Creating my own large cap crypto fund on Coinbase Pro

Creating my own large cap crypto fund on Coinbase Pro


I created a large cap crypto fund for myself on Coinbase Pro -- here's how. 

Motivation and constraints

The way I see it an amateur crypto investor needs at least one of three things to profit -- more research time, smarter trades, or luck. I have a full time job so my research time is limited and luck is a graveyard, so I need to trade smarter. 

I'm willing to sacrifice first-mover buys and potential moonshot returns because I don't want to spend all my leisure time hunting crypto. I'm also confident in blockchain technology's potential, so I'm willing to hold assets mid-to-long term. 

These constraints have a well-defined solution in traditional investments -- large cap or index funds. There seems to be limited large cap crypto funds available to US consumers and I want to stay on Coinbase Pro, so I'll to create my own. 

Choosing the coins

I took the top 30ish cryptocurrencies by marketcap from coinmarketcap.com and applied three filters:

  • Remove the stablecoins -- I'm only interested in coins whose dollar value I expect to increase
  • Remove the memecoins -- I'm not confident memecoins have long-term value
  • Remove the coins not available on Coinbase Pro -- I'm most comfortable with Coinbase Pro's API so its the most straightforward exchange for me to use
  • Remove Stellar Lumens (XLM) -- XLM is a promising coin but I'm already exposed to its growth with my Coinbase debit card rewards

These filters produce a list of 18 coins, shown in no particular order below: 

  • AAVE, ADA, ALGO, ATOM
  • BTC, DOT, EOS, ETH
  • FIL, ICP, LINK, LTC
  • MATIC, QNT, SOL, UNI
  • LUNA (substituted with WLUNA on cbpro), and XTZ

Investment strategy

I now have 18 coins I'm reasonably certain will continue to grow in value. The easiest investment strategy I could think of that satisfies my time and knowledge constraints is Dollar Cost Averaging (DCA). 

In laymen's terms, DCA operates on a simple assumption --  the cumulative value of regular, periodic asset purchases will exceed the total money spent over mid-to-long term time frames. 

Amateur crypto traders have already applied DCA to crypto markets with general success. Check out dcabtc.com to play around with historical DCA returns with Bitcoin -- its the easiest tool I ran across when I was researching this post. 

9-month case study

I want an idea of how DCA investments into my 18 cryptocurrencies might perform, so I simulated my fund's DCA performance from Jan. 1, 2021 through mid September with the setup described below:

  • Daily coin market prices for 16 out of my 18 cryptocurrencies were extracted from Coinbase Pro's API. The two remaining coin price histories -- MATIC and SOL -- were manually copied from investing.com because Coinbase Pro's API returned incomplete datasets for them. 
  • 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 coins -- in other words, 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 results are very encouraging. After nine months my large-cap fund was about 3x greater than the total USD deposits. 

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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, MATIC, QNT, SOL, and WLUNA. Overall, nearly all the coins produced DCA returns greater than 1x. 

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Bitcoin is always a good standard to compare time ranges to, so I'll plot BTC's share of the simulated fund performance below. BTC's individual performance over the same time range is much more subdued. I think its notable that the BTC portfolio value never dropped significantly below the cumulative BTC-USD deposits even during BTC's May 2021 crash. To my amateur eye, the simulated results suggest DCA is really good at filtering extreme price swings. 

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Its important to contextualize the study results:

  • The crypto market experienced a bull run and crash during the study's time frame. This likely skewed the results somewhat, but to what degree and in what direction I'm not sure.
  • Several of the coins were listed on Coinbase Pro during the study's time frame, so the study may be biased towards higher returns on these coins since coins typically get a price bump after major exchange listings. 
  • Historical performance does not always suggest future performance. This is especially true for cryptocurrencies. 

Wrapping up

I chose 18 cryptocurrency funds based on current market cap 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 cryptocurrency fund based on these 18 coins and DCA since Jan. 1, 2021. The 9-month study suggests regular, periodic coin purchases into this large cap fund are likely profitable despite extreme market volatility. 

I plan to execute this strategy in some shape or form for at least several months but hopefully for years. There's plenty of analysis I can do to better refine the fund's predicted performance. I also really need to figure out a way to do quality control checks on the analysis code that's driving these results. I'll probably write up a post or two that describes the code behind these results. 

Appreciate the time reading. I would love comments or critiques, especially if anyone has experience with DCA applied to crypto. 

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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