
import unittest
import user_code
import ast
import re   
import importlib
import csv
import unittest
import importlib
import io
from contextlib import redirect_stdout
import numpy as np

class TestTask(unittest.TestCase):
    def setUp(self):
        self.expected_returns = [
            0.4*0.02 + 0.3*0.015 + 0.2*0.01 + 0.1*(-0.005),
            0.4*0.01 + 0.3*0.02 + 0.2*0.005 + 0.1*0.0,
            0.4*0.015 + 0.3*0.01 + 0.2*0.02 + 0.1*0.01,
            0.4*(-0.005) + 0.3*0.0 + 0.2*0.01 + 0.1*0.02,
            0.4*0.03 + 0.3*0.025 + 0.2*0.015 + 0.1*0.005,
            0.4*0.025 + 0.3*0.02 + 0.2*0.01 + 0.1*0.015
        ]
        self.average_return = np.mean(self.expected_returns)
        self.volatility = np.std(self.expected_returns, ddof=0)
        self.risk_free_rate = 0.005
        self.sharpe_ratio = (self.average_return - self.risk_free_rate) / self.volatility

    def test_portfolio_returns_calculation(self):
        import user_code
        import importlib
        importlib.reload(user_code)
        # Try to get variable portfolio_returns from user code
        portfolio_returns = getattr(user_code, 'portfolio_returns', None)
        if portfolio_returns is None:
            # Try to get as a function result
            returns_df = getattr(user_code, 'returns_df', None)
            weights = getattr(user_code, 'weights', None)
            if returns_df is not None and weights is not None:
                try:
                    portfolio_returns = returns_df.dot(weights)
                except Exception:
                    portfolio_returns = None
        _dynamic_test(
            self,
            portfolio_returns is not None and np.allclose(
                np.array(portfolio_returns), np.array(self.expected_returns), atol=1e-6),
            "Portfolio monthly returns are correctly calculated.",
            f"Expected: {self.expected_returns}, got: {portfolio_returns}"
        )

    def test_average_return(self):
        import user_code
        import importlib
        importlib.reload(user_code)
        average_return = getattr(user_code, 'average_return', None)
        if average_return is None:
            # Try to compute from portfolio_returns
            portfolio_returns = getattr(user_code, 'portfolio_returns', None)
            if portfolio_returns is not None:
                average_return = np.mean(portfolio_returns)
        _dynamic_test(
            self,
            average_return is not None and np.isclose(average_return, self.average_return, atol=1e-6),
            "Average monthly return is correctly calculated.",
            f"Expected: {self.average_return}, got: {average_return}"
        )

    def test_volatility(self):
        import user_code
        import importlib
        importlib.reload(user_code)
        volatility = getattr(user_code, 'volatility', None)
        if volatility is None:
            portfolio_returns = getattr(user_code, 'portfolio_returns', None)
            if portfolio_returns is not None:
                volatility = np.std(portfolio_returns, ddof=0)
        _dynamic_test(
            self,
            volatility is not None and np.isclose(volatility, self.volatility, atol=1e-6),
            "Portfolio volatility (std) is correctly calculated.",
            f"Expected: {self.volatility}, got: {volatility}"
        )

    def test_sharpe_ratio(self):
        import user_code
        import importlib
        importlib.reload(user_code)
        sharpe_ratio = getattr(user_code, 'sharpe_ratio', None)
        if sharpe_ratio is None:
            average_return = getattr(user_code, 'average_return', None)
            volatility = getattr(user_code, 'volatility', None)
            if average_return is not None and volatility is not None:
                sharpe_ratio = (average_return - self.risk_free_rate) / volatility
        _dynamic_test(
            self,
            sharpe_ratio is not None and np.isclose(sharpe_ratio, self.sharpe_ratio, atol=1e-6),
            "Sharpe Ratio is correctly calculated.",
            f"Expected: {self.sharpe_ratio}, got: {sharpe_ratio}"
        )

    def test_printed_summary(self):
        import user_code
        import importlib
        import io
        from contextlib import redirect_stdout
        importlib.reload(user_code)
        f = io.StringIO()
        with redirect_stdout(f):
            importlib.reload(user_code)
        output = normalize_text(f.getvalue())
        expected_avg = f"average monthly return: {self.average_return:.4f}"
        expected_vol = f"portfolio volatility: {self.volatility:.4f}"
        expected_sharpe = f"sharpe ratio: {self.sharpe_ratio:.4f}"
        _dynamic_test(
            self,
            expected_avg in output,
            "Output includes average monthly return.",
            f"Expected average monthly return line: '{expected_avg}', got: '{output}'"
        )
        _dynamic_test(
            self,
            expected_vol in output,
            "Output includes portfolio volatility.",
            f"Expected portfolio volatility line: '{expected_vol}', got: '{output}'"
        )
        _dynamic_test(
            self,
            expected_sharpe in output,
            "Output includes Sharpe Ratio.",
            f"Expected Sharpe Ratio line: '{expected_sharpe}', got: '{output}'"
        )

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: Portfolio Return and Risk Calculation

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Challenge: Portfolio Return and Risk Calculation

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