u/Thiru_7223 ·
Reddit — r/algotrading
· March 25, 2026 at 11:12
· ⬆ 15 pts
· 💬 31 comments
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AI Summary
Summary
The author is discussing the common algorithmic trading pitfall of overfitting a strategy to historical data through continuous parameter tweaking.
They highlight the theoretical flaw in using out-of-sample data repeatedly, as it effectively turns into in-sample data over multiple iterations.
Quality assessment: This is a methodological discussion regarding quantitative system development, not investment due diligence or market speculation.
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Been backtesting a strategy for a few weeks now. Every time I tweak something entry condition, stop placement, position sizing the numbers improve. So I tweak again. Better again.
At some point I caught myself thinking... am I actually building a solid strategy, or just slowly sculpting something that only works on this one dataset?
Walk-forward testing helped, but I'm still not fully convinced. And the "just use out-of-sample data" advice makes sense until you realize if you keep peeking at OOS to validate each iteration, doesn't it eventually become in-sample too?
Curious where people here draw the line. Do you have a hard rule for when to stop optimizing? Or is there a point where you just accept the uncertainty and let it run?