What you will learn
- Event conditions
- Spread limits as a hypothesis
- Missing data
- Conflicting exposure
Event conditions
Event exclusions should specify which information or execution conditions invalidate the study assumptions. A blanket description such as avoid news is ambiguous unless the relevant events and time windows are defined.
Spread limits as a hypothesis
A spread threshold can be a research hypothesis about feasibility. Record how spread is observed and whether order size matters. Select the threshold before evaluating results rather than excluding costly losses afterward.
Missing data
Missing required data should produce an unavailable decision, not an invented input. A playbook must define whether it waits, cancels or remains inactive when the necessary observation cannot be established.
Conflicting exposure
Conflicting exposure includes multiple positions driven by the same risk factor. Assess the account-level plan before adding a setup that looks independent on a separate chart. Different labels do not guarantee diversified losses.
Worked example
A learner excludes every losing trade whose spread was high, but keeps winners from the same conditions. This is an outcome-dependent filter, not a valid implementation rule.
Try it yourself
Define a symmetric spread exclusion that can be applied before entry to winners and losers alike. State what data must be retained to verify it.
Show the worked solution
Specify the measurement source, time, size convention and threshold before outcomes are known. Retain the observed spread and eligibility decision for every candidate, including excluded cases.
Apply this to your course project
Create two contrasting playbooks and replay them on a reserved chart sample.
Keep the calculation inputs, assumptions and decisions with your work. Practical exercises are self-reviewed; the scored knowledge checks assess the questions shown, not an independent certification of practical competence.