Financial data analysis pattern in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Calculate Moving Average of Stock Prices

Given daily closing prices of a stock, what is the output of the following code that calculates a 3-day moving average?

Data Analysis Python

import pandas as pd
prices = pd.Series([100, 102, 101, 105, 107])
moving_avg = prices.rolling(window=3).mean()
print(moving_avg.tolist())

A [nan, nan, 101.0, 103.0, 106.0]

B [100, 102, 101, 105, 107]

C [100.0, 101.0, 101.0, 102.66666666666667, 104.33333333333333]

D [nan, nan, 101.0, 102.66666666666667, 104.33333333333333]

💡 Hint

Think about how rolling averages handle the first few values when the window is not full.

The rolling function with window=3 calculates the mean of the current and previous two values. The first two values are NaN because there are not enough data points to fill the window.

❓ data_output

intermediate

Identify the Date with Highest Daily Return

Given a DataFrame of stock closing prices indexed by date, which date has the highest daily return calculated by percentage change?

Data Analysis Python

import pandas as pd

data = {'Close': [100, 105, 103, 110, 108]}
dates = pd.to_datetime(['2024-01-01', '2024-01-02', '2024-01-03', '2024-01-04', '2024-01-05'])
df = pd.DataFrame(data, index=dates)
df['Return'] = df['Close'].pct_change()
max_return_date = df['Return'].idxmax()
print(max_return_date.strftime('%Y-%m-%d'))

A 2024-01-04

B 2024-01-02

C 2024-01-05

D 2024-01-03

💡 Hint

Calculate percentage change between consecutive days and find the max.

The highest daily return is on 2024-01-04 because the price jumped from 103 to 110, which is the largest percentage increase.

❓ visualization

advanced

Plot Cumulative Returns Over Time

Which option produces the correct plot of cumulative returns from daily returns in a DataFrame?

Data Analysis Python

import pandas as pd
import matplotlib.pyplot as plt

returns = pd.Series([0.01, -0.005, 0.02, -0.01, 0.015])
cum_returns = (1 + returns).cumprod() - 1
plt.plot(cum_returns)
plt.title('Cumulative Returns')
plt.xlabel('Days')
plt.ylabel('Cumulative Return')
plt.show()

A Line plot starting near 0 and ending around 0.03 showing cumulative growth

B Bar plot showing daily returns as bars

C Scatter plot of daily returns with no cumulative calculation

D Pie chart showing proportion of positive vs negative returns

💡 Hint

Cumulative returns multiply daily returns over time, showing growth trend.

The code calculates cumulative returns and plots them as a line chart showing growth over days.

🔧 Debug

advanced

Identify the Error in Volatility Calculation

What error does the following code raise when calculating volatility (standard deviation of returns)?

Data Analysis Python

import pandas as pd
prices = pd.Series([100, 102, 101, 105])
returns = prices.pct_change()
volatility = returns.std(ddof=0)
print(volatility.values)

A ValueError: std() got an unexpected keyword argument 'ddof'

B TypeError: pct_change() missing required argument

C AttributeError: 'float' object has no attribute 'values'

D No error, prints the volatility value

💡 Hint

Check the type of the volatility variable before accessing .values.

The std() method returns a float, which does not have a .values attribute, causing an AttributeError.

🚀 Application

expert

Determine the Number of Trading Days with Positive Returns

Given a DataFrame of daily closing prices, how many days have positive returns?

Data Analysis Python

import pandas as pd
prices = pd.Series([100, 101, 99, 102, 104, 103])
returns = prices.pct_change()
positive_days = returns[returns > 0].count()
print(positive_days)

A 5

B 3

C 2

D 4

💡 Hint

Count how many daily returns are greater than zero, ignoring the first NaN.

The positive returns occur on days where price increased compared to previous day: from 100 to 101, 99 to 102, and 102 to 104, totaling 3 days.