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Timeframes and history depth

The minimum backtesting interval is one minute, with aggregation available at 2, 3, 4 minutes or any larger interval. Historical data goes back to 2005, with a 15-minute market data delay.

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Cost modeling

Broker commissions are factored into backtests by default, so results are closer to reality. Commissions can also be configured in detail for a specific case.

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Bringing strategies from other platforms

Many users already have strategies built on other platforms. Ziplime’s AI can convert that code into Python for its own engine. Supported sources include:
  • MetaTrader (MQL4 / MQL5)
  • Quik (Lua)
  • TradingView (Pine Script)
  • Quantopian and Zipline
  • QuantConnect and Lean
  • Any existing Python code
This means the platform doesn’t lock users into one framework — years of accumulated work can be brought over and continued inside Ziplime. See Migrating to Ziplime for platform-specific notes.

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Algorithm transparency

The trading algorithm in Ziplime is never a black box. You always see your strategy’s code and understand the exact rules it trades by. This sets it apart from closed robo-advisors and signal services, where the logic is hidden.

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Where Ziplime doesn’t fit

The engine is not built for ultra-fast strategies — high-frequency trading (HFT) and latency arbitrage, where fractions of a second decide the outcome. The engine is Python-based (with select components accelerated in Rust), which isn’t sufficient for that class of problem. This is a deliberate positioning choice, not a shortcoming: Ziplime is about meaningful strategies on one-minute-and-above intervals, not a race for milliseconds. High-frequency trading isn’t accessible to retail users in the first place — it’s the domain of large professional participants. See Limitations for more detail.