About Quawd

Research the idea, not the infrastructure.

Turn a plain-English trading idea into a backtested, paper-traded strategy in minutes — not weeks. Quawd is the AI agent that does the quant work for you, across equities and crypto.

What Quawd is

Quawd is a SaaS platform for individual quants and small teams. You describe a strategy in plain English to an AI agent, Quawd compiles it into a deterministic intermediate representation, and the same engine runs your backtests and paper trades. Sign up, build a strategy, run it against years of historical data, and watch it trade live in a sandbox — no notebooks, no broker SDKs, no infrastructure to manage.

Quawd is a commercial product operated as a subscription SaaS business: usage-based plans for backtesting and AI-agent workflows, billed monthly with a free tier to start. See Pricing for details.

Quawd also ships an MCP server, so you're not limited to the web UI — point Claude, or any other MCP-compatible AI agent, at Quawd and it can create strategies, run backtests, and manage paper-trading sessions directly through the same tools the product uses internally.

Who builds it

David Bentz

David Bentz — Founder

Software engineer at Apple, based in the Bay Area. Dad to five kids, which is roughly why Quawd exists — turning a trading idea into a validated strategy needs to take minutes, not weekends.

Quawd started in October 2025 as a side project testing whether an AI agent could reason about strategy logic carefully enough for a human to trust the result.

Get in touch

For product questions, partnership inquiries, or press, reach out at david@quawd.bot. For account or billing support, use the Support page.

Quawd

A subscription SaaS platform for designing, backtesting, and paper-trading algorithmic trading strategies on equities and crypto — described in plain English to an AI agent, no code required.

© 2026 Quawd. All rights reserved.

Quawd is a software tool, not a broker-dealer or investment adviser, and does not provide investment advice. Trading involves substantial risk of loss. Backtested and hypothetical results have inherent limitations and are not indicative of future performance.