Plotting Charts & Special NumPy Functions | 22/100 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 22: Plotting Charts & Special NumPy Functions

1. What is the primary purpose of using candlestick charts in algorithmic trading?

A) To display the opening and closing times of exchanges

B) To predict the exact price at future dates

C) To visualize price movements within a specific period

D) To compare different stocks directly

2. Which matplotlib function is typically used to plot a simple line chart representing stock prices over time?

A) plt.pie()

B) plt.bar()

C) plt.scatter()

D) plt.plot()

3. When plotting a moving average on a stock chart, which parameter is crucial for smoothing the data?

A) The color of the line

B) The window size of the moving average

C) The title of the chart

D) The type of chart

4. For a trader analyzing high-frequency trading data, which plot type provides the most information about price volatility within a single day?

A) Line chart

B) Histogram

C) Candlestick chart

D) Pie chart

5. In a subplot arrangement where multiple stock indices are compared, what feature should be synchronized across all subplots to ensure accurate analysis?

A) X-axis limits

B) Plot colors

C) Legend position

D) Chart titles

6. Which Python library would you use to interactively explore stock market data with zooming and panning features?

A) Matplotlib

B) Seaborn

C) Plotly

D) ggplot

7. What type of visualization would best depict the correlation between different stocks’ returns?

A) Line chart

B) Correlation matrix heatmap

C) Candlestick chart

D) Bar chart

8. How would you visually represent an algorithm's buy and sell signals on a stock price chart?

A) Using different colors for up and down movements

B) Placing arrows at buy and sell points

C) Using a pie chart

D) Labeling high and low points

9. Which chart type would be most useful for visualizing the distribution of returns from a trading algorithm?

A) Scatter plot

B) Histogram

C) Box plot

D) Area chart

10. What feature of a chart is essential when comparing the performance of an algo-trading strategy against its benchmark?

A) The use of a secondary y-axis

B) The chart’s background color

C) The inclusion of a legend

D) 3D visual effects

11. Which NumPy function can be used to calculate the weighted average of stock prices?

A) np.mean()

B) np.cumsum()

C) np.average()

D) np.sum()

12. In NumPy, which function would you use to find the standard deviation for a dataset representing trading volumes?

A) np.var()

B) np.std()

C) np.mean()

D) np.max()

13. What is the purpose of using np.log() in financial modeling?

A) To compute the logarithmic return of stocks

B) To plot data on a logarithmic scale

C) To calculate the mean return

D) To add up values

14. Which NumPy function is best for generating random numbers that simulate stock price movements for a Monte Carlo simulation?

A) np.random.rand()

B) np.random.randint()

C) np.random.normal()

D) np.linspace()

15. How would you use NumPy to calculate the exponential moving average (EMA) from a series of prices?

A) By applying np.mean() over an exponentially weighted window

B) By using np.cumsum() with exponential decay

C) By using np.convolve() with an exponential decay vector

D) There is no direct NumPy function; it requires manual calculation

16. For a strategy that involves frequent recalculations of portfolio weights based on volatility, which NumPy function can quickly calculate inverse variances?

A) np.linalg.inv()

B) np.sqrt()

C) np.divide()

D) np.power()

17. What advantage does np.matmul() offer when calculating returns from multiple securities over several time periods?

A) It adds up elements directly

B) It performs element-wise multiplication

C) It performs matrix product operations

D) It finds the maximum product

18. Which function can be utilized to ensure that all trading algorithm calculations are executed on NumPy arrays without loops for efficiency?

A) np.apply_along_axis()

B) np.vectorize()

C) np.broadcast()

D) np.fromfunction()

19. In the context of algo trading, why would you use np.clip() on an array of trading signals?

A) To convert all signals to a standard size

B) To limit the maximum and minimum values a signal can have

C) To count the number of signals

D) To plot the signals

20. What is the primary use of np.diff() in analyzing stock price data?

A) To calculate the percent change from one day to the next

B) To sum up all stock prices

C) To find the average price

D) To create a plot of prices

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