Simulate your grid bot
before it costs you money.

Monte Carlo simulation and historical backtesting for perpetual futures grid strategies. Free, client-side, no sign-up.

Launch Simulator → See how it works
No sign-up required Runs in your browser Open source
Mechanics
How a grid bot works
1
Place orders

The bot places a ladder of buy and sell limit orders at fixed price intervals across a defined range. No manual trading required once deployed.

2
Price oscillates

When price drops, a buy order fills. When it recovers, the corresponding sell order fills. Each round trip captures the spread between adjacent grid levels.

3
Collect the spread

Repeat across hundreds of oscillations. The bot earns steadily as long as price stays within the range — independent of whether the market is trending up or down.

The risk: if price breaks out of the range and trends strongly in one direction, the bot accumulates an underwater position. Leverage amplifies this. Simulation tells you how likely that is before you commit capital.
Capabilities
Four tools in one
Monte Carlo Simulation

Run hundreds of simulated price paths using your IV, drift, and grid parameters. See the full distribution of outcomes — not just the best case.

CSV Backtesting

Upload real OHLCV data from Binance, Bybit, or TradingView. The engine replays your grid bot tick-by-tick against actual historical prices.

Live Market Data

Entry price, funding rate, and volatility auto-populate from live exchange data. The Market Analysis panel flags regime conditions and suggests a grid range calibrated to recent price behaviour.

A/B Strategy Comparison

Run two configurations against the same set of price paths and compare every metric side by side. Change one variable — direction, leverage, grid count — and see exactly what difference it makes.

Use cases
Built for two kinds of trader
Available now
Before you deploy

You're evaluating whether a grid bot makes sense for a market, leverage level, or capital size. Use Monte Carlo to stress-test assumptions. Use A/B compare to choose between setups. Know your liquidation probability before you touch the exchange.

Coming soon
After you're live

You've already got a bot running. Connect a read-only API key and monitor your live position, P&L breakdown, and distance to liquidation in real time — compared against your original simulation.

Results
What you'll see
Simulation Results — 500 paths · 30 days
Median P&L
+$1,240
Win Rate
71.4%
Sharpe Ratio
1.82
Calmar Ratio
2.14
Liq Rate
3.2%
Median outcome across 500 paths
Liquidation probability in this scenario
10th–90th percentile range
Annualised ROI at median
Questions
Common questions
Markets that oscillate within a range rather than trend strongly in one direction. ADX below 20 indicates ranging conditions — the simulator's Market Analysis panel calculates this from live data. High-volume, liquid perpetual futures pairs (BTC, ETH, SOL) are the most commonly used.
The minimum is determined by the exchange's minimum order size (typically $5 notional per grid level). With 20 grids at 5× leverage, that requires $20 of margin at an absolute minimum — but fees will dominate at that scale. A more realistic starting point is $500–$1,000 margin for a 20-grid setup.
Monte Carlo generates hundreds of synthetic price paths using your IV and drift assumptions — it shows a probability distribution of what could happen. Backtest mode uses real historical OHLCV data you upload as a CSV — it shows exactly what would have happened in a specific period. Use Monte Carlo for scenario planning; backtesting for validation.
See all FAQs →