VARRD

Turn any trading idea into a statistically validated edge in about 3 minutes.

pip install varrd

Ask it anything

varrd research "Does buying SPY after a 3-day losing streak actually work?"

varrd research "When VIX spikes above 30, is there a bounce in ES?"

varrd research "Is there a seasonal pattern in wheat before harvest?"

varrd research "What happens to gold when the dollar drops 3 days straight?"

varrd research "Does Bitcoin rally after the halving?"

varrd research "When crude oil drops 5% in a week, what happens next?"

Every question gets real data, a chart with signals marked, a statistical test, and a definitive answer.

What you get back

Edge found

STRONG EDGE — Statistically significant vs both zero and market baseline.

  Direction:    LONG
  Win Rate:     62%
  Sharpe:       1.45
  Signals:      247

  Trade Setup:
    Entry:       $5,150.25
    Stop Loss:   $5,122.00
    Take Profit: $5,192.50
    Risk/Reward: 1:1.5

No edge

NO EDGE — Neither test passed. No tradeable signal found.

You found out for 25 cents instead of $25,000 in live losses.

Both are valuable results.

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Why can't I just ask Claude / ChatGPT to do this?

Because testing trading ideas properly is really hard to get right, and there are a dozen ways to accidentally produce fake results that look great but lose money in production.

An LLM by itself will happily write you a backtest, show you a beautiful equity curve, and tell you it has a 70% win rate. The problem: none of it is real. The LLM doesn't have market data, doesn't have a testing environment, and has no guardrails preventing it from overfitting, cherry-picking, or just making numbers up.

Even if you give an LLM real data (like in Claude Code or Cursor), it still can't do this properly. Here's why:

What can go wrong when testing trading ideas — and what VARRD handles:

• Overfitting — Tweaking a strategy until it looks good on past data. VARRD holds out unseen data and tests on it once. You can't re-run it after peeking at results.
• Cherry-picking results — Testing 50 variations and only showing the winner. VARRD tracks every test you run and raises the significance bar automatically the more you test.
• p-hacking — Massaging the numbers until you get a "significant" result. VARRD corrects for multiple comparisons so a lucky result doesn't pass as real.
• Lookahead bias — Accidentally using future data in your formula. VARRD runs in a sandboxed kernel that makes this structurally impossible.
• Wrong test type — Some ideas need forward-return analysis, others need full simulations with stops and targets. VARRD has a team of specialized agents that determine the right test for each question.
• Cross-market contamination — Testing on one market but the signal actually came from another. VARRD isolates and aligns data across markets and timeframes.
• Fabricated statistics — LLMs will invent numbers to sound confident. In VARRD, every stat comes from a deterministic calculation. The AI interprets results, it never generates them.
• ATR-based position sizing — Real edges need real risk management. VARRD calculates stop losses and take profits based on actual volatility, not arbitrary percentages.
• Showing what's happening right now — A validated edge is useless if you can't see when it's firing. VARRD scans live data and tells you exactly when your signals are active, with fresh entry and exit levels.

An LLM is a brain without a lab. It can reason about trading ideas, but it can't test them in a controlled environment. VARRD is the lab — purpose-built infrastructure where every test is tracked, every result is verified, and the dozen ways to accidentally cheat are blocked at the system level, not the prompt level.

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Quick start — Python

from varrd import VARRD

v = VARRD()  # auto-creates free account, $2 in credits

# Research a trading idea
r = v.research("When RSI drops below 25 on ES, is there a bounce?")
r = v.research("test it", session_id=r.session_id)

print(r.context.edge_verdict)  # "STRONG EDGE" / "NO EDGE"

# Get exact trade levels
r = v.research("show me the trade setup", session_id=r.session_id)

# What's firing right now across all your strategies?
signals = v.scan(only_firing=True)
for s in signals.results:
    print(f"{s.name}: {s.direction} {s.market} @ ${s.entry_price}")

# Let VARRD discover edges autonomously
result = v.discover("mean reversion on futures")
print(result.edge_verdict, result.market, result.win_rate)

Quick start — CLI

# Full research workflow (auto-follows chart → test → trade setup)
varrd research "When wheat drops 3 days in a row, is there a snap-back?"

# What's firing right now?
varrd scan --only-firing

# Search saved strategies
varrd search "momentum on grains"

# Let VARRD discover edges on its own
varrd discover "mean reversion on futures"

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Use with AI agents

Claude Desktop / Claude Code / Cursor

Add to your MCP config — no API key needed:

{
  "mcpServers": {
    "varrd": {
      "transport": {
        "type": "streamable-http",
        "url": "https://app.varrd.com/mcp"
      }
    }
  }
}

Then just ask: "Is there a pattern when gold spikes after a Fed rate decision?"

OpenBB Workspace

VARRD plugs directly into OpenBB Workspace as an MCP server:

1. Open Workspace → click "+" in the MCP server panel
2. Enter https://app.varrd.com/mcp
3. VARRD's tools appear in your Copilot — research ideas, scan signals, search strategies

OpenBB gives you the data. VARRD tells you if your idea has an edge.

