Why my strategy research used to end in nothing — and what fixed it
For years my process looked like this: build a multi-combinatory system, throw millions of parameter combinations at the optimizer, and hunt for the ones that "work."
Sounds like research. It isn't. It's sampling noise.
Run enough combinations and pure chance guarantees some look brilliant in-sample. You can't tell the lucky ones from the real ones, so you chase ghosts — they shine in the backtest, crumble out-of-sample, and weeks evaporate. The search space grows exponentially; the information per iteration approaches zero.
The fix turned out to be one sentence:
An edge hypothesis must be falsifiable.
Before I code anything, I now write down: the condition (what setup), the expected behavior (what price should do after it), and — the key part — what test result would prove me wrong.
"Breakouts work on momentum instruments" is not a hypothesis. It can't fail, so it can't be researched — only endlessly excused.
"After X happens, price continues often enough that entering with a stop at Y is profitable before costs" — that can be killed by data. And a hypothesis that can be killed can also be confirmed.
This flips the whole workflow:
❌ Old: search millions of combos → find something that scores → invent an explanation after
✅ New: state one specific claim → test it raw, default settings, fixed basket → it survives or it dies
Optimization still exists — but only at the end, walk-forward, tuning an edge that already proved it has signal. Search tunes an edge. Search never finds one.
Iterations went from millions to dozens. Every one ends with a written verdict — including the kills. A dead idea with a recorded reason is progress. A folder of "promising" optimizer runs is not.
Explain first. Test second. Kill fast. 🧠
I'm now building this whole pipeline — idea → falsifiable hypothesis → screening → walk-forward → SIM, with hard kill-gates at every step — directly into my platform. Will show you how it looks soon. 👀
