A correlation is not yet a usable forecast

An observed correlation summarizes a relationship in the data you supplied. It does not establish that the relationship was available before the outcome, will persist on later observations, or supports a feasible decision after costs.

For a portfolio project, the next useful step is to specify the information and evaluation contract. A large number without that contract is difficult to interpret.

An obvious relationship that cannot be used

Let y_t be an outcome that resolves tomorrow. Create x_t = y_t and calculate their correlation over a nonconstant sample. It is one. A model can also predict y_t perfectly from x_t.

The relationship is mathematically real in the constructed table. It is unavailable at today's decision because x_t contains tomorrow's answer. Sorting the rows chronologically does not fix the feature.

Less obvious versions include centered rolling statistics, revised historical values and labels accidentally included in a normalization or selection step. Trace an input back to when its particular value became available.

A usable relationship still needs a comparison

Suppose x_t is genuinely observed now. State the forecast target and compare the candidate with a simple rule on matching later rows. A constant forecast, lag-only rule or other task-appropriate baseline helps identify what the additional input contributes.

Keep fitting and selection separate. A model fitted on early rows can still have benefited from later outcomes if you chose its feature after repeatedly inspecting their performance.

Prediction and action are separate

A lower forecast error does not define an executable strategy. A decision rule adds position sizing, timing and constraints. Any cost illustration needs units, turnover accounting and boundary conventions. Without prices and capital, a synthetic outcome score cannot be relabeled a percentage return.

This is why our lab calls its position example an educational accounting exercise. Its purpose is to expose sensitivity, not supply investment advice or claim discovered alpha.

Four questions for the project memo

1. Was every feature available before the decision?
2. What simple comparison used the same evaluation rows?
3. Which choices were made after looking at those outcomes?
4. What additional assumptions would be needed to interpret a forecast as an action?

Answering these questions can reduce the strength of the initial claim. That is useful progress: the project becomes easier to defend.

scikit-learn's leakage guidance explains why evaluation information must be isolated from fitting and preprocessing. Our research lab supplies an original synthetic example, including an intentionally impossible feature and a recorded failure case. Continue with the research memo template to present the conclusion clearly.