The Day You Became A Better Crypto Fund
Investing in crypto used to be simple but the low-hanging alphas are long gone. What to do now?
The bear market has all sorts of collateral damages. It depletes retail investors’ wallets, affects consumers morale and slows down product adoption. Broadly speaking it challenges fundamentals.
And the crypto funds that raised on the promises of the 2017 boom also count amongst the casualties.
2018 — A Bloodbath (also) for Crypto Funds
- Pantera Capital’s Digital Asset fund: -77% (as of Oct 2018)
- Galaxy Digital’s Benchmark Crypto Index fund: -50.6% (as of Oct 2018)
- Grayscale’s Bitcoin Trust: -54.7% (as of Oct 2018)
- Polychain Capital: -40% (in 2018)
- Multicoin Capital: -32.9% (in 2018)
- And the countless funds operating as mere indexes suffered the same faith. Bitwise 10, 20, 70 & 100 all grossly underperformed BTC.
According to data group HFR, crypto hedge funds were down 70% on average in 2018.
The HODL days are behind us and the existing crypto funds that bet on outdated narratives have no other choice but to evolve in order to survive.
One of the best options is to become Active in the markets.
Become an Actively Managed Crypto Fund
Cryptos are liquid and highly volatile. Investors who are able to exploit that volatility in a risk-managed and market agnostic way are more likely to achieve alpha in the medium and long-term.
As the cryptocurrency market matures, the days of 1000% returns are behind us, which means that crypto funds are no longer in a special league of their own, but competing with traditional hedge funds which are looking at double-digit yearly returns.
In such circumstances, long-biased crypto funds will have difficulty raising larger pools of assets under management. Given the uncertain outlook, lack of safeguards as well as regulatory uncertainty, the risk-reward ratio for investors is out of sync.
Crypto funds should take on profits from both upward and downward trends. Moving forward, algorithmic funds or actively managed funds will be the new norm.
However, setting up a fully fledged algorithmic fund to trade Bitcoin futures might not be right down your alley… nor what your LPs bought into.
But there are simpler solutions that let you automate portfolio management, reduce risk and help you sleep at night.
Three options to automate your portfolio management
In this section, we won‘t touch on alpha creation (data, indicators, algo training, backtesting, trade signals) which widely varies from fund to fund based on their investment thesis. We will solely focus on trade execution.
Let’s break down our solutions into three levels, ranging from the easiest to the most complex to implement.
Level 1: Shrimpy
Shrimpy is a simple automatic rebalancing platform. Select the cryptos you’re interested in, specify their respective percentage allocations, enter your exchanges’ API keys, and off you go to the beach sipping a mojito.
We’ve been using Shrimpy for months and it delivered on the expectations. It integrates with most exchanges, offers hourly/daily/weekly rebalancing across the top 100 cryptos, and even rebalances across multiple exchanges.
Their case-study on rebalancing is quite convincing, although BTC has outperformed most cryptos and indexes over the past few months.
Pros:
- Zero technical work needed.
- You can create your own crypto basket, and adjust the weight allocations over time based on strong/poor fundamentals.
- They offer cold storage integration.
Cons:
- 0.25% trading fee per transaction. That is steep for active management.
- Very low touch. It is a static allocation and doesn’t let you create rules, make one-off trades, or incorporate logic.
- Long only. No shorting.
→ Recommended for small funds and non-technical portfolio managers.
And if you are looking to build your own rebalancer, here is a great hack to get you started: a portfolio rebalancer on Google Sheet.
Level 2: Enigma Catalyst
Why reinvent the wheel when great teams are doing the heavy lifting for you?
Catalyst is an open-source algorithmic investing platform to trade crypto-assets. It lets you focus on developing strategies and offset the concerns of data warehousing, execution, and other tedious obstacles that would otherwise stand in the way.
“Users can quickly write and simulate algorithms across years of daily and minute-resolution historical data, analyze performance, and switch to live trading mode on a number of leading exchanges.”
→ Recommended for medium-sized Funds with a technical background.
Pros:
- Fully open source.
- Speed of implementation. They claim you can set up Algo Trading in under 20-minutes.
- Easy switch from backtesting to live-trading mode. It also comes with a library of basic strategies.
Cons:
- Catalyst is a Python-based tool and requires decent programming skills.
- The platform is still in alpha stage. That means you will certainly uncover bugs and have to invest engineering resources to make it fully functional.
- Only four exchanges are fully supported at the moment: Binance, Bitfinex, Bittrex, and Poloniex.
Level 3: Building Your Proprietary Cloud-based Trading Engine
The crème de la crème is obviously to implement your own solution. You have developed your own strategies, be-it rule-based/momentum-based or market-making/arbitrage, and are now ready to automate the live execution.
Here are a few tools that will do the job for you:
- Deploy the strategies on Google Cloud Virtual Machine instances. Load, modify and run your algos with simple SSH command lines.
- Monitor the individual strategies’ trades and performances on a Plotly dashboard.
- Monitor your P&L in aggregate form across all your strategies and all your exchanges on Coinigy.
And here is what your end-to-end integrated trading engine looks like when you tie it all together:
Pros:
- Cover the full spectrum of trading possibilities: long/short, leverage, market/limit/stop orders.
- Deploy, pause/stop, fine-tune dozens of strategies in parallel based on their individual performances.
- Embed risk management with algorithmic rebalancing and automatic stop-losses (absolutely necessary when leveraging positions).
Cons:
- Higher barrier to entry with some non-negligible upfront technical costs.
- Requires an internal IT department to construct, implement and maintain the stack.
- Requires in-house trading expertise to design alpha strategies.
→ Recommended for large Funds, OTC desks, and institutions whose core expertise is active trading.
At Supernova, we can help you implement this third level solution. We have packaged a kit to expedite the implementation and turn you into an actively managed fund.
We also offer complementary Service Packages: automated rebalancing, backtesting, alpha creation, and automated execution.
Get in touch at services@gosupernova.co.
Disclaimer: we have no commercial or partnership agreements with Shrimpy, Enigma or Coinigy. We are simple users that like to advertise the work of other great teams.
