On January 30, 2026, we launched MarketCrunch AI on Product Hunt with a simple promise: AI stock forecasts that show their work - so traders can decide to trade, size, or skip.
If you've tried "AI stock tools" and felt like they were just screeners with better copy (or worse, confident hallucinations), this post is for you.
Quick links: Support on Product Hunt • Coverage • Try MarketCrunch AI
What's different about MarketCrunch AI
A lot of "AI research" is a chat wrapper. Our approach is quant-style decision support:
- We analyze 300M+ data points daily (macro, price action, news) to generate next-day + weekly price targets.
- We show "receipts" on every call: confidence markers, backtest context, and clear drivers - so you can sanity-check the signal.
- We publish AI Picks ~5pm PT (model-driven) and run Pulse during market hours to scan thousands of tickers so you don't have to.
- We're building for active traders (stocks/options) who want a repeatable workflow - not vibes.
Not investment advice. MarketCrunch AI is research tooling - always do your own due diligence.
What an influencer got right
In a post that resonated with traders, Dipanshu Kushwaha summed up the gap in the market:
- Traders don't lose money because of "bad execution."
- They lose money because they don't know why a price might move, how confident the signal is, or when to size up vs sit out. He also used a line we love because it's exactly the product thesis:
"Bloomberg Terminal logic... built for Robinhood users."
The toughest questions we got (and our answers)
These are real questions from Product Hunt + LinkedIn comments - written in the same language people search.
1) "Do the confidence markers correlate with actual outcome accuracy?"
Confidence is primarily uncertainty, not accuracy. In plain terms: it's how tight the forecast distribution is, not a promise the model is right. We run repeated simulations (Monte Carlo-style) to measure stability. When regimes get noisy, dispersion widens and confidence typically drops.
2) "How should I interpret the confidence score in practice - trade bigger, more often, or trust it more?"
Short answer: no - "high confidence" should not automatically mean "increase size." We treat confidence as a risk lens: combine it with your own rules (liquidity, volatility, time horizon, max loss) to decide whether to engage or skip.
3) "What guardrails prevent people from over-trusting the target?"
We design the product to repeatedly communicate target ≠ promise, and we're exploring stronger "skip" cues and onboarding checkpoints that force a more disciplined read. We also ask the community what they'd want as a hard guardrail: max-position guidance, confidence thresholds, or a "what changed since yesterday" diff.
4) "Backtests are where most tools get hand-wavy. How do I judge overfit vs legit?"
This is the correct skepticism. The minimum bar usually includes some mix of:
- Walk-forward / rolling windows
- Out-of-sample periods
- Regime splits
- Live tracking (paper or real) that updates daily
We're biased toward "show receipts without fake certainty," and we're actively gathering what your minimum bar is.
5) "Is this really 'deep AI' if you're using GARCH + isotonic + XGBoost?"
Great catch - and yes, those aren't deep nets. That's the calibration layer (post-processing) that stabilizes outputs across regimes. The core forecast comes from an ensemble of deep models (feed-forward nets + LSTMs), and the calibration stack is used to make results more reliable and interpretable.
6) "When do you plan to cover data beyond US stocks?"
We're excited about international expansion (India was explicitly called out), and we're already having those conversations.
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Try your first ticker at MarketCrunch.AI. Not a financial advice.