Here we compare our monthly net performance to Bitcoin and other crypto funds.
Table 1: Monthly ROI % (net after costs and fees)
*) Preliminary figures as of 9 Dec 2022, based on 95 funds for October and 30 for November.
**) As of November 1st, the fund is live and returns are calculated by the fund administrator (AssetCare).
Because the fund was started only recently, a simulation may provide better insight into the long-term risks and returns.
Figure 1: Simulated Fund Equity Curve 2018-2021
2018 – 2021
We are looking at 2018 through 2022 because Bitcoin reached an all time high at the end of 2022, and the highest point before that was in December 2017. So we make a fair comparison, from Bitcoin peak to peak.
The average return in our backtest, at 144% per year, is a lot higher than Bitcoin (37%) and Ethereum (49%) over the same period.
The risk in terms of maximum drawdown, at -33%, is a lot lower compared to Bitcoin (-75%) and Ethereum (-90%) over the same period.
Is this Simulation Reliable?
The trouble with doing a simulation on the past, is that you can accidentally “cheat” by using what we know today in selecting the strategy. This knowledge can easily slip in, in all kinds of ways, without being noticed.
For example, if you want to invest in the top 10 cryptos in terms of market share, you should not take the current top 10, but check which cryptos were big in early 2018 and start your simulation with those. Or if you choose a particular category (such as DeFi or NFT-coins) you’re also making a mistake, because at the beginning of 2018 it was not at all clear that any of those would become a “category” as such.
The great advantage of our fully automated analysis process is that we are able to construct a reliable backtest.
We do this by disregarding our current knowledge about recent market developments, and give the computer only the knowledge that was available at the relevant point in history. We set the boundary conditions as broadly as possible, and then leave the computer free to select the crypto assets, input data, pattern recognition techniques, and parameter settings that are used to make the predictions.
So we run a simulation of what the fund would have done in the past:
Each quarter, we check what assets and data were available at the time, and select a model based only on those data.
Every day, we make predictions with that model and simulate the buying and selling.
All costs are included: transaction costs, spread, price impact and fund management fees.
If you have questions about this or would like to discuss this further, we’d love to meet you in person or online.