Ask what an experience actually explains
Trading products encounter small but valuable problems: a user changes a parameter, research misses contrary evidence, a risk check rejects a stale request, or actual fees differ from an estimate. These experiences can become useful learning cases only when the original conditions, handling and reconciled outcome are preserved.
Process example: learning from a correction
This is a process example, not an actual user trade. A request leads to a candidate plan; the user adds a condition against overnight holdings. The revised plan passes programmatic checks, simulation and human review. The useful record explains why the condition changed, which rule followed it, and how the outcome was verified.
Existing records and bounded persistence provide part of the foundation. Cross-step links, training consent and sample governance still need development. Acceptance is not proof of correctness, and short-term profit is not a sufficient training label.
The design lets people use the product without contributing private training data. Training participation needs a separate opt-in; payment, trading approval and training consent are distinct. Records enter a versioned dataset only after permission, provenance, quality and timing checks. This is an R&D governance principle, not an available training-consent interface or a completed dataset.
What each stage should produce This is the planned learning cycle. Consent, model evaluation and release permissions remain separate.
Training is not proof of learning
Performance on a training set may reflect memorization, future-information leakage or one market regime. A trading model needs independent evaluation separated in time, then shadow observation of its research, refusals and handling of missing evidence.
Feedback is a lead to verify. A more articulate model does not automatically acquire trading authority.
Initial specialist tasks focus on intent clarification, missing rules, citation checks, risk explanation and review summaries. Permission and de-identification, deduplication and label review precede time-separated training and testing. Evaluation compares the candidate with the base model and current workflow; profitable prediction is not an established result.
Build a path that can be reversed
A validated version can return to research through controlled release while an earlier version remains available for rollback. Active strategies and account permissions keep their own rules.
This is the R&D direction for LensWave Learning Engine and LensWave Trading Model, not a claim of existing training gains. The goal is to make consented, tested experience gradually reusable as specialist capability.