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How to backtest a crypto strategy on real exchange data with Freqtrade

Create a user directory and strategy, download candles, and simulate your buy and sell rules.

Short answer: Follow the Docker Quickstart in the docs, or install natively with Python 3.12 or newer; then run Freqtrade with create-userdir, then new-config and new-strategy to scaffold your setup; download candles with download-data for your pairs and timeframe. All steps.

  • Top pick
  • Money
  • Intermediate
  • 1 hour
  • From the README
  • Any agent

Who it is forA developer with a crypto strategy idea who wants to see how it would have done before running it.

The problemWithout a simulation you only learn a strategy is bad after it loses money.

The outcomeBacktest results for your strategy on downloaded exchange data, plus a check for look ahead bias.

What you need first

  • Python 3.12 or newer, pip, git and TA-Lib, or Docker
  • An accurate system clock synced to NTP
  • About 2GB RAM, 1GB disk and 2 vCPU recommended
  • Exchange API keys only when you move past dry-run

Steps

  1. Follow the Docker Quickstart in the docs, or install natively with Python 3.12 or newer
  2. Run Freqtrade with create-userdir, then new-config and new-strategy to scaffold your setup
  3. Download candles with download-data for your pairs and timeframe
  4. Run backtesting and review past results with backtesting-show
  5. Check the strategy for look ahead bias with lookahead-analysis

Prompt for your agent

Using freqtrade/freqtrade, scaffold a user directory and a strategy for <idea> on <pairs> and <timeframe>, download data, backtest it over <period>, and run lookahead-analysis. Report profit, drawdown and trade count, and any bias it found.

Caveat. Backtests on past candles do not guarantee future results. Not financial advice: paper trade first, and remember you can lose money.

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