> ## 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. # Common Backtesting Pitfalls > The most common ways traders misread backtests - and how to avoid them. ## Why pitfalls happen Backtests feel objective because they produce numbers and charts. But most backtest mistakes come from: * wrong assumptions (timeframe, execution, intrabar behavior) * overfitting (optimizing to the past) * ignoring risk (drawdown, tail losses) * ignoring data limitations (coverage, gaps, warmup) This page helps you avoid the traps that create false confidence. *** ## Pitfall 1: Starting with total return If you start with the biggest number (PnL / CAGR), you will: * overfit quickly * ignore risk * misunderstand behavior Fix: * read results in the correct order Use a stable workflow for every backtest. *** ## Pitfall 2: Ignoring trade count A strategy with 7 trades can look incredible by chance. Fix: * require a meaningful trade sample * validate behavior across multiple ranges Low trade count = high uncertainty. *** ## Pitfall 3: Expecting intrabar triggers Many traders expect: * entries on candle highs/lows * instant crossovers mid-candle Trinigence evaluates logic on **candle close** by default. Fix: * design strategies for candle-close evaluation * validate triggers using trade history *** ## Pitfall 4: Confusing events and states Common mistake: * using a crossover (event) as if it were a persistent condition (state) Example: * “EMA crosses above EMA” triggers once * “EMA is above EMA” stays true Fix: * use events for triggers * use states for filters Events vs states explained clearly. *** ## Pitfall 5: Over-filtering the strategy Too many filters often lead to: * almost no trades * misleading metrics * “perfect” equity curves that are just inactivity Fix: * start with a baseline strategy * add one filter at a time *** ## Pitfall 6: Exit logic dominates (but you don’t notice) A strategy can appear profitable because: * TP/SL choices dominate outcomes * exits are too wide/tight * one extreme trade drives results Fix: * simplify exits * inspect the biggest wins/losses * validate distribution, not just averages *** ## Pitfall 7: Over-optimizing parameters Tuning parameters to maximize past performance often creates: * fragile strategies * collapse out of sample * regime dependence Fix: * small parameter changes should not destroy performance * test across multiple time ranges * prefer robust parameter zones Understand how parameters change behavior. *** ## Pitfall 8: Ignoring data coverage and warmup Common symptoms: * “strategy doesn’t trade early in the backtest” * “results look broken in old periods” * “backtest range got adjusted” Fix: * confirm data coverage * understand indicator warmup behavior Learn how coverage affects results. *** ## Pitfall 9: Assuming one market equals all markets A strategy that works on BTC may fail on: * ETH * altcoins * other volatility regimes Fix: * test on multiple markets * look for behavior consistency, not peak returns *** ## Pitfall 10: Ignoring drawdown duration Many traders only look at max drawdown, not how long it lasts. A strategy can have: * acceptable max drawdown * but extremely long recovery periods Fix: * evaluate drawdown duration alongside max drawdown *** ## Quick self-check Before trusting a backtest, ask: * Do I understand the first 10 trades? * Do I have enough trades for confidence? * Is risk acceptable (DD + duration + tail trades)? * Does it work across multiple ranges? * Does it survive small parameter/timeframe changes? *** ## What to read next A step-by-step workflow. Interpret numbers correctly. Debug trade-by-trade. Iterate without overfitting. The easiest backtests to believe are often the most dangerous.