Periodic Rebalancing

Periodic rebalancing is the act of rebalancing a portfolio at a regular interval or “period”. At the end of each interval, the portfolio will be rebalanced to once again match the target allocations.

Some examples of common rebalancing periods include 1 hour, 1 day, 1 week, and 1 month rebalance periods. Each of these periods will be evaluated later in this study.

Periodic rebalancing has been a trusted strategy by new cryptocurrency investors due to the simplicity of understanding when and how the portfolio will be maintained.

For example, if a 1 day rebalance period is set for a portfolio, the portfolio will be realigned with the target allocations at the same time every day. This consistency provides a clear expectation for how the strategy will be implemented.

Portfolio Rebalancing for Cryptocurrency

Threshold Rebalancing

Threshold rebalancing uses the same core concepts as periodic rebalancing, but instead of implementing a consistent interval to decide when to rebalance, threshold uses the deviation from the target (desired) allocations to determine when to trigger a rebalance.

The threshold that is evaluated for triggering a threshold-based rebalance is based on the following formula:

Formula: ((C - D) / D) x 100

Where,

C is the current allocation.

D is the desired allocation.

We multiply by 100 to convert from a decimal to a percentage.

If we use the previous example where BTC had a current allocation of 30% and the desired allocation of 25%, we would, therefore, be able to calculate its current deviation by plugging those values into the formula.

BTC Deviation = ((.30 - .25) / .25) x 100

BTC Deviation = 20%

With a current deviation of 20% that means if we had a threshold below 20%, the entire portfolio would be rebalanced.

In this study, we will be evaluating thresholds that range from 1% to 30%. That way we can observe a wide variety of strategies and their performances.

Threshold Rebalancing - The Evolution of Cryptocurrency Portfolio Management

Data & Trade Calculations

The data for this study was collected in real-time from each individual exchange. That means the backtests for Bittrex, for example, use exact market data collected from Bittrex.

We have never used aggregated or estimated data in order to calculate our backtests and this study is no exception. Every trade is calculated using precise order book data from each specific exchange.

During each rebalance event, the historical order book is evaluated, precise trades are simulated based on the actual state of the order book at that time, and the resultant balances are calculated.

Each simulated trade uses the appropriate trading fees that are currently active on the exchange. Since each rebalance will only use maker trades, the spread is crossed in every trade, meaning the spread and trading fees are included in the cost of the rebalance.

In order to provide the most accurate calculations, these backtests will all trade using BTC as the only quote currency. That means if a trade needs to take place between LTC and ETH during the rebalance, the backtest will simulate this as first a trade from LTC to BTC, then a trade from BTC to ETH. There are no optimizations for situations where there are opportunities for direct trading, even if there are direct trading pairs available.

The data for this study begins on January 1, 2019, and ends on January 1, 2020. That way we restrict the evaluation period to only 2019.

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.

Backtesting

Each individual strategy was evaluated by running 1,000 backtests on each exchange. That means when we were evaluating a 1-hour rebalancing strategy, for example, we ran 1,000 backtests using that strategy on each exchange.

In total, 66,000 backtests were run to construct the complete results we will be discussing throughout this study.

Portfolio Size

In order to restrict the number of variables, we did not vary the number of assets in the portfolio across backtests. In our past studies, we have found that diversity does impact the performance of the portfolio, so if you’re interested in those results, you can find them here.

Crypto Users Who Diversify Perform Better

This study will use 10 assets for every portfolio.

Allocations

As discussed in previous sections, allocations are the desired percentages for each of the assets in a portfolio. During each rebalance, trades will be made to reach those target allocations.

For simplicity, this study will use even allocations across all assets in each portfolio that is evaluated. That means since there are 10 assets in each portfolio, every asset will hold a percent allocation of 10% in the portfolio.

During each simulated rebalance, the backtest will buy or sell each asset to reach the 10% allocation that was assigned to each asset.

The Best Asset Distribution for Cryptocurrency Rebalancing

Funds

At the start of each backtest, the portfolio is funded with $5,000 as the initial portfolio value. The resulting portfolio values are then calculated based on this starting value.

Asset Selection

In order for the study to remain unbiased towards specific assets, we needed to carefully implement a selection process that incorporated all available assets on an exchange. This was done by first finding the assets that were available throughout the entire backtesting period (January 1, 2019, to January 1, 2020) on each exchange. From the assets that were available, each backtest randomly selected the 10 assets that would participate in the backtest.

The randomization of the asset selection process removes bias for specific assets. Instead of evaluating the performance of specific portfolios, we are attempting to understand the performance of the general strategy.

Performance Calculations

At the end of the backtest, the results are two different values. These values are the final value of the portfolio if a rebalancing strategy had been used and the final value of the portfolio if a HODL strategy had been used.

To determine how these strategies compare, we calculate the performance of the rebalancing strategy against the HODL strategy by using the following formula.

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

where,

R is the final value of the portfolio that used a rebalancing strategy.

H is the final value of the portfolio that used the HODL strategy.

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

Notice that all of the results discussed in this study will be comparing a rebalanced portfolio to the exact same HODLed portfolio. That means these values are not relative to the starting value of the portfolio, but the final values resulting from these two strategies.

If a value of 5% is displayed, that means the final result for the rebalanced portfolio is 5% higher than the HODLed portfolio. There will be no discussion on comparing the initial and final values for the portfolio since that is not the purpose of this study.

Selected Exchanges

This study will provide a comprehensive analysis of the rebalance vs HODL performance across 6 major exchanges.

The selected exchanges include:

These exchanges were selected based on their popularity, data availability, and selection of assets.

Note: Exchanges like Coinbase Pro, Gemini, and Bitstamp were excluded from this study due to the limited number of assets that were available on these exchange between January 1, 2019 and January 1, 2020. Since we decided to use a default portfolio size of 10 assets, these exchanges did not provide a diverse enough selection to participate.

Each of the selected exchanges will go through the same backtesting process, that way the results can be directly compared across exchanges.

Results

These results cover both threshold rebalancing and periodic rebalancing. For each exchange, we provide the performance histogram for the best performing strategy in both the threshold rebalancing and periodic rebalancing categories.

Binance Backtests

Binance is the most popular exchange in the world. This can be attributed to the exchange’s high liquidity, low trading fees, and reliable services.

Periodic Rebalancing Results