In our last study, we analyzed the effects of trading fees on threshold rebalancing.
We found out that trading fees have a massive impact on rebalancing.
Some fees can almost halve your profits.
Despite high trading fees, rebalancing still almost always outperforms HODL.
We decided to take a deeper look into trading fees by analyzing their effects on rebalancing performance by taking into account specific exchanges and their fee schedules.
The purpose of this case study is to compare how rebalancing performs on different exchanges, including: Binance, Coinbase, Kraken, Bybit, Gemini, and so on.
Our conclusions show which exchange brings the best returns and how threshold rebalancing compares to HODL-ing at various tolerance bands and trading fees.
If this is your first time reading our research, I recommend reading our previous four case studies:
This case study was made with the help of Shrimpy's native backtesting solution. You can use Shrimpy to analyze thousands of cryptocurrencies and compare their performance against different strategies. Backtesting is the easiest way to analyze the potential performance of your portfolio without having to deploy real capital.
Cryptocurrency exchanges offer a plethora of trading fees. The amount you pay depends on which trading pair you’re trading, whether you’re trading spot, margin, or futures, and whether you bring enough volume to the table to warrant lower VIP fees.
Above is the fee schedule for cryptocurrency exchange Bybit. The schedule shows different fee rates for spot trading, futures trading, and options trading. Each section is further divided into two parts: taker fees and maker fees. In short, you pay taker fees when executing market orders, and maker fees when executing limit orders.
The fee schedule also has a VIP level section. Your VIP level is determined by the amount of volume you’re trading on Bybit. Exchanges tend to reward traders with lower fees the more capital they exchange. All exchanges have a VIP mechanism.
For the sake of this case study, we’re focusing on taker fees, non-VIP accounts, and spot trading. We have made this decision in order for the case study to be inclusive and target the average Joe.
This case study focuses on the impact of exchange trading fees on the performance of a cryptocurrency portfolio utilizing a threshold rebalancing strategy. Our backtest tracks the portfolio’s performance during a 3-year time period (Jan 1, 2020, to Dec 31, 2022).
We've analyzed 15 settings based on threshold rebalancing tolerance bands and exchange trading fees. We've run 15,000 backtests in total, amounting to 1,000 backtests per setting.
This time we’ve only analyzed a 15-asset portfolio.
Our threshold rebalancing includes the following tolerance bands:
These were the best-performing tolerance bands in our previous trading fee case study.
We have carried out the case study by taking the non-VIP taker fee for spot markets for every major exchange and applying it to our rebalancing trades. The trading fees include 5 groups:
This case study compares the performance of each rebalancing strategy against HODL (buy-and-hold). We've also compared the performance of each strategy against the initial portfolio value.
Note that the asset selection process during our backtests was completely randomized.
The case study includes one portfolio group in terms of portfolio size. The portfolio has 15 assets in total.
The assets in the portfolio group are evenly distributed.
The rebalancing process simulated in the backtest executes trades to keep these initial allocation targets in check. The point of rebalancing is to maintain targeted allocations.
Note that each executed trade includes a trading fee.
Each portfolio starts with an initial balance of $5,000.
The results of this case study show the value of each portfolio by the end of the backtest period (January 1st, 2020 - December 31st, 2022.)
The asset selection process in this case study is completely randomized for the purposes of objectivity and accuracy.
Our list of assets includes all cryptocurrencies that were available on the top 10 crypto exchanges during the backtest period.
Each backtest picked random assets for each portfolio group based on the list of assets available at that time. This is done so that the focus of our analysis is placed not on the assets themselves but on the strategy.
Each backtest outputs two results. One result shows a portfolio's final value if it had been rebalanced, while the other shows a portfolio's final value if it had used the HODL strategy.
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
You can read the formula in the following way:
Our results contain graphs that show the relationship between the number of assets held in a portfolio with the median value of the portfolio by the end of the backtesting range. We have results for HODL, monthly rebalancing, weekly rebalancing, daily rebalancing, and hourly rebalancing.
Our final results (Portfolio Rebalancing vs. HODL) compare portfolio rebalancing against the HODL strategy. This section focuses not on the median value of each portfolio by the end of the backtest, but on the performance of a rebalanced portfolio compared to the same portfolio utilizing a HODL strategy.
Please note that any values displayed in this section are not relative to the starting value of a portfolio but to the same portfolio had it been HODL’ed. If a value of 5% is displayed, that means the final result for the rebalanced portfolio is 5% higher than the HODLed portfolio, and not 5% higher than the initial portfolio fund.
Our case study focuses on the following market period: Jan 1, 2020 - Dec 31st, 2022.
Given the nature of the market during that time period, our case study predominantly analyzes the price data of a bull market.
The market has seen extremely volatile price action during this backtest period. Bitcoin has seen a price increase of 851% from Jan 1st, 2020, to the November ATH of 2021. The market has also seen a 76% price decrease from the ATH to Dec 31st, 2022.
Considering that our case study involves mostly altcoins (due to the nature of diversification), the results are far more volatile.
Our results start by individually analyzing the effects of different exchanges and their trading fees on each threshold rebalancing tolerance band.
The list of tolerance bands includes:
We use the following trading fees: 0.1%, 0.2%, 0.26%, 0.4%, and 0.6%
Our combined results section compares the combined performance of all the strategies listed above. Note that the performance in the combined results section is denominated in dollars. Their performance is relative to the initial portfolio value (IPV).
Our final results compare the combined performance of each rebalancing strategy against HODL.
The values in the final results section are percentages, not dollars. The values are relative to a portfolio utilizing HODL – the performance difference between a rebalanced portfolio and a HODL’ed portfolio – and not to the initial value of a portfolio.
This graph shows the results of a $5,000 portfolio utilizing a 10%, 15%, and 20% threshold rebalancing combined with 0.1% trading fees after three years. The X-axis shows the trading fee used. The Y-axis shows the portfolio's final dollar value at the end of the backtest period.
The results above show the effects of a 0.1% trading fee on three tolerance bands (10%, 15%, and 20%. This portfolio group includes Binance, Kucoin, Bybit, and OKX.
Changing the tolerance band from 10% to 15% resulted in a 4.65% performance increase. Increasing the band further led to an additional 7.04% boost in performance.
This graph shows the results of a $5,000 portfolio utilizing a 10%, 15%, and 20% threshold rebalancing combined with 0.2% trading fees after three years. The X-axis shows the trading fee used. The Y-axis shows the portfolio's final dollar value at the end of the backtest period.
The results above show the effects of a 0.2% trading fee on three tolerance bands (10%, 15%, and 20%. This portfolio group includes Bitfinex and Gate.io.
Changing the tolerance band from 10% to 15% resulted in a 7.31% performance increase. Increasing the band further led to an additional 3.67% boost in performance.
This graph shows the results of a $5,000 portfolio utilizing a 10%, 15%, and 20% threshold rebalancing combined with 0.26% trading fees after three years. The X-axis shows the trading fee used. The Y-axis shows the portfolio's final dollar value at the end of the backtest period.
The results above show the effects of a 0.6% trading fee on three tolerance bands (10%, 15%, and 20%. This portfolio group includes Kraken.
Changing the tolerance band from 10% to 15% resulted in a 4.88% performance increase. Increasing the band further led to an additional 1.73% boost in performance.
This graph shows the results of a $5,000 portfolio utilizing a 10%, 15%, and 20% threshold rebalancing combined with 0.4% trading fees after three years. The X-axis shows the trading fee used. The Y-axis shows the portfolio's final dollar value at the end of the backtest period.
The results above show the effects of a 0.4% trading fee on three tolerance bands (10%, 15%, and 20%. This portfolio group includes Bitstamp and Gemini.
Changing the tolerance band from 10% to 15% resulted in a 12.9% performance increase. Increasing the band further led to an additional 3.32% boost in performance.
This graph shows the results of a $5,000 portfolio utilizing a 10%, 15%, and 20% threshold rebalancing combined with 0.6% trading fees after three years. The X-axis shows the trading fee used. The Y-axis shows the portfolio's final dollar value at the end of the backtest period.
The results above show the effects of a 0.1% trading fee on three tolerance bands (10%, 15%, and 20%. This portfolio group includes Coinbase.
Changing the tolerance band from 10% to 15% resulted in a 15.88% performance increase. Increasing the band further led to an additional 1.93% boost in performance.
This graph shows the combined results of all the previous threshold strategies and trading fees for a $5,000 portfolio after three years. The X-axis shows the trading fee for each portfolio group. The Y-axis shows the portfolio's final dollar value at the end of the backtest period.
X-Axis from left to right: Binance, Bitfinex, Kraken, Bitstamp, Coinbase.
As expected, the results above show that increasing trading fees leads to lower profits when utilizing a threshold rebalancing strategy. 10% tolerance bands are the most affected by increased trading fees. Meanwhile, a 20% band outperforms the other two at all trading fees.
The results also show that the portfolios we’ve analyzed perform the best when trading on exchanges that offer 0.1%, 0.2%, and 0.26% fees. In translation, these groups include Binance, Bybit, Kucoin, OKX, Bitfinex, Gate.io, and Kraken.
The image above shows the impact of each trading fee on each threshold strategy. The best returns are concentrated around the 20% tolerance band, along with 0.1%, 0.2%, and 0.26% trading fees.
Our results show that a 10% threshold strategy with a 0.6% trading fee performed the worst. The best-performing strategy is the 20% threshold strategy with a 0.1% trading fee.
The graph above compares how well each rebalancing strategy has performed against HODL at varying trading fees. The X-axis shows the trading fee used. The Y-axis shows the performance, defined in percentages, of a threshold rebalancing strategy compared to a HODL strategy at the end of the backtest period.
X-Axis from left to right: Binance, Bitfinex, Kraken, Bitstamp, Coinbase.
Our results show that every single threshold strategy outperforms HODL on every single exchange we’ve tested and their respective trading fee.
10% threshold rebalancing outperformed HODL on exchanges like Binance and Coinbase. 20% threshold rebalancing outperformed HODL on exchanges like Bitfinex, Kraken, and Bitstamp.
If we exclude Coinbase from this analysis, we see that threshold rebalancing outperforms HODL by 50% on average. These are amazing results considering that trading fees have a huge toll on portfolio rebalancing.
The image above shows how well each threshold strategy performed with each trading fee versus a HODL strategy.
We conclude that threshold rebalancing performs best when using higher tolerance bands. The results of this case study show that 20% rebalancing offers the greatest returns.
We also conclude that 0.1%, 0.26%, and 0.3% trading fees are the most optimal for threshold rebalancing. Cryptocurrency exchanges that offer these fees are Binance, Bybit, Kucoin, OKX< Bitfinex, Gate.io, and Kraken.
Another important conclusion is that threshold rebalancing outperforms HODL across all crypto exchanges and their respective trading fees. Not a single tolerance band performed worse than HODL per the results of this case study.
However, you may want to optimize your crypto strategy by picking the highest tolerance band and lowest trading fee. Portfolios with a 15% tolerance band and a 0.1% fee performed the best against HODL, scoring 72.93% higher.
The backtests and rebalancing strategies were carried out using our very own portfolio rebalancing tool, Shrimpy.
Shrimpy is an automated portfolio management platform that helps you not only rebalance but also diversify your crypto portfolio.
You can connect over 25 exchange accounts and wallets with Shrimpy and start rebalancing your portfolio right away. Shrimpy is one of the easiest-to-use rebalancing tools in the industry.
Sign up now by clicking here.
Each day Shrimpy executes over 200,000 automated trades on behalf of our investor community. And joining them is easy.
After you sign up and connect your first exchange account, you’ll deploy an investment-maximizing strategy in as few as 5-minutes.
Whether you create your own rebalancing strategy or completely custom automation, the ability to walk your own path belongs in the hands of every crypto investor.
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