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Ziplime strategies are written in Python and run on an engine built on Zipline — the backtesting engine that once powered the Quantopian platform. It’s open source, and Ziplime maintains its own fork: github.com/Limex-com/ziplime. Unlike a proprietary scripting language, writing a Ziplime strategy means writing Python against the Ziplime/Zipline API. That has a practical benefit: any Python code you already have — your own logic, helper libraries, data processing — can, in principle, be used inside a strategy.

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What the engine adds on top of stock Zipline

  • Fundamental data — company financials, valuation multiples.
  • Alternative data — non-traditional signals beyond price and volume.
  • Asynchronous core — key functions are async, so a strategy behaves consistently whether it’s running on historical data or live.
  • Unified backtest / live logic — the same strategy code runs in both modes, so a backtest result is a meaningful preview of live behavior.
See Under the Hood for the performance layer, and Writing Strategies for how you actually build one.