Markets reward intelligence,
not intuition.
Every competitive advantage in trading eventually becomes infrastructure. We're building the infrastructure from day one.
AI is the edge
Financial markets are information-processing machines. The participant with the best signal extraction wins. Wolf provides a foundation for testing market hypotheses with training and backtesting components. Its current roadmap focuses on autonomous research for COIN shares and options; no sustainable trading edge is established today.
Vertical integration
We built our own ML framework. We control the full stack from tensor operations to order execution — no dependency on Python runtimes, no vendor lock-in, no performance ceilings we can't break through ourselves.
Compounding advantages
Technology improves. Track records lengthen. Data accumulates. Relationships deepen. Wolf's roadmap separates candidate research from independent evaluation and controlled paper execution. This workflow remains under development.
Two systems. One objective.
Wolf supplies quantitative research components. Zerfoo supplies general-purpose ML infrastructure. Together, they provide a technical foundation for further development.
Wolf
Wolf has reusable Go services, training and backtesting components, and risk, portfolio, and broker integration primitives. Its current research mandate covers COIN shares and options.
SwiftUI tooling exists alongside the research services. Evaluation and recovery gaps remain; controlled Alpaca paper trading requires acceptance gates and approvals. No live trading is authorized.
Zerfoo
A production-grade ML inference and training framework written entirely in Go with zero CGo dependency. Runs 10+ model architectures at 245 tok/s — 20% faster than Ollama. Every trading signal we generate flows through Zerfoo.
Open source under Apache 2.0. 25+ custom CUDA kernels. OpenAI-compatible API. The Go ecosystem's answer to PyTorch — and our unfair advantage in production latency.
One language. One codebase. Zero compromise.
Most quant firms stitch together Python for research, C++ for execution, Java for infrastructure, and pray the interfaces don't break. We write everything in Go — from CUDA kernel bindings to REST APIs to model inference.
Single binary deployment. No runtime dependencies. No GIL. No framework lock-in. Research in Python, production in Go — same architecture, verified identical outputs.
A research foundation you can build on.
Reusable software exists; a sustainable trading edge is not yet established. Wolf's current work addresses evaluation and recovery gaps before controlled paper trading can be activated. Existing components are a starting point, not evidence of live trading performance.
A decade of compounding.
We think in decades. Each phase builds the foundation for the next — expanding into new markets and compounding both capital and capability.
Research Foundation
Existing research components are being evaluated and reconciled. Independent evaluation and tightly controlled paper trading are the current roadmap; activation requires approved risk limits and acceptance gates.
Expansion
New asset classes. Multi-strategy portfolio management. 12-month audited track record. Institutional-grade reporting.
Scale
Fund launch. Global markets. The system expands into new territories and begins discovering strategies autonomously.
Apex
Full-spectrum market coverage. The world's most valuable trading intelligence — built by machines, for machines.
Our ML engine is open source. On purpose.
Zerfoo is the ML inference framework that powers every trading signal we produce. We open-sourced it because the best infrastructure gets built in the open — by the community, for the community.
A stronger foundation benefits everyone. What we build on top of it is ours.
Explore Zerfoo →"I spent eight years watching human emotion destroy perfectly good trading strategies — including my own. So I decided to remove the human from the loop entirely."
David built distributed systems at scale at Zendesk, then taught himself machine learning from first principles. Feza is not a side project or an experiment — it's the convergence of deep infrastructure expertise and an obsessive conviction that markets belong to machines.
One founder. A fleet of AI agents. Building the most automated trading company on earth.