A positive statistical result is only the beginning of a trading investigation. In financial markets, the difference between a predictive effect and a usable strategy is often the difference between theory and execution.
The first question is whether the signal survives outside the period in which it was discovered. A pattern that looks impressive in one regime can disappear when volatility, liquidity or participant behaviour changes. This is why chronological out-of-sample testing matters more than another round of parameter tuning.
The second question is execution. A signal may predict a ten-basis-point move, yet the real trade may require paying spread, crossing depth, suffering slippage and paying fees. If the all-in friction is larger than the expected move, the signal is economically irrelevant even if the statistics are real.
The third question is capacity. A result that works for a theoretical one-dollar trade may disappear at one thousand dollars. Liquidity should therefore be treated as part of the model, not as an afterthought.
My preferred workflow is simple: first establish whether information exists, then test whether it is incremental, then model execution, and only after that discuss strategy design. This sequence prevents a common research error — spending weeks optimizing an effect that was never large enough to trade.