Any AI can run a backtest. VARRD guarantees it was done right. Describe any trading idea -we test it with real data, real statistics, and the same guardrails that institutional quant firms enforce internally.
Equities · Futures · Crypto · Commodities · FX · Fixed Income
Ranked #1 trading MCP server · #12 globally out of 12,865 servers on PulseMCP · Above AWS, Notion, Figma, Zapier, and Salesforce
Demo
Type any trading intuition in plain English. VARRD finds the pattern in decades of data, builds the formula, and tells you if the edge is real.
The 3 most common ways backtests lie - and how VARRD stops all of them.
Most AI tools let the model do the math. We don't. Our engines calculate. The AI interprets. That distinction is everything.
| Capability | VARRD | ChatGPT (web) |
Claude Code (IDE) |
TradingView | QuantConnect | Composer | Robinhood Cortex |
Public.com Alpha AI |
|---|---|---|---|---|---|---|---|---|
| Natural language idea input | ✓ | ✓ | ✓ | ✗ | ✗ | ✓ | ✓ | ✓ |
| Real backtesting engine (not AI-generated stats) | ✓ | ✗ | ✓* | ✓ | ✓ | ✓ | ✗ | ✗ |
| Overfitting guardrails that account for research history | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| Lookahead bias detection | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| One-shot OOS enforcement & slippage/commission modeling | ✓ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ |
| Autonomous edge discovery AI researches, tests, and validates overnight |
✓ | ✗ | ✓* | ✗ | ✗ | ✗ | ✗ | ✗ |
| Saved edge library with live scan | ✓ | ✗ | ✗ | Alerts only | ✗ | Community library | ✗ | Activity only |
| Edge persistence and decay tracking | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| Portfolio simulation & SL/TP optimization | ✓ | ✗ | ✓* | ✗ | ✓ | ✓ | ✗ | ✗ |
| No coding required | ✓ | ✓ | ✗ | ✗ | ✗ | ✓ | ✓ | ✓ |
* With significant limitations. · Based on publicly available product documentation as of March 2026.
Anyone can ask an AI to find a 4.0 Sharpe ratio setup. It'll happily oblige... with numbers it fabricated. The hard part isn't generating results. It's guaranteeing the results are honest. That's what we built.
Before we ever showed this to anyone, we spent years building the system underneath. We went through libraries. The actual teachings and literature of the best investors and traders in history. Raschke, Niederhoffer, Williams, Carver, Whaley, and dozens more. We built vectorized embeddings and knowledge graphs from their work, but we were ruthless about what went in. No statistics. No specific numbers. Only ideologies and theories about how markets behave. The why, not the what.
Then we built the most important piece: a system that turns any idea into a quantifiable, testable formula, and watches for every pitfall of backtesting while it runs. We had to build our own domain-specific programming language to do it. A proprietary system that translates plain-English trading ideas into executable, testable, and statistically rigorous research. It's not Pine Script. It's not Python. It's purpose-built for this problem and it doesn't exist anywhere else. On top of that, K-tracking counts every test. Bonferroni correction penalizes multiple comparisons. Lookahead bias detection catches future data leakage. Fingerprint deduplication prevents re-testing the same thing. Out-of-sample validation is locked to one sacred shot. And more.
The math is done by a deterministic engine. Not AI. The AI orchestrates the workflow and interprets results in plain English, but it never touches a number. Every statistic is computed on real market data by code that cannot hallucinate. We track where every idea came from and give it the proper statistical penalties. That's the foundation everything else sits on.
We offered the system to people and they loved what came out of it. Validated edges with exact entries, stops, and targets, all backed by real statistics. But most people don't have the time or mental bandwidth to sit down and grind through hypothesis testing for hours. They have jobs, families, lives.
So we built autonomous mode. You point the AI in a direction. Something like "find opening range breakouts in energy futures, setups that have structural weight behind them." And it goes to work. It uses cosine similarity functions across our vectorized embeddings and knowledge graphs to find relevant concepts, then pulls bits and pieces of market ideologies (not tested ideas, just theories about how markets behave) and recombines them with insights from its own sandbox research, data it's pulled from the web, or context the user has given it. From there it forms a hypothesis and follows the scientific method. The system routes the idea to the right type of test with proper data handling, all done by deterministic math.
No hypothesis is allowed to be tested for the first time twice. We map every edge every user has ever tested and use cosine similarity to check if what you're asking has already been explored. If it has, the system resumes from that session with the proper penalties and statistical context already applied. No starting over. No gaming the numbers. The full guardrailed pipeline runs on every single idea, from generation to validation, and delivers results while you sleep. Same strict guardrails. Same deterministic math. No shortcuts.
To be clear: our AI is not brute-forcing millions of random combinations and hoping something sticks. That's the definition of overfitting, and it's what most "AI trading" systems actually do. Ours uses complex engineering to research and form deep hypotheses rooted in the ideologies of markets, drawn from the teachings of the best traders and investors in history. Then it tests those ideas quantitatively through the full statistical pipeline. Every hypothesis has a why before it has a what. That's how you find edges that are real, not artifacts of randomness.
"AI has driven the cost of idea generation down to almost zero, in a very similar way to how the internet drove the cost of communication down to almost zero. Now the bottleneck is different. Now we have to verify them, evaluate them."
