https://github.com/augiemazza/varrd
From idea to statistically validated trade setup. VARRD is more than a back tester you can query with natural language — it's your home for safely researching market edges without the invisible statistical mistakes that invalidate most backtests. Let your agent autonomously discover edges with peace of mind.
https://github.com/augiemazza/varrd
Last synced: 5 months ago
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From idea to statistically validated trade setup. VARRD is more than a back tester you can query with natural language — it's your home for safely researching market edges without the invisible statistical mistakes that invalidate most backtests. Let your agent autonomously discover edges with peace of mind.
- Host: GitHub
- URL: https://github.com/augiemazza/varrd
- Owner: augiemazza
- License: mit
- Created: 2026-03-01T14:03:47.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2026-03-01T16:51:44.000Z (5 months ago)
- Last Synced: 2026-03-01T17:40:39.145Z (5 months ago)
- Language: Python
- Size: 34.2 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Agents: AGENTS.md
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README
# VARRD — Trading Edge Discovery
[](https://pypi.org/project/varrd/)
[](https://app.varrd.com/mcp)
[](https://app.varrd.com/mcp)
[](LICENSE)
**Institutional-grade quant research. Describe any trading idea in plain English, get statistically validated results with exact trade levels.**
Any AI can backtest a strategy. VARRD guarantees it was done right — with K-tracking, Bonferroni correction, OOS lock, lookahead detection, and 4 other integrity guardrails enforced at infrastructure level.
---
## MCP Server — 7 Tools, 4 Prompts
**Endpoint:** `https://app.varrd.com/mcp`
**Transport:** Streamable HTTP (2025-03-26 spec)
**Auth:** Anonymous access with auto-provisioned credits. No API key required.
```json
{
"mcpServers": {
"varrd": {
"transport": {
"type": "streamable-http",
"url": "https://app.varrd.com/mcp"
}
}
}
}
```
Works with **Claude Desktop**, **Claude Code**, **Cursor**, and any MCP-compatible client.
### MCP Tools
| Tool | Cost | What It Does |
|------|------|--------------|
| `research` | ~$0.25 | Multi-turn quant research with VARRD AI. Orchestrates 15 internal tools (data loaders, charting, event studies, backtests, optimization). Follow `context.next_actions` each turn. |
| `autonomous_research` | ~$0.25 | AI discovers edges for you. Give it a topic, it generates hypotheses from its market knowledge base, runs the full pipeline, returns validated results. |
| `scan` | Free | Scan saved strategies against live market data. Returns exact dollar entry, stop-loss, and take-profit prices for every active signal. |
| `search` | Free | Find saved strategies by keyword or natural language. Returns matches ranked by relevance with win rate, Sharpe, edge status. |
| `get_hypothesis` | Free | Full details on any strategy: formula, entry/exit rules, win rate, Sharpe, profit factor, max drawdown, version history. |
| `check_balance` | Free | View credit balance and available credit packs. |
| `reset_session` | Free | Kill a broken research session and start fresh. |
### MCP Prompts
| Prompt | Description |
|--------|-------------|
| `test-trading-idea` | Test any trading idea with real market data and statistical validation |
| `whats-firing-now` | Scan your validated strategies and show what's actively firing |
| `discover-edges` | Let VARRD's autonomous AI discover trading edges on a topic |
| `find-strategies` | Search your strategy library by keyword or concept |
---
## Quick Start — CLI
```bash
pip install varrd
# Research an idea (auto-follows the full workflow)
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 your saved strategies
varrd search "momentum on grains"
# Let VARRD discover edges autonomously
varrd discover "mean reversion on futures"
# Check credits
varrd balance
```
## Quick Start — Python
```python
from varrd import VARRD
v = VARRD() # auto-creates free account
# What's firing right now?
signals = v.scan(only_firing=True)
for s in signals.results:
print(f"{s.name}: {s.direction} {s.market} @ ${s.entry_price}")
# 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.has_edge) # True / False
print(r.context.edge_verdict) # "STRONG EDGE" / "NO EDGE" / etc.
