Moving averages are widely used in financial analysis to smooth out short-term fluctuations and highlight longer-term trends in price data. By averaging a set number of past data points, a moving average helps you see the underlying direction of a financial time series, making it easier to identify trends and potential turning points. This is especially useful when analyzing volatile markets, as it reduces noise and provides a clearer picture of price movement. The **simple moving average** (SMA) is the most basic type, calculated by taking the mean of a fixed window of consecutive data points as you move through the series.


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

class TestTask(unittest.TestCase):
    def setUp(self):
        # Patch plt.show to avoid displaying plots during tests
        self._original_show = plt.show
        plt.show = lambda *args, **kwargs: None
        self.user_code = importlib.import_module("user_code")

    def tearDown(self):
        plt.show = self._original_show

    def test_window_size_too_small(self):
        importlib.reload(self.user_code)
        prices = [100, 101, 102]
        window = 0
        raised = False
        try:
            self.user_code.plot_moving_average(prices, window)
        except ValueError as e:
            raised = True
        _dynamic_test(
            self,
            raised,
            "Correctly raises ValueError for window size < 1",
            "Did not raise ValueError for window size < 1",
        )

    def test_window_size_too_large(self):
        importlib.reload(self.user_code)
        prices = [100, 101, 102]
        window = 5
        raised = False
        try:
            self.user_code.plot_moving_average(prices, window)
        except ValueError as e:
            raised = True
        _dynamic_test(
            self,
            raised,
            "Correctly raises ValueError for window size > len(prices)",
            "Did not raise ValueError for window size > len(prices)",
        )

    def test_sma_values_and_none_placeholders(self):
        importlib.reload(self.user_code)
        prices = [10, 20, 30, 40, 50]
        window = 3
        # We'll patch plt.plot to capture the y values for SMA
        plotted = {"prices": None, "sma": None}
        orig_plot = plt.plot
        def fake_plot(y, *args, **kwargs):
            # First call: prices, second call: SMA
            if plotted["prices"] is None:
                plotted["prices"] = y
            else:
                plotted["sma"] = y
            return orig_plot(y, *args, **kwargs)
        plt.plot = fake_plot
        try:
            self.user_code.plot_moving_average(prices, window)
        finally:
            plt.plot = orig_plot
        expected_sma = [None, None, 20.0, 30.0, 40.0]
        _dynamic_test(
            self,
            plotted["sma"] == expected_sma,
            f"Correct SMA values: {expected_sma}",
            f"Incorrect SMA values: expected {expected_sma}, got {plotted['sma']}",
        )
        _dynamic_test(
            self,
            plotted["prices"] == prices,
            f"Original prices plotted correctly",
            f"Original prices not plotted correctly: expected {prices}, got {plotted['prices']}"
        )

    def test_plot_labels_and_legend(self):
        importlib.reload(self.user_code)
        prices = [100, 105, 110, 115, 120]
        window = 2
        # Patch plt functions to capture calls
        called = {"xlabel": False, "ylabel": False, "title": False, "legend": False}
        orig_xlabel = plt.xlabel
        orig_ylabel = plt.ylabel
        orig_title = plt.title
        orig_legend = plt.legend
        plt.xlabel = lambda *args, **kwargs: called.update({"xlabel": True})
        plt.ylabel = lambda *args, **kwargs: called.update({"ylabel": True})
        plt.title = lambda *args, **kwargs: called.update({"title": True})
        plt.legend = lambda *args, **kwargs: called.update({"legend": True})
        try:
            self.user_code.plot_moving_average(prices, window)
        finally:
            plt.xlabel = orig_xlabel
            plt.ylabel = orig_ylabel
            plt.title = orig_title
            plt.legend = orig_legend
        for label in ["xlabel", "ylabel", "title", "legend"]:
            _dynamic_test(
                self,
                called[label],
                f"Plot includes {label}",
                f"Plot missing {label}",
            )

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: Plot Moving Average

Lösung

Challenge: Plot Moving Average

Lösung