Stock chart pattern search, built for AI agents.
Anchor any chart. Get back the 500 historical days that look like it. Bucket them by regime. That’s the edge — empirical analogs, not predictions.
Pattern search has always been mathematical.
We made it shape-based.
Search by formulas someone wrote.
- • Define a “rising wedge” with a math expression.
- • Scan the universe for charts matching the rule.
- • You query in indicator space. Same query for everyone.
- • Limited to patterns someone bothered to formalize.
Search by raw chart shape.
- • Take an actual chart — a real stock on a real date.
- • Get back the 500 historical days that look like it as raw shape.
- • Bucket those 500 by regime — vol, sector, news, earnings window.
- • Conditional distribution shows which conditions actually predicted what happened. No formulas. The shape is the query.
From anchor to actionable read in one MCP call.
Anchor
Your agent picks any (symbol, date, timeframe). NVDA · today · 1h. The chart you want to understand.
Match
The system finds the 500 historical days that look most like your anchor as raw price + volume shape. No indicators. No formulas.
Stratify
Bucket the 500 analogs by regime — vol, sector, news, earnings. The conditional distribution shows which conditions historically predicted what happened next.
We don’t tell the agent what to say. We give it labels.
The MCP tool returns deterministic classification flags — verdict, edge, regime alignment, swing factors. Your agent reads them and writes the answer in its own voice. No scripted prose. Numbers stay in parentheses.
Honestly, this one reads like a coin flip. NVDA’s in-regime analogs printed about the same as everything else — there’s no real edge in either direction here.
Of the 132 closest historical analogs, 21 printed under a regime like today’s. Their 5-day median was +0.09% with a 52% hit rate. The other 111 returned +0.27% on the same hit rate — the gap is small enough that the regime isn’t doing much work here.
- ·Sector lagging hard. XLK 60-day RS is −10.4 — that skews historical reads on tech setups lower until it turns.
- ·News without conviction. Pulse is +0.29 with the tape active — looks like vol repricing, not a real narrative change.
- ·Earnings in 16 sessions. Pattern reads tend to weaken this close to the print — keep that on the radar.
Bottom line: no meaningful edge, low conviction. The in-regime sample is soft (n=21) and the vol regime was derived — treat this as a lean, not a thesis.
Every read is grounded in the underlying tools your agent can also call directly: search, cohort, discover, analyze, context, narrative, explain, portfolio, and the new decision_brief orchestrator that bundles them.
AI agents using Chart Library beat identical agents without it 50–0.
Two identical Claude Haiku agents. 50 out-of-sample trading scenarios. Blind LLM judge scored both on 6 dimensions of reasoning quality. The agent with Chart Library tools won every single scenario. Paired t- statistic greater than 10 on every dimension. Full methodology public.
Drop-in chart pattern intelligence for any AI agent.
MCP-native. Works with Claude Desktop, Cursor, Hermes Agent, or any MCP-capable client. Under a minute to install.
The next ten years of markets × AI runs on grounded retrieval.
Every AI agent that talks about stocks today is making up cohort statistics. The interfaces are getting smarter; the grounding hasn't caught up. Chart Library is the layer.
MCP-native retrieval
9 canonical tools. 25M+ patterns. Calibrated forward distributions, regime stratification, Layer 5 memory that compounds with every query.
Live + multi-timeframe
Real-time intraday updates. One anchor, simultaneous matches across 5min / 15min / 1h / 1d.
Cross-asset
Crypto first (active agent-trading market). Then futures, then international equities. Same architecture, broader universe.