Terence Tao, Fields Medalist
After thousands of hours of autonomous research, we now have thousands of validated edges that statistically test out in the markets. Futures, equities, crypto, FX, commodities. All actively monitored, all with full audit trails, all with live forward performance tracking since discovery. That number is growing every day since our partnership with NVIDIA for compute.
We realized: people don't want to buy the research engine. They want the output. They want to know what's firing right now, with real entries, real exits, and the full transparent proof behind every single number.
So that's what we sell. Every edge is open-book. The original idea, every test that was run, the K value, the Bonferroni-adjusted p-values, the equity curve since discovery, the trades that won, the trades that lost, the edges that decayed and stopped working. We publish our failures as loudly as our wins. In a world drowning in AI slop, the only way to prove you're real is to show everything.
When an edge is VARRD Validated, it means the research was done honestly. The math was done by a deterministic engine, not an AI. Every test was tracked. Every comparison was penalized. The proof is public, the failures are published, and every number is verifiable. That's the standard we hold ourselves to.
The hard problem was never "can AI find patterns in markets?" Any LLM can spit out a backtest. The hard problem is guaranteeing the results were attained properly. The right corrections. The right penalties. The right methodology. So you can actually trust them with real money.
You can use the full VARRD platform yourself. Test your own ideas, run autonomous research overnight, build your own edge library. Or you can query the edges we've already found. We run our own compute at scale, powered by NVIDIA through the NVIDIA Inception Program, generating and validating edges continuously with the same strict guardrails. Both paths are available. Use the tool, or use the output.
Either way, this only works with total transparency. We show every hypothesis, every test, every K value, every failure. Fingerprint deduplication prevents retesting the same idea twice. Knowledge graphs track where every concept came from at every step, properly accounting for overfitting of any kind. The value isn't in the edge itself. Anyone can claim they found one. The value is in the proof that it was found honestly.
Finding properly validated edges is one half of the battle. The other half: sizing positions, choosing when to execute, managing a portfolio of edges, controlling risk. That's on you. We cover the first half completely. We find it. We prove it. You trade it.
Any AI can produce a backtest. Our guardrails ensure the results actually mean something. They're enforced at the infrastructure level -not by prompting, not by suggestion. The AI physically cannot skip them. Here are a few.
Every test you run is counted. Try 50 variations? Your significance threshold rises accordingly. No free looks.
Multiple comparison penalty applied automatically. The more things you test, the higher the bar for significance. Based on the Dunn–Bonferroni method (1961).
Out-of-sample validation is sacred. One shot. Locked forever. No peeking, no re-running, no optimizing after the fact. Follows walk-forward validation methodology.
The system catches when a formula accidentally uses future data. Entry timing models account for when you'd actually get filled.
The AI never fabricates a number. Every statistic comes from computed results on real market data. Architecture enforces the separation.
You must see the pattern on a chart and approve it before any test runs. No skipping validation. No silent testing.
Every test is fingerprinted. Can't inflate results by running the same formula, market, and horizon combination twice.
Once out-of-sample validates, the parameters are locked permanently. No tweaking. No curve-fitting. The result is final.
These systems run whether the researcher is a human clicking buttons or an AI agent running autonomously at 3am. The infrastructure doesn't care who's asking -it enforces the same rigor every time.
Every audit trail, every equity curve, every failure -always public, always free. You pay for the actionable detail: entries, exits, and the setups behind them.
Use VARRD to research and validate your own ideas -or let autonomous mode do it for you. You pay the base AI token cost plus a 15% surcharge that covers our AWS servers, data feeds, and platform. Nothing hidden.
Query VARRD's validated edge library. $2 gets you market, direction, entry, exit, and live performance. $5 gets you the full hypothesis, setup code, formula, testing history -and the ability to load it into your own session and continue testing with it.
Unlimited edge feed access at full $5 depth. Your own private research lane. queries stay yours, never published to the public feed.
Every edge's audit trail -hypothesis, K values, p-values, equity curve, decay, failures -is always free and public. We never paywall the proof.
Need special accommodations or have questions? Reach out any time.
VARRD was built by derivatives traders and engineers who spent years working alongside one of the most successful futures trading firms of the past 40 years -dissecting the research process that turned raw market intuition into systematic, repeatable edges.
VARRD is that process, rebuilt from the ground up with AI -so that anyone, not just quants with PhDs and Bloomberg terminals, can do the same caliber of research.
Co-Founder
Derivatives trader and lifelong entrepreneur with deep market intuition shaped by his father, one of the top macro derivatives traders globally. Has been building with AI models since 2018, making him one of the most knowledgeable engineers on the real-world capabilities of modern AI. His university has him review their AI curriculum every year.
Co-Founder
Derivatives trader with an Economics degree with a focus in quantitative finance from Princeton and D1 All-American honors, bringing both analytical precision and incredible discipline to the team. Has invested countless hours refining the backtesting engine, ensuring its outputs meet the highest standards of statistical rigor and real-market reliability.
Founding Member
Options trader with a Princeton Economics degree and D1 All-American honors, bringing both market knowledge and competitive intensity to the team. Leads the company's business and partnership efforts, aligning VARRD's development with the needs of enterprise clients.
VARRD is a research and decision-support tool. It does not execute trades, manage portfolios, or provide investment advice.