Taking the speculation out of crypto, one study at a time.

“In God we trust. All others must bring data.” — W. Edwards Deming

The following study will provide a glimpse into how market capitalization of crypto assets affects the performance of a portfolio. The data presented is based on actual market data. This allows us to accurately calculate the past performance of strategies in the crypto market without speculation.

Our last article evaluated the performance implications of diversifying a portfolio. You can read more about this topic here:

The Setup

The design for this study was such that assets were divided into 3 categories; large ($70M — $26B), mid ($9M — $69M), and small ($900k — $7.8M) market capitalization. The capitalization ranges were defined by dividing the assets into 3 even groups based on the market cap of each asset on May 4, 2017.

The following constraints were used when performing each backtest.

Trade Fee: .25%

Data: Market data was collected from exchanges over the last year.

Data Time Period: May 4, 2017 to May 3, 2018

Asset Distribution: Evenly weighted among all assets.

Trade Path: All trades are performed through BTC for simplicity.

Asset Selection: Random within each market cap group.

Assets Included: The complete list can be found in our backtest tool.

Initial Investment: Each portfolio is seeded with a $5,000 investment.

Number of Backtests: 1,000 for each portfolio size, strategy type, and market capitalization grouping.

Strategy: The rebalancing method used is outlined in our previous article.

Backtest: Read more about backtests or run your own.

A more in depth discussion of the backtest procedure and study setup can be found in our previous article:

Performance

Four separate groups were evaluated: Large, mid, small, and combined market cap assets. Combined market cap represents all assets regardless of market capitalization.

Large Market Cap