Quant research & backtesting · Open-source community

Backtrader

Open-source Python backtesting and live-trading framework for building and evaluating trading strategies.

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Backtrader is a Python library for backtesting and live trading of quantitative strategies, offering a strategy/indicator/broker object model, built-in performance analyzers, and plotting for visualizing trades against price data. It supports multiple data feeds and timeframes simultaneously, custom indicators and order types, and can route live orders through supported broker integrations such as Interactive Brokers and Oanda. As a self-hosted library rather than a hosted service, it runs entirely on infrastructure the user controls, with no cloud component, account system or subscription. Development activity has slowed in recent years, so users typically pin versions and maintain their own forks for newer Python compatibility.

At a glance

Vendor Open-source community
Pricing model Open source + paid options
Free tier Yes
Deployment Self-hosted
Open source Yes (GPL-3.0)
Best for Python developers who want full control over a self-hosted backtesting engine with no vendor lock-in.

Pricing

Free, open-source Python library with no paid tier or hosted offering.

Pricing has not been verified yet — see the vendor's site.

Features

  • Strategy, indicator and broker object model
  • Built-in performance analyzers (Sharpe, drawdown, returns)
  • Multiple simultaneous data feeds and timeframes
  • Custom indicators and order types
  • Backtest visualization/plotting
  • Live trading via broker integrations (Interactive Brokers, Oanda)

Integrations

Profile last reviewed September 21, 2026

Head to head

Alternatives

Zipline Python backtesting library originally built by Quantopian; still usable but largely unmaintained since Quantopian shut down. vectorbt Open-source Python library for vectorized backtesting, using NumPy/pandas array operations to test thousands of parameter combinations fast. NautilusTrader Open-source, event-driven algorithmic trading platform in Python/Rust for high-performance backtesting and live deployment.

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Terms to know

Time series forecasting Predicting future values of a metric using patterns found in its own historical sequence over time. Backtesting Testing a trading or investment strategy against historical data to see how it would have performed. Vendor lock-in The difficulty and cost of switching away from a vendor once an organization is deeply dependent on that vendor's proprietary systems.

Related guides

How to backtest a trading strategy Data quality, look-ahead and survivorship bias, realistic costs, and the evaluation metrics that separate a real edge from an overfit curve. How to choose a quant research and backtesting platform The choice is really about who owns the code — a library you control, a desktop license, or a hosted platform with data included.