Automating the evaluation of portfolio performance is a key step in modern FinTech workflows. The **Sharpe Ratio** is a foundational metric for assessing **risk-adjusted returns**, helping you compare portfolios with different risk profiles on a level playing field. By calculating the Sharpe Ratio programmatically, you can quickly determine whether a portfolio's returns justify the risks taken, and categorize its performance for better investment decision-making.


import unittest
import user_code
import ast
import re   
import importlib
import csv
import unittest
import importlib
import io
from contextlib import redirect_stdout

class TestTask(unittest.TestCase):
    def test_sharpe_ratio_value(self):
        import user_code
        import numpy as np
        importlib.reload(user_code)
        returns = [0.05, 0.02, 0.04, -0.01, 0.03]
        risk_free_rate = 0.01
        expected = (np.mean(returns) - risk_free_rate) / np.std(returns)
        result = user_code.analyze_sharpe_ratio(returns, risk_free_rate)
        _dynamic_test(
            self,
            result is not None and abs(result - expected) < 1e-6,
            f"Sharpe Ratio is correctly calculated: {expected}",
            f"Sharpe Ratio calculation incorrect: expected {expected}, got {result}",
        )

    def test_category_good(self):
        import user_code
        importlib.reload(user_code)
        returns = [0.15, 0.16, 0.14, 0.17, 0.15]
        risk_free_rate = 0.01
        f = io.StringIO()
        with redirect_stdout(f):
            result = user_code.analyze_sharpe_ratio(returns, risk_free_rate)
        output = normalize_text(f.getvalue())
        _dynamic_test(
            self,
            "good" in output,
            'Correctly categorizes as "good" when Sharpe Ratio > 1',
            f'Expected "good" in output, got: {output}',
        )

    def test_category_average(self):
        import user_code
        importlib.reload(user_code)
        returns = [0.02, 0.01, 0.015, 0.012, 0.011]
        risk_free_rate = 0.01
        f = io.StringIO()
        with redirect_stdout(f):
            result = user_code.analyze_sharpe_ratio(returns, risk_free_rate)
        output = normalize_text(f.getvalue())
        _dynamic_test(
            self,
            "average" in output,
            'Correctly categorizes as "average" when 0 < Sharpe Ratio <= 1',
            f'Expected "average" in output, got: {output}',
        )

    def test_category_poor(self):
        import user_code
        importlib.reload(user_code)
        returns = [-0.01, 0.0, -0.02, 0.01, -0.03]
        risk_free_rate = 0.01
        f = io.StringIO()
        with redirect_stdout(f):
            result = user_code.analyze_sharpe_ratio(returns, risk_free_rate)
        output = normalize_text(f.getvalue())
        _dynamic_test(
            self,
            "poor" in output,
            'Correctly categorizes as "poor" when Sharpe Ratio <= 0',
            f'Expected "poor" in output, got: {output}',
        )

    def test_zero_std(self):
        import user_code
        importlib.reload(user_code)
        returns = [0.05, 0.05, 0.05, 0.05]
        risk_free_rate = 0.05
        f = io.StringIO()
        with redirect_stdout(f):
            result = user_code.analyze_sharpe_ratio(returns, risk_free_rate)
        _dynamic_test(
            self,
            result == 0,
            'Handles zero standard deviation and returns Sharpe Ratio 0',
            f'Expected Sharpe Ratio 0 for zero std, 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

Explore how Python empowers FinTech solutions by automating financial analysis, modeling, and data-driven decision making. This course blends practical coding with real-world finance scenarios, guiding you through essential FinTech concepts and hands-on challenges.

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

Challenge: Sharpe Ratio Analyzer

Lösung

Challenge: Sharpe Ratio Analyzer

Lösung