VARRD

The governed live edge layer.
Statistically validated market behaviors, monitored in real time.

Web App · MCP Endpoint · PyPI · varrd.com

Every edge shows exact entry, stop, target, and the full statistical methodology behind it.

Equity curves, Monte Carlo simulations, regime analysis, edge decay, and full audit trail.

Two ways to use VARRD

1. Tell your AI. Add the MCP config below, then ask: "What VARRD edges are firing right now?" or "What happens to gold when silver ETFs are making 100-day new lows?" Your AI browses the edge library, shows you what's actionable, and can test any idea you throw at it.

2. Use the web app. Go to app.varrd.com, sign up, and do the same thing — browse the edge library, ask questions like "Is there a seasonal pattern in wheat before harvest?", and watch the full research pipeline run visually.

{
  "mcpServers": {
    "varrd": { "url": "https://app.varrd.com/mcp" }
  }
}

Works with Claude Desktop, Cursor, OpenBB, or any MCP client. No API key needed.

What VARRD does

VARRD turns trading ideas into quantitative formulas using a domain-specific language we built from the ground up — purpose-built to express market behaviors in a way that's both machine-testable and human-readable. Those formulas are then tested with the right guardrails so the results actually mean something.

The AI generates hypotheses. A purpose-built backtesting engine does the math. The AI never calculates statistics, never fabricates results, and never touches the numbers. Every stat comes from a deterministic computation running in a sandboxed kernel. This matters because most people's first question is: "How do I know the AI isn't just making this up?" It can't. The engine is separate from the model.

VARRD also maintains a growing library of validated edges — patterns that survived the full testing gauntlet — running 24/7 against live market data across futures, equities, and crypto. When an edge fires, you get exact entry, stop, target, hold period, and the complete audit trail of how it was discovered, tested, and validated.

Full transparency

Every edge in the library shows you everything. Not summaries — the actual work.

How it was found: The discovery story — what pattern was hypothesized, why it might work, what market structure theory it's based on. You can read the exact thinking that led to the formula.

The formula itself: Written in our domain-specific language built to quantify market behaviors and make them replicable. You can read every condition, understand the logic, and veri

... View full README on GitHub