The intention of this study is to paint a fair picture of how rebalancing as a strategy stacks up to a simple buy and hold strategy. In order for us to make this comparison, we thought carefully about how we would design this study. Ensuring we were precise with our data, variables, and backtesting.

If you aren’t familiar with the concept of backtesting, you can read more about strategy backtesting here or join our active Telegram community that is always prepared to discuss the details of backtesting.

Trades & Data

A complete year of market data was collected from exchanges. This data was used to evaluate the cost of each trade at the exact time a rebalance would have been performed. In order to accurately calculate the fees from trading, a .25% fee which is standard for Bittrex at the time of writing this study.

A trade from LTC to SNT would trade from LTC to BTC and then BTC to SNT. In this instance, both trades incur the .25% fee (as it would when trading on the exchange). This allows us to create the most accurate model possible for rebalancing performance.

Rebalance Period

The first variable that is necessary for this study is the rebalance period. A rebalance period is the specific amount of time between each rebalance. In the traditional financial space, this period of time is usually rather long. For example, this article by Investopedia suggests a monthly or quarterly rebalance. However, the cryptocurrency market is far more volatile and can result in the need for more frequent rebalances. Therefore, a rebalance period of 1 hour or 1 day may even be desirable. A 1 day rebalance period would result in a rebalance every day at the same time. The purpose of varying this value is to determine if the frequency of rebalances affects the performance of a portfolio. In this study, we selected rebalance periods of 1 hour, 1day, 1 week, and 1 month. Learn more about rebalancing for cryptocurrency.

Portfolio Size

The second variable we decided to investigate for this study was the number of assets in a portfolio. The hypothesis being that the number of assets in a portfolio has a strong influence on the performance. This hypothesis is tested with 5 groups of asset sizes. Since 2 is the smallest number of assets that will produce any difference when comparing rebalancing and HODLing, we started with a 2 asset portfolio. Then, we increased by 2 to obtain 2, 4, 6, 8, and 10 as the number of assets in each portfolio group. Learn more about how the number of assets in a portfolio affects performance.

Asset Selection

In order to determine which assets should be considered during the process of constructing a portfolio, we used a cross-section of Bittrex and Poloniex. This means we took all the assets from Poloniex for which we had 1 year of data and compared them to the list of Bittrex assets for which we had a year of data. Any asset which was in both lists was included in our pool for the selection process. When a portfolio was constructed, assets were randomly selected from the pool to create a portfolio.

While our study randomly selects assets, we strongly discourage this as a strategy for creating a portfolio. Learn more about how to successfully build a strong portfolio.

Backtest

A backtest is the process of using the trade and order book data from the exchange to simulate how a strategy would have performed over a given amount of time. This is often used to test the viability of a strategy by running it through these large data sets. In this study, we used backtests to compare the results of rebalancing to those of HODL. The number of backtests we ran for each portfolio size and rebalance period pair was set to 1000. This was determined to be sufficiently large to produce an obvious trend. Read more about backtests or run your own.

Now that we know how the study was set up, let’s walk through the entire process we used for completing this study. First, the rebalance period was set to 1 hour and the number of assets was set to 2. This means the portfolio would contain 2 assets and rebalance 1 time every hour. Next, 2 assets were selected at random from the pool of assets. If there were no duplicates, the backtest was run. Once complete, the software then randomly selected 2 new assets at random and ran another backtest. This process continued until it successful ran 1,000 backtests. Once complete, the number of assets was increased from 2 to 4 and 1,000 more backtests were run. This process continued until each combination of the number of assets and rebalance periods was backtested.