I can appreciate your honesty. Setting up successful algo trading isn't like just baking a cake
There's no golden standard for robustness/over-fitting (both are -IMO- pretty much the same), so I can only speak for myself. I look for decent OOS statistics, enough trades (say 50+), combined with a decent PF (say 1.25+). More precise would be to look at the performance offsets between your in- and out-of-sample stats.
Believe me, even with 10 years of data with 1000+ trades you can have over-fitting, so a large in-sample set is not a guarantee for robustness. This largely depends on the complexity of your approach, the more complex, the higher the likelihood of over-fitting.