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Multi-Indicator Analysis with QuantWave

In this notebook, we demonstrate how to combine multiple QuantWave indicators (classic moving averages + Ehlers DSP suite) using Polars' high-performance lazy expressions and method chaining.

What you'll see

  • Generating realistic synthetic OHLCV data
  • Applying SMA, EMA, Momentum, and SuperTrend in a single vectorized pass via the .ta namespace
  • Unnesting struct outputs for clean analysis
  • Clean, readable Polars code that runs efficiently

Run the interactive notebook locally

For the full interactive experience with real QuantWave execution:

# Install dependencies
pip install quantwave marimo polars numpy

# Edit and run the notebook
marimo edit docs/examples/notebooks/multi_indicator_analysis.py

The notebook will use the native Rust + Polars backend for fast indicator computation.

View the source

Note on the documentation site

This page exists because the live documentation is a static site. Full interactive marimo notebooks that depend on compiled Rust extensions (like QuantWave) cannot run directly in the browser on GitHub Pages.

The best experience is always running the .py notebook locally with marimo edit.

Related: See the Strategy Backtesting notebook for an end-to-end example using these indicators inside a backtester.