... swing strategy showed +6.17% per trade on the holdout set. It would pass your first two checks: clear number, clear period. Only when I re-ran the parameter grid with the periods swapped did it turn out to be overfit. So I'd add a fourth: the lesson should say what test could have proven it wrong, and whether anyone ran it. Comment from Amanda Phillips(@amanndaphillips): I think that's the main challenge with letting AI take the reins ...

... results look great because you picked it. So I built free checks for that: tell them how many versions you tried and they tell you how much of the result is luck (deflated Sharpe ratio, probability of backtest overfitting, minimum track record). They run in the browser, as a free API, or as MCP servers so you can ask Claude or Cursor "is this backtest real?". Every result comes with what it does not prove. The part I'm proudest ...

... data stays mine, and I can drop into Python whenever. What's there today: - AI Loop for equity, options, and cross-sectional strategies - Monte Carlo significance testing (p-values, deflated Sharpe) so I can tell real edge from overfitting - Visual Flow Builder for non-coders - Paper and live trading via Alpaca Excited to be here. Would love to meet other indie builders in fintech, dev tools, or AI. Also genuinely curious: what's worked for you in pre-launch hustle ...

... Here's the dirty secret of trading: almost every "edge" you see online is fake. Not intentionally people just don't know how to test properly. They overfit to historical data. They don't correct for multiple testing. They cherry-pick the one timeframe where it worked. They look at in-sample results and think they found something. I saw this from the inside. I'm a quant I spent years watching even smart traders make the same statistical mistakes ... ... over and over. And the tools out there? They let you do it. Pine Script doesn't stop you from overfitting

... success criteria regularly, reliability degrades quietly like you said. How you think about detecting that decay early, before users feel it? Comment from Ghost Kitty(@ghost-kitty-9381936): Comment Deleted Comment from Musa Molla(@musa_molla): @dougli   Prompt overfitting is a great way to describe it. Prompts silently bind themselves to model behavior, so upgrades can regress quality even when capability improves. This is why evals need to track distribution shifts, not just accuracy. Comment from Musa Molla(@musa ... ... review outputs to make sure quality hasn t shifted. We use a synthetic data pipeline, but even small changes in how a model writes can subtly change the output distribution. A lot of this feels like prompt overfitting