I have designed an algorithm that uses Support Vector Machines to classify the next day's price movement for several prominent cryptocurrencies on a
[1,0,-1] (buy/hold/sell) basis. These cryptocurrencies are namely Bitcoin, Ethereum & Litecoin. I have desgined a backtest that takes into account fees, spread and depth too and therefore can assume a backtesting environment somewhat more realistic than most. When aggregating the returns from across these 3 cryptocurrencies, the overall return is positive.
But there must be other, more insightful and prudent metrics that indicate the success of a strategy other than just pure returns. The only metric that I have so far compared the strategy to is a
Buy and hold (B&H) and comparing returns to the CCi30 index, an index for cryptocurrencies. Whilst I have seen a similar answer here, I found the answers slightly vague and not necessarily relevant to this particular asset class.
Are there any other metrics that can evaluate the performance of a backtest? And which ones should receive the most weighting?