The intention of this study is to paint a fair picture of how rebalancing as a strategy stacks up to HODLing. In order for this to happen, we thought carefully about how we would design the backtests, data, and variables.

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. Additionally, all trades included a .25% fee which is standard for most exchanges. So, a trade from LTC to SNT would trade from LTC to BTC and then BTC to SNT. In this instance, both trades incurred a .25% fee. 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. So, a period of 1 day results in a rebalance every single day at the exact 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 was that the number of assets in a portfolio has a strong influence on the performance. This hypothesis was 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 would be considered during the process of constructing a portfolio, we used a cross section of Bittrex and Poloniex. This means we took all of 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 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. 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 number of assets and rebalance periods were backtested.

Performance2 Asset Portfolio

This group compares the performance of portfolios which contain two assets, but differ by rebalance period. This performance varies from 1 hour (top left chart) to 1 month (bottom right chart). Each histogram incorporates 1,000 backtests, where the x-axis is the percent gain better than HODL. The y-axis is the number of backtests which fell into the performance buckets that are defined on the x-axis. (Example: A backtest was run with a rebalance period of 1 hour and 2 assets in the portfolio. The results of a backtest was a 50% increase over buy and hold. This would mean you add a 1 to the top left chart in the x-axis bucket which has the range of 44 and 67. This process is then repeated until 1,000 backtests have been run.)

This demonstrates the median percent for which rebalancing at varying intervals outperformed HODL for a portfolio which contains two assets.

A two asset portfolio represents the most simple option for a portfolio. In this instance, the cryptocurrencies simply trade back and forth to each other during each rebalance period. We can see from these histograms that the shorter rebalancing periods result in a larger spread in performance. There are significantly less outliers for shorter rebalancing periods and the results are consistently higher. As the rebalance period increases, the spread actually decreases. This results in less variance in results, but a higher observance of outliers. This suggests higher periods produce lower returns consistently, but also produce more sporadic outliers. The portfolios which used a 1 hour rebalance period outperformed buy and hold by the largest difference of 93%.

4 Asset Portfolio

This group compares the performance of portfolios which contain four assets, but differ by rebalance period. This performance varies from 1 hour (top left chart) to 1 month (bottom right chart). Each histogram incorporates 1,000 backtests, where the x-axis is the percent gain over HODL. The y-axis is the number of backtests which fell into the performance buckets that are defined on the x-axis. (Example: A backtest was run with a rebalance period of 1 hour and 4 assets in the portfolio. The results of a backtest was a 50% increase over buy and hold. This would mean you add a 1 to the top left chart in the x-axis bucket which has the range of 32 and 66. This process is then repeated until 1,000 backtests have been run.)

This demonstrates the median percent for which rebalancing at varying intervals outperformed HODL for a portfolio which contains four assets.

Continuing the trends from the 2 asset portfolio study, we see that shorter rebalance periods have larger spreads in performance in the 4 asset portfolios as well. This results in fewer outliers and a significantly higher median performance than the longer rebalance periods. It can also be observed that the highest performing portfolios all utilized a 1 hour rebalance period. This is even the case when including all outliers. A period of one hour performed the best with a 177% gain OVER buy and hold.

6 Asset Portfolio

This group compares the performance of portfolios which contain six assets, but differ by rebalance period. This performance varies from 1 hour (top left chart) to 1 month (bottom right chart). Each histogram incorporates 1,000 backtests, where the x-axis is the percent gain over HODL. The y-axis is the number of backtests which fell into the performance buckets that are defined on the x-axis. (Example: A backtest was run with a rebalance period of 1 hour and 6 assets in the portfolio. The results of a backtest was a 50% increase over buy and hold. This would mean you add a 1 to the top left chart in the x-axis bucket which has the range of 22 and 55. This process is then repeated until 1,000 backtests have been run.)

This demonstrates the median percent for which rebalancing at varying intervals outperformed HODL for a portfolio which contains six assets.

We observe from the 6 asset portfolio results that the trends discussed in the 2 and 4 asset portfolios continue. This includes the larger spread for shorter rebalance periods and a higher average performance for shorter rebalance periods. At this time, we can also begin to conclude that there is an increasing spread between the 1 hour rebalance period and the 1 month rebalance period as we increase the number of assets in the portfolio. We can keep that in mind as we continue the study. A portfolio which contains 6 assets and has a rebalance period of 1 hour outperformed HODL by 203%.

8 Asset Portfolio

This group compares the performance of portfolios which contain eight assets, but differ by rebalance period. This performance varies from 1 hour (top left chart) to 1 month (bottom right chart). Each histogram incorporates 1,000 backtests, where the x-axis is the percent gain over HODL. The y-axis is the number of backtests which fell into the performance buckets that are defined on the x-axis. (Example: A backtest was run with a rebalance period of 1 hour and 8 assets in the portfolio. The results of a backtest was a 50% increase over buy and hold. This would mean you add a 1 to the top left chart in the x-axis bucket which has the range of 50 and 80. This process is then repeated until 1,000 backtests have been run.)

This demonstrates the median percent for which rebalancing at varying intervals outperformed HODL for a portfolio which contains eight assets.

We observe from the 8 asset portfolio results that the trends discussed in the 2, 4, and 6 asset portfolios continue. This includes the larger spread for shorter rebalance periods and a higher average performance for shorter rebalance periods. What we can also see is that there is only one histogram in this study of 8 asset portfolios that contained results which performed worse than HODL. This can be seen in the bottom right chart which represents the portfolios which used a 1 month rebalance period. The median 8 asset portfolio which rebalanced every 1 hour outperformed HODL by 224%.

10 Asset Portfolio

This group compares the performance of portfolios which contain ten assets, but differ by rebalance period. This performance varies from 1 hour (top left chart) to 1 month (bottom right chart). Each histogram incorporates 1,000 backtests, where the x-axis is the percent gain over HODL. The y-axis is the number of backtests which fell into the performance buckets that are defined on the x-axis. (Example: A backtest was run with a rebalance period of 1 hour and 10 assets in the portfolio. The results of a backtest was a 50% increase over buy and hold. This would mean you add a 1 to the top left chart in the x-axis bucket which has the range of 44 and 72. This process is then repeated until 1,000 backtests have been run.)

This demonstrates the median percent for which rebalancing at varying intervals outperformed HODL for a portfolio which contains ten assets.

We observe from the 10 asset portfolio results that the trends discussed in the 2, 4, 6, and 8 asset portfolios continue. This includes the larger spread for shorter rebalance periods and a higher average performance for shorter rebalance periods. We can also see from these results that only 10 portfolios out of 4,000 performed worse than HODL if they had rebalanced even 1 time each month. This means if you randomly selected 10 assets and rebalanced at least once a month, you would have had a 99.75% chance of outperforming buy and hold over the last year. This is truly incredible. The median performance for a portfolio with 10 assets and a rebalance period of 1 hour was 234% BETTER than HODL.

Complete Comparison

Now that we have all of the data, we can simplify the results into a 4 x 5 grid that illustrates the performance of each portfolio and rebalance period. Since the upper bound on most graphs is much higher than the lower bound, we will calculate the median. This also means that 50% of the portfolios were above and 50% of the portfolios were below this value. So, if when creating your portfolio, you randomly selected assets without performing any research, you would have a 50% chance of performing better than the listed value.

Also, the listed value is the percent gain over buy and hold. So, a value of 10% would mean rebalancing performed 10% BETTER than HODL.

The median performance demonstrates that the higher the rebalance period with the higher number of assets presents the highest gains for rebalancing. Each value represents a percent increase OVER buy and hold. That means a value of 18 means the median of that group performed 18 percent BETTER than buy and hold. This demonstrates, even the absolute worst case performs better than by and hold, even after considering taxes.

We can draw two major conclusions from this grid. First, we have obvious correlations between the rebalance period and the performance. As the rebalance period becomes shorter, the performance of the portfolio increases. A second correlation we can see is between the number of assets and performance. As the number of assets in the portfolio increases, there is an increase in performance. Therefore, the best performing portfolios were those that have both a short rebalance period and a large number of assets.

To round out the complete comparison, we will combine every backtest to create an overall comparison.

Combining all of the backtests over all portfolios and rebalancing periods produces a complete picture comparing rebalancing and HODL. We observe a median complete performance of 64%. This means, if you were to randomly select a portfolio size between 2 and 10, randomly select a rebalance period between 1 hour and 1 month, and randomly select the assets in your portfolio, you would have a 50% chance of performing 64% better than buy and hold if the only difference was rebalancing.

The results show a median performance increase of 64% over all portfolio sizes, rebalance periods, and coin selections.

Tax Implications (US Specific)

According to the latest news, crypto trades are taxed as short-term capital gains at the rate of your current income bracket if the assets were held for less than a year. Long-term capital gains will be taxed at a discount when assets are held for more than a year. Since there are a lot of misunderstandings revolving around taxes, I will try to break down some of the implications here. All calculations will be based on an individual income of $120,000.

An individual making $120,000 is well within the top 10% of incomes in the US. They are also in a federal income tax bracket of 24%. This means any short-term capital gains will be taxed at 24%, which is equal to the personal income tax. That same individual making $120,000 will pay long-term capital gains at 15%.

We can see quickly that there is a 9% difference in taxes between long and short term capital gains. We can compare this difference to the 64% boost in returns observed through rebalancing. What we see is that rebalancing significantly outperforms HODL even after factoring in tax implications of frequent trading. In fact, 92% of all portfolios which rebalanced over the past year beat HODL, after taxes.

That’s not the entire story, however. Rebalancing only trades a portion of the portfolio at any given time. This means part of a portfolio which uses rebalancing would not have been traded by the end of one year. These untouched portions can be taxed as long-term capital gains, reducing the overall taxes that are incurred as a result of rebalancing. The amount can be quantified by examining the volatility difference between all cryptocurrencies over the last several years. This would give us an idea of what percentage of a portfolio would typically be considered long-term capital gains. Since a proper simulation would require careful design, we will save this analysis for another post.

Conclusions

There are two major relations we can draw from this study. The first relation is that increasing the number of assets increased the performance of a portfolio.The second relation is that decreasing the rebalance period (increasing rebalance frequency) increased the performance of a portfolio. Therefore, the ideal portfolio was rebalanced frequently and also contain numerous assets.

It should be remembered that all of these portfolios were selected on a completely random basis. There was no research or elimination process when determining which assets should be incorporated into the portfolios. There is a significant amount of improvement potential for an individual who actively researches and selects promising assets.

Rebalancing beat HODL by a median of 64%. After taxes, this represented 92% of all possible cryptocurrency portfolios.

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