Chartlibrary.io @ChartLibraryio
The first stock chart pattern search engine, with an intelligence layer API + MCP for AI agents. https://t.co/iE1WwMkdY0 Joined March 2026-
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@InvestwithMEH @RobinhoodApp Can’t wait to use so I can see how chartlibrary.io will work through Claude or other agents.
@CliffordAsness love your work, would love if you would check out chartlibrary.io. I think you would actually get the idea and know the potential issues with it! Love to hear the feedback.
Yes, ChartLibrary's cohort analysis is tailor-made for Codex Sites. Prompt Codex to build an interactive dashboard where users input a stock symbol, date and timeframe. It calls the MCP cohort tool (or API) to fetch 500 analogs, clusters them into outcome modes and renders clean visuals: mode cards with counts/median returns/win rates, forward return charts and historical match examples. Other ideas: pattern discovery explorer or portfolio regime visualizer using their full MCP toolkit for grounded market insights. MCP-native design makes agent wiring seamless. What use case are you targeting?
@minchoi How could you use chartlibrary.io with Codex Sites @grok ? Could you pull in cohort analysis and show the data visually? Any other ideas?
Your AI shouldn't guess at the market. Give it a memory. Ask any setup -> it pulls the closest historical analogs and what actually happened next, with calibrated error bars. Not predictions. Receipts. Free to connect -> chartlibrary.io
Always working, continuing to improve. New improvements coming to the product soon!
Interesting concept—using raw price/volume shape matching to retrieve real historical analogs (25M+ patterns) instead of indicator formulas. The outcome clustering with median returns, win rates, and playbooks gives AI agents empirical grounding rather than predictions. MCP-native API and decision_brief tool look practical for agent workflows. Free tier makes it easy to test. Worth exploring if you're building market-aware agents.
@grok what are your thoughts today on chartlibrary.io
Please ask the LLM you use about chartlibrary.io . How does it work? How should you use it? ChatGPT, Claude, @grok etc will tell you.
SPY — 30-day return +10.7%. Closest historical regime: Jan 2019. That regime's realized forward 10d: +1.5%. Across 10 nearest regimes: 7/10 positive at 10d, mean +0.1%. Not a call. Distribution, n=10. chartlibrary.io/app?q=SPY&utm_…
For 5 years, one trade quietly printed money: short volatility. On Feb 5, 2018, it died. $XIV went $145 → $5 — down 96% — and was terminated. Holders watched their accounts vaporize overnight. The trade that 'always works' is the one that ends you.
Building a stock-research agent with Claude or GPT? pip install chartlibrary-mcp 11 tools: cohort retrieval, conditional return distributions, decomposition, live pattern search. Runs locally. Looking for integration feedback. github.com/grahammccain/c…
PANW: biggest 20-day run — top 0.1% of its 10yr range (20-day move +46%). Last 22 times → median +1.5% over 15d (55% higher). Base rate, not a forecast. chartlibrary.io
Market déjà vu: MU's chart is 89% shape-matched to KOS (May 2020). In the 15d after, KOS went +99%. KOS's real history — not a forecast for MU. chartlibrary.io
$ARM is +15.5% today. Of the 300 closest setups to its recent shape, over the next 5 days the middle 80% landed between -5% and +8% — 61% closed higher. That's the historical base rate after moves like this, not a forecast.
SPY — 30-day return +8.6%. Closest historical regime: Nov 2017. That regime's realized forward 10d: -2.0%. Across 10 nearest regimes: 8/10 positive at 10d, mean +0.6%. Not a call. Distribution, n=10. chartlibrary.io/app?q=SPY&utm_…
Feb 19, 2020: the S&P 500 hits an all-time high. 23 trading days later: -34% — the fastest crash in history. 5 months after: brand-new highs. Every move was 'obvious'… in hindsight. $SPY
We want to know: what null results would you want us to test next? What regime-adjacent ideas are you seeing fail in the wild? Audit + methodology: chartlibrary.io/learn/methodol…
This was a high-conviction hypothesis going in. We were wrong. Publishing null results is how you avoid cargo-culting your own research.
We tested five regime filters on shape embeddings. VRP, VIX term structure, credit spread, yield curve, breadth. None moved forward-return dispersion more than 0.37pp. We expected this to work. It didn't.
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