Leer Challenge: Compute and Interpret Returns | Financial Data Analysis with Python

In this challenge, you will work with a hardcoded pandas DataFrame containing the closing prices of two different stocks over a period of 10 days. Your goal is to analyze these prices by calculating both the daily absolute returns and the daily percentage returns for each stock. Once you have these returns, you will identify which stock experienced the highest single-day percentage gain, and on which day that occurred. You will also add the calculated return columns to the original DataFrame and display the relevant results.

To begin, you need a DataFrame with closing prices for two stocks, labeled "StockA" and "StockB", with dates as the index. Calculating the daily absolute return involves subtracting the previous day's closing price from the current day's closing price. The daily percentage return is calculated by dividing the absolute return by the previous day's closing price and multiplying by 100 to express it as a percent.


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import pandas as pd

# Create the DataFrame with closing prices
data = {
    "StockA": [100, 102, 101, 105, 107, 110, 108, 111, 115, 117],
    "StockB": [50, 51, 53, 52, 56, 58, 57, 59, 60, 62]
}
dates = pd.date_range(start="2023-01-01", periods=10, freq="D")
df = pd.DataFrame(data, index=dates)

# Calculate daily absolute returns
df["StockA_AbsReturn"] = df["StockA"].diff()
df["StockB_AbsReturn"] = df["StockB"].diff()

# Calculate daily percentage returns
df["StockA_PctReturn"] = df["StockA"].pct_change() * 100
df["StockB_PctReturn"] = df["StockB"].pct_change() * 100

# Identify the highest single-day percentage gain
stock_a_max = df["StockA_PctReturn"].idxmax()
stock_b_max = df["StockB_PctReturn"].idxmax()

stock_a_gain = df.loc[stock_a_max, "StockA_PctReturn"]
stock_b_gain = df.loc[stock_b_max, "StockB_PctReturn"]

if stock_a_gain > stock_b_gain:
    best_stock = "StockA"
    best_day = stock_a_max
    best_gain = stock_a_gain
else:
    best_stock = "StockB"
    best_day = stock_b_max
    best_gain = stock_b_gain

# Print the DataFrame with new columns
print(df)

# Print the result for the highest single-day percentage gain
print(f"\nThe highest single-day percentage gain was for {best_stock} on {best_day.date()} with a return of {best_gain:.2f}%")

This approach allows you to observe the day-to-day changes in both absolute and percentage terms for each stock. By comparing the maximum values in the percentage return columns, you can determine which stock had the most significant single-day jump, which is an important metric for understanding volatility and potential trading opportunities. The DataFrame now contains all the calculated columns, making it easy for you to review the results and interpret the performance of each stock over the given period.

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Create a pandas DataFrame with the provided closing prices for "StockA" and "StockB" over 10 days.
Calculate the daily absolute returns and daily percentage returns for both stocks, adding them as new columns.
Identify which stock had the highest single-day percentage gain and on which day it occurred.
Print the updated DataFrame and display the highest single-day percentage gain with its corresponding stock and date.

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import pandas as pd

# Create the DataFrame with closing prices
data = {
    "StockA": [100, 102, 101, 105, 107, 110, 108, 111, 115, 117],
    "StockB": [50, 51, 53, 52, 56, 58, 57, 59, 60, 62]
}
dates = pd.date_range(start="2023-01-01", periods=10, freq="D")
df = pd.DataFrame(data, index=dates)

# Calculate daily absolute returns
df["StockA_AbsReturn"] = df["StockA"].diff()
df["StockB_AbsReturn"] = df["StockB"].diff()

# Calculate daily percentage returns
df["StockA_PctReturn"] = df["StockA"].pct_change() * 100
df["StockB_PctReturn"] = df["StockB"].pct_change() * 100

# Identify the highest single-day percentage gain
stock_a_max = df["StockA_PctReturn"].idxmax()
stock_b_max = df["StockB_PctReturn"].idxmax()

stock_a_gain = df.loc[stock_a_max, "StockA_PctReturn"]
stock_b_gain = df.loc[stock_b_max, "StockB_PctReturn"]

if stock_a_gain > stock_b_gain:
    best_stock = "StockA"
    best_day = stock_a_max
    best_gain = stock_a_gain
else:
    best_stock = "StockB"
    best_day = stock_b_max
    best_gain = stock_b_gain

# Print the DataFrame with new columns
print(df)

# Print the result for the highest single-day percentage gain
print(f"\nThe highest single-day percentage gain was for {best_stock} on {best_day.date()} with a return of {best_gain:.2f}%")