> ## Documentation Index > Fetch the complete documentation index at: https://docs.trinigence.com/llms.txt > Use this file to discover all available pages before exploring further. # Risk Warnings & Suggestions > How Trinigence surfaces risk warnings, what they mean, and how to use suggestions to improve strategy robustness. ## What this feature is When you create a strategy, Trinigence may provide: * **warnings** (risk flags and potential issues) * **suggestions** (improvements you can apply) The goal is not to judge the strategy -\ it’s to prevent the most common failure modes before you go live. Warnings are about survivability and clarity - not about “good” or “bad” trading. *** ## When warnings appear Warnings usually appear when Trinigence detects patterns like: * missing risk controls (no stop loss) * unrealistic TP/SL for the timeframe * too few trades (weak statistical confidence) * excessive filters (almost no trades) * high exposure / always-in-market behavior * ambiguous logic (can be interpreted multiple ways) * potential overfitting signals (fragile parameters) *** ## Types of warnings ### Missing or weak downside protection Examples: * no stop loss defined * stop loss extremely wide relative to volatility * stop loss defined for long but not for short Why it matters: * tail events can dominate outcomes * backtests can look fine until they don’t Best practices for TP/SL. *** ### Too few trades If trade count is very low, metrics become unreliable. Why it matters: * results can be driven by luck * small sample hides real risk Suggestion: * test a longer period * loosen filters slightly * validate on multiple markets *** ### Over-filtering (strategy inactivity) If filters remove most entries, the strategy may: * look “perfect” because it barely trades * be impossible to evaluate meaningfully Suggestion: * start with baseline entries/exits * add one filter at a time *** ### High exposure / constant market risk If exposure is high, the strategy is almost always in a position. Why it matters: * drawdowns can compound * risk rises in volatility spikes Suggestion: * add regime filters * restrict trading windows * reduce position sizing Understand exposure as real risk. *** ### Parameter fragility (over-optimization risk) If small parameter changes alter behavior dramatically, the strategy may be fragile. Suggestion: * simplify indicators * use standard parameters * test stability across ranges Learn how parameters affect robustness. *** ## How to interpret suggestions Suggestions are typically framed as: * “Add SL if missing” * “Consider a volatility filter” * “Try reducing trade frequency” * “Validate across multiple ranges” A good workflow: 1. accept safety suggestions first (SL, exposure) 2. validate that behavior stays aligned with intent 3. only then optimize performance *** ## What suggestions are not Suggestions are **not**: * guaranteed improvements * financial advice * a promise of better returns They are structured guidance to reduce obvious risk. *** ## Common suggestion patterns Most common. Prevents tail losses and makes strategies backtestable under stress. Helps avoid never-hitting TP or constant stop-outs. Improves statistical confidence and makes debugging easier. Reduces exposure in high-risk or low-liquidity periods. Checks robustness and regime dependence. *** ## Best practices * Treat warnings as “must-review”, not “must-fix” * Fix survivability issues before performance tuning * Use suggestions to guide iterations, not to chase metrics * Always validate changes using trade history Use a stable process to evaluate improvements. *** ## What to read next Mistakes that warnings try to prevent. Sizing-driven risk warnings. Reduce exposure intelligently. Turn suggestions into a clean iteration loop. If a warning surprises you, it usually means the risk was invisible - not absent.