Quantitative analysis gives your assumptions a form that can be tested. It is useful when the data describes what you could actually have known and traded at the time. A complicated model built on future information or unrealistic fills can look excellent while offering little practical insight.
Create a timestamped dataset
For each observation, record the market question, your forecast, the observation time, available price and eventual outcome. Keep rule changes and missing data explicit. A result observed after the event cannot be used as if it were a pre-event feature. If several markets depend on the same underlying development, tag that relationship so the dataset does not exaggerate the number of independent observations.
Measure forecasting quality
For a binary outcome coded as zero or one, the Brier score is the squared difference between your probability and the result. A 0.70 forecast followed by an outcome of one contributes 0.09. Lower average scores indicate better performance within a comparable evaluation set. Also inspect calibration groups, since a single average can hide systematically overconfident or underconfident forecasts.
Use chronological evaluation
Train or choose assumptions using earlier observations and evaluate them on later ones. Randomly mixing closely related events can leak information across the boundary. Preserve a final sample that has not influenced feature selection. Compare with simple baselines before celebrating a small gain, and describe the number of decisions involved so readers can assess whether the apparent improvement could be noise.
Separate prediction from execution
A model can forecast well without producing a profitable trading strategy. Include fees, spread, available size and delayed execution in any trade simulation. Specify how unfilled orders are treated. Review sensitivity to a less favorable fill assumption instead of picking the best scenario. Official market-data documentation helps identify available observations, but your testing choices determine whether the conclusions are credible.
Questions & answers
Is forecasting accuracy the same as profit?
No. Entry price, execution costs and exposure determine the financial result alongside the outcome forecast.
Why use a simple baseline?
It reveals whether model complexity adds value compared with an easily reproduced alternative.