A Trading Research Platform in 12 Weeks

We built this platform to show our method. Trading is a strict test. Small errors in data, timing or logic show immediately in the results.

This is an internal R&D project. It uses a paper (simulated) account only. This page does not give investment advice. It makes no claim about trading performance.

What we built

• One code path. The same strategy code runs in simulation and in paper trading. The two modes cannot give different decisions.
• Correct data. The system keeps raw market data. It applies adjustments only when it reads the data. Thus, no future data gets into a test.
• Strict validation. Each parameter search keeps a holdout period that the search does not see. A strategy must also pass robustness checks before it can go live.
• Safe execution. Each order has a unique identifier. The database prevents two trading cycles at the same time. A risk halt stops all orders for an account.
• Operations. A web console shows deployments and live status. Each push to the main branch deploys the system automatically.

How we kept quality high

• Design first. Each large change starts with a written design and acceptance criteria. The AI agents write code to that design.
• Tests as the contract. All tests run on each change. A change that fails a test does not ship.
• Incidents become rules. One incident counted some fills two times. We found the root cause. Then we added a permanent rule and a repair tool.
• Honest limits. We document known weaknesses in the code. The next engineer sees them immediately.

What this means for you

We use the same method for data pipelines, internal tools, AI features and automation. You get speed, and you get proof that the system works.