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S&P 500 PE Ratio The Buffett Indicator: Market Cap to GDP Stocks vs. Gold and Silver Stocks vs. Bonds The 60/40 Portfolio Cyclical vs. Defensive Stocks Market-cap Weight vs. Equal Weight Growth vs. Value Stocks Large-cap vs. Small-cap Stocks Nasdaq to S&P 500 Ratio US Stocks vs. The World Emerging vs. Developed Markets US Stock Market Sectors Factor Investing Thematic Investing: Megatrends MSCI Country Indices The Business Cycle Stocks to Commodities Ratio Stocks vs. Real Estate Dow to Gold Ratio Gold to Oil Ratio Mining Stocks vs. Gold & Silver S&P 500 Dividend Yield Developed Markets Emerging Markets Frontier Markets
The Macro Compass Stocks vs. Bonds The 60/40 Portfolio US Yield Curve Bond Yield Credit Spreads M2 Money Supply Growth vs. Inflation Breaking Down US Inflation The Real Interest Rate S&P 500 Dividend Yield
The Macro Compass Stocks vs. Gold and Silver Commodity Futures Dow to Gold Ratio Bitcoin vs. Gold Real Estate to Gold Ratio Copper to Gold Ratio Gold to Oil Ratio Gold to Silver Ratio Gold vs. Real Yields Mining Stocks vs. Gold & Silver
The Macro Compass Worldwide GDP Comparison The Business Cycle US Debt to GDP US Yield Curve Bond Yield Credit Spreads M2 Money Supply Growth vs. Inflation Breaking Down US Inflation The Real Interest Rate
Home Price to Income Ratio The Real Home Price Stocks vs. Real Estate Real Estate to Gold Ratio Bond Yield Credit Spreads
Stocks vs. Gold and Silver Commodity Futures The Business Cycle Stocks to Commodities Ratio Copper to Gold Ratio Gold to Oil Ratio Gold to Silver Ratio Mining Stocks vs. Gold & Silver
Cryptocurrencies Bitcoin vs. Gold Ethereum vs. Bitcoin
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Cryptocurrencies

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Interpretation

The charts above show the historical prices of the leading cryptocurrencies, organized into distinct groups, each targeting specific functionalities:

While this categorization provides a snapshot of the diverse technologies and applications within the cryptocurrency market, it's important to recognize the fluidity within these categories. For instance, platforms originally designed for specific purposes, such as Solana's high-performance blockchain for dApps, have expanded their utility to support DeFi applications and services, demonstrating a fluidity that transcends traditional categorization. Similarly, Chainlink, while a Web3 token providing oracle services, has become integral to the functioning of DeFi protocols. This overlap highlights the dynamic nature of cryptocurrencies, necessitating a flexible and nuanced approach to understanding this ever evolving market.

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Correlation Heat Map

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Interpretation

This heatmap shows how the top cryptocurrencies move in relation to each other. Red squares mean the tokens are strongly correlated (moving together), while blue indicates a negative correlation (moving in opposite directions). As you can see, the crypto market is highly interconnected, with most tokens showing a strong positive correlation, often influenced by the performance of Bitcoin.

Understanding these high correlations is crucial for crypto investors. While diversification is more challenging in a tightly linked market, identifying less correlated assets can still help manage risk. As Ray Dalio highlights in his book Principles, combining even partially uncorrelated assets is key to building a more resilient portfolio.

To create this chart, weekly price changes are calculated for each token, and the Pearson correlation is computed for every pair. The heatmap is then organized using hierarchical clustering to group the most similar tokens together, making market patterns easier to see.

Correlation Spanning Tree

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Interpretation

The Minimum Spanning Tree (MST) simplifies the correlation matrix by showing only the strongest connections between tokens. If two tokens are linked, they have a strong positive correlation and tend to move in tandem. This helps identify clusters of related assets and is useful for portfolio diversification.
The tree is constructed by converting the correlations into distances and then finding the set of connections that links all tokens with the minimum total distance. As noted by Marti, Gautier, et al. (2017), the optimal Markowitz portfolio is often found at the tree's outskirts, and the tree tends to shrink during a financial crisis.

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An error appeared while loading the data. Maybe there is a technical problem with the data source. Please let me know if this happens regularly @silvan_frank.