Hi everyone, I built Vaquero because I was spending more time maintaining Python backtesting pipelines than actually researching trading edges. Most independent quants and retail traders face two structural bottlenecks: 1. The technical barrier of building robust data and execution environments takes months. 2. Zero-friction backtests (ignoring slippage, spread, and perpetual funding rates) create illusions of profitability that inevitably bleed out in live markets. Vaquero solves this by bypassing the code layer entirely. You describe your trading logic in plain English, and our engine parses the exact conditions to run a rigid backtest. By default, it applies real-world market friction and statistical penalties to filter out overfitted noise. Our goal is to let you validate ideas instantly without lying to yourself about execution costs. I would love to hear your thoughts on the platform. Specifically, I’m curious to know how you currently handle execution modeling and statistical validation (like avoiding survivorship bias) in your own research workflows. Happy to answer any questions about the engine under the hood.

Vaquero

Code-free backtesting with real market friction.

Code-free backtesting with real market friction.