The math of leveraging returns using quant estimates
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-mathematics-of-turning-2-into-15-in-a-day-building-an-options-strategy-engine This is not an investment advice and purely for educational purpose. Past performance doesn't...
25.9x vs 2.6x isn’t the point. The point: what make's it trustworthy?
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.
A practical way to use AI price targets without overconfidence (looking for critique)
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...