Vespera Capital is a quantitative strategy engine — a systematic pipeline that identifies, validates, and deploys trading strategies across futures markets.
For decades, quantitative trading followed a familiar pattern: hire PhDs, spend months building models, deploy capital, and hope the edge survives long enough to justify the overhead. That model worked when cycle time was the bottleneck — when testing a single hypothesis required weeks of data engineering and backtesting infrastructure.
AI removed that bottleneck. The constraint is no longer how fast you can test a strategy — it's whether your validation pipeline is rigorous enough to reject the ones that don't hold. Most of what looks like edge is noise. The firms that win are the ones with automated gates strict enough to kill bad ideas before capital touches them.
Vespera was built from this premise. We don't run a single strategy. We operate a strategy engine — a pipeline that continuously explores edge across options microstructure, volume dynamics, and order flow patterns. Dealer hedging flows create gravitational pull around key price levels. Volume profiles reveal where institutional participation concentrates and where it thins. These are structural phenomena, not predictions — and structural phenomena are testable.
Our founder's background is in enterprise technology architecture, not traditional finance. Before Vespera, he designed and integrated twenty modules across a $1B+ fleet management platform — systems where reliability, automated feedback loops, and rigorous testing weren't optional. We apply that same discipline here. Every strategy passes through walk-forward validation, regime-aware calibration, and Monte Carlo stress testing before it sees live capital. Engineers, not traders.
Every candidate strategy passes through a multi-stage pipeline: historical backtest, walk-forward analysis, regime filtering, and Monte Carlo simulation. Strategies that fail any gate are rejected automatically. No discretionary overrides, no exceptions.
We focus on observable market mechanics — dealer hedging dynamics, options-driven price magnetism, volume concentration patterns. These are physics, not forecasts. When structural conditions align, we act. When they don't, we wait.
What once took a team of researchers months to hypothesize, build, and test now runs in hours through our AI-integrated pipeline. This doesn't make us faster at guessing — it makes us faster at eliminating what doesn't work.
Founded by Mike Abdo — a technology architect who spent years building large-scale integrated systems before applying the same engineering discipline to quantitative markets.
We welcome conversations with allocators and family offices who value systematic discipline over discretionary conviction.
Start a Conversation