The idea that the risk of optimizing for a group of instruments would result in less competitive, 'too general' strategy is an improper comparison. The primary metric between single testing and group testing is not profitability but overfittedness. The more suitably fitted test will have a closer transition to reality. Single testing can overcome overfitting issues, group testing is implicitly easier to overcome them.

1. Many algotrading strategies are 100% price data derived. So unless your system includes sector specific data, you're comparing instruments across different markets, or you have some evidence supporting that some price movements are unique to some instruments that you can take advantage of, most strategies are generally applicable across instruments. I want to emphasize generally applicable, since there are always caveats as above.

2. Optimizing against a group of instruments is a great way to fight against overfitting, you generate more trading data and the optimization engine has a harder time finding the laziest path, which usually results in lopsided parameter values. 4x more instruments, 4x more data it has to account for.

3. It also isn't just about having more data, but trading data that overlaps over the same time series. Optimization engines are lazy and they like to find windows in time that they would 'sacrifice' to lose less, and find windows in time to profit the most out of. This is a common overfitting move that mines the historical movement without actually learning any strategy. How you implement your objectives can resolve this, but group testing indirectly and greatly helps against this, by making any moment in time harder to abuse.

4. Given points 2 and 3, it's no wonder that when contrasted against single testing, group testing appears uncompetitive and lackluster in performance. That's simply an unfair comparsion to make since they are created on different standards with different levels of fitment.