Fast Python backtesting with a Rust engine: parameter sweeps, walk-forward, Monte Carlo, realistic execution, Polymarket and Hyperliquid data
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Updated
Nov 12, 2026 - Python
Fast Python backtesting with a Rust engine: parameter sweeps, walk-forward, Monte Carlo, realistic execution, Polymarket and Hyperliquid data
GUI für VectorBT Pro und KI-gestützte Research-Werkbank: algorithmische Crypto-Trading-Strategien als JSON-Spec entwickeln, in Multiparameter-Läufen backtesten und über Test-Sets und Leaderboard vergleichen. Kein Live-Trading.
VelaQuant: event-driven US equities research, backtesting, and paper-trading infrastructure
Institutional quantitative research suite and systematic alpha lab. Contains Jupyter notebooks, statistical arbitrage factor models, cointegration tests, market regime classifiers, and vectorbt backtesting pipelines for cross-asset quantitative strategies.
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