Skip to content

Strategy Backtesting with QuantWave

A practical example of building and evaluating a trading strategy using QuantWave's Polars-native indicators and the vectorized backtesting engine.

Highlights

  • Synthetic data generation
  • Computing indicators (e.g. SuperTrend) with the real .ta extension
  • Using the backtester with rich signals and position sizing
  • Evaluating performance
pip install quantwave marimo polars numpy pandas
marimo edit docs/examples/notebooks/strategy_backtest.py

This notebook demonstrates the high-fidelity execution path and rich metadata support added in the v0.5 backtester improvements.

View source

Live Notebook (Exported)

The notebook below is a pre-exported self-contained version generated during the docs build. It shows the structure, code, and any captured outputs.

Note: The exported HTML on this page was generated during the docs build after installing the released quantwave package from PyPI. This is why real outputs are visible. Full re-execution of cells that use the native extension is limited in the browser. For the complete interactive experience, run locally with the command above.

See the Multi-Indicator Analysis notebook for more examples of chaining indicators.