If you test on the same data you optimized on, you don't have a backtest

This is the first thing we check when someone shows us a strategy, and it's where most "great" backtests fall apart. If you tune your parameters on a stretch of data and then measure performance on that same stretch, the result tells you nothing. The system didn't learn an edge, it fit the noise in that specific window. It'll look pristine in testing and unravel the moment it touches data it hasn't seen. So this is what discipline can help fix that: Carve off the last 20-30% of your data before you do anything and treat it as untouchable. Optimize, tweak, and iterate entirely on the earlier portion. When you're genuinely finished, run the strategy once on the slice you set aside. That single number is the best indicator of whether you have something. The technique is trivial, but holding the line is not. The instinct to **"adjust one more thing"** after you've glanced at the holdout is exactly how the holdout gets contaminated, and once it's contaminated it's gone. If the out-of-sample result holds, you might be onto a real edge. If it doesn't, you just saved yourself a drawdown. Let us know, how tightly does everyone here actually guard that final slice while iterating?