Challenge: Compute Returns for Multiple Stocks
Before you take on the challenge of computing returns for multiple stocks, recall the essentials of financial time series analysis. Calculating simple returns involves measuring the percentage change from one closing price to the next, which helps you assess the day-to-day performance of a stock. Log returns, on the other hand, use the natural logarithm of price ratios and are especially useful for modeling and statistical analysis because they are time-additive and better suited for handling compounding effects. Both return types are fundamental in financial analytics and portfolio management.
1234567891011121314import pandas as pd import numpy as np # Simulated daily closing prices for 5 stocks over 20 trading days np.random.seed(42) dates = pd.date_range("2023-01-01", periods=20, freq="B") prices = np.cumprod(1 + np.random.normal(0.001, 0.02, (20, 5)), axis=0) * 100 df = pd.DataFrame( prices, index=dates, columns=["AAPL", "MSFT", "GOOGL", "AMZN", "TSLA"] ) print(df.head())
To calculate returns for each stock, you can use the pct_change() method from pandas to get simple returns, and apply the np.log() function to compute log returns. Add each result as a new column in your DataFrame, such as "AAPL_return" for simple returns and "AAPL_log_return" for log returns. This approach lets you compare both metrics side by side for every stock in your dataset.
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Given a DataFrame with daily closing prices for five stocks, your goal is to compute both simple and log returns for each stock and display the updated DataFrame.
- For each stock column, calculate the simple return and add it as a new column named with the pattern "
<ticker>_return". - For each stock column, calculate the log return and add it as a new column named with the pattern "
<ticker>_log_return". - Display the first five rows of the DataFrame after all new columns are added.
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Before you take on the challenge of computing returns for multiple stocks, recall the essentials of financial time series analysis. Calculating simple returns involves measuring the percentage change from one closing price to the next, which helps you assess the day-to-day performance of a stock. Log returns, on the other hand, use the natural logarithm of price ratios and are especially useful for modeling and statistical analysis because they are time-additive and better suited for handling compounding effects. Both return types are fundamental in financial analytics and portfolio management.
1234567891011121314import pandas as pd import numpy as np # Simulated daily closing prices for 5 stocks over 20 trading days np.random.seed(42) dates = pd.date_range("2023-01-01", periods=20, freq="B") prices = np.cumprod(1 + np.random.normal(0.001, 0.02, (20, 5)), axis=0) * 100 df = pd.DataFrame( prices, index=dates, columns=["AAPL", "MSFT", "GOOGL", "AMZN", "TSLA"] ) print(df.head())
To calculate returns for each stock, you can use the pct_change() method from pandas to get simple returns, and apply the np.log() function to compute log returns. Add each result as a new column in your DataFrame, such as "AAPL_return" for simple returns and "AAPL_log_return" for log returns. This approach lets you compare both metrics side by side for every stock in your dataset.
Swipe to start coding
Given a DataFrame with daily closing prices for five stocks, your goal is to compute both simple and log returns for each stock and display the updated DataFrame.
- For each stock column, calculate the simple return and add it as a new column named with the pattern "
<ticker>_return". - For each stock column, calculate the log return and add it as a new column named with the pattern "
<ticker>_log_return". - Display the first five rows of the DataFrame after all new columns are added.
Ratkaisu
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