# Get the trade setup
r = v.research("show me the trade setup", session_id=r.session_id)
```
---
## Authentication & Passkey
First use auto-creates an account. You'll receive a **passkey** (`VARRD-XXXXXXXXXXXXXXXX`) saved to `~/.varrd/credentials`.
```
VARRD account created.
Your passkey: VARRD-A3X9K2B7T4M8P1Q6
Saved to: ~/.varrd/credentials
```
**Link to browser:** Go to [app.varrd.com](https://app.varrd.com), click "Link your AI agent", enter your passkey with an email and password. Your agent's strategies and credits merge into your account.
```python
v = VARRD(api_key="your-key") # Python
```
```bash
varrd --key your-key scan # CLI
export VARRD_API_KEY=your-key # Environment variable
```
---
## What You Get Back
### Edge Found
```
STRONG EDGE: Statistically significant vs both zero and market baseline.
The pattern produces real returns that beat market drift.
Direction: LONG
Win Rate: 62%
Sharpe: 1.45
Trades: 247
K: 3 (tests run on this hypothesis)
Trade Setup:
Entry: $5,150.25
Stop Loss: $5,122.00
Take Profit: $5,192.50
Risk/Reward: 1.5:1
```
### No Edge
```
NO EDGE: Neither test passed — no tradeable signal found.
This is a valid result. You found out for 25 cents
instead of $25,000 in live losses.
```
---
## The Research Flow
```
Your idea (plain English)
|
v
Chart pattern — see the actual signals on real price data
|
v
You approve — sanity check before spending statistical power
|
v
Statistical test — event study or backtest with proper controls
| (K increments, fingerprints logged)
v
Edge verdict: STRONG EDGE — beats zero AND beats market
MARGINAL — beats zero, doesn't clearly beat market
NO EDGE — no signal found
|
v
Trade setup — exact dollar entry, stop-loss, take-profit
```
A typical session is 3-5 turns and costs ~$0.25.
## 8 Statistical Guardrails (Infrastructure-Enforced)
| Guardrail | What It Does |
|-----------|--------------|
| **K-Tracking** | Counts every test. 50 variations = higher significance bar. |
| **Bonferroni Correction** | Multiple comparison penalty, automatic. |
| **OOS Lock** | Out-of-sample is sacred. One shot. Locked forever. |
| **Lookahead Detection** | Catches formulas that accidentally use future data. |
| **Tools Calculate, AI Interprets** | AI never fabricates a number. Every stat from real data. |
| **Chart > Approve > Test** | Must see and approve pattern before testing. |
| **Fingerprint Deduplication** | Can't retest same formula/market/horizon twice. |
| **No Post-OOS Optimization** | Parameters locked after OOS validates. |
## 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.
## Pricing
- **$2 free** on signup — enough for 6-8 full research sessions
- **Research:** ~$0.20-0.30 per complete workflow
- **ELROND council** (8 expert investigators): ~$0.40-0.60
- **Multi-market** (3+ markets): up to ~$1
- **Free tools:** scan, search, get_hypothesis, check_balance, reset_session
- **Credit packs:** $5 / $20 / $50 via Stripe
- Credits never expire
---
## Examples
See [`examples/`](examples/):
- [`quick_start.py`](examples/quick_start.py) — 5 lines to get started
- [`scan_portfolio.py`](examples/scan_portfolio.py) — Scan all strategies, show what's firing
- [`research_idea.py`](examples/research_idea.py) — Full multi-turn research workflow
- [`multi_idea_loop.py`](examples/multi_idea_loop.py) — Test many ideas in a loop
- [`mcp_config.json`](examples/mcp_config.json) — MCP config for Claude Desktop
## For AI Agents
See [`AGENTS.md`](AGENTS.md) for a structured guide with complete tool reference, response formats, and integration patterns.
---
## Links
- **Web app:** [app.varrd.com](https://app.varrd.com)
- **Website:** [varrd.com](https://www.varrd.com)
- **MCP endpoint:** `https://app.varrd.com/mcp`
- **MCP Registry:** [registry.modelcontextprotocol.io](https://registry.modelcontextprotocol.io)