Most cryptocurrency strategies involve some kind of active trading.
Depending on the trading frequency of your strategy, fees might play a significant role in your performance.
For example, a high-frequency trader executes tons of trades every single day. For him to be profitable, he must also take into account how much he spends on trading fees.
Someone with a $100,000 account can execute only a single trade per day and still end the month paying $3,100 in fees. Although fees don’t seem like a lot at first glance, they can add up rather quickly and make a hole in your portfolio.
Rebalancing is a strategy that can involve a high level of trading activity. A periodic rebalancing strategy can execute a trade every hour, day, week, or month. You can easily calculate the effects of trading fees on your periodic rebalancing strategy. But what about a threshold rebalancing strategy where the trading frequency is unknown?
Our newest case study focuses on the effects of trading fees on threshold rebalancing strategies with varying tolerance bands. The purpose of this case study is to discover which tolerance bands perform the best in combination with different fees.
If this is your first time reading our research, I recommend reading our previous three 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.
Exchanges collect fees from traders in order to fund their operations. You have to pay a small fee every time you trade crypto, open a trading position, or use any other type of financial service.
Trading fees create a ton of competition between exchanges. An exchange might offer cheaper fees in hopes of attracting more liquidity. The additional liquidity will fill the gap left by decreased fees trading and even bring additional profits into the business.
Centralized exchanges operate on the basis of order books. Order books store orders from users, and therefore, their liquidity. You pay different fees depending on whether you take or add liquidity from the order book.
This case study focuses on taker fees. Taker fees are trading fees that you pay when you take liquidity. For example, you might execute a market order and instantly execute your trade – which requires taking liquidity from the exchange right away.
This case study focuses on the impact of varying trading fees on the performance of a portfolio utilizing a threshold rebalancing strategy on cryptocurrency portfolio performance during a 3-year time period. The time period includes the 2020-2021 bull market, as well as the 2022 bear market.
We've analyzed 35 settings based on threshold rebalancing tolerance bands and trading fees. We've run 35,000 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:
Our trading fees include: 0.1%, 0.3%, 0.5%, 0.7%, and 0.9%
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 various trading fees on each threshold rebalancing tolerance band.
The list of tolerance bands includes:
We use the following trading fees: 0.1%, 0.3%, 0.5%, 0.7%, and 0.9%
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 1% threshold rebalancing after three years. The X-axis shows the trading fees 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 different trading fees on a threshold strategy using a 1% tolerance band. You can see that higher trading fees dramatically decrease the portfolio’s value. This is because lower tolerance bands are more likely to execute trades in order to rebalance your portfolio. As a result, fees start adding up and take a big chunk of your portfolio.
Deviating 3x from the standard crypto exchange taker fee (0.1%) results in an 18% loss.
Deviating up to 9x from the same fee results in a 52.3% performance loss.
This graph shows the results of a $5,000 portfolio utilizing a 5% threshold rebalancing after three years. The X-axis shows the trading fees 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 different trading fees on a threshold strategy using a 5% tolerance band.
Deviating 3x from the standard crypto exchange taker fee (0.1%) results in a 16.59% loss.
Deviating up to 9x from the same fee results in a 45.2% performance loss.
This graph shows the results of a $5,000 portfolio utilizing a 10% threshold rebalancing after three years. The X-axis shows the trading fees 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 different trading fees on a threshold strategy using a 10% tolerance band.
Deviating 3x from the standard crypto exchange taker fee (0.1%) results in a 10.38% loss.
Deviating up to 9x from the same fee results in a 35.9% performance loss.
This graph shows the results of a $5,000 portfolio utilizing a 15% threshold rebalancing after three years. The X-axis shows the trading fees 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 different trading fees on a threshold strategy using a 15% tolerance band.
Deviating 3x from the standard crypto exchange taker fee (0.1%) results in a 16.17% loss.
Deviating up to 9x from the same fee results in a 37% performance loss.
This graph shows the results of a $5,000 portfolio utilizing a 20% threshold rebalancing after three years. The X-axis shows the trading fees 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 different trading fees on a threshold strategy using a 20% tolerance band.
Deviating 3x from the standard crypto exchange taker fee (0.1%) results in a 2.18% loss.
Deviating up to 9x from the same fee results in a 31.6% performance loss.
This graph shows the results of a $5,000 portfolio utilizing a 25% threshold rebalancing after three years. The X-axis shows the trading fees 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 different trading fees on a threshold strategy using a 25% tolerance band.
Deviating 3x from the standard crypto exchange taker fee (0.1%) results in a 9.6% loss.
Deviating up to 9x from the same fee results in a 25.3% performance loss.
This graph shows the results of a $5,000 portfolio utilizing a 30% threshold rebalancing after three years. The X-axis shows the trading fees 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 different trading fees on a threshold strategy using a 5% tolerance band.
Deviating 3x from the standard crypto exchange taker fee (0.1%) results in a 7.7% loss.
Deviating up to 9x from the same fee results in a 24.49% performance loss.
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 tolerance band for each portfolio group. The Y-axis shows the portfolio's final dollar value at the end of the backtest period.
Our results show that increased trading fees have a large negative effect on portfolio performance when utilizing a threshold rebalancing strategy.
What’s important to note is that the impact of trading fees is even larger for threshold strategies with low tolerance bands. Lower tolerance bands lead to more frequent rebalancing, which in return leads to more fees.
The image above shows the impact of each trading fee on each threshold strategy. The best returns are concentrated around the 15% tolerance band, along with 0.1%, 0.3%, and 0.5% trading fees.
Our results show that a 1% threshold strategy with a 0.9% trading fee performed the worst. The best-performing strategy is the 15% 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.
Nearly all of the threshold strategies have outperformed a HODL strategy in spite of the effects of trading fees. Only threshold strategies with tolerance bands on the lower end (i.e. 1% and 5%) with 0.5% trading fees and higher had performed worse than HODL.
In this case, it’s important to note that the 25% tolerance band brought the best performance against HODL for the threshold strategy. This is the only tolerance band that consistently outperforms all other strategies against HODL.
The image above shows our results for how well each threshold strategy performed with each trading fee versus a HODL strategy. of each trading fee on each threshold strategy. The best returns are concentrated around the 15% tolerance band, along with 0.1%, 0.3%, and 0.5% trading fees.
The results of our case study show that higher trading fees drastically decrease the returns of a cryptocurrency portfolio utilizing a threshold strategy.
The best-performing portfolios were the ones that had higher tolerance bands: 15%, 20%, 25%, and 30%. This is because these portfolios had executed trades less frequently, thus incurring less damage from trading fees.
The performance decrease of increased trading fees is gradual and predictable. What’s noticeable is that the gap in performance between trading fees is drastically bigger on threshold strategies with lower-end tolerance bands.
When comparing the performance of each threshold strategy against HODL, we conclude that portfolio rebalancing still outperforms HODL in spite of the added trading fees. Only strategies with lower-end tolerance bands failed to outperform HODL (e.g. 1% and 5%).
We conclude that threshold rebalancing completely outperforms HODL most of the time, even at incredibly high trading fees.
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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