Our team recently published a blog on leveraging our quant estimates into math-aware options strategy, so you take the guesswork out of it. Give it a read and let us know what you think

https://marketcrunch.ai/blog/the...

This is not an investment advice and purely for educational purpose. Past performance doesn't guarantee future returns.

See the supporting performance metrics (Sharpe/Sortino, max drawdown)
We built MarketCrunch AI to show receipts, not vibes. You enjoy that for every ticker prediction we analyze for you so you get to decide how to trust. Internally, we traded on a basket strategy using our own signal and this was the cumulative performance (return multiple) for our strategy vs S&P 500 from 2018 2026.

Before launch, based on the current UI, I want to pressure-test a simple framework for using price targets responsibly: (1) target is a scenario, not a promise, (2) confidence should change sizing or skip , (3) always sanity-check volatility/regime, (4) decide rules for entry/exit before the open.
What am I missing? If you ve been burned by AI picks, what went wrong?
Context: Most AI stock research tools are dressed-up ChatGPTs, or worse, hallucinating bots. At MarketCrunch AI, we built a deep-learning quantitative AI model that analyzes 300 million+ points daily - including macro, price action, news - to give you 1-click next-day + weekly price targets. Our AI shows its work: each price target has confidence markers, backtest, and clear drivers to help you can decide on trade / size / skip. Our fans say, its Bloomberg terminal for Robinhood users.