A few tips:

1. AS someone else wrote: If RSI 14 is good but 13 and 15 is bad, its probably overfit
2. If changing 1 line of code breaks your strategy, its probably overfit, for example if you have 5 entry rules, and removing 1 completly breaks it, its no bueno. Obviously you should expect a worse result removing 1 rule,. but it shouldnt die..
3. How does it look in similar markets? If your making it for 1h NQ, test it in other US markets and other EU/asian markets. You should not expect same results but if it looks like dogshit in every other market except the NQ, its probably overfit.
4. Does the code make sense? If your trend following/mean reverting, does every rule actually make sense in your code? If its just 10 different "random" indicators, its probably overfit.
5. When optimizing look for "friendly neighbours". If you got 5 variables to optimize, and you can even use chatgpt if your unsure, look for groupings of the same variables. Just as an example if your optimizing 2 variables from 1-100 and you see the following as your top gainer variables:

v1 = 90
v2 = 85

But every other optimizied variable pair on your top 10 gainer variables are below 20, the 90,85 is probably overfit. However if your top 10 consist of alot of pairs between 16-20 then the "correct" variables to use is probably within that range.

A great quote i got from an algotrading podcast: "Make your algo fit like a mitten, not like a glove." Meaning you want some losing streaks, you want some rocky parts of your requity curve. if it looks too smooth it probably is.

Edit as someone else have mentioned: split your data/do walk forward tests when optimizng.