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Backtesting simulates your strategy against historical exchange data. Use it to understand parameter sensitivity and validate that your strategy behaves as expected—not to predict exact live results.

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Philosophy

Backtesting market making strategies cannot perfectly simulate reality:
  1. Queue Position — Order books are FIFO (First In, First Out). You never know your position in the queue because exchanges don’t reveal participant order positions.
  2. Path Dependency — If one fill differs between backtest and live, all subsequent behavior diverges. The correlation breaks with a single mismatched order.
  3. Order Book Opacity — Thick order books (like USDT/BRL with $600K at the first level) mean your orders may never fill even if price touches your level.
Don’t trust absolute P&L numbers from backtests. Use backtesting as a parameter research tool, not a prediction engine.

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What Backtesting Is Good For

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Supported Executors

The backtesting engine works with controllers using these executors:

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Candle Resolution

One-second candles are crucial for market making backtests. With one-minute candles, the engine can only simulate one fill per minute. Real market making often has 30+ fills per minute. One-second resolution captures the granularity needed.
Currently, only Binance Spot provides one-second candles. You may need a server in a region where Binance is accessible, or use Tailscale to route requests through an allowed region.

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Running Backtests

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Via Hummingbot Scripts

Hummingbot includes backtesting scripts in the scripts/ folder:
The script outputs:
  • Processing time (e.g., “34 seconds for 0.5 days”)
  • Interactive Plotly chart in browser

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Via Condor Web Dashboard

  1. Navigate to Bots → Backtest
  2. Select a controller config
  3. Set the time range
  4. Run and view results

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Interpreting Results

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Chart Panes

The Plotly output has three panes:

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P&L Lines

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Executor Colors

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Example Analysis

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Portfolio Allocation: 2% vs 10%

2% allocation:
  • Inventory builds gradually
  • Trading continues through drawdowns
  • More consistent activity
10% allocation:
  • Inventory builds quickly
  • May hit max position during drawdowns
  • Trading stops if position is underwater and at max (profit protection enabled)

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PMM Mister Parameters

Key parameters that affect backtesting behavior:

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Trading Bots vs Trading Agents

Agents can oversee and modify bot parameters, but they operate at a higher level (every minute+) rather than every tick. This separation keeps HFT performance while adding intelligent oversight.