Everyone building AI trading tools wants a smarter one. Faster signals. Sharper predictions. A model that "learns" your account and adjusts on the fly.
We went the other direction.
When Glenn and I (Reid) sat down with Claude to build our own position-sizing risk tool, we gave it one job. Get dumber, not smarter. It's allowed to do less. It's never allowed to do more.
That sounds backwards until you remember what this tool touches. Money. Real risk. The kind of decision that, if it's wrong, doesn't just cost you a bad trade. It costs you the account.
When AI touches anything near your capital, "smart" is the wrong goal. "Constrained" is.
Before we wrote a single line of code, we wrote a sentence.
"This tool may only ever REDUCE position size. It may never increase it."
We had Claude repeat that rule back to us before anything else happened. Then every design decision after that got checked against that one line. Every feature. Every...
"If the tests pass, the tool works." That's the assumption almost everyone makes about AI-built software. We made it too. For about twenty minutes.
Earlier this year we had Claude build us a position-sizing guardrail tool. Something to catch us before we sized a trade too big. The first version came back looking sharp. Every test passed. The documentation read like a senior engineer wrote it on a good day. Our gut said ship it.
We didn't. And that decision is the whole point of this post.
I (Reid) run point on our AI builds, so I was the one staring at that first version, ready to call it done. Then we did what we tell every student to do with a new strategy before it touches real money. We audited it instead of trusting it.
What we found wasn't a small bug. It was three of them, stacked underneath a shiny surface.
The tool was reading from a dead data file. A source that no longer existed in the pipeline it was supposedly checking. Run...
Our position-sizing tool sat quiet for 30 trades before we let it touch anything. It watched real trades come in. It logged what it would have told us to do. It changed nothing.
That wasn’t caution for caution’s sake. That was the plan from day one.
This is post one in a ten-part series on what we’ve actually learned building tools and testing strategies with Claude, Anthropic’s AI. Glenn and I aren’t AI developers by trade. We’re traders who started using Claude to build things we needed and couldn’t buy off the shelf. Some of what we built worked. Some of it didn’t, until we fixed how we were building it. This series is the honest version of that process. What to do, what not to do, no polish added.
Today’s post covers the do’s. Specifically, the three things that kept an AI-built risk tool from ever putting our account in danger, even while it was still rough around the edges.
I (Reid) handle most of the AI and content systems at ...