_var, atol=1e-8),
            f"VaR is correctly calculated: {var}",
            f"Expected VaR: {self.expected_var}, got: {var}"
        )

    def test_var_is_positive(self):
        import user_code
        importlib.reload(user_code)
        var = user_code.calculate_var(self.returns, self.confidence_level)
        _dynamic_test(
            self,
            var >= 0,
            "VaR is returned as a positive value.",
            f"VaR is not positive: {var}"
        )

    def test_var_with_different_confidence(self):
        import user_code
        importlib.reload(user_code)
        conf = 0.99
        expected = abs(np.percentile(self.returns, 100 * (1 - conf)))
        var = user_code.calculate_var(self.returns, conf)
        _dynamic_test(
            self,
            np.isclose(var, expected, atol=1e-8),
            f"VaR is correct for 99% confidence: {var}",
            f"Expected VaR: {expected}, got: {var}"
        )

    def test_plot_var(self):
        import user_code
        importlib.reload(user_code)
        var = user_code.calculate_var(self.returns, self.confidence_level)
        # Intercept the plotting call
        plt.close('all')
        user_code.plot_var(self.returns, var)
        figs = [plt.figure(i) for i in plt.get_fignums()]
        _dynamic_test(
            self,
            len(figs) > 0,
            "plot_var produces a matplotlib figure.",
            "plot_var did not produce a plot."
        )
        # Check if a vertical line at -VaR is present
        found = False
        for fig in figs:
            for ax in fig.get_axes():
                for line in ax.get_lines():
                    x = line.get_xdata()
                    if np.any(np.isclose(x, -var, atol=1e-8)):
                        found = True
        _dynamic_test(
            self,
            found,
            "plot_var marks the VaR threshold on the plot.",
            "plot_var did not mark the VaR threshold."
        )

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: Calculate and Visualize Portfolio VaR

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