Creating my own large cap crypto fund on Coinbase Pro (PART 2)


I created a large cap crypto fund for myself and published it's simulated dollar-cost-averaging 2021 returns in an earlier post -- read it here

I received a lot of fantastic feedback in the post's comments section and from discussions within my professional network. Several folks pointed out the original post's conclusions were positively biased because the top-30 coins by marketcap today were not necessarily in the top-30 on Jan. 1, 2021 when my simulated portfolio began. 

This is an excellent observation, and I want to provide the Publish0x community my updated results. I re-ran the analysis with the top-30 coins by market cap as they were when the portfolio began. The results are more subdued but still profitable.

A brief summary of the original analysis

I chose 18 cryptocurrencies based on current market cap rankings and exchange availability that I think are likely to increase in value over mid-to-long term time ranges. I then simulated the performance of a large cap fund based on these 18 coins and dollar cost averaging (DCA) since Jan. 1, 2021. 

The entire post is available here

The updated analysis approach

My original approach filtered today's top-30 cryptocurrencies by marketcap into a list of 18 coins. The update instead filters the top-30 coins by marketcap from the week of Jan. 3, 2021. Historical coin marketcap rankings per week are available on coinmarketcap.com -- the Jan. 3, 2021 snapshot is linked here

The updated coin list is presented below:

  • BTC, ETH, LTC, DOT
  • ADA, LINK, EOS, XTZ
  • UNI, ATOM, AAVE, SNX
  • FIL, DASH, YFI, MKIR
  • COMP, ZEC

Notably absent from this updated list are SOL, ALGO, LUNA (or WLUNA for Coinbase Pro), and MATIC. 

I was also curious to see how the fund's marketcap ranking cutoff affects the simulated portfolio's performance. I updated the analysis to run three separate times with a different marketcap cutoff -- the first run considered all 18 coins listed above, the second run considered only the top 10 from the list, and the final run considered only the top 5. 

I kept the other simulation parameters as they were:

  • I simulated $100 bi-weekly purchases on Thursdays starting Jan. 7, 2021. I am a full-time, salaried employee with bi-weekly Thursday paychecks, so every-other-Thursday works for me. $100 per paycheck is also reasonable for my current financial situation. 
  • The $100 bi-weekly deposits were equally split between the 18 (then 10, then 5) coins -- in other words, during the first run that considered all 18 coins I bought $5.56 worth of each coin every two weeks. 
  • I assumed feeless trades executed at daily market open prices. Coinbase Pro charges between 0.00%-0.5% per trade. I'll definitely account for this in the future, but for simplicity's sake I assume they are negligible in this study. 

The updated results

The results are more subdued than the original but still profitable:

  • The DCA portfolio using all 18 coins would have a current return of 1.23x relative to its cumulative deposits. 
  • The DCA portfolio using the top 10 coins would have a higher return of 1.47 relative to its cumulative deposits. 
  • The DCA portfolio using the top 5 coins performs slightly below its top-10-coin portfolio peer with a current return of 1.41x. 

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We can examine the performance of each individual coin to see if any particular coins drove the majority of the returns. The top five coins among the large cap fund's 18 cryptocurrencies were ADA, ATOM, DOT, ETH, and XTZ. The top five performing coins change slightly when we run the analysis again with a marketcap cutoff of 10, and then 5. 

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Final thoughts

I'm happy the simulated fund still generates profit. While the profitable return is smaller than the original analysis, its still promising to me for two reasons:

  • 1.25x-1.5x over less than a year is still pretty good in my book. For comparison, the S&P500 is up 1.2x over the same time range. 
  • The returns were made without any fund optimization. I'm confident the fund's returns would increase with even some minor tweaks throughout the year. 

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I was very surprised at how volatile the coin marketcap's top-30 list changes. The coins that ended up driving the original analysis' returns -- MATIC, SOL, WLUNA, and QNT -- were no where near the top-30. 

I want to investigate how the fund's performance changes if I periodically rebalance to capture the changing coin marketcaps. I'll be working on this analysis for the next weekend or so, and I'll certainly post an update whenever I finish. 

As always, thanks for reading and for the constructive criticism. It really helps me grow as an analyst and as an investor. 

Cheers! 

Thumbnail photo by Executium on Unsplash.

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simplyrangel
simplyrangel

Aerospace engineer interested in all things data science and cryptocurrency. Based in Houston, Texas.


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