Best-In-Class Crypto Index 10
Current Index Value
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BICCX10 tracks the performance of 10 cryptoassets projects identified as "best-in-class" after applying an in-house evaluation framework that is inspired by venture capital investors.

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Disclaimer: The information provided on this page should not be considered as an offer to sell, purchase or invest and doesn’t constitute the provision of investment advice.
Past performance does not constitute an indicator of future performance. Data is provided for information only; While every effort has been made to ensure the accuracy of the information provided, the companies of the Kapilendo group shall not be responsible or liable for the accuracy of the information provided. The BICCX™ are protected by various trademarks and intellectual property rights. You require a license to launch any product whose performance is linked to the value of a particular BICCX™ index.

The performance chart contains index performance data based on backtesting, i.e. calculations of how the index might have performed prior to launch if it had existed using the same index methodology and based on historical constituents. In order to preclude hindsight bias, no dynamic management decisions are included in this evaluation; the BICCX™ is determined based on the standard parametrisation alone. Backtested performance information is purely hypothetical and is provided in this document solely for information purposes. Backtested performance does not represent actual performance and should not be interpreted as an indication of actual performance.
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α = 2.00 1 Month (30d) 6 Months (180d) 1 Year (365d)
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We define \(\operatorname{M}({c,t})\) as the market capitalization of crypto asset \(c\) at time \(t\).
The classic weighting by market capitalization is given by
\[w_{c,t} = \frac{\operatorname{M}({c,t})}{\sum\limits_{k\in C}\operatorname{M}({k,t})}\text{.}\]

The problem with this approach becomes visible by looking at the current market capitalizations of the crypto asset market.
Bitcoin dominates the general crypto asset market and also our selection of assets.
To fight the dominance of Bitcoin we would like to reduce the proportion of large crypto assets and shift over some weight to smaller ones.

Therefore we define a function that has a decreasing gradient.
This function will relatively increase small values and decrease big values.
In our case we choose
We call \(\alpha\) the redistribution factor.
By applying \(f\) to \(w\) we shift the weights.
w_\text{new}=\frac{f(w)}{\sum_{i} f(w_i)}

By choosing an \(\alpha\) you can refine the degree of redistribution.
An \(\alpha\) of 1 implies \(f(x)=x\), meaning the transformation won't have any effect.
This represents the base case where we choose the weights by their proportional market capitalization.
An \(\alpha>1\) will overweigh the assets with relatively small market caps.
When \(\alpha=\infty\) this portfolio is equal to the \(1/N\)-Portfolio.
An \(0<\alpha<1\) will overweigh assets that already have a huger market cap.
An negative \(\alpha\) will flip the roles and the smallest assets become the biggest ones.

The weighting function is then given by
w_{c,t} = \frac{f(\operatorname{M}({c,t}))}{\sum\limits_{k\in C}f(\operatorname{M}({k,t}))} = \frac{\operatorname{M}({c,t})^{\frac{1}{\alpha}}}{\sum\limits_{k\in C}\operatorname{M}({k,t})^{\frac{1}{\alpha}}}\text{.}
Below we attached an animation of how the weights behave for different redistribution values of \(\alpha\).
Every investor may choose his own \(\alpha\). BICCX uses an default \(\alpha\) value of 2.

You can also smooth the market cap using a rolling window mean
\hat{\operatorname{M}}({c,t}) = \frac{1}{T} \sum_{i=0}^{T-1}\operatorname{M}({c, t-i})
This will reduce variance in weight changes. We provide an option to smooth the market cap by using an 7 day mean \((T=7)\).

The value of the BICCX is calculated as

I(t)=\sum_{c\in C} w_{c,t} \frac{P({c,t})}{P({c,T_{c,0}})}

where \(T_{c,0}\) corresponds to the 01.07.2018 and \(P({c,t})\) is the price of coin \(c\) at time \(t\).
The index is calculated once a day with data recorded at 00:00 UTC

Data Provider

CoinMetrics is a leading crypto financial data provider for institutions. The company emerged as the trusted brand for building institutional-grade crypto asset infrastructure. Besides, it provides valuation frameworks for investors and empowers them to better understand, value, use, and ultimately steward public crypto networks. 


Didier Goepfert - Project Lead

Didier is specialized in investments and new product/market launch in the financial services industry. He has shared the journey with several successful and innovative start-ups such as Raisin (online savings marketplace), Machinio (search engine) or Streamr (blockchain-powered data marketplace) navigating in various industries and countries. Didier graduated from ESMT Berlin (MBA) and Clermont-Ferrand University (MSc in Financial Markets). 

Paulo Morales Castillo - Data Lead

At Kapilendo, Paulo oversees the company’s data strategy, including the implementation of database solutions, prediction algorithms, and reporting systems in the organization. Before joining Kapilendo, he was a senior data analyst at the credit-comparison site Smava, where he worked for 4 years. Paulo earned bachelor’s degrees in mathematics and economics from the California State University at Sacramento, and an M.S. in economics from the University of Bonn.