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Quantitative Research Laboratory

You have the intuition.
We built the lab to prove it.

TheoryCraft is an AI research platform for traders and investors. Describe an idea in plain words, and it builds the strategy and backtests it against years of real market data, so you can see whether your edge is real before you risk money.

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1,600+ instruments 15+ yrs tick data No code required
theorycraft · validation
XAU/USD · London breakout Validated
1.51Profit factor
1.18Sharpe
-7.1%Max DD
47.2%Win rate
2010 to 2025 · spread, slippage and commission modeled overfit 18/100
theorycraft · exploration · PEA SP500 DCA Live
A TheoryCraft research notebook with strategy research charts

A live exploration notebook in TheoryCraft, comparing DCA vs lump sum strategies on the S&P 500.

The problem

You think you have an edge.
But do you really?

Most traders rely on gut feel and a handful of screenshots. Without enough data behind a strategy, you are trading on hope instead of evidence.

01 ✕
No rigorous way to validate. Testing manually on charts can't separate a real edge from random noise.
02 ✕
Not enough data to trust results. 50 journal trades or 6 months of backtest can't support a conclusion.
03 ✕
Testing takes weeks, not minutes. Building the infrastructure from scratch burns your iteration loop.
04 ✕
Great on paper, fails live. Overfitting stays invisible without proper out-of-sample testing.
05 ✕
Skill, or luck? Without statistical proof you'll never know which one you're holding.
The scientific method, applied to markets

Observation becomes proof

Every profitable system starts from a repeatable market event called an Edge. TheoryCraft helps you isolate that Edge and measure how reliable it is, then build filters and risk rules on top of it. Each layer is validated on its own before you add the next.

Edge

Your idea, tested

Find a repeatable pattern and measure its real probability of success.

+
Filters

Better entries

Keep only the highest-quality setups. Each filter is tested to prove it actually helps.

+
Risk

Capital protected

Sizing, stops and drawdown caps, verified to improve the strategy.

=
Strategy

Ready to deploy

An engineered, tradable framework backed by data you can trust.

Institutional-grade validation

Every strategy comes back with a verdict

The same methods hedge funds use to confirm an edge is real: walk-forward analysis, Monte Carlo simulation, and overfitting checks. This is the screen you will live in.

Walk-forward analysis

Tested out-of-sample on rolling windows. Does the edge hold on data it has never seen?

Monte Carlo

Thousands of resampled paths show the full range of possible outcomes, so you do not bet everything on one lucky run.

Overfit score

18 / 100 · low curve-fit risk

One honest number: how much of the performance is real, and how much is curve-fitting?

From idea to proof · four steps

Four steps to a strategy you can trust

01

Describe your idea

Say it in plain words, or write the logic yourself. Either way, it becomes testable.

02

Refine & protect

Add filters and risk rules. Each addition is tested so you keep only what helps.

03

Optimize & validate

Find robust parameters across years and assets, then run out-of-sample validation.

04

Incubate & deploy

Watch it run live before risking real money. Deploy only when the numbers earn it.

The research notebook

Readable code you own

You do not need to write code, yet every strategy is real, readable Python that you fully own. What the assistant writes is exactly what a developer would write, so you can audit it and run it on your own infrastructure.

  • Open, reproducible notebooks
  • Live charts & data exploration
  • Export & self-host anytime
gold_breakout_research.ipynb · connected
In [1]
# Hypothesis: London-session breakouts on XAU/USD have an edge
strat = Edge.breakout(asset="XAU/USD", session="london", lookback=20)
In [2]
res = backtest(strat, data="2010..2025", costs=True)
res.summary()
Out [2]
trades: 1,284 win%: 47.2 profit_factor: 1.51 sharpe: 1.18 max_dd: -7.1% edge: CONFIRMED ✓
Plot
research chat · TheoryCraft AI
You
Does adding an ATR volatility filter improve the gold breakout?
AI
Testing two thresholds across 2010-2025. Filtering setups below ATR<0.4% raised profit factor 1.38 -> 1.51 and cut max drawdown to -7.1%. Want me to walk-forward validate it?
You
Yes, and check for overfitting.
AI
Holds across 6 of 7 windows. Overfit score 18/100. Robust. I've saved the notebook to your workspace.
AI research assistant

Your research partner
in the lab

The assistant creates notebooks, writes the code, runs your backtests, and reads the results with you. It does not place trades or send buy and sell signals. Its job is to turn your question into evidence you can check yourself.

  • Plain-language idea → working, testable strategy
  • Reads results and suggests the next iteration
  • Bring your own agent through MCP, including Claude and Codex
Open by design

No black boxes.
The engine is yours.

The core engine is fully open source (Apache 2.0) and free forever. Every algorithm that touches your data is transparent and auditable. The platform adds AI, optimization and analytics on top.

Star on GitHub View the engine

Core engine

Tick-level backtest engine. Open, verifiable, built for accuracy.

Connectors

Open integrations for brokers, exchanges & data feeds.

Built for trust

Audit, extend or fork any module. The AI uses the same libraries you do.

Build your edge alongside other researchers

Discord is where the project lives. You will find daily research discussion, build updates, and a direct line to the founder.

Join the Discord
DarkyZ
Why we built this

"The rigor to know whether an edge is real used to live only inside hedge funds. TheoryCraft puts that same rigor in the hands of independent traders and investors."

DarkyZ
Founder · builds TheoryCraft & its open-source engine
Pricing

Join early. Keep founder pricing.

One plan at launch, with full access to the platform. Early members lock in founder pricing while the product is still taking shape, and they keep that price as it grows.

Early Access · available at launch
$10/month $100/year
Early-access price. Regularly $29/mo at launch. Cancel anytime.
Early-access price. Regularly $290/yr at launch. Two months free.
Get Early Access

Join the first generation of TheoryCraft researchers and get the full platform, with nothing held back.

  • Full platform & all Lab features
  • Integrated research notebooks
  • AI research assistant
  • MCP support (Claude, Codex, and more)
  • Historical data · 1,600+ instruments
  • Multi-year backtesting
  • Public notebook sharing
  • Every update during Early Access

Researcher

Coming later
$49/mo

For active traders running regular research.

  • Everything in Early Access
  • Multiple workspaces & parallel runs
  • More powerful machines + storage

Quant

Coming later
$99/mo

For advanced, intensive research workflows.

  • Everything in Researcher
  • High compute & premium machines
  • Large-scale workflows · priority support

These tiers show where TheoryCraft is heading. They are not available yet.

FAQ

Questions,
answered

Still curious? Ask us anything on Discord.

What is TheoryCraft?
An AI-powered research platform that helps traders and investors test, validate, and improve their market ideas using historical data, notebooks, and AI.
Is TheoryCraft an AI trading bot?
No. TheoryCraft does not generate automatic buy and sell signals. It helps you research and validate strategies before risking real money.
Who is TheoryCraft for?
Anyone interested in systematic trading, quantitative research, or evidence-based investing.
Do I need to know how to code?
No. The AI assistant can help create notebooks, write code, analyze data, and run research workflows for you.
What market data is available?
TheoryCraft provides access to historical market data across 1,600+ instruments. Additional providers and datasets will be added over time.
Can I share my research?
Yes. You can share notebooks and collaborate with the community.
Can I use Claude, Codex, or other AI agents?
Yes. TheoryCraft is being designed to integrate with external AI agents through MCP.
When is the launch?
The first public release is planned for mid-June 2026.
Laboratory opens in 0 days

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