Understanding how a stock's price changes from one day to the next is crucial for anyone working in finance. One of the most common ways to measure these changes is by calculating **daily returns**. A daily return tells you the percentage change in a stock's price compared to the previous day's closing price. This measure helps you assess how volatile a stock is, compare performance across different assets, and even build more advanced financial models. By expressing returns as percentages, you can easily compare stocks with different price levels, making it a fundamental metric for both analysis and reporting in the world of finance.


import unittest
import user_code
import ast
import re   
import importlib
import csv
import unittest
import importlib

class TestTask(unittest.TestCase):
    def test_length_of_returns(self):
        import user_code
        importlib.reload(user_code)
        prices = [100, 102, 101, 105]
        result = user_code.calculate_daily_returns(prices)
        _dynamic_test(
            self,
            isinstance(result, list) and len(result) == len(prices) - 1,
            "Returns list has correct length (one less than prices)",
            f"Expected length {len(prices)-1}, got {len(result)}",
        )

    def test_percentage_calculation_positive(self):
        import user_code
        importlib.reload(user_code)
        prices = [100, 102, 101]
        result = user_code.calculate_daily_returns(prices)
        expected = [2.0, -0.9804]
        _dynamic_test(
            self,
            isinstance(result, list) and all(abs(a-b) < 1e-4 for a, b in zip(result, expected)),
            f"Correct daily returns for [100, 102, 101]",
            f"Expected {expected}, got {result}",
        )

    def test_percentage_calculation_negative(self):
        import user_code
        importlib.reload(user_code)
        prices = [150, 100, 90]
        result = user_code.calculate_daily_returns(prices)
        expected = [-33.3333, -10.0]
        _dynamic_test(
            self,
            isinstance(result, list) and all(abs(a-b) < 1e-4 for a, b in zip(result, expected)),
            f"Correct daily returns for [150, 100, 90]",
            f"Expected {expected}, got {result}",
        )

    def test_rounding(self):
        import user_code
        importlib.reload(user_code)
        prices = [100, 100.005, 100.0001]
        result = user_code.calculate_daily_returns(prices)
        expected = [0.005, -0.0049]
        _dynamic_test(
            self,
            isinstance(result, list) and all(abs(a-b) < 1e-4 for a, b in zip(result, expected)),
            f"Daily returns rounded to four decimal places",
            f"Expected {expected}, got {result}",
        )

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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Apply machine learning techniques to financial data for prediction, classification, and decision support in FinTech applications.

Challenge: Calculate Daily Returns

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Challenge: Calculate Daily Returns

Рішення