
import unittest
import user_code
import ast
import re   
import importlib
import csv
import unittest
import importlib
import pandas as pd
import numpy as np
import sys
import io

class TestTask(unittest.TestCase):
    def setUp(self):
        self.closing_prices = pd.DataFrame({
            'Stock_A': [100, 102, 101, 104, 106, 105, 107, 110, 108, 111],
            'Stock_B': [50, 51, 52, 53, 54, 56, 55, 57, 58, 60],
            'Stock_C': [200, 202, 203, 205, 207, 210, 208, 211, 213, 215]
        })
        # Calculate expected returns
        self.daily_returns = self.closing_prices.pct_change().iloc[1:]
        self.expected_avg = self.daily_returns.mean()
        self.expected_vol = self.daily_returns.std()
        # Prepare expected summary DataFrame for output check
        self.expected_summary = pd.DataFrame({
            'Average Return': self.expected_avg,
            'Volatility': self.expected_vol
        })

    def test_daily_returns_correct(self):
        import user_code
        importlib.reload(user_code)
        # Try to find a variable with daily returns (should be named daily_returns)
        daily_returns = getattr(user_code, 'daily_returns', None)
        _dynamic_test(
            self,
            isinstance(daily_returns, pd.DataFrame),
            "Found daily_returns DataFrame.",
            "Variable 'daily_returns' is missing or not a DataFrame."
        )
        # Check values
        correct = daily_returns is not None and np.allclose(daily_returns.values, self.daily_returns.values, atol=1e-8)
        _dynamic_test(
            self,
            correct,
            "Daily returns computed correctly.",
            f"Daily returns are incorrect. Expected:\\n{self.daily_returns}\\nGot:\\n{daily_returns}"
        )

    def test_summary_table_values(self):
        import user_code
        importlib.reload(user_code)
        summary = getattr(user_code, 'summary', None)
        _dynamic_test(
            self,
            isinstance(summary, pd.DataFrame),
            "Summary is a DataFrame.",
            "Variable 'summary' is missing or not a DataFrame."
        )
        # Check index and columns
        correct_index = list(summary.index) == list(self.expected_summary.index)
        correct_cols = list(summary.columns) == list(self.expected_summary.columns)
        _dynamic_test(
            self,
            correct_index and correct_cols,
            "Summary table has correct index and columns.",
            f"Summary table index or columns incorrect. Expected index: {self.expected_summary.index}, columns: {self.expected_summary.columns}. Got index: {summary.index}, columns: {summary.columns}."
        )
        # Check values
        correct_vals = np.allclose(summary.values, self.expected_summary.values, atol=1e-8)
        _dynamic_test(
            self,
            correct_vals,
            "Summary table values are correct.",
            f"Summary values incorrect. Expected:\\n{self.expected_summary}\\nGot:\\n{summary}"
        )

    def test_output_prints_summary(self):
        import user_code
        import importlib
        f = io.StringIO()
        sys_stdout = sys.stdout
        sys.stdout = f
        importlib.reload(user_code)
        sys.stdout = sys_stdout
        output = f.getvalue()
        # The output should contain the summary table as a string
        expected = self.expected_summary.round(6).to_string()
        # Normalize both for easier matching (ignore whitespace, rounding)
        output_norm = normalize_text(output)
        expected_norm = normalize_text(expected)
        _dynamic_test(
            self,
            all(stock.lower() in output_norm for stock in ['stock_a', 'stock_b', 'stock_c']),
            "Output includes all stock names.",
            f"Output is missing one or more stock names. Output: {output}"
        )
        _dynamic_test(
            self,
            all(col.lower() in output_norm for col in ['average return', 'volatility']),
            "Output includes both column names.",
            f"Output is missing one or both column names. Output: {output}"
        )
        # Check that at least the first row of expected output appears in actual output (rough check)
        first_row = self.expected_summary.round(6).iloc[[0]].to_string()
        first_row_norm = normalize_text(first_row)
        _dynamic_test(
            self,
            first_row_norm.split()[0] in output_norm,
            "Output includes summary table values.",
            f"Output is missing summary table values. Output: {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: Compute Stock Returns and Volatility

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