Backtest strategies against 9 years of independently audited trading signals from a World Trading Championship competitor. Monte Carlo simulation, equity curves, and monthly heatmaps across 6 markets.
| # | Date | Asset | Dir | Result | P&L % | Cum P&L |
|---|
Disclaimer: Past performance is not indicative of future results. This simulator uses real historical signal data but actual results depend on execution timing, slippage, and market conditions. All performance data independently audited by AuditedTrader.com.
Backtesting is the process of applying a trading strategy to historical data to see how it would have performed. It is the most important step between developing a strategy and risking real capital. Yet most traders backtest incorrectly, producing results that look fantastic on paper but fail catastrophically in live trading.
This simulator avoids the most common backtesting pitfalls by using a fundamentally different approach: instead of testing a hypothetical strategy against price data, it replays actual trading signals that were issued in real time, traded with real money, and independently audited. There is no curve-fitting, no parameter optimization, and no survivorship bias.
The critical flaw in traditional backtesting is that the strategy being tested was created with knowledge of the historical data it is being tested against. This is like taking an exam when you already know the answers. Even sophisticated traders fall into this trap through a process called data snooping: testing dozens of parameter combinations until one works.
This simulator eliminates that problem entirely. The signals in the database were generated in real time, before the outcomes were known. The trader behind these signals had no more information than any other market participant. The results are what actually happened, not what could have happened under perfect conditions.
Two factors that separate backtest results from live performance are slippage and commissions. Slippage occurs because the price you execute at is slightly different from the signal price due to market movement, liquidity, and order processing time. Commissions are the fees your broker charges per trade.
Our simulator lets you adjust both slippage and commission assumptions. Start with the defaults (0.5% slippage, $2 commission) for a realistic estimate, then stress-test with higher values to see how sensitive your results are to execution quality.
The historical sequence of trades that actually occurred is just one of millions of possible orderings. Monte Carlo simulation randomizes the trade order and runs 1,000 independent simulations. This reveals the full distribution of possible outcomes from the same set of trades.
Why does order matter? Because of compounding. A large loss early in the sequence has a different impact than the same loss occurring later when the account has grown. Monte Carlo shows you the best case, worst case, and median outcomes across all possible trade orderings, giving you a much more robust understanding of the strategy's risk profile.
The monthly heatmap provides a visual summary of returns across years and months. Green cells indicate profitable months, red cells indicate losses. The intensity of color reflects the magnitude. Key patterns to look for:
Use this simulator in the following order for maximum insight:
The goal is not to find the parameters that produce the highest return. It is to understand the risk-reward profile under realistic conditions so you can make an informed decision about subscribing.
Grade A-E conviction-rated signals across 6 markets, delivered in real time. Start with a 14-day free trial.
Start Free TrialAudited historical signals from a verified World Trading Championship competitor, 2016 to present. All signals including losses are included. Performance verified by AuditedTrader.com.
Most backtesters test hypothetical strategies against price data. This tool replays real signals that were issued live, traded with real capital, and independently audited. No curve-fitting or hindsight optimization.
It randomizes trade order and runs 1,000 simulations to show the full range of possible outcomes from the same signals. This reveals best-case, worst-case, and median scenarios.
Slippage reduces returns by modeling the gap between signal price and actual execution. Default 0.5% is realistic for most markets. Increase it to stress-test execution quality sensitivity.
Above 1.0 is good, above 2.0 is excellent. The VR signal history shows Sharpe ratios between 0.35 and 2.57 depending on year, with a long-term average around 1.0-1.5.
Green cells are profitable months, red are losses. Color intensity shows magnitude. Look for consistency (mostly light green) rather than extreme swings.
Yes. Filter by Forex, Crypto, Futures, Indices, Equities, or Polymarket to see which markets drove performance historically.
The largest peak-to-trough equity decline during the period. It represents the worst-case scenario. Lower drawdown means less risk, even if absolute returns are lower.
Backtests assume execution at signal price. Live trading involves slippage, partial fills, and timing differences. The slippage and commission settings help model this gap.
Gross profits divided by gross losses. Above 1.0 is profitable, above 1.5 is good, above 2.0 is very good. It measures dollars earned per dollar lost.
No. Past performance is never a guarantee. This tool is educational. Market conditions change and future results may differ from historical performance.
Set realistic expectations. Focus on risk-adjusted metrics (Sharpe, drawdown) over raw returns. Stress-test with high slippage and commission. Use Monte Carlo for worst-case analysis.
