Qantify: GPU-Accelerated Python Quant Library for Modern Traders
Most trading libraries were never built for today’s data scale.
Backtesting five years of minute data still takes hours, and parameter tuning feels endless.
Qantify changes that.
It’s an open-source, GPU-accelerated quant trading library built entirely in Python — designed to make backtesting, machine learning, and quantitative research dramatically faster.
Why Qantify
GPU-accelerated backtesting (up to 1000× faster)
160+ vectorized technical indicators
AutoML pipeline for intelligent model selection
Advanced econometric models (Heston, GARCH, Almgren–Chriss)
30+ exchange integrations (Binance, OKX, Bybit, Kraken, etc.)
100% local, open-source, and production-ready
Current Milestones
100+ users in less than 24 hours after launch
1767+ tests with full coverage
Verified GPU backtesting performance (RTX 3070: ~30s for 2.6M rows)
The Goal
Qantify’s mission is simple — to bring hedge-fund-grade tooling to independent quant developers.
No cloud fees, no vendor lock-in, no slow iterations.
Just fast, local, open quant research.
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