Your backtest shows 67% win rate and 2.1R expectancy. Looks great on paper. But you don't know if that edge survives transaction costs at scale, if the winning streaks are clustered in one market regime, or if the max drawdown happened during a period you would have actually kept trading through.
The single most misleading metric in any backtest is average R-multiple. A strategy with 2.0R average can still lose money if the distribution is fat-tailed — 10 trades at -1R followed by one trade at +12R produces a great average but requires surviving 10 consecutive losses first. Most traders quit before the payoff arrives.
Upload your backtest results as a CSV or screenshot. The AI reads your trade log and runs five analyses that standard backtest reports skip entirely.
Win rate stability — your overall win rate gets broken down by market regime (trending vs. ranging). A 67% win rate that drops to 38% in ranging markets is not a 67% strategy.
Drawdown clustering — are your worst losses consecutive or spread across time? Clustered drawdowns cause account blowups. Spread drawdowns are survivable.
Regime sensitivity — identifies which market conditions your strategy depends on, and flags when those conditions are absent from your backtest sample.
Transaction cost impact — recalculates your equity curve with realistic slippage and commission assumptions at your expected trade frequency.
Psychological survivability — models whether you would realistically hold through the worst drawdown period, based on drawdown depth, duration, and recovery time.
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