Leer Challenge: Analyze Asset Correlations | Financial Data Visualization and Trend Analysis

In recent chapters, you have explored how to analyze the relationships between financial assets using correlation matrices and how to visualize these relationships for better interpretation. Correlation analysis helps you understand how different asset returns move in relation to each other, which is essential for portfolio construction and risk management. Visualizations such as heatmaps allow you to quickly spot patterns—strong positive or negative correlations—among multiple assets, making it easier to identify diversification opportunities or potential risks.


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import pandas as pd
import numpy as np

# Example DataFrame: daily returns for six assets (A, B, C, D, E, F)
np.random.seed(42)
dates = pd.date_range("2023-01-01", periods=10)
returns_data = {
    "Asset_A": np.random.normal(0, 0.01, size=10),
    "Asset_B": np.random.normal(0, 0.01, size=10),
    "Asset_C": np.random.normal(0, 0.01, size=10),
    "Asset_D": np.random.normal(0, 0.01, size=10),
    "Asset_E": np.random.normal(0, 0.01, size=10),
    "Asset_F": np.random.normal(0, 0.01, size=10)
}
returns_df = pd.DataFrame(returns_data, index=dates)
print(returns_df)

To analyze asset correlations, you compute the correlation matrix using the DataFrame's corr() method. The resulting matrix shows the correlation coefficient for every pair of assets, with values ranging from -1 (perfect negative correlation) to +1 (perfect positive correlation). Visualizing this matrix as a heatmap using seaborn's heatmap function provides an intuitive, color-coded overview. When interpreting the matrix, focus on identifying which pairs of assets have the strongest positive and negative correlations. These pairs represent the assets that tend to move most closely together, or in opposite directions, which can inform your diversification and hedging strategies.

Taak

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You are given a DataFrame containing daily returns for six financial assets. Your task is to perform correlation analysis and visualization.

Compute the correlation matrix for all asset columns in the given DataFrame.
Create a heatmap of the correlation matrix using seaborn, with asset labels and a color bar.
Identify the pair of assets with the highest positive correlation (excluding self-correlation).
Identify the pair of assets with the highest negative correlation (excluding self-correlation).
Print the names of the asset pairs and their correlation values as formatted strings.

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              12345678910111213141516
            
import pandas as pd
import numpy as np

# Example DataFrame: daily returns for six assets (A, B, C, D, E, F)
np.random.seed(42)
dates = pd.date_range("2023-01-01", periods=10)
returns_data = {
    "Asset_A": np.random.normal(0, 0.01, size=10),
    "Asset_B": np.random.normal(0, 0.01, size=10),
    "Asset_C": np.random.normal(0, 0.01, size=10),
    "Asset_D": np.random.normal(0, 0.01, size=10),
    "Asset_E": np.random.normal(0, 0.01, size=10),
    "Asset_F": np.random.normal(0, 0.01, size=10)
}
returns_df = pd.DataFrame(returns_data, index=dates)
print(returns_df)