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Bot Backtest Reality Check: 11 AI prompts for backtest validation

A pack for checking whether a trading bot backtest is robust enough to move toward paper trading or live execution.

Added August 27, 2026 11 copy-ready prompts Library-matched

Copy-ready prompts from Bot Backtest Reality Check

Backtest Reality Scorecard

Start here Pro

Assesses whether a trading-bot backtest deserves deeper validation or still resembles an overfit demonstration.

Strategy Backtesting
ID 390
Act as an independent trading-strategy reviewer. Evaluate strategy rules, sample period, trade count, P/L, costs, and assumptions. For each dimension (data quality, sample size, costs, slippage, leakage, overfitting, regime coverage, and execution realism), rate the evidence as insufficient, needs work, or ready for deeper validation. Cite the evidence for every rating, list missing inputs, and rank the largest failure risks first.

Look-Ahead Bias Detector

Medium

Checks whether a backtest accidentally used information that would not have existed at decision time.

Strategy Backtesting
ID 391
Audit these strategy rules for look-ahead bias: trend-following strategy. Check signal timestamps, indicator calculations, candle-close handling, revised data, corporate actions, historical index membership, model-training windows, and order timing. For each possible leak, explain how to test for it and how to correct it.

Slippage, Fees, and Fill Realism Check

Pro

Adds real execution costs to a backtest so perfect fills do not hide a fragile strategy.

Strategy Backtesting
ID 392
Review my backtest assumptions for the crypto market. Ask for the venue, period, order types, trade size, and missing execution data before estimating costs. Use dated venue or broker evidence to model fees, spreads, slippage, partial fills, latency, and stressed liquidity. Show results under base, adverse, and severe cost assumptions, clearly label estimates, and identify inputs that cannot be supported.

Trade Count and Sample Size Sanity Check

Beginner

Prevents users from trusting a high Sharpe ratio, win rate, or profit curve built on too few trades.

Strategy Backtesting
ID 393
Evaluate the strength of evidence in my backtest: 240 trades over 4 hours, win rate 20%, and expectancy 0.12 R per trade. Estimate uncertainty where possible, examine regime coverage and dependence on outliers, state what cannot be inferred, and define the additional testing required. Do not invent a universal minimum sample size.

Walk-Forward Split Builder

Medium

Turns a single backtest into rolling in-sample and out-of-sample windows.

Walk-Forward Validation
ID 394
Design and justify a walk-forward validation plan for my strategy on the crypto market using 15-minute data. Define in-sample and out-of-sample window lengths, the step size, re-optimization frequency, embargo rules, pass/fail metrics, and how to combine all out-of-sample results. Explain how the design reflects the strategy horizon, available history, and likely regime changes.

Monte Carlo Drawdown Stress Test

Medium

Tests sensitivity to trade order, losing streaks, and adverse sequencing.

Bot Infrastructure
ID 395
Create a Monte Carlo stress-test plan for my backtest trades. Include trade reshuffling, bootstrap assumptions, dependence limitations, adverse sequencing, fee and slippage shocks, drawdown distributions, probability-of-ruin estimates, and illustrative position-size limits. Explain why simulated results do not guarantee live survival.

Parameter Sensitivity Heatmap

Medium

Checks whether performance depends on one lucky parameter setting instead of a robust zone.

Strategy Backtesting
ID 396
Build a parameter-sensitivity test for this strategy: trend-following strategy. Identify the key parameters, define sensible ranges, plan a heatmap, and explain how to distinguish a robust plateau from a fragile peak caused by overfitting.

Regime Robustness Review

Medium

Tests how a bot behaves across volatility, trend, liquidity, and correlation regimes.

Bot Infrastructure
ID 397
Stress-test my bot across market regimes: low-volatility chop, high-volatility trend, crash, rebound, liquidity drought, and correlation breakdown. For each regime, list expected failure modes, metrics to monitor, and rules for reducing or pausing the bot.

Paper Trading Graduation Gate

Medium

Creates objective rules for moving from backtest to paper trading, then to small live size.

Walk-Forward Validation
ID 398
Create evidence-based graduation criteria from backtesting to paper trading and then to minimum live size. Cover the required observation period and sample, execution reconciliation, slippage tolerance, alert quality, drawdown limits, logging, rollback conditions, and stop rules. Explain how to set the thresholds from the strategy and venue rather than inventing universal values.

Survivorship and Universe Audit

Pro

Checks whether the backtest universe quietly excluded assets that failed, delisted, or became untradeable.

Strategy Backtesting
ID 399
Audit the asset universe used in my backtest: ETF portfolio. Check delisted assets, current-members-only data, missing historical constituents, corporate actions, liquidity filters, listing and delisting dates, and whether the strategy could actually trade each asset at the time.

Production Kill-Switch Rules

Pro

Defines when a bot should reduce size, stop trading, or require human review after deployment.

Bot Infrastructure
ID 400
Design production kill-switch rules for my trading bot. Cover daily loss, drawdown, slippage, stale or missing data, rejected orders, latency, abnormal fills, position mismatches, and venue incidents. For every trigger, define who sets and approves the threshold, the safe state, cancellation and reconciliation behavior, alerting, authorized manual intervention, recovery evidence, and a tested restart procedure.

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Find why a trading bot backtest may be too good to be true

A strong equity curve can still come from leakage, overfitting, weak samples, or fills that could not occur in the real market. Use this pack to challenge the test design before interpreting performance or considering deployment.

  • Begin with the strategy rules, market, timeframe, data source, timestamps, sample period, position sizing, order logic, fees, spread, slippage, latency, and available capacity.
  • Test data integrity, look-ahead and survivorship bias, parameter sensitivity, realistic execution, regime stability, and out-of-sample behavior as separate checks rather than one broad review.
  • Keep the evidence for every pass or failure, define rejection and pause criteria in advance, and require paper or minimum-size live validation before risking meaningful capital.