Trading bots (Freqtrade, Jesse, Hummingbot, OctoBot, NautilusTrader)

VARRD validates that your strategy has a real edge before you deploy it. Works with any bot:

from varrd import VARRD
from varrd.freqtrade import generate_strategy

v = VARRD()
result = v.discover("RSI oversold reversal on BTC")

if result.has_edge:
    hyp = v.get_hypothesis(result.hypothesis_id)
    strategy_code, config = generate_strategy(hyp)
    # Drop into your bot's strategies/ folder and run it

Bot	How VARRD plugs in
Freqtrade	varrd.freqtrade generates ready-to-run IStrategy files with ATR stops
Jesse	varrd.jesse generates ready-to-run Strategy files with ATR stops
Hummingbot	Validate directional signals before deploying to market-making
OctoBot	Pre-validate any tentacle strategy through VARRD's MCP server
NautilusTrader	Statistical edge validation before live deployment

The pattern: validate first, deploy second. Most strategies don't survive statistical testing — better to find out for $0.25 than $25,000.

CrewAI

from crewai import Agent, Task, Crew

researcher = Agent(
    role="Trading Researcher",
    goal="Find statistically validated trading edges",
    backstory="You are a quantitative researcher who tests trading ideas rigorously.",
    mcps=[{"type": "streamable-http", "url": "https://app.varrd.com/mcp"}]
)

task = Task(
    description="Research whether RSI oversold conditions on ES lead to a bounce within 5 days.",
    agent=researcher,
    expected_output="Edge verdict with trade setup if edge is found."
)

crew = Crew(agents=[researcher], tasks=[task])
result = crew.kickoff()

LangChain / LangGraph

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_anthropic import ChatAnthropic

model = ChatAnthropic(model="claude-sonnet-4-20250514")

async with MultiServerMCPClient({
    "varrd": {"url": "https://app.varrd.com/mcp", "transport": "streamable_http"}
}) as client:
    agent = create_react_agent(model, client.get_tools())
    result = await agent.ainvoke({"messages": [
        {"role": "user", "content": "Does gold rally when the dollar drops 3 days in a row?"}
    ]})

Raw MCP (any client)

# Any MCP-compatible client can connect to:
https://app.varrd.com/mcp
# Transport: Streamable HTTP | No auth required | $2 free credits

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8 statistical guardrails (infrastructure-enforced)

Every test runs through these automatically. You can't skip them.

Guardrail	What It Prevents
K-Tracking	Tests 50 variations of the same idea? Significance bar goes up automatically.
Bonferroni Correction	Multiple comparison penalty. No p-hacking.
OOS Lock	Out-of-sample is one shot. Can't re-run after seeing results.
Lookahead Detection	Catches formulas that accidentally use future data.
Tools Calculate, AI Interprets	Every number comes from real data. AI never fabricates stats.
Chart → Approve → Test	You see and approve the pattern before spending statistical power.
Fingerprint Dedup	Can't retest the same formula/market/horizon twice.
No Post-OOS Optimization	Parameters lock after out-of-sample validates.

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Data coverage

Asset Class	Markets	Timeframes
Futures (CME)	ES, NQ, CL, GC, SI, ZW, ZC, ZS, ZB, TY, HG, NG + 20 more	1h and above
Stocks / ETFs	Any US equity	Daily
Crypto (Binance)	BTC, ETH, SOL + more	10min and above

15,000+ instruments total.

MCP tools

Tool	Cost	What It Does
research	~$0.25	Multi-turn quant research. Orchestrates 15 internal tools.
autonomous_research	~$0.25	AI discovers edges for you. Give it a topic, get validated results.
scan	Free	Scan strategies against live data. Fresh entry/stop/target prices.
search	Free	Find strategies by keyword or natural language.
get_hypothesis	Free	Full details on any strategy.
check_balance	Free	View credits and available packs.
buy_credits	Free	Buy credits with USDC on Base or Stripe.
reset_session	Free	Kill a broken session and start fresh.

Pricing

• $2 free on signup — enough for 6–8 research sessions
• Research: ~$0.20–0.30 per idea tested
• Discovery (autonomous): ~$0.20–0.30
• ELROND council (8 expert investigators): ~$0.40–0.60
• Multi-market (3+ markets): ~$1
• Scan, search, balance: Always free
• Credit packs: $5 / $20 / $50 via Stripe
• Credits never expire

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Examples

See examples/ for runnable scripts:

• quick_start.py — 5 lines to scan all strategies
• research_idea.py — Full multi-turn research workflow
• multi_idea_loop.py — Test many ideas in a loop
• scan_portfolio.py — Portfolio scan with trade levels
• mcp_config.json — MCP config for Claude Desktop / Cursor

For AI agent builders

See AGENTS.md for the complete integration guide — tool reference, response formats, authentication, and workflow patterns.

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Links

• Web app: app.varrd.com
• Website: varrd.com
• MCP endpoint: https://app.varrd.com/mcp
• PyPI: pypi.org/project/varrd