Challenge: Cluster Asset Returns
Clustering is a practical approach in finance for grouping assets with similar return profiles, helping you understand which assets behave alike and thus manage portfolio risk more effectively. By applying clustering algorithms such as KMeans, you can identify patterns and relationships among assets that might not be obvious at first glance. This is especially valuable for risk management, as you can diversify your portfolio by ensuring assets from different clusters are included, reducing the chance of simultaneous losses.
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Implement a function that clusters assets based on their return profiles and returns a mapping of each asset to its assigned cluster.
- The function must use KMeans from scikit-learn to cluster the assets into two groups based on their return data.
- The input is a dictionary where each key is an asset name and each value is a list of returns.
- The function must return a dictionary mapping each asset name to its assigned cluster (0 or 1).
- Asset return lists must be used as features for clustering.
- The number of clusters must be set to 2.
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Clustering is a practical approach in finance for grouping assets with similar return profiles, helping you understand which assets behave alike and thus manage portfolio risk more effectively. By applying clustering algorithms such as KMeans, you can identify patterns and relationships among assets that might not be obvious at first glance. This is especially valuable for risk management, as you can diversify your portfolio by ensuring assets from different clusters are included, reducing the chance of simultaneous losses.
Swipe to start coding
Implement a function that clusters assets based on their return profiles and returns a mapping of each asset to its assigned cluster.
- The function must use KMeans from scikit-learn to cluster the assets into two groups based on their return data.
- The input is a dictionary where each key is an asset name and each value is a list of returns.
- The function must return a dictionary mapping each asset name to its assigned cluster (0 or 1).
- Asset return lists must be used as features for clustering.
- The number of clusters must be set to 2.
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