Lære Calculating Returns and Price Changes | Financial Data Analysis with Python

Understanding how to measure and interpret returns is a fundamental skill for any trader or analyst. Returns provide a standardized way to evaluate how much a price has changed over time, making it possible to compare the performance of different assets or strategies. There are two primary types of returns you should know: absolute returns and percentage returns. Absolute returns simply measure the difference in price from one period to the next, while percentage returns express this change as a proportion of the initial price, allowing for meaningful comparisons across assets of different prices or scales.


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import pandas as pd

# Sample DataFrame with closing prices
data = {
    "Date": ["2024-06-01", "2024-06-02", "2024-06-03", "2024-06-04"],
    "Close": [100.0, 102.0, 101.5, 104.0]
}
df = pd.DataFrame(data)
df["Date"] = pd.to_datetime(df["Date"])
df.set_index("Date", inplace=True)

# Calculate daily price changes (absolute returns)
df["Price Change"] = df["Close"] - df["Close"].shift(1)

# Calculate daily percentage returns
df["Pct Return"] = df["Close"].pct_change()

print(df)

To calculate returns, you start by looking at the closing prices for each trading day. The absolute return is found by subtracting the previous day's closing price from the current day's closing price. In pandas, you can use the .shift(1) method to align the previous day's price with the current row, making this calculation straightforward. The percentage return is calculated by dividing the absolute return by the previous day's closing price. Pandas simplifies this with the .pct_change() method, which computes the percentage change from one row to the next. Both of these operations add valuable insights to your price data, and pandas makes it easy to add them as new columns in your DataFrame.


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# Add absolute and percentage returns as new columns (if not already added)
df["Absolute Return"] = df["Close"] - df["Close"].shift(1)
df["Percentage Return"] = df["Close"].pct_change()

print(df)

1. What does the `.pct_change()` method compute in a pandas DataFrame?

2. Why are percentage returns preferred over absolute price changes for comparing different stocks?

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Can you explain the difference between absolute and percentage returns in more detail?

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              123456789101112131415161718
            
import pandas as pd

# Sample DataFrame with closing prices
data = {
    "Date": ["2024-06-01", "2024-06-02", "2024-06-03", "2024-06-04"],
    "Close": [100.0, 102.0, 101.5, 104.0]
}
df = pd.DataFrame(data)
df["Date"] = pd.to_datetime(df["Date"])
df.set_index("Date", inplace=True)

# Calculate daily price changes (absolute returns)
df["Price Change"] = df["Close"] - df["Close"].shift(1)

# Calculate daily percentage returns
df["Pct Return"] = df["Close"].pct_change()

print(df)


              12345
            
# Add absolute and percentage returns as new columns (if not already added)
df["Absolute Return"] = df["Close"] - df["Close"].shift(1)
df["Percentage Return"] = df["Close"].pct_change()

print(df)

1. What does the `.pct_change()` method compute in a pandas DataFrame?

2. Why are percentage returns preferred over absolute price changes for comparing different stocks?

Var alt klart?

Tak for dine kommentarer!

Sektion 1. Kapitel 2

Calculating Returns and Price Changes

1. What does the .pct_change() method compute in a pandas DataFrame?

2. Why are percentage returns preferred over absolute price changes for comparing different stocks?

Calculating Returns and Price Changes

1. What does the .pct_change() method compute in a pandas DataFrame?

2. Why are percentage returns preferred over absolute price changes for comparing different stocks?

1. What does the `.pct_change()` method compute in a pandas DataFrame?

1. What does the `.pct_change()` method compute in a pandas DataFrame?