Use Strategy Analyzer as the starting point
Net profit, drawdown, percent profitable and profit factor summarize a run. They do not explain why a system produced those numbers. Exported trade results can be reorganized by direction, time, session, instrument and market context to expose concentration and fragility.
Diagnose losing trades in groups
Instead of asking why one individual trade lost, ask whether many losing trades share the same observable conditions. Examples include time-of-day clusters, volatility states, trend conditions, distance from reference levels, or unusually poor excursion after entry.
Analyze exits separately
A strategy can have a valid entry model but inefficient exits. Compare realized outcomes with maximum favorable and adverse excursion. This can reveal early exits, excessive giveback, stops that are consistently too tight, or targets that capture only a small fraction of available opportunity.
Test market-regime dependence
A strong aggregate backtest may rely on one market environment. Regime analysis can show whether performance is concentrated in trending, ranging, high-volatility or low-volatility conditions.
Avoid optimizing the explanation away
The research objective should be to understand the mechanism before changing parameters. When every discovery immediately becomes another optimization variable, it becomes difficult to tell whether you found a real relationship or simply fit the history more tightly.
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.