This study will stand as the first major analysis of threshold based rebalancing for cryptocurrency portfolios. The objective of this study is to not only accurately describe the historical performance of threshold rebalancing, but compare the results to those of a simple buy and hold strategy as well as periodic rebalancing. Due to the recent announcements of our support for threshold rebalancing in our portfolio management application, it is appropriate for us to thoroughly understand the historical implications of executing this strategy over long time periods.

To provide a foundation for understanding of how threshold rebalancing works, we’ve published a brief explanatory piece which will walk you through the strategy and how it has been implemented in Shrimpy. Before continuing, we recommend at least skimming that article here:

Threshold Rebalancing - The Evolution of Cryptocurrency Portfolio Management

Now that you have a general understand of how threshold based rebalancing works, stay up to date with all the studies we release on portfolio strategies by joining our Telegram group.

Past studies which evaluated the performance of periodic rebalancing can be found on our blog here.

Introduction

Before we get to the data, let’s discuss the methods for how this study was set up. Without a robust understanding of the way the study was conducted, we can’t rely on the accuracy of the results.

Data & Trade Calculations

The data for this study was collected directly from the Bittrex cryptocurrency exchange through the third party service CoinAPI. CoinAPI is a data provider which collects and archives order book data across every major cryptocurrency exchange. Using the data APIs, developers are able to access this data for backtests, market analytics, and even real-time pricing data. Our team collected each historical order book snapshot from CoinAPI such that we were provided with exact bid-ask pricing data. This ensures the simulated rebalances will be as accurate as possible when calculating trades.

Caution: Only exact bid-ask data should be used when running backtests. Using aggregated data from CoinMarketCap or other similar services will result in highly inaccurate calculations. Shrimpy studies have never been compromised by the use of such data.

The data for this study begins on March 15, 2017 and extends until June 20, 2019. This includes both the bull run of 2017 and the bear market of 2018 in an attempt to highlight a complete market cycle.

During each simulated rebalance, trades will be calculated by routing each trade through BTC. Each of these simulated trades will include the .25% trading fee which is standard for Bittrex. That means if our portfolio is selling LTC for ETH for example, the backtest will simulate 2 trades, one from LTC to BTC and one from BTC to ETH. Both of these trades will incur the .25% trading fee. Note that in this example Bittrex does have a direct ETH-LTC market. For simplicity our backtests will not consider these optimizations.

There are more advanced algorithms available for how to execute rebalances. In a previous article of ours, we discuss more intricate algorithms. You can find this article here:

Portfolio Rebalancing Algorithms In Crypto (Part I)

Threshold

The thresholds used in this study range from 1% to 50%. After the initial 1% threshold, the backtests evaluated each 5% increment. Therefor evaluating 5%, 10%, 15%, and so on, up until 50%. This provided us with a wide range of thresholds to evaluate in the hopes of identifying a trend.

Threshold rebalancing is a strategy which employs deviation bands (aka “thresholds”). When an individual asset deviates from its target allocation such that the current allocation is outside the deviation bands for this asset, the entire portfolio is rebalanced. This means even if a single asset crosses the threshold, every asset is rebalanced.

In this study, the deviation each asset exhibits is compared to the target allocation on an hourly period. That means once an hour, the data is evaluated and determined if the threshold has been crossed. If the threshold has been crossed, a rebalance is triggered. Therefore, the maximum rebalancing frequency that can be obtained is an hourly rebalance.

Portfolio Size & Asset Selection

During the portfolio construction process, each portfolio is allocated 10 assets. These ten assets are randomly selected from the available assets over the time period of March 15, 2017 to June 20, 2019. That means if the asset was not available throughout the entire period, the asset is not included in this study.

The follow assets are those which were included in this study:

ABY, AEON, AMP, ARDR, BAY, BITB, BLK, BLOCK, BTC, BTS, BURST, CLOAK, CRW, CURE, DASH, DCR, DGB, DGD, DMD, DOGE, EMC, EMC2, ETC, ETH, EXCL, EXP, FCT, FLDC, FLO, FTC, GAME, GEO, GRC, GRS, IOC, ION, IOP, KMD, KORE, LBC, LSK, LTC, MAID, MEME, MONA, MUE, NAV, NEOS, NLG, NXS, NXT, OK, PINK, PIVX, POT, PPC, QWARK, RADS, RDD, REP, SBD, SHIFT, SIB, SLR, SLS, SPHR, STEEM, STRAT, SWT, SYNX, SYS, TX, UBQ, VIA, VRC, VTC, WAVES, XDN, XEM, XLM, XMR, XRP, XST, XVG, XWC, XZC, ZCL, ZEC.

Our team has been fascinated with the way portfolio diversity affects performance. For a complete study on the topic, please visit our previous study here:

Crypto Users who Diversify Perform Better

Backtesting

To analyze each percent threshold, our study will combine the results of 1,000 backtests. The outcome is a histogram at each threshold which provides a distribution which can be both visually and analytically studied. In total 15,000 backtests were run for this study.

At the beginning of each backtest, the portfolio was seeded with $5,000 of initial capital which was used to construct the portfolio and rebalance throughout the backtest. For the sake of simplicity, the portfolio was allocated as an evenly distributed portfolio. That means each of the 10 assets were given exactly 10% weight in the portfolio.

If you’re interested in learning more about how different distributions affect performance, we published a study on this topic here:

Optimizing Asset Distribution For Cryptocurrency Rebalancing

Performance Calculations

At the end of the day, none of this matters if we don’t calculate performance in a way which people understand.

At the conclusion of each backtest, the result is a single dollar amount which is the value of the portfolio. This value is received for both rebalanced backtests and backtests which used the HODL strategy. The two resulting values are compared to one another in the following way to calculate performance:

Performance = ((R - H) / H) x 100

where,

R is the value of the rebalanced portfolio.

H is the value of the HODLed portfolio.

The result is multiplied by 100 to convert from a decimal to a percent.

Results

The following results will cover 15,000 backtests in an attempt to evaluate the performance difference between portfolios which were historically rebalanced and those which have HODLed. As an extra resource at the end, these results are compared to those which have used a periodic rebalancing strategy instead of threshold rebalancing.

In each section, we evaluate 4 primary attributes for the distribution. These attributes are the following:

Primary Performance Distribution: The range for which the majority of the backtests fall in the performance distribution. We calculate this range by taking the first bucket which has more than 10 backtests in the range and include all backtests until there are less than 10 backtests in a bucket.

Percent of Threshold Backtests that Outperformed HODL: The percent of portfolios which performed better using a threshold based rebalancing strategy instead of HODLing.

Average Performance Increase: The average percent boost that was observed for a portfolio that used the threshold rebalancing strategy instead of HODLing.

Median Performance Increase: The median percent boost that was observed for a portfolio that used the threshold rebalancing strategy instead of HODLing.

Threshold Rebalancing Backtests

Results from the threshold rebalancing backtests.

1% Threshold