Relative Strength in Cryptocurrencies - Research Article #57
How does a relative strength strategy written in 2010 perform in the cryptocurrency market?
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Today’s paper that we’re going to analyze is “Relative Strength Strategies for Investing” (Faber, 2010).
We will apply the rules and concepts described there, to the crypto market, and find out how it performs.
I will briefly explain the differences between absolute and relative momentum in the sections below.
Throughout this short article we will analyze how the strategy performed, the rules, different variations of those rules, and my thoughts on its performance.
Today I am testing a new format for this newsletter.
I build a lot of stuff, and sometimes what slows me down, is having to write long articles, that people might or might not take all the value from.
Today I’ve focused on maximum value per word, rather than a lot of words to get to the same point.
After all, we are practitioners over here, and we put more value on the strategy’s performance, rules and implementation!
So why not, keep these articles under 5 minutes?
Let’s try it today, and please if you have a minute, let me know what you think of this format, I’d greatly appreciate it. 👇
Let’s dig into it!
Index
Introduction
Index
What is Relative Momentum?
Research Paper Highlights
Crypto Performance Highlights
Other Performance Analysis (Highest Risk-Adjusted Settings)
Conclusion
What is Relative Momentum?
Relative momentum is the evaluation of performance of assets relative to each other over a specific period.
Unlike absolute momentum, which measures an asset’s performance against its past performance, relative momentum identifies the leaders in a group and focuses on their leaders.
For instance, in a universe of 100 stocks, a relative momentum strategy might rank them by 3- or 6-month returns and allocate capital to the top-performing quartile. This ranking method allows traders to consistently align with strength, avoiding laggards.
Research Paper Highlights
In the first few pages of the paper, the author describes the performance findings, from 1928 to 2009.
Momentum is a known effect for many decades, so it’s not surprising that the returns seem fairly stable when looking at it from a zoomed out perspective.
But as many momentum strategies, the sharpe is relatively low and the maximum drawdowns are pretty steep.
Everything looks great in a zoomed out plot right?
At the end of the testing period, they did produce interesting returns above their chosen benchmark, so I went ahead and proceeded with the analysis.
There’s an important factor to mention, although the paper does not include commissions and slippage, I did include them in my own analysis below.
Never neglect the importance of costs!
When it comes to the rules, they are rather simple.
The ranking is a simple trailing of total returns, over a specified period like 30 days.
The sizing is based on how many assets we’re trading. So let’s say we’re trading 10 assets, each asset would have 10% allocation based on starting capital.
There’s no sell rule, except when the asset falls from the ranking we discussed above.
If an asset is no longer performing against our basket, we remove it, and add the performing ones.
Another rule that the author used, was to not add any exposure, when the benchmark or the individual sector, fell below its 10 simple moving average.
I’ve added another filter to the testing process to ensure we’re trading the most liquid coins.
We require coins to be within the 50 market cap group, at the time of their trade.
(For historical market cap, I’ve used CoinGecko’s historical data.)
Crypto Performance Highlights
After the strategy passed my initial threshold, I’ve went ahead and tested multiple configurations, within the same parameters provided in the paper.
Here’s my findings below by Best Strategy Group by Sharpe, Best CAGR by Individual Strategy, Highest Sharpe by Individual Strategy and Lowest Drawdown.
Best Strategy Group by Sharpe:
Settings:
Entry/Exit Rule: Every Monday rebalance towards the top 20 coins by relative momentum
Position Size: Fixed 5% position or volatility target of 40% at the time a position is opened
Market Cap Filter: Top 50 by market capitalization
SMA Filter: Close above the 50 SMA
Variable: Lookback period (30, 90, 180, 365 days)
Best CAGR by Individual Strategy:
Strategy Name: relative_momo_365_top_50_fixed_size_long_10
CAGR: 43%
Sharpe: 0.87
Total Return: 880%.
Maximum Drawdown: -86%
Settings:
Entry/Exit Rule: Every Monday rebalance towards the top 20 coins by relative momentum
Position Size: Fixed 5% position
Market Cap Filter: Top 50 by market capitalization
SMA Filter: Close above the 10 SMA
Variable: Lookback period of 365 days
Highest Sharpe by Individual Strategy:
Strategy Name: relative_momo_365_top_50_volatility_targeting_long_10
CAGR: 29%
Sharpe: 0.98
Total Return: 416%.
Maximum Drawdown: -61%
Settings:
Entry/Exit Rule: Every Monday rebalance towards the top 20 coins by relative momentum
Position Size: Volatility Targeted at 40% (calculated at entry signal)
Market Cap Filter: Top 50 by market capitalization
SMA Filter: Close above the 10 SMA
Variable: Lookback period of 365 days
Lowest Drawdown by Individual Strategy:
Strategy Name: relative_momo_30_top_50_volatility_targeting_long_50
CAGR: 22%
Sharpe: 0.89
Total Return: 305%.
Maximum Drawdown: -46%
Settings:
Entry/Exit Rule: Every Monday rebalance towards the top 20 coins by relative momentum
Position Size: Volatility Targeted at 40% (calculated at entry signal)
Market Cap Filter: Top 50 by market capitalization
SMA Filter: Close above the 50 SMA
Variable: Lookback period of 30 days
Other Performance Analysis (Highest Risk-Adjusted Settings)
Conclusion
This strategy suffers from some flaws in my perspective.
I don’t like stacking filters on top of filters to make a strategy perform better. For example, adding the 10-SMA as the author suggested, might be acceptable within a sector like equities or crypto, but I’d rather not do it, if I have other available methods.
If we study the top trend/momentum traders of all time, we find that none of them use these kinds of filters.
They’d rather hedge their positions other ways or add short biased strategies to mitigate the losses when the markets turn.
The paper had no mention of a short bias variation because of the “up-only” nature that we find in equities. It’s really hard to trade negative momentum in an asset that has been going up for 100 years.
Another potential problem are the lengthy drawdowns. Even though we were able to reduce it by using sensible volatility targets, the best strategy by sharpe still suffered a 61% drawdown, which can be quite steep.
However, the bright side is that this strategy is a good starting ground for more study, as momentum is something sticky across market classes.
When we have a strategy, that has the tendency to work decently well for many decades, even in other asset classes, it’s something that we know it’s probably robust, and that we can expect returns going forward.
But nothing is guaranteed and doesn’t come without it’s volatility!
We will continue studying momentum strategies in the future and not only on the long-side.
I hope you’ve enjoyed today’s article!
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