A very interesting post in the blog of Enigma project by Rodrigo Gomez-Grassi shows how beneficial an application of financial theory to cryptocurrencies could be.
In the first part of the article he briefly explains the Markowitz theory and then moves to the most interesting part: a backtest.
He took 5 cryptocurrencies (BTC, Eth, Monero, Dash and LTC), the starting point was 1st of January 2017. He used a window of historical prices of 180 days, and made rebalances every 30 days (hence, 5 changes during the test period were made). Then he programmed his model on Python (making it rebalancing on Sharpe Ratio).
Rodrigo had a concern regarding high correlation of crypto assets. If this was true, the optimization would barely give extraordinary results. However, the correlation matrix showed that no correlation higher than 0.5 was observed.
Then he moved to testing his ideas. He generated 50 000 thousand portfolios and marked a maximum Sharpe portfolio. This portfolio actively switched between coins during rebalancing periods and showed great performance achieving a return of 359% and Sharpe Ratio of 3.4.
Although this portfolio outperformed Bitcoin, Monero and Litecoin, it lost in terms of Sharpe Ratio to ETH, Dash and equally weighted portfolio. Rodrigo marks that even though his hypothesis did not work out perfectly, his findings suggest using traditional financial theory in crypto optimization might secure your portfolio from drastic single-coins falls.