Calculating Returns and Simple Metrics
Understanding how your investments perform over time is essential for making informed decisions. The concept of returns helps you measure how much you have gained or lost on an investment over a specific period. Returns are a core metric for investors because they allow you to compare the performance of different assets, periods, or strategies on a consistent basis. By analyzing returns, you gain insight into both the rewards and risks associated with your investments.
12345678910111213import pandas as pd # Sample list of closing prices for a stock closing_prices = [100, 102, 101, 105, 107] # Create a pandas Series prices = pd.Series(closing_prices) # Calculate daily returns as percentage change daily_returns = prices.pct_change() print("Daily returns:") print(daily_returns)
To compute daily returns, you first take the closing price for each day and compare it to the previous day's price. In the code above, you use the pct_change() method from pandas, which calculates the percentage change between consecutive elements in the prices Series. This gives you the percentage return for each day, which is often more useful than the absolute price change because it shows you the relative gain or loss. For example, a price move from 100 to 102 is a 2% return, but a move from 200 to 202 is only a 1% return, even though both are a $2 change. By using percentage returns, you can fairly compare performance across different assets or time periods.
123456# Compute mean (average) and standard deviation of returns mean_return = daily_returns.mean() std_return = daily_returns.std() print("Mean daily return:", mean_return) print("Standard deviation of daily returns:", std_return)
1. What does the standard deviation of returns indicate to an investor?
2. Why is it important to calculate returns instead of just looking at price changes?
3. Which pandas method can be used to calculate the mean of a column in a DataFrame?
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Understanding how your investments perform over time is essential for making informed decisions. The concept of returns helps you measure how much you have gained or lost on an investment over a specific period. Returns are a core metric for investors because they allow you to compare the performance of different assets, periods, or strategies on a consistent basis. By analyzing returns, you gain insight into both the rewards and risks associated with your investments.
12345678910111213import pandas as pd # Sample list of closing prices for a stock closing_prices = [100, 102, 101, 105, 107] # Create a pandas Series prices = pd.Series(closing_prices) # Calculate daily returns as percentage change daily_returns = prices.pct_change() print("Daily returns:") print(daily_returns)
To compute daily returns, you first take the closing price for each day and compare it to the previous day's price. In the code above, you use the pct_change() method from pandas, which calculates the percentage change between consecutive elements in the prices Series. This gives you the percentage return for each day, which is often more useful than the absolute price change because it shows you the relative gain or loss. For example, a price move from 100 to 102 is a 2% return, but a move from 200 to 202 is only a 1% return, even though both are a $2 change. By using percentage returns, you can fairly compare performance across different assets or time periods.
123456# Compute mean (average) and standard deviation of returns mean_return = daily_returns.mean() std_return = daily_returns.std() print("Mean daily return:", mean_return) print("Standard deviation of daily returns:", std_return)
1. What does the standard deviation of returns indicate to an investor?
2. Why is it important to calculate returns instead of just looking at price changes?
3. Which pandas method can be used to calculate the mean of a column in a DataFrame?
Obrigado pelo seu feedback!
