Using Morlet Wavelets we are able to capture both frequency and time. We can then reconstruct the curve to uncover any mid-long term trends we were looking for. This method is fairly effective at producing a pure denoised signal, but more research has to be done as to why the y-axis is scaled so disproportionately.
One disadvantage of using an Fast Fourier Transform is that it only takes frequency into consideration and not time, thus it is more difficult to rebuild an accurate denoised signal. Further, the FFT assumes stationarity, thus Morlet Wavelets are more robust then the common FFT.
| Metric | Crypto ETF Value |
|---|---|
| Annualized Return | 0.916497 |
| Volatility | 0.652803 |
| Sharpe Ratio | 1.373304 |
| Sortino Ratio | 1.896141 |
| Max Drawdown | -0.800194 |
| Alpha | 0.647176 |
| Beta | 1.608164 |
The following formulas form the foundation for calculating and managing an equal-weighted ETF. The cryptocurrencies that make up the ETF are as follows: Bitcoin, BNB, Cardano, Ethereum, Solana, XRP.
The number of initial shares to hold for each asset is calculated as:
The index level at any time ( t ) is calculated as:
Where:
- Total Value is the sum of the current value of each asset's investment.
- Base Value sets the initial index value (e.g., 100).
During rebalancing, the following formulas are used:
After rebalancing, the new shares for each asset are calculated as:


