Advanced NumPy | 21/100 Days of Python Algo Trading

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If you’re here, it means you’re truly dedicated to mastering Python. Directly below, you’ll find a quiz designed to help you reinforce what you’ve just learned. This isn’t just about recalling facts; it’s about deeply understanding the concepts. Take a moment to answer the questions and see how well you can apply the knowledge from the video. You’re doing great—every question you tackle brings you one step closer to becoming a Python expert!

Day 21: Advanced Numpy

1. Which of the following is a major advantage of NumPy arrays over Python lists in algorithmic trading?

A. NumPy arrays can store heterogeneous data types.

B. NumPy arrays support element-wise operations and broadcasting.

C. Python lists are more memory efficient.

D. Python lists are faster for numerical computations.

2. Given the need to handle large datasets in algorithmic trading, why would you prefer NumPy arrays over Python lists?

A. NumPy arrays are immutable.

B. NumPy arrays have a dynamic size.

C. NumPy arrays are optimized for numerical operations and use less memory.

D. NumPy arrays can store mixed data types.

3. How would you use Boolean indexing to select all positive returns from an array returns?
```python
positive_returns = returns[________]
```

A. returns > 0

B. returns[returns > 0]

C. (returns > 0)

D. [returns > 0]

4. Given an array prices, how do you use fancy indexing to select elements at indices 2, 4, and 6?
```python
selected_prices = prices[________]
```

A. 2, 4, 6

B. [2, 4, 6]

C. (2, 4, 6)

D. np.array([2, 4, 6])

5. If you have an array prices of shape (10,) and you want to add a constant value 5 to each element, how does broadcasting work in this case?
```python
adjusted_prices = prices + 5
```

A. NumPy throws a shape mismatch error.

B. NumPy replicates the value 5 to match the shape of prices.

C. NumPy ignores the operation.

D. NumPy subtracts 5 from each element.

6. How does broadcasting work when adding a 1D array of shape (3,) to a 2D array of shape (3, 3)?
```python
result = array_2d + array_1d
```

A. Raises a ValueError due to shape mismatch.

B. The 1D array is broadcasted across each row of the 2D array.

C. The 2D array is flattened and then added element-wise.

D. Only the first row is added to the 1D array.

7. How do you calculate the exponential moving average (EMA) of an array prices with a smoothing factor alpha?
```python
ema = np.zeros_like(prices)
ema[0] = prices[0]
for t in range(1, len(prices)):
ema[t] = alpha * prices[t] + (1 - alpha) * ema[t-1]
```

A. This code calculates a simple moving average.

B. This code correctly calculates the EMA.

C. This code incorrectly calculates the EMA.

D. This code calculates the standard deviation.

8. How would you use NumPy to compute the standard deviation of daily returns in an array daily_returns?
```python
std_dev = np.________(daily_returns)
```

A. stddev

B. variance

C. std

D. sqrt(var)

9. What is the correct NumPy implementation of the sigmoid function applied to an array x?
```python
def sigmoid(x):
return np.________(1 / (1 + np.exp(-x)))
```

A. log

B. sqrt

C. exp

D. clip

10. Why is the sigmoid function useful in algorithmic trading models, particularly in logistic regression?

A. It maps any real-valued number to the range [0, 1], useful for probability predictions.

B. It maps any real-valued number to the range [-1, 1], useful for scaling.

C. It computes the derivative of the loss function.

D. It is used for optimizing the trading model's performance.

11. How do you calculate the Mean Squared Error (MSE) between predicted and actual returns in NumPy?
```python
mse = np.________((predicted_returns - actual_returns)**2)
```

A. mean

B. sum

C. average

D. median

12. Why is the Mean Squared Error important in evaluating algorithmic trading models?

A. It measures the total number of errors.

B. It measures the average squared difference between predicted and actual values, indicating the accuracy of predictions.

C. It measures the variance of predictions.

D. It is used to calculate the standard deviation.

13. How can you replace missing values (NaNs) in an array data with the mean of the non-missing values?
```python
mean_value = np.nanmean(data)
data[np.isnan(data)] = mean_value
```

A. This code correctly replaces NaNs with the mean value.

B. This code raises an error.

C. This code replaces NaNs with zero.

D. This code replaces all values with the mean.

14. Which function is used to identify missing values in a NumPy array?
```python
missing_values = np.________(data)
```

A. isnan

B. isnull

C. isna

D. isfinite

15. Which library is commonly used alongside NumPy for plotting graphs in Python?

A. matplotlib

B. seaborn

C. plotly

D. pandas

16. How would you plot a time series of stock prices stored in a NumPy array prices using Matplotlib?
```python
import matplotlib.pyplot as plt
plt.plot(________)
plt.show()
```

A. prices

B. np.plot(prices)

C. prices.plot()

D. plt.plot(prices)

17. How do you perform element-wise addition of two arrays a and b with broadcasting, given that a has shape (3, 1) and b has shape (1, 3)?
```python
result = a + b
```

A. This code will raise a shape mismatch error.

B. This code will correctly perform broadcasting.

C. This code requires explicit reshaping.

D. This code will add the elements without broadcasting.

18. In algorithmic trading, how can you efficiently calculate the daily returns from a time series array prices using NumPy?
```python
daily_returns = np.diff(prices) / prices[:-1]
```

A. This code calculates the daily percentage change.

B. This code calculates the cumulative returns.

C. This code calculates the moving average.

D. This code calculates the exponential moving average.

19. Given an array weights representing the weights of a portfolio, how do you normalize them to sum to 1 using NumPy?
```python
normalized_weights = weights / np.sum(weights)
```

A. This code normalizes the weights correctly.

B. This code throws a division by zero error.

C. This code requires the use of a loop.

D. This code normalizes the weights to sum to 100.

20. How can you use advanced indexing to select specific rows from a 2D array data where the first column is greater than a threshold value?
```python
selected_rows = data[data[:, 0] > threshold]
```

A. This code selects the correct rows using advanced indexing.

B. This code raises an indexing error.

C. This code selects columns instead of rows.

D. This code does not handle thresholds correctly.

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