Common warning signs
- Performance changes dramatically with tiny parameter adjustments.
- A small number of trades create most of the profit.
- The system only works during one narrow period or regime without a clear mechanism.
- Adding filters repeatedly improves the development sample but not untouched data.
- Out-of-sample performance degrades far more than normal variance would suggest.
Use parameter neighborhoods
Robust logic often produces a region of acceptable parameter values rather than one magical setting. If performance collapses immediately on either side of the chosen value, investigate whether the strategy is exploiting historical noise.
Preserve untouched data
Out-of-sample data loses its value once you repeatedly inspect it and modify the strategy in response. Treat it as a finite research resource. Development, validation and forward testing should have distinct roles.
Inspect regime and outlier dependence
A strategy may appear robust until you discover that almost all profit came from a brief volatility event or a handful of exceptional trades. Segment performance and measure contribution concentration before trusting aggregate statistics.
Prefer explanations that survive simplification
If a finding only exists after numerous interacting conditions are added, skepticism is appropriate. A simpler, persistent relationship is usually easier to validate and understand than a highly specific historical rule set.
Ask better questions about your strategy.
EdgeQuery is being built to analyze automated trading-system history beyond headline statistics. The founding beta focuses on trade-level behavior, market context, failure patterns and research priorities.