⚡ BACKTESTING LAB

? How it works
Test your strategies against real historical data. Monte Carlo simulation reveals true edge vs. luck.
Results
Trades
Monte Carlo
Compare
Optimize
History
Running backtest...
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Select a strategy, date range, and symbol above, then hit RUN BACKTEST to see results.

📋

Run a backtest to see the individual trade log.

🎲

Click MONTE CARLO to run 500 randomized iterations and see the probability distribution of outcomes.

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Click COMPARE ALL to run all saved strategies on the same data and rank them head-to-head.

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Click OPTIMIZE to sweep parameter combinations and find the best settings for your strategy.

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No backtest history yet. Run some backtests and they'll appear here.

✦ How backtesting works ×

Replay a strategy bar-by-bar over real history, count the trades it would have taken, and judge the result on whether the edge is real — not just one lucky run.
Strategy→ Historical bars→ Simulate trades→ Metrics→ Monte Carlo
Win rate
Share of simulated trades that closed green. Not edge on its own.
Profit factor
Gross profit ÷ gross loss. Above 1 profitable; 1.5+ is strong.
Expectancy
Average $ per trade you'd expect over time — the core "worth trading?" number.
Sharpe / Sortino
Return per unit of risk. Sortino only counts downside risk — the kinder, truer one.
Max drawdown
Worst peak-to-trough dip. How much pain the curve put you through.
R-multiple
Profit measured in units of the risk taken. +2R = twice what you risked.
Monte Carlo
Reshuffles the trade order hundreds of times to show the range of outcomes — luck vs. real edge.
Percentiles
The 5th/median/95th outcomes from those reshuffles. Judge on the pessimistic tail, not the best case.
▶ What to do: pick a strategy + date range and Run. Read Profit Factor and Expectancy first — then the Monte Carlo spread: a tight, mostly-positive distribution means a real edge; a wide one that dips negative means you may be looking at